quality control charts case study

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A CASE STUDY OF QUALITY CONTROL CHARTS IN A MANUFACTURING INDUSTRY

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Eng Mohamed Hamdy

– Most of the modern industrial processes are naturally multivariate. Multivariate control charts are supplanted univariate control charts, as it takes into account the relationship between variables and identifies the real process changes, which are undetectable by univariate control charts. In practice, the basic assumption that the measurements are independently and identically distributed about a target value is not always valid. Violation of this assumption increases the False Alarm Rate (FAR) and deteriorates the separation of assignable causes from common causes. This paper presents the application of Multivariate Statistical Process Control (MSPC) charts (e. g., Hotelling , MEWMA) to monitor the flare making process in a straight fluorescent light bulb industry. Furthermore, it develops the appropriate procedure for monitoring a multivariate autocorrelated data variable (i. e., dynamic behavior) by using Autoregressive Integrated Moving Average (ARIMA) models. Univariate SPC charts and decomposition approach are used to identify the out-of-control signals that are generated from multivariate SPC charts. Software packages such as Minitab 17 and Statgraphics Centurion XVI are used to construct the control charts.

Jonathan Quiroz

cristian palacio

control estadistico de la calidad

Abidemi Adeniyi

This project is aimed at providing a stock price prediction system which can be used to forecast the future stock price of the Nigerian Stock Exchange using the artificial neural network. This study will attempt to reduce the stress people have in analyzing large amount of data in order to predict stock. This study will have to look into the problem areas of the stock market prediction and devise a method of proffering solutions to all these problems.

Seyed Taghi Akhavan Niaki

In this paper, two control charts based on the generalized linear test (GLT) and contingency table are proposed for Phase-II monitoring of multivariate categorical processes. The performances of the proposed methods are compared with the exponentially weighted moving average-generalized likelihood ratio test (EWMA-GLRT) control chart proposed in the literature. The results show the better performance of the proposed control charts under moderate and large shifts. Moreover, a new scheme is proposed to identify the parameter responsible for an out-of-control signal. The performance of the proposed diagnosing procedure is evaluated through some simulation experiments.

OLUMA URGESSA

Statistical Quality Control, 6th Edition

Regiz Faria

Anwar Shaker

sanika kamtekar

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• Volume 16, Issue 5
• Application of statistical process control in healthcare improvement: systematic review
• Article Text
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• Johan Thor ,
• Jonas Lundberg ,
• Jakob Ask ,
• Jesper Olsson ,
• Cheryl Carli ,
• Karin Pukk Härenstam ,
• Mats Brommels
• Medical Management Centre, Karolinska Institutet, Stockholm, Sweden
• Correspondence to:
 Dr Johan Thor
 Medical Management Centre, Berzelius väg 3, 5th floor, Karolinska Institutet, S-171 77 Stockholm, Sweden; johan.thor{at}ki.se

Objective: To systematically review the literature regarding how statistical process control—with control charts as a core tool—has been applied to healthcare quality improvement, and to examine the benefits, limitations, barriers and facilitating factors related to such application.

Data sources: Original articles found in relevant databases, including Web of Science and Medline, covering the period 1966 to June 2004.

Study selection: From 311 articles, 57 empirical studies, published between 1990 and 2004, met the inclusion criteria.

Methods: A standardised data abstraction form was used for extracting data relevant to the review questions, and the data were analysed thematically.

Results: Statistical process control was applied in a wide range of settings and specialties, at diverse levels of organisation and directly by patients, using 97 different variables. The review revealed 12 categories of benefits, 6 categories of limitations, 10 categories of barriers, and 23 factors that facilitate its application and all are fully referenced in this report. Statistical process control helped different actors manage change and improve healthcare processes. It also enabled patients with, for example asthma or diabetes mellitus, to manage their own health, and thus has therapeutic qualities. Its power hinges on correct and smart application, which is not necessarily a trivial task. This review catalogues 11 approaches to such smart application, including risk adjustment and data stratification.

Conclusion: Statistical process control is a versatile tool which can help diverse stakeholders to manage change in healthcare and improve patients’ health.

• MRSA, methicillin resistant Staphylococcus aureus
• PEFR, peak expiratory flow rate
• QI, quality improvement
• RCT, randomised controlled trial
• SPC, statistical process control

https://doi.org/10.1136/qshc.2006.022194

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Quality improvement (QI) practices represent a leading approach to the essential, and often challenging, task of managing organisational change. 1 Statistical process control (SPC) is, in turn, a key approach to QI. 2 SPC was developed in the 1920s by the physicist Walter Shewhart to improve industrial manufacturing. It migrated to healthcare, first in laboratory settings (eg, Fisher and Humphries 3 ) and then into direct patient care applications, along with other approaches to QI. Before we report on our systematic review of the literature on how SPC has been applied to QI in healthcare, there is a need to define SPC and its role in QI.


 “Statistical process control (SPC) is a philosophy, a strategy, and a set of methods for ongoing improvement of systems, processes, and outcomes. The SPC approach is based on learning through data and has its foundation in the theory of variation (understanding common and special causes). The SPC strategy incorporates the concepts of an analytic study, process thinking, prevention, stratification, stability, capability, and prediction. SPC incorporates measurement, data collection methods, and planned experimentation. Graphical methods, such as Shewhart charts (more commonly called ‘control charts’), run charts, frequency plots, histograms, Pareto analysis, scatter diagrams, and flow diagrams are the primary tools used in SPC.” (Carey 4 , p xviii)

The terms “statistical process control” and “statistical quality control” are often used interchangeably, 5 although sometimes the latter is used to describe a broader organisational approach to quality management that evolved into the concept of total quality management. 6

One of the tenets of QI is that to improve healthcare performance we must change our way of working. 7 But change does not always mean improvement. To discriminate between changes that yield improvement and those that do not, relevant aspects of performance need to be measured. In addition, measurement guides decisions about where improvement efforts should be focused in the first place. SPC may facilitate such decision making. Control charts, central to SPC, are used to visualise and analyse the performance of a process—including biological processes such as blood pressure homoeostasis or organisational processes such as patient care in a hospital—over time, sometimes in real time. Statistically derived decision rules help users to determine whether the performance of a process is stable and predictable or whether there is variation in the performance that makes the process unstable and unpredictable. One source of such variation can be a successful intervention aimed at improvement that changes performance for the better. If the improvement is maintained, the process will stabilise again at its new level of performance. All of this can be easily determined by using SPC. 4

Although there are theoretical propositions that SPC can facilitate decision making and QI in healthcare (eg, Berwick, 8 Benneyan et al , 9 Plsek 10 ) it is not clear what empirical support there is in the literature for such a position 11 :


 “The techniques of statistical process control, which have proved to be invaluable in other settings, appear not to have realised their potential in health care. ... Is this because they are, as yet, rarely used in this way in health care? Is it because they are unsuccessful when used in this way and thus not published (publication bias)? Or is it that they are being successfully used but not by people who have the inclination to share their experience in academic journals?” (p 200)

The present systematic review aimed to answer these questions. We examined the literature for how and where SPC has been applied in QI of clinical/patient care processes and the benefits, limitations, barriers and facilitating factors related to such application.

MATERIALS AND METHODS

Drawing on the principles and procedures for systematic review of QI interventions 12 we searched for articles on the application of SPC in healthcare QI published between 1966 and June 2004 (see appendix A) in the following databases: Web of Science, Ovid Medline(R), EMBASE, CINAHL (Cumulative Index to Nursing and Allied Health Literature), PsycInfo, and the Centre for Reviews and Dissemination databases. We also included articles found by searching reference lists or from elsewhere which we were aware of, if they met our inclusion criteria: original empirical studies of SPC application in improvement of clinical/patient care processes in healthcare organisations, published in English. We excluded articles dealing with application of SPC in laboratory or technical processes (eg, film processing) and in surveillance/monitoring (unless they also contained empirical data about improvement efforts), as well as tutorials (unless they contained empirical case studies), letters, book reviews and dissertations.

We reviewed abstracts, when available, or else other information about the publication provided in the database (eg, publication type such as letters, book reviews or original articles). Articles that did not meet the inclusion criterion were excluded. We retrieved and read the full text of the remaining articles, again excluding the articles that did not meet the inclusion criterion.

We developed, pilot tested and modified a data abstraction form which we then used to consistently capture information of relevance to our review questions on reading the full text articles. The information recorded was: whether and how the article met the inclusion criterion; study objective(s); study design; whether the study empirically compared application of SPC with any other method for process data display and analysis; reported benefits, limitations, barriers and facilitating factors related to SPC; organisational setting; country where study was conducted; clinical specialty; unit of analysis; variables for SPC analysis; and other observations. Some questions in the form required a yes/no or brief response (eg, country where study was conducted) and others required answers in the form of direct quotes from the article or the a summary of the article written by the reviewer. Each article was read and data abstracted by one member of the review team (the coauthors of this review). Following this, all the data abstraction forms were reviewed by the first author, who solicited clarification and checked for any missing or incomplete data to ensure consistency in reporting across all articles reviewed. He also conducted the initial data synthesis, which was then reviewed by the entire team.

We determined the study design for each article and whether the investigators intended to test the utility of SPC application, alone or in combination with other interventions. In several articles, the study design or study objectives were not explicitly stated. Our determination of such intention in such cases was based on our reading of the full text papers.

Simple descriptive statistics—for example, the number of publications per year of publication or per country—were used to characterise the included studies. The qualitative nature of our research questions and of the abstracted data shaped our analysis and synthesis of findings regarding benefits, limitations, SPC variables, etc. 13 The abstracted data was reviewed one question at a time and data from each article was classified into one or more thematic categories, each with a descriptive heading. Informed by our present understanding of QI and healthcare, we developed these categories as we reviewed the data, rather than using categories derived a priori from theory. For data that did not fit into an existing category, we developed a new one. Thus the categories emerged as we synthesised the data. We report the categorised data in tabular form, illustrated with examples, and give the references of all the source studies.

To strengthen our review through investigator triangulation, 14 we sought feedback on an earlier version of this manuscript from two SPC experts: one was the most frequent coauthor in the included studies and the other was an expert on SPC application also in settings other than healthcare. Their comments helped us refine our data synthesis and distil our findings.

The database searches yielded 311 references. The initial review (abstracts etc.) yielded 100 articles which we read in full text form. Of these, 57 articles met the inclusion criteria and have been included in the review. 15– 71 To characterise the body of liferature, figure 1 shows the year of publication and whether the studies were conducted in USA or elsewhere (further specified below); table 1 gives the study designs and objectives—whether or not to test SPC utility.

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 Study design and objectives of the studies included in the systematic review*

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 The number of included articles by year of publication. (A total of 55 articles were published in 1990–2003; the two articles from 2004 are not included in this graph since the database searches were conducted in June 2004.) Black bars: studies conducted in the USA; grey bars: studies conducted outside the USA.

Most of the articles (45/57) concerned application of SPC in healthcare improvement in the USA. 15– 35, 37– 40, 42, 43, 45, 47, 49– 56, 59, 60, 63, 67– 71 While the first US-based article was published in 1990, the non-US articles were published between 1998 and 2003: three articles were from the UK, 61, 62, 66 three were from Switzerland, 36, 41, 46 and one each were from Australia, 58 Finland, 65 France, 57 Indonesia, 44 Norway 64 and Spain. 48 The intention to test the utility of SPC is exemplified by a study aiming to reduce the rate of acquisition of methicillin-resistant Staphylococcus aureus (MRSA) on wards and units at Glasgow Royal Infirmary hospitals. 61 Annotated control charts displaying data on MRSA acquisition were fed back monthly to medical staff, managers and hotel services. Sustained reductions in the rate of acquisition from the baseline, which could not otherwise be accounted for, started 2 months later. In contrast, investigators at a paediatric emergency department used SPC to demonstrate a decline in the rate of contamination following the introduction of a new approach to drawing blood for culture specimens, 68 but the study had no intention to test the utility of SPC per se.

To characterise the content of the articles, we first present how and where SPC has been applied to healthcare QI. Tables 2–4 present the study settings (ie, hospital etc. where SPC was applied; table 2), the field of healthcare (ie, specialties or forms of care; table 3), and the units of analysis (table 4). Table 5 enlists the 97 distinct SPC variables that have been reported. Tables 6–9 convey our synthesis of the reported benefits, limitations, barriers and facilitating factors related to SPC application. For each category, we have given explanations or examples and references to the source articles.

 How and where SPC was applied: study settings*

 How and where SPC was applied: fields of healthcare*

 How and where SPC was applied: units of analysis*

 SPC variables*

 Benefits of using SPC to improve clinical processes*

SPC has been applied to healthcare improvement in a wide range of settings and specialties, at diverse levels of organisations and directly by patients, using many types of variables (fig 1, tables 2–5). We found reports of substantial benefits of SPC application, as well as important limitations of, barriers to and factors that facilitate SPC application (tables 6–9). These findings indicate that SPC can indeed be a powerful and versatile tool for managing changes in healthcare through QI. Besides helping diverse stakeholders manage and improve healthcare processes, SPC can also help clinicians and patients understand and improve patients’ health when applied directly to health indicators such as PEFR in asthma or blood sugar concentrations in diabetes. In healthcare, the “study subject” can thus also be an active agent in the process, as when patients apply SPC to their own health. Several studies indicated the empowering effects this may have on patients. 35, 38, 40, 50 SPC application thus has therapeutic potential as it can help patients manage their own health. We agree with Alemi and Neuhauser 70 that this potential merits further investigation.

Most of the included articles concerned application of SPC in healthcare improvement in the USA. Articles from other countries appeared only towards the end of the study period (fig 1). We have no explanation for this finding, but we speculate that it is related to differences between US and other healthcare systems with regard to QI awareness and implementation. 73

Only 22 studies included in the review were intended to test the utility of SPC (table 1). Of the four controlled studies, only one included a control chart in the intervention (as a minor component which did not fully exploit the features of SPC). In 35 articles we did not find an intention to test the utility of SPC application. In those cases, SPC was applied for other reasons (ie, to evaluate the impact of other interventions). Even though many articles thus did not address the utility of SPC, all studies offered information—to varying degrees—relevant to our review’s question of how SPC has been applied to healthcare. The utility of SPC is reflected in benefits reported regarding SPC application (table 6).

SPC has been applied in over 20 specialties or fields of healthcare, at a wide range of levels (tables 3 and 4), suggesting that SPC has broad applicability in healthcare. The dominance of anaesthesia and intensive care can be explained in large part by the fact that many studies included their services in conjunction with other specialties. This reflects the way in which anaesthesia has a vital supporting role in many clinical care processes. The 97 SPC variables reported (table 5) demonstrate a diversity of situations in which SPC has been applied, ranging from process indicators of patients’ health to health outcomes and many aspects of healthcare processes and organisational performance. This indicates that SPC is a versatile QI tool.

The benefits of SPC application (table 6) mirror those given in books and tutorials on SPC (exemplified by the quote in the Introduction to this review). As noted in a report from a top-ranked healthcare system which has applied SPC widely:


 “Among the most powerful quality management tools that IHC [Intermountain Health Care, USA] has applied is statistical process control, SPC. Most notable among those tools are control charts. Under optimal conditions, these graphical depictions of process performance allow participants to know what is happening within their processes as ‘real time’ data enable them to make appropriate decisions. The capability of truly understanding processes and variation in a timely manner has resulted in the most dramatic, immediate, and ongoing improvements of any management technique applied at IHC.” (Shaha, 26 p 22)

The limitations of SPC application (table 7) identified by this review are important, and yet perhaps less emphasised than the benefits in books and tutorials on SPC. SPC cannot solve all problems and must be applied wisely. There are many opportunities to “go wrong”, as illustrated by the case where incorrect application was highlighted by other authors (limitation number 5 in table 7). In several cases, our own understanding of SPC suggested that investigators had not used it correctly or fully (eg, standard decision rules to detect special causes were not applied to identify process changes). In the worst case scenario, incorrect application of SPC could lead to erroneous conclusions about process performance and waste time, effort and spirit and even contribute to patient harm. In the more authoritative studies we reviewed, co-investigators included experts in industrial engineering or statistics or authors who otherwise had developed considerable expertise in SPC methodology. On the basis of these observations, we conclude that although SPC charts may be easy to use even for patients, clinicians or managers without extensive SPC training, they may not be equally simple to construct correctly. To apply SPC is, paradoxically, both simple and difficult at the same time. Its power hinges on correct and smart application, which is not necessarily a trivial task. The key, then, is to develop or recruit the expertise necessary to use SPC correctly and fully and to make SPC easy for non-experts to use, before using it widely.

 Limitations of SPC application in improvement of clinical processes*

Autocorrelation is another limitation of SPC highlighted by this review. Our review, and published books, offer limited advice on how to manage it:


 “There is no single acceptable way of dealing with autocorrelation. Some would say simply to ignore it. [Others] would disagree and suggest various measures to deal with the phenomenon. One way is to avoid the autocorrelation by sampling less frequently. ... Others argue against plotting autocorrelated data on control charts and recommend that the data be plotted on a line chart (without any centerline or control limits).” (Carey, 4 p 68)

Just over a quarter of the articles reported barriers to SPC application (table 8). The three broad divisions of barriers—people, data and chart construction, and IT—indicate where extra care should be taken when introducing SPC in a healthcare organisation. Ideas on how to manage the limitations of and barriers to SPC application can be found among the factors reported to facilitate SPC application (table 9). They deal with, and go beyond, the areas of barriers we found. We noted the prominence of learning and also of focusing on topics of interest to clinicians and patients. The 11 categories under the heading “Smart application of SPC can be helpful” contain valuable approaches that can be used to improve SPC application. Examples include risk adjustment 51, 52, 71 and stratification 30, 37, 59 to enable correct SPC analysis of data from heterogeneous populations of patients (or organisational units). Basic understanding of SPC must be taught to stakeholders and substantial skill and experience is required to set up successful SPC application. Experts, or facilitators, in healthcare organisations can help, as indicated in table 9, and as we have described for other QI methods. 74

 Barriers to SPC application*

 Factors or conditions facilitating application of SPC*

We found more information on SPC benefits and facilitating factors than on limitations and barriers, and this may represent a form of publication bias, as indicated by the quote in the Introduction. 11 We did not find any study that reported failed SPC application. We can speculate that there have been situations when SPC application failed, just as there must be many cases of successful SPC application that have not been reported in the literature. Studies of failed SPC application efforts, as well as studies designed to identify successful ways to apply SPC to manage change, would help inform future SPC application efforts. On the basis of this review, we agree with the argument that “medical quality improvement will not reach its full potential unless accurate and transparent reports of improvement work are published frequently and widely (p 319),” 75 and also that the way forward is to strengthen QI research rather than to lower the bar for publication. 76

Methodological considerations regarding the included studies

None of the studies we found was designed to evaluate the effectiveness quantitatively—that is, the magnitude of benefits—of SPC application. This would have required other study designs such as cluster randomised trials or quasi-experimental studies. 12 Although the “methods of evaluating complex interventions such as quality improvement interventions are less well described [than those to evaluate less complex interventions such as drugs]”, Eccles et al argue that the “general principle underlying the choice of evaluative design is ... simple—those conducting such evaluations should use the most robust design possible to minimise bias and maximise generalisability. [The] design and conduct of quantitative evaluative studies should build upon the findings of other quality improvement research (p 47).” 77 This review can provide such a foundation for future evaluative studies.

An important distinction is warranted here: we believe that SPC rests on a solid theoretical, statistical foundation and is a highly robust method for analysing process performance. The designs of the studies included in this systematic review were, however, not particularly robust with regard to evaluating the effectiveness of SPC application, and that was not their objective. This does not mean that SPC is not a useful tool for QI in healthcare, only that the studies reviewed here were more vulnerable to bias than more robust study designs, even if they do indicate many clear benefits of SPC application (table 6). Despite the studies not being designed to evaluate the effectiveness of SPC, many used SPC to effectively show the impact of QI or other change initiatives. In this way, SPC analysis can be just as powerful and robust as study designs often deemed superior, such as randomised controlled trials (RCTs). 77 The key to this power is the statistical and practical ability to detect significant changes over time in process performance when applying SPC. 9 On the basis of a theoretical comparison between control charts and RCTs, Solodky et al 38 argue that control charts can complement RCTs, and sometimes even be preferable to RCTs, since they are so robust and enable replication—“the gold standard” for research quality—at much lower cost than do RCTs. These points have been further elaborated in subsequent work. 78, 79

A curious methodological wrinkle in our review is: can you evaluate the application of a method (eg, SPC) using that same method for the evaluation? Several of the included studies used SPC both as (part of) an intervention and as a method to evaluate the impact of that intervention. For example, Curran et al used annotated control charts to feed information on MRSA acquisition rates back to stakeholders and used these same control charts to show the effectiveness of the feedback programme. 61

Relationship between monitoring and improvement

When SPC is applied for monitoring, rather than for managing change, the aims are different—for example, to detect even small but clinically important deviations in performance—as are the methodological challenges. 80, 81 This review focused on the latter. Thus although studies on SPC application for monitoring healthcare performance were excluded from this review, we recognise the importance of such monitoring. The demarcation between monitoring and improvement is not absolute. Indeed, there are important connections between measurement, monitoring and improvement, even if improvement does not follow automatically from indications of dissatisfactory performance. “To improve performance, organizations and individuals need the capability to control, improve, and design processes, and then to monitor the effects of this improvement work on the results. Measurement alone will not suffice (pp 1–35).” 82

Monitoring performance by way of control charts has been suggested as a better approach to clinical governance in the British National Health Service. Through six case studies, Mohammed et al demonstrate how control chart monitoring of performance can distinguish normal performance from performance that is either substandard or better than usual care. “These case studies illustrate an important role for Shewhart’s approach to understanding and reducing variation. They demonstrate the simplicity and power of control charts at guiding their users towards appropriate action for improvement (p 466).” 83

Comments on the review methodology

No search strategy is perfect, and we may well have missed some studies where SPC was applied to healthcare QI. There are no SPC specific keywords (eg, Medical Subject Headings, MeSH) so we had to rely on text words. Studies not containing our search terms in the title or abstract could still be of potential interest although presumably we found most of the articles where SPC application was a central element. We believe the risk that we systematically missed relevant studies to be small. Therefore, our findings would probably not have changed much due to such studies that we might have missed.

The review draws on our reading, interpretation and selection of predominantly qualitative data—in the form of text and figures—in the included articles to answer the questions in our data abstraction form. The questions we addressed, the answers we derived from the studies, and the ways we synthesised the findings are not the only ways to approach this dataset. Furthermore, each member of the review team brought different knowledge and experiences of relevance to the review, potentially challenging the reliability of our analysis. An attempt was made to reduce that risk by having one investigator read all data abstraction forms, and obtain clarifications or additional data from the original articles when needed. That investigator also conducted the initial data synthesis, which was then reviewed by the entire team and the two outside experts. Although other interpretations and syntheses of these data are possible, we believe that ours are plausible and hope they are useful.

The methods for reviewing studies based primarily on qualitative data in healthcare are less well developed than the more established methods for quantitative systematic reviews, and they are in a phase of development and diversification. 13, 84, 85 Among the different methods for synthesising evidence, our approach is best characterised as an interpretive (rather than integrative) review applying thematic analysis—it “involves the identification of prominent or recurrent themes in the literature, and summarising the findings of different studies under thematic headings”. 86 There is no gold standard for how to conduct reviews of primarily qualitative studies. Our response to this uncertainty has been to use the best ideas we could find, and to be explicit about our approach to allow readers to assess the findings and their provenance.

The main limitation of this review is the uncertainty regarding the methodological quality of many of the primary studies. Assessment of quality of qualitative studies is still under debate, and there is no consensus on whether at all, or, if so, how to conduct such assessments. 84 We reviewed all the studies that satisfied our inclusion criteria and made no further quality assessment. Therefore our findings should be considered as tentative indications of benefits, limitations, etc to be corroborated, or rejected, by future research. The main strength of this review is our systematic and explicit approach to searching and including studies for review, and to data abstraction using a standardised form. It has helped generate an overview of how SPC has been applied to healthcare QI with both breadth and depth—similar to the benefits of thematic analysis reported by investigators reviewing young people’s views on health and health behaviour. 87

In conclusion, this review indicates how SPC has been applied to healthcare QI with substantial benefits to diverse stakeholders. Although there are important limitations and barriers regarding its application, when applied correctly SPC is a versatile tool which can enable stakeholders to manage change in healthcare and improve patients’ health.

Database search strategy

Web of Science (1986 – 11 June 2004)

TS [topic search]  =  ((statistical process control or statistical quality control or control chart* or (design of experiment and doe)) and (medical or nurs* or patient* or clinic* or healthcare or health care))

We limited the search to articles in English only which reduced the number of hits from 167 to 159. We saved these 159 titles with abstracts in an EndNote library. Using a similar strategy, we searched the following databases through Ovid:

Ovid MEDLINE(R) (1966 to week 1, June 2004)

EMBASE (1988 to week 24, 2004)

CINAHL (1982 to week 1, June 2004)

PsycINFO (1985 to week 5, May 2004)

This yielded 287 hits, including many duplicates, which we saved in the same EndNote library as above.

Centre for Reviews and Dissemination (CRD)

We searched all CRD databases and found two articles which we also added to our EndNote library.

Acknowledgments

We thank Ms Christine Wickman, Information Specialist at the Karolinska Institutet Library, for expert assistance in conducting the database searches. We also acknowledge the pilot work conducted by Ms Miia Maunuaho as a student project at Helsinki University, supervised by Professor Brommels, which provided a starting point for this study. We thank Professor Duncan Neuhauser, Case Western Reserve University, Cleveland, Ohio, USA, and Professor Bo Bergman, Chalmers University of Technology, Gothenburg, Sweden, for their helpful comments on an earlier version of this manuscript. We thank Dr Rebecca Popenoe for her editorial assistance.

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Funding: No dedicated funding was received for this study. All coauthors were supported by their respective employers in conducting this research as part of their work.

Competing interests: None.

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Statistics By Jim

Making statistics intuitive

Control Chart: Uses, Example, and Types

By Jim Frost 2 Comments

What is a Control Chart?

Control charts determine whether a process is stable and in control or whether it is out of control and in need of adjustment. Some degree of variation is inevitable in any process. Control charts help prevent overreactions to normal process variability while prompting quick responses to unusual variation. Control charts are also known as Shewhart charts.

A stable process operates within an ordinary, expected range of variation. It is predictable and consistent and is not influenced by special causes of variation, such as changes in the process itself, changes in the environment, or changes in the input materials or equipment. Stable processes are more likely to produce high-quality products or services. Conversely, an out-of-control process is unpredictable and more likely to make defects or errors.

A control chart displays process data by time, along with upper and lower control limits that delineate the expected range of variation for the process. These limits let you know when unusual variability occurs. Statistical formulas use historical records or sample data to calculate the control limits. Unusual patterns and out-of-control points on a control chart suggest that special cause variation exists.

Control charts can be valuable aids for tracking a continuous process and gaining insight into a newly established one. They can help with the following:

• Determine whether a process is stable.
• Find problems as they occur in an ongoing process.
• Assess the effectiveness of a process change.
• Predict the range of outcomes for a process.
• Assess patterns of special cause variation to identify non-routine events.
• Determine whether improvements should target non-routine events or the underlying process itself.

Control Chart Example

Quality engineers at a manufacturing plant monitor part lengths. They use process data to create an X-bar-R chart, a control chart that evaluates both the process mean (X-bar) and spread (R chart for range).

Control charts typically contain the following elements:

• Data points representing process outcomes.
• Control limits depict the range of normal process variability.
• Centerline locates the process’s center value.
• Red dots indicate out-of-control points.

Learn more about Variability in a Dataset .

Interpretation

For the part length example, we must ensure the R chart (bottom) is in control before analyzing the X-bar chart. If the R chart is unstable, the control limits for the X-bar chart will be invalid, potentially leading to false signals of an out-of-control situation on the X-bar chart.

The R chart does not flag any points in red. They’re all in control. However, the X-bar chart on the top is a different story because it flags six points. Red data points fail a statistical test and suggest that special cause variation exists.

Point 8 is out-of-control because it is below the lower control limit. But there are five more red points within the control limits. Why?

Control charts can test for various statistically improbable patterns.

The chart flags points 12, 13, 19, and 20 because 4 out of 5 points in a row are more than one standard deviation from the centerline on one side of the mean. That’s unlikely to occur by chance. Additionally, #17 is flagged because 2 out of 3 points are more than two standard deviations from the centerline on one side of the mean.

All the red dots suggest special cause variation exists because those patterns are unlikely to occur with only common cause variation. Assessing these patterns in conjunction with process knowledge might help us identify its source.

Learn more about the Mean , Standard Deviation , and Range .

Types of Control Charts

Various types of control charts monitor different process properties over time. The following are standard control charts:

• X-bar: Average performance of a process using subgroups.
• I: Average performance without subgroups.
• R: Variation (range) of a process with subgroups.
• S: Variation (standard deviation) with subgroups.
• MR: Variation (moving range) without subgroups.
• C: Number of defects in a subgroup.
• P: Proportion of defective products.
• U: Number of defects in a unit of product or service.

Control charts for continuous data , such as lengths and weights, typically have two panels. The top panel assesses the process mean over time, while the bottom evaluates its variability. In this manner, X-bar-R, X-bar-S, and I-MR charts are common pairings because they assess both the mean and variability.

Control charts for attribute data, such as pass or fail for defect data, have only one panel and evaluate either the proportion of defects or the number of defects per subgroup.

While analysts frequently use control charts for quality improvement projects, learn how it can be helpful Using Control Charts with Hypothesis Tests .

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Reader Interactions

April 25, 2024 at 5:25 pm

What, if anything, can be learned by generating a control chart from 30 consecutive samples pulled originally to generate a capability study.

April 25, 2024 at 7:39 pm

Typically, you should create a control chart before performing a capability study. You need to know that your process is in statistical control (i.e., predictable). If it’s not in control, the capability results are meaningless.

At any rate, you should be able to create an I-MR chart using those 30 consecutive samples. However, they usually recommend at least 100 samples for those charts. But you’d get some preliminary information at least.

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The Complete Guide to Understanding Control Charts

Published: February 18, 2013 by Carl Berardinelli

Control charts have two general uses in an improvement project.

The most common application is as a tool to monitor process stability and control.

A less common, although some might argue more powerful, use of control charts is as an analysis tool.

The descriptions below provide an overview of the different types of control charts to help practitioners identify the best chart for any monitoring situation, followed by a description of the method for using control charts for analysis.

Identifying Variation

When a process is stable and in control, it displays common cause variation, variation that is inherent to the process. A process is in control when based on past experience it can be predicted how the process will vary (within limits) in the future. If the process is unstable, the process displays special cause variation, non-random variation from external factors.

Control charts are simple, robust tools for understanding process variability.

The Four Process States

Processes fall into one of four states: 1) the ideal, 2) the threshold, 3) the brink of chaos and 4) the state of chaos (Figure 1). 3

When a process operates in the ideal state , that process is in statistical control and produces 100 percent conformance. This process has proven stability and target performance over time. This process is predictable and its output meets customer expectations.

A process that is in the threshold state is characterized by being in statistical control but still producing the occasional nonconformance. This type of process will produce a constant level of nonconformances and exhibits low capability. Although predictable, this process does not consistently meet customer needs.

The brink of chaos state reflects a process that is not in statistical control, but also is not producing defects. In other words, the process is unpredictable, but the outputs of the process still meet customer requirements. The lack of defects leads to a false sense of security, however, as such a process can produce nonconformances at any moment. It is only a matter of time.

The fourth process state is the state of chaos . Here, the process is not in statistical control and produces unpredictable levels of nonconformance.

Every process falls into one of these states at any given time, but will not remain in that state. All processes will migrate toward the state of chaos. Companies typically begin some type of improvement effort when a process reaches the state of chaos (although arguably they would be better served to initiate improvement plans at the brink of chaos or threshold state). Control charts are robust and effective tools to use as part of the strategy used to detect this natural process degradation (Figure 2). 3

Elements of a Control Chart

There are three main elements of a control chart as shown in Figure 3.

• A control chart begins with a time series graph.
• A central line (X) is added as a visual reference for detecting shifts or trends – this is also referred to as the process location.
• Upper and lower control limits (UCL and LCL) are computed from available data and placed equidistant from the central line. This is also referred to as process dispersion.

Control limits (CLs) ensure time is not wasted looking for unnecessary trouble – the goal of any process improvement practitioner should be to only take action when warranted. Control limits are calculated by:

• Estimating the standard deviation , σ, of the sample data
• Multiplying that number by three
• Adding (3 x σ to the average) for the UCL and subtracting (3 x σ from the average) for the LCL

Mathematically, the calculation of control limits looks like:

(Note: The hat over the sigma symbol indicates that this is an estimate of standard deviation, not the true population standard deviation.)

Because control limits are calculated from process data, they are independent of customer expectations or specification limits.

Control rules take advantage of the normal curve in which 68.26 percent of all data is within plus or minus one standard deviation from the average, 95.44 percent of all data is within plus or minus two standard deviations from the average, and 99.73 percent of data will be within plus or minus three standard deviations from the average. As such, data should be normally distributed (or transformed) when using control charts, or the chart may signal an unexpectedly high rate of false alarms.

Controlled Variation

Controlled variation is characterized by a stable and consistent pattern of variation over time, and is associated with common causes. A process operating with controlled variation has an outcome that is predictable within the bounds of the control limits.

Uncontrolled Variation

Uncontrolled variation is characterized by variation that changes over time and is associated with special causes. The outcomes of this process are unpredictable; a customer may be satisfied or unsatisfied given this unpredictability.

Please note: process control and process capability  are two different things. A process should be stable and in control before process capability is assessed.

Control Charts for Continuous Data

Individuals and Moving Range Chart

The individuals and moving range (I-MR) chart is one of the most commonly used control charts for continuous data; it is applicable when one data point is collected at each point in time. The I-MR control chart is actually two charts used in tandem (Figure 7). Together they monitor the process average as well as process variation. With x-axes that are time based, the chart shows a history of the process.

The I chart is used to detect trends and shifts in the data, and thus in the process. The individuals chart must have the data time-ordered; that is, the data must be entered in the sequence in which it was generated. If data is not correctly tracked, trends or shifts in the process may not be detected and may be incorrectly attributed to random (common cause) variation. There are advanced control chart analysis techniques that forego the detection of shifts and trends, but before applying these advanced methods, the data should be plotted and analyzed in time sequence.

The MR chart shows short-term variability in a process – an assessment of the stability of process variation. The moving range is the difference between consecutive observations. It is expected that the difference between consecutive points is predictable. Points outside the control limits indicate instability. If there are any out of control points, the special causes must be eliminated.

Once the effect of any out-of-control points is removed from the MR chart, look at the I chart. Be sure to remove the point by correcting the process – not by simply erasing the data point.

The I-MR chart is best used when:

• The natural subgroup size is unknown.
• The integrity of the data prevents a clear picture of a logical subgroup.
• The data is scarce (therefore subgrouping is not yet practical).
• The natural subgroup needing to be assessed is not yet defined.

Xbar-Range Charts

Another commonly used control chart for continuous data is the Xbar and range (Xbar-R) chart (Figure 8). Like the I-MR chart, it is comprised of two charts used in tandem. The Xbar-R chart is used when you can rationally collect measurements in subgroups of between two and 10 observations. Each subgroup is a snapshot of the process at a given point in time. The chart’s x-axes are time based, so that the chart shows a history of the process. For this reason, it is important that the data is in time-order.

The Xbar chart is used to evaluate consistency of process averages by plotting the average of each subgroup. It is efficient at detecting relatively large shifts (typically plus or minus 1.5 σ or larger) in the process average.

The R chart , on the other hand, plot the ranges of each subgroup. The R chart is used to evaluate the consistency of process variation. Look at the R chart first; if the R chart is out of control, then the control limits on the Xbar chart are meaningless.

Table 1 shows the formulas for calculating control limits. Many software packages do these calculations without much user effort. (Note: For an I-MR chart, use a sample size, n ,  of 2.) Notice that the control limits are a function of the average range (Rbar). This is the technical reason why the R chart needs to be in control before further analysis. If the range is unstable, the control limits will be inflated, which could cause an errant analysis and subsequent work in the wrong area of the process.

Can these constants be calculated? Yes, based on d 2 , where d 2 is a control chart constant that depends on subgroup size.

The I-MR and Xbar-R charts use the relationship of Rbar/ d 2 as the estimate for standard deviation. For sample sizes less than 10, that estimate is more accurate than the sum of squares estimate. The constant, d 2 , is dependent on sample size. For this reason most software packages automatically change from Xbar-R to Xbar-S charts around sample sizes of 10. The difference between these two charts is simply the estimate of standard deviation.

Control Charts for Discrete Data

Used when identifying the total count of defects per unit ( c ) that occurred during the sampling period, the c -chart allows the practitioner to assign each sample more than one defect. This chart is used when the number of samples of each sampling period is essentially the same.

Similar to a c -chart, the u -chart is used to track the total count of defects per unit ( u ) that occur during the sampling period and can track a sample having more than one defect. However, unlike a c -chart, a u -chart is used when the number of samples of each sampling period may vary significantly.

Use an np -chart when identifying the total count of defective units (the unit may have one or more defects) with a constant sampling size.

Used when each unit can be considered pass or fail – no matter the number of defects – a p -chart shows the number of tracked failures ( np ) divided by the number of total units ( n ).

Notice that no discrete control charts have corresponding range charts as with the variable charts. The standard deviation is estimated from the parameter itself ( p , u or c ); therefore, a range is not required.

How to Select a Control Chart

Although this article describes a plethora of control charts, there are simple questions a practitioner can ask to find the appropriate chart for any given use. Figure 13 walks through these questions and directs the user to the appropriate chart.

A number of points may be taken into consideration when identifying the type of control chart to use, such as:

• Variables control charts (those that measure variation on a continuous scale) are more sensitive to change than attribute control charts (those that measure variation on a discrete scale).
• Variables charts are useful for processes such as measuring tool wear.
• Use an individuals chart when few measurements are available (e.g., when they are infrequent or are particularly costly). These charts should be used when the natural subgroup is not yet known.
• A measure of defective units is found with u – and c -charts.
• In a u -chart, the defects within the unit must be independent of one another, such as with component failures on a printed circuit board or the number of defects on a billing statement.
• Use a u -chart for continuous items, such as fabric (e.g., defects per square meter of cloth).
• A c -chart is a useful alternative to a u-chart when there are a lot of possible defects on a unit, but there is only a small chance of any one defect occurring (e.g., flaws in a roll of material).
• When charting proportions, p – and np -charts are useful (e.g., compliance rates or process yields).

Subgrouping: Control Charts as a Tool for Analysis

Subgrouping is the method for using control charts as an analysis tool. The concept of subgrouping is one of the most important components of the control chart method. The technique organizes data from the process to show the greatest similarity among the data in each subgroup and the greatest difference among the data in different subgroups.

The aim of subgrouping is to include only common causes of variation within subgroups and to have all special causes of variation occur among subgroups. When the within-group and between-group variation is understood, the number of potential variables – that is, the number of potential sources of unacceptable variation – is reduced considerably, and where to expend improvement efforts can more easily be determined.

Within-subgroup Variation

For each subgroup, the within variation is represented by the range.

The R chart displays change in the within subgroup dispersion of the process and answers the question: Is the variation within subgroups consistent? If the range chart is out of control, the system is not stable. It tells you that you need to look for the source of the instability, such as poor measurement repeatability. Analytically it is important because the control limits in the X chart are a function of R-bar. If the range chart is out of control then R-bar is inflated as are the control limit. This could increase the likelihood of calling between subgroup variation within subgroup variation and send you off working on the wrong area.

Within variation is consistent when the R chart – and thus the process it represents – is in control. The R chart must be in control to draw the Xbar chart.

Between Subgroup Variation

Between-subgroup variation is represented by the difference in subgroup averages.

Xbar Chart, Take Two

The Xbar chart shows any changes in the average value of the process and answers the question: Is the variation between the averages of the subgroups more than the variation within the subgroup?

If the Xbar chart is in control, the variation “between” is lower than the variation “within.” If the Xbar chart is not in control, the variation “between” is greater than the variation “within.”

This is close to being a graphical analysis of variance (ANOVA). The between and within analyses provide a helpful graphical representation while also providing the ability to assess stability that ANOVA lacks. Using this analysis along with ANOVA is a powerful combination.

Knowing which control chart to use in a given situation will assure accurate monitoring of process stability. It will eliminate erroneous results and wasted effort, focusing attention on the true opportunities for meaningful improvement.

• Quality Council of Indiana. The Certified Six Sigma Black Belt Prime r , Second Edition, Quality Council of Indiana, West Terre Haute, Ind., 2012.
• Tubiak, T.M. and Benbow, Donald W. T he Certified Six Sigma Black Belt Handbook , Second Edition, ASQ Quality Press, Milwaukee, Wisc., 2009.
• Wheeler, Donald J. and Chambers, David S. Understanding Statistical Process Control . SPC Press, Knoxville, Tenn., 1992.

About the Author

Carl Berardinelli

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The Contribution of Variable Control Charts to Quality Improvement in Healthcare: A Literature Review

Line slyngstad.

1 Molde University College, Molde, 6410, Norway

To conduct a literature review to determine where and how variable control charts have contributed to quality improvement in healthcare.

A targeted literature search of the ABI/INFORM Global, Science Direct, Medline and Google Scholar databases was conducted, which yielded 6875 papers. Screening articles on the basis of title and abstract resulted in references to 163 articles, leading to the identification of 29 articles published between 1992 and 2019 that met the inclusion criteria. Common themes, variables and units of analysis were then analyzed.

Variable control charts have been applied in 11 different healthcare contexts, using 17 different variables, at various levels within healthcare organizations. The main reason for applying variable control charts is to demonstrate a process change, usually following a specific change or quality intervention. The study identified various limitations and benefits of applying variable control charts. The charts are visually easy to understand for both management and employees, but they are limited by their requirement for potentially complex and resource-intensive data collection.

Variable control charts contribute to quality improvement in healthcare by enabling visualization and monitoring of variations and changes in healthcare processes. The methodology has been most frequently used to demonstrate process shifts after quality interventions. There still is a great potential for more studies applying variable control charts.

Introduction

Quality of care can be defined as the

degree to which health services for individuals and population increase the likelihood of desired health outcomes and care consistent with professional knowledge. 1

Quality of care can be measured through dimensions, most commonly effectiveness, safety, responsiveness, accessibility, equity and efficiency. 2

In recent decades, quality improvement (QI) strategies and methods from industry have been applied more commonly to healthcare. Quality improvement involves concerted and continuous efforts to make changes that will improve patient outcomes, system performance and professional development. 3 With rapid developments in technology and more complex healthcare systems, it is becoming increasingly important to apply methods to monitor whether changes lead to better quality and patient outcomes.

One of the most challenging tasks for healthcare leadership, is to control the processes leading to the desired quality. Over the last decade, researchers have demonstrated how control charts can be an efficient tool for monitoring processes and quality.

Statistical process control (SPC) methods are management tool for measuring and visualizing stability and monitoring QI from one point in time to another. These methods provide a powerful statistical tool for QI that distinguishes between special-cause variation and common variation. The literature on SPC in healthcare is growing, including literature reviews, tutorials and practitioners note, focusing on everything from general use of SPC in healthcare, 4 , 5 to more specified topics, such as surgery 6 or pressure ulcer prevention. 7 Tutorials do also cover a wide range of areas such as detecting and monitoring hospital acquired infections, 8 charts for data involving very large sample sizes, 9 or the application of risk-adjusted control charts in health care. 10

In addition, SPC is a practical tool that benefits different organizational levels, at the management level and front-line staff levels. Management refers to the leaders of healthcare organizations, and front-line staff is the employees dealing with the patients.

For the service quality to meet patients’ expectations, the process delivery must be stable and repeatable. 11 Quality is sometimes best expressed through numerical measurements, such as the waiting time, 12 length of stay 13 or door-to-needle-time, 14 where it is feasible to use variable control charts. 11 These are SPC charts that use variables as a quality indicator. However, there appears to have been no previous review of the use of variable control charts in the context of health care.

The main objective of this literature review is to examine where and how variable control charts have contributed to QI in healthcare, including their objectives, outcomes, limitations, and benefits, for both management and front-line staff.

Materials and Methods

A targeted literature search in relevant databases was conducted by the author, to identify studies that meets the inclusion criteria. There was one reviewer of this article. Following sections describe the process of selecting articles included in the review.

Eligibility Criteria

Inclusion criteria.

The criteria for inclusion in the literature review were that the research should apply variable control charts in healthcare, and should be conducted in organizations providing care, such as hospitals, nursing homes and home healthcare.

Exclusion Criteria

Articles concerning pharmacies, laboratories and organizations providing mental health care, such as psychiatric hospitals, were excluded from the study, as were studies of random samples of patients across different institutions. Articles examining all levels of institutions were included, from organization to individual performance level. Only empirical articles were included, while tutorials, letters, reviews and books were excluded. All articles reviewed were written in English and published in peer-reviewed journals.

Study Identification

A search of the ABI/INFORM Global, Science Direct, Medline and Google Scholar databases was performed to identify research articles that applied SPC in healthcare for all years to date (2021). The search terms used were: control chart healthcare, statistical process control healthcare, variable control chart application healthcare, Shewhart control chart healthcare, process improvement healthcare, process analysis healthcare, six sigma healthcare, LSS healthcare and total quality management (TQM) healthcare. Relevant articles found in reference lists was included further in the review. When the searches were conducted, title and abstract were crucial if the article was included further in the review. It was often from the title or abstract which charts were used, since the type of variable determines what charts are appropriate to use.

Study Selection and Data Extraction

The title and abstract were reviewed to understand the content and ascertain whether the article met the inclusion criteria. If the inclusion criteria were not met, the article was excluded. Included articles were read in full, and information relevant to this review was extracted and organized in an Excel sheet, including title, authors, year of publication, country, criteria for inclusion, study objective, study outcome, output variable, journal, unit of analysis, study context and level of analysis, and length of period for the data used in the SPC charts. It was also noted whether variable control charts were used with additional methods of analysis, such as regression analysis or interviews. Visual statistics in the form of graphs were used to present relevant information from the data, such as country and year of publication. The review also extracted qualitative information on the objectives of the studies, their outcomes, and the limitations and benefits of applying SPC in healthcare.

Results of the Search

The database search resulted in references to 6875 articles (see Figure 1 ). Screening articles on the basis of title and abstract resulted in references to 163 articles, of which 120 of the records were excluded. A reading of the abstracts suggested that 43 articles might meet the inclusion criteria. 15 of these articles were excluded; 7 applied run chart, 6 used attributes in their control chart, and 4 were was tutorial notes, and 26 of the articles met the inclusion criteria. 13–40 The articles reference list were checked for possible articles, found 3 more. Thus, a total of 29 articles met the inclusion criteria.

Flow diagram.

Characteristics of the Studies

First, the articles were sorted by year of publication and the country in which the research was conducted (see Figure 2 ). The earliest study was published in 1992 41 and the latest in 2019. 38 Both of these were conducted in the USA. Between 1992 and 2006, there were long gaps between publications, but from 2006 they became more frequent. The highest numbers of studies were published in 2014 and 2016, with four in 2014 15 , 24 , 26 , 33 and five in 2016. 16 , 23 , 29–31

Year of publication and country.

Research for 11 of the articles was conducted in the USA, 13 , 18 , 21 , 24 , 28 , 32 , 35–37 , 39 , 42 and one comparative study of the USA and Turkey 38 was also categorized as USA. Three articles each were published in the UK, 19 , 33 , 34 Sweden 23 , 25 , 26 and India, 15–17 and two each in Italy 30 , 31 and Taiwan. 20 , 40 Single studies were conducted in Brazil, 29 Switzerland, 14 Australia 27 and Israel. 20 Because the majority of articles were based on research in the USA, the others were classified into a single “other countries” category.

With regard to content, Table 1 summarizes the research contexts, output variables and units of analysis. Twelve studies were conducted in surgery departments, 13 , 20 , 21 , 24 , 28–32 , 35 , 36 , 39 five in emergency departments 19 , 25–27 , 33 and two in intensive care 14 , 22 and health information departments. 15 , 17 Single studies were conducted in a urology department, 23 internal medicine, 38 a medical record department, 16 a registration department, 37 a women, infants and children clinic 18 and a general practice. 34 One study focused on an entire hospital. 41

Content of Articles

The output variables used varied considerably. The most frequent was waiting time, which was used in six studies. 13 , 18 , 25 , 26 , 34 , 37 Turnaround time was used in four studies, 16 , 17 , 30 , 31 operative time in three 20 , 24 , 32 and non-operative time in two. 32 , 39 The remaining variables were used only once. The most frequent unit of analysis was department level, used in 15 articles, with the remainder conducted at individual patient, individual surgeon, individual observation and organizational levels. The follow up period in the papers, varied from 4 days to 34 months.

Reasons for Applying SPC and the Use of Additional Methods

There were different objectives for applying variable control charts in healthcare. They were generally applied to demonstrate a shift in a process, or for various reasons in retrospective studies. Fifteen studies applied variable control charts to demonstrate a change resulting from an improvement project. 14 , 16–18 , 21 , 23 , 25–27 , 29 , 33 , 35–37 , 40 Waiting time was the most frequently used performance measure, used in four articles. 18 , 25 , 26 , 37 Turnaround time was used as a measure in two articles, 16 , 17 while the other variables were represented only once.

In the remaining studies, the reasons for applying variable control charts were diverse. In some articles, the charts were used to determine steady-state behavior or benchmarks, 20 , 30 , 31 to measure variation or process performance, 15 , 34 , 38 to identify links between changes and opportunities for improvement between hospital and micro- or/macro-systems, 28 to evaluate whether nurse staffing was meeting needs, 22 or for other reasons. 19 , 24 , 39

It was noted that additional methods were often used when applying variable control charts. As illustrated in Figure 3 , variable control charts were the only method used in 12 studies, while additional methods were used in 16 cases. In nine studies that used additional methods, quantitative methods were applied, while in seven articles a mixed-methods approach was adopted.

Were methods other than variable control charts used?

Limitations of Applying Variable Control Charts

Limitations to data collection.

The most common limitation of applying variable control charts is that, for various reasons, collecting data may be resource-intensive and time-consuming. In some cases, where data are not readily available, collection may be complex and require people to collect sufficient data. 19 Examples in the literature include undertaking manual patient chart reviews, 28 collecting data on waiting times through data collection sheets, 37 and measuring turnaround time for health records preparation with a stopwatch. 17 Due to the time and resources required for collection, sample sizes may limit the results, since monitoring a process over time requires several months data. 21 The analysis may therefore be limited to a specific period, such as one day or one shift, which may be unrepresentative of day-to-day occurrences, as the day on which the data were collected may be anomalous. 19 For the similar reasons, data may be limited to one department. 23

Limitations of Data Used in the Charts

A frequently raised issue is that variable control charts must sometimes be used alongside other methods. Variable control charts visualizes the performance of the output variable and do not necessarily have a descriptive function, so they must be supplemented with methods such as semi-structured interviews, 25 , 26 descriptive statistics or value stream mapping. 15 , 16 This is also the case when the data are retrospective and links between particular causes and events in the process cannot be identified. 28 , 35 Such cases require the investigation of out-of-control points by care providers, 38 so additional methods must be used.

Furthermore, observational bias may affect the recording of data, as in the Hawthorne effect, because observing people may improve their performance. 19 In some studies this effect has been observed from the outset of the formal process analysis, but the effects are transient and should not be mistaken for definitive results. 14

Other limitations reported include the difficulty of defining a true baseline or “before” period for comparison when seasonal differences affect the variables, 27 and the fact that traditional control chart approaches make no adjustment for varying risk profiles. Medical contexts must deal with heterogeneous patient cases. Information on patient-related risk adjustment is rarely used, 38 and few articles discuss the use of risk-adjusted Shewhart charts. 20 In some cases, information on risk was unavailable, or it required information from systems to construct patient mix adjustment models, and information from a hospital-wide cost accounting system to measure total in-hospital cost. 21

Like many other statistical methods, variable control charts require the data to be normally distributed. Sometimes they require transformed data, making it more difficult to present the data to others. 21 It mayalso not involve the use of control groups, 18 making generalization more challenging. For instance, when the unit of analysis is a single surgeon, 39 the results are unlikely to be readily transposable to other surgical populations. 35

Benefits of Applying Variable Control Charts

Variable control charts are visually easy to understand.

The selected articles mention several benefits of applying variable control charts. First, charts are reported to be visually easy to understand, and may reveal otherwise unobtainable insights. 18 They may inspire the development of patient diagnosis-to-treatment routines and work process measurements, and enable the visualization of resource use. 23 They may demonstrate whether a process is stable or unstable, 34 and provide a method for assessing undisturbed or steady-state process behavior. 30 Variable control charts measure effects over time through simple statistics. 20 They provide early warning of systematic change taking place in a process, 27 , 30 , 32 , 39 and enables the nature of an identified shift in performance, whether gradual or sudden, to be determined. 39 Applying variable control charts in process analysis may significantly improve the quality of a time-sensitive process. 14

Variable Control Charts are Useful for Front-Line Staff

Some articles suggest that variable control charts are helpful for employees. Front-line staff can see what is happening, with an opportunity to stand back from daily routines to view things differently. 19 Variable control charts may also help to motivate cultural change and moves toward continuous improvement and excellence. 15 , 19

Variable Control Charts are Useful for Management

Variable control charts benefit managers, enabling them to make decisions based on science rather than intuition. 15 The charts can support public health agencies in developing service delivery processes, 18 and allow comparison of an organization with and its partners or competitors. 34 They provide insights into the processes investigated, allow the monitoring of consecutive events, 14 and improve performance quality through early detection of problems. 31 Variable control charts may be used as a monitoring tool for one-off interpretations. 27 They may also help decision makers to achieve institutional goals and objectives, 30 and to pinpoint performance shifts 39 and the direction they are taking. 34

The review findings demonstrate that variable control charts contribute to QI in various healthcare settings, with different types of output variables, at different levels within healthcare organizations. The review also reveals the reasons for applying variable control charts and using additional methods, and it highlights the limitations and benefits of their use. The amount of previous research is less than expected, with only 29 articles meeting the inclusion criteria. The current healthcare environment, involving complex systems, presents enormous potential for using such methodologies in research and practical applications.

As illustrated by Figures 1 and ​ and2, 2 , relevant research has increased since 2006. Primarily in the USA, with only nine studies in other countries. This may reflect a stronger tradition in the USA than in Europe regarding using applications from industry in healthcare.

There are several of benefits and limitations of applying variable control charts to QI in healthcare. The benefits and limitations mentioned in the articles indicate that variable control charts may be a powerful tool, especially in settings where data can easily be obtained. Several papers have noted that the charts are beneficial for both front-line staff and management because the charts are visual, and are good tools for understanding processes and supporting decision-making. Several articles have suggested that the application of variable control charts is limited by the resources required to collect data that are not readily available. Amassing data may require both time and resources that many institutions do not have, which may explain why few studies have applied variable control charts. However, technological developments in IT systems and applications are likely to make data collection easier and make the use of such methodologies more available. The studies included in this review suggest many potential research opportunities in new healthcare settings, as well environments where variable control charts have already been applied.

Variable control charts are a data-driven method. Application of variable control charts would not lead to changes but is often used to demonstrate process variation and effects of different quality interventions, for instance lean 25 or six sigma. 13 When data collection is time-consuming, and frequently used with other methods ( Figure 2 ), it requires clear and present management to apply the charts, which might be an additional reason for the limited number of studies applying variable control charts.

From the origin SPC was applied to monitor variations in manufactured products. 43 Because it sometimes difficult to provide products with equal quality from unit to unit, charts were used to monitor the variability. An example of this is the blade thickness in the production of jet turbines. 11 To date, applying variable control charts in healthcare seems to have the most gain in cases where quality interventions have been made, usually after a process change or quality intervention. In such research, there would typically be more focus on whether a shift has occurred in the process, rather than on monitoring the variation itself. This concerns 15 of the selected articles in this review. The reason for this is uncertain but might relate to the service intangibility and heterogeneity, and the difficulty in research to find feasible measures to monitor. It would typically be easier to concretize a physical product than a service.

Variable control charts have been applied in 11 different healthcare fields, although some of these have been investigated more frequently than others. Some areas and variables have a more obvious connection to the quality of care than others, but most studies have a connection to the most common quality dimensions which are effectiveness, safety, responsiveness, accessibility, and efficiency. 2 The results reveal great potential for applying variable control charts in QI in healthcare.

Twelve of the selected studies were conducted in surgery departments, of which five used operative and non-operative time as output variables. 20 , 24 , 32 , 35 , 39 In these five studies, operative and non-operative time were used to monitor cost and efficiency. In other research, operative and non-operative time are related to safety. For example they correlate the duration of surgery with complications and higher risk. 44 , 45 Longer operating times and increased use of theatres have also been shown to expose patients to greater risk of surgical site infections, 24 and faster operating times are associated with better outcomes. 46 Although variable control charts could be important for monitoring cost and efficiency, there might be potential for other measurements to be applied to SPC charts from surgery departments.

Concerning other measures monitored in surgery, reduced costs and increased efficiency seem to motivate the monitoring of turnaround time, first-case on-time start, and first-case tardiness. 21 , 29–31 , 36 For first-case tardiness, delays, and patient satisfaction are mentioned as important factors. 29 Days between incidences of HAPUs are considered “never events” that should be minimized. 28 In the research in the review, cost, efficiency, safety and accessibility were cited as the primary reasons for monitoring operating rooms.

Eight studies were conducted in contexts where processes were highly time-sensitive, emergency departments 19 , 25–27 , 33 and intensive care. 14 , 22 In such settings, time may have significant consequences for the quality of treatment and patient outcomes. For instance, when treating myocardial infarction, 14 , 40 door-to-needle and cycle times must be reduced, because treatments are most effective in the first hours. 47 Variable control charts could provide valuable quality information when monitoring length and variation in time in such settings. Another example where variable control charts could provide valuable information, is ambulance response time after different interventions.

Waiting time is the most frequently used output variable in the articles 13 , 18 , 25 , 26 , 34 , 37 motivated by efficiency and patient safety. Waiting time is a strong indicator of quality and patient satisfaction, 18 , 34 , 37 and may affect mortality rates and lead to inefficient use of resources. 25 , 26 It is broadly accepted in the literature that waiting times may significantly influence healthcare quality. Waiting time affect customer satisfaction, 12 where total waiting time to visit a clinician is one of the most significant predictors. 48 This variable is a strong quality predictor and should be readily available for institutions, it is surprising that it has not received greater attention in the literature on adopting a control chart approach. Unlike many other quality predictors in health care, waiting time is relatively easy to interpret, because it is desirable to minimize it in many settings.

Length of stay is used as an output variable in three articles 13 , 27 , 38 concerning monitoring quality and performance. Although the connection between length of stay and quality of care is unclear in the selected studies, it has been accepted in the literature, 49 and is often used as a metric of efficiency and effectiveness. 50–52

In some studies, the objective of improvement projects was to make processes more efficient by reducing the time spent on time-consuming activities. Time spent on the registration process and preparing medical processes and health records are examples of this. 15–17 In all these cases, variable control charts were used to monitor efficiency and reduce costs. For instance, reducing turnaround time on the preparation of health or medical record may improve department productivity and performance, 16 , 17 or improve the registration process and reduce waiting times in the system. 15

Variable control charts are a data-driven method. This review demonstrates that in health care, variable control charts have been most frequently used to demonstrate process shifts after quality interventions, such as for instance lean and Six Sigma. The review also reveals that even shown that even variable control charts are applied in various context to QI in healthcare, there still is a great potential for more studies applying variable control charts. To date, the surgery department and departments where process are highly time-sensitive, are the most frequent settings of applying variable control charts.

There are both limitations and benefits to applying variable control charts. The data for applying variable control charts often require resources to collect, and the charts are often used with other methods, which most likely explains the scarce amount of research applying variable control charts. Such research would also require clear and consistent management to apply the charts. Despite the limitations for application, the benefits of the charts` may help both employees and leaders understand the processes and monitor changes in healthcare QI.

The author reports no conflicts of interest for this work.

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ChartExpo Survey

Control Charts Explained: A Visual Guide to Process Stability

By ChartExpo Content Team

Introduction

If you’re looking to keep your processes on track, Control Charts are your best buddy. Picture them as the hall monitors of the manufacturing or service process world—they make sure everything’s running smoothly, and nobody’s stepping out of line.

Control Charts help you detect when things are veering off course due to unusual events (that’s your special causes) or just the normal ebb and flow (those are your common causes).

Table of Contents:

What is a control chart, what are the components of a control chart, control chart example, step-by-step: creating a control chart, benefits of control charts: what’s in it for you, control charts rules, why are control charts in quality control a game-changer, when to use a control chart, types of control charts, reading control charts, setting up control charts: a quick guide, constructing and utilizing a control chart, control charts in practice.

• Control Chart Challenges and Solutions
• Advanced Techniques in Control Charting
• Leveraging Control Charts for Enhanced Quality
• Delving into Analysis and Prediction with Control Charts
• Control Charts for Non-Manufacturing Processes

Control Chart Definition

Definition : A Control Chart, also known as a statistical process Control Chart, is a statistical tool used to monitor, control, and improve the quality of processes. It visually displays process data over time and allows you to detect whether a process is in statistical control or not.

In the 1920s, Walter A. Shewhart, while working at Bell Labs, thought, “Hey, why don’t we catch problems before they blow up?” He created the Shewhart chart.

Imagine you’ve got a process – could be anything from brewing the perfect cup of coffee to manufacturing car parts. You want this process to be as predictable as your aunt’s holiday sweaters (spoiler alert: very predictable). That’s where Control Charts swoop in. They’re like the process’s report card, showing you how it’s performing over time. But instead of grades, we’re looking at data points.

The Secret Sauce: UCL, LCL, and All That Jazz

Control Charts have these cool things called the Upper Control Limit (UCL) and Lower Control Limit (LCL). Picture them as the goalposts. If your process is kicking the data ball within these posts, you’re golden. But if it’s kicking them way out of bounds, it’s time to sit down and have a little chat with your process about its life choices.

Control Chart Vs. Run Chart

Now, don’t confuse Control Charts with their distant cousin, the Run Chart. Run Charts are like Control Charts without the superpowers. They show data over time, sure, but they’re missing the control limits. It’s like trying to play soccer without goalposts – where’s the fun in that?

Data Points: The Leading Actors

Each data point is a snapshot, capturing a specific value of your process at a given moment. These are not random numbers but are the heartbeats of your process; each beat telling you how well you’re performing against your set standards. Think of these as individual scenes in a movie, each crucial for the storyline.

Type of Data: Setting the Scene

Data comes in two main genres:

Variables Data:

This type is quantitative, meaning it can be measured on a continuous scale. Think of it as measuring the length of your road trip in miles.

Attributes Data:

This type is qualitative, focusing on the count or presence of a characteristic. It’s akin to counting the number of pit stops you make.

Control Limits: Defining the Drama’s Boundaries

There are two types of control limits:

Upper Control Limit (UCL):

The threshold above which your process might be too erratic or out of control. It’s like setting a speed limit to prevent accidents.

Lower Control Limit (LCL):

The threshold below which your process might also be losing its grip. Together, UCL and LCL frame the stage where the story of your process unfolds, marking the limits of normal variations.

Specification Limits: The Audience’s Expectation

These are the thresholds set by customer requirements or industry standards, outlining the acceptable range of process outputs. They are the critics’ reviews of your movie, setting the bar for what is considered a success and what is deemed a failure.

Trend Lines and Patterns: Foreshadowing and Flashbacks

Just as foreshadowing and flashbacks add depth to a story, revealing underlying themes or hinting at future developments, trend lines and patterns in Control Charts signal underlying changes in your process. These could be gradual improvements, sudden shifts, or recurring issues, each pattern telling its own subplot within the larger narrative.

X-axis (Time or Sequence): The Timeline

Every story unfolds over time, and in the case of Control Charts, the X-axis is the timeline narrating this progression. Whether it’s time, sequence, or any other orderly progression, this axis grounds the data points in a temporal or sequential context, adding depth to the process’s story.

Y-axis (Measurement): The Narrative Scale

Opposite to the X-axis stands the Y-axis, the scale against which the story’s metrics are measured. Be it quality, quantity, or any other measure of performance, the Y-axis quantifies the tale, offering a lens through which to view the data points’ highs and lows.

Let’s say you work at a car manufacturing plant, and your job is to ensure that the paint finish on each car is flawless. You’d use a Control Chart to monitor this. Each day, as each car comes off the line, it’s inspected for any imperfections in the paint.

1.  Collect Data:

Each car is checked, and the number of paint flaws is recorded daily.

2.  Plot the Data:

These numbers are plotted on a Control Chart.

3.  Determine Limits:

You calculate the average flaws per day and set upper and lower control limits. This might be based on historical data, where you say, “Okay, if we stay within these bounds, we’re good. If not, it’s trouble.”

Over the weeks, you notice the points on the chart are creeping up, inching closer to that upper control limit.

Alarm bells ring!

This trend could indicate a problem with the paint spraying equipment or maybe the quality of the paint itself. Because you’ve been tracking this data, you catch the issue early. You flag it, the equipment gets inspected and voila – a potential crisis is averted. The process is tweaked, and the number of flaws goes back down, well within your happy limits.

Using a Control Chart is like having a health monitor for your manufacturing process . In our car paint example, it helped maintain high-quality standards and prevent the extra costs of rework and unhappy customers. And while our example didn’t feature thrilling car chases or dramatic explosions, remember, in the world of quality control, no news is good news. Keeping things boringly consistent is exactly what you want!

By integrating Control Charts into your operations, you’re not just adopting a tool; you’re embracing a culture of continuous improvement. They empower you to manage processes proactively, enhance decision-making , save costs through early detection, and consistently meet quality standards. The result? A smoother, more predictable workflow that not only meets but exceeds expectations.

Enhanced Decision-Making

Control Charts are not just about tracking data; they’re about making informed decisions quickly. With real-time feedback on process variations, you can make adjustments before small issues become big problems. Whether it’s a spike in temperature on a production line or a sudden shift in software test outcomes, Control Charts highlight these changes, allowing you to act swiftly and decisively.

Proactive Problem Solving

Imagine being able to predict a storm before it hits. Control Charts function similarly by identifying trends and patterns that could lead to defects or inefficiencies. By understanding these trends, you can implement preventive measures, avoiding the costs and disruptions of firefighting after the fact.

Improvement of Process Stability and Quality

Stability is king in any process. Control Charts help maintain this stability by signaling when processes are deviating from their intended path. Consistent quality is crucial, and with Control Charts, you can ensure that your product or service remains top-notch, meeting both compliance standards and customer expectations.

Cost Reduction Through Early Detection

The early bird catches the worm, and the early user of Control Charts detects issues before they escalate into costly errors. By spotting a deviation early, you can save substantial resources and expenses that would otherwise go towards rectifying defects, not to mention avoiding the potential loss of customer trust.

Boost in Productivity

With Control Charts, your team won’t waste time guessing about the state of your processes. They provide a clear picture of performance, identifying whether variances are within acceptable limits or if corrective action is needed. This clarity leads to more effective team actions and less downtime, thereby boosting overall productivity.

Control Chart rules or guidelines are used to interpret Control Charts, helping to identify patterns that suggest a process is out of statistical control. Here are some common Control Chart rules:

One Point Beyond Control Limits:

Any single point outside the control limits on a Control Chart suggests an out-of-control process.

Two of Three Points Beyond 2 Sigma:

If two out of three consecutive points fall beyond the 2 sigma (standard deviation) limit from the centerline and on the same side, this suggests a shift in the process.

Four of Five Points Beyond 1 Sigma:

If four out of five consecutive points are more than 1 sigma away from the mean and on the same side, it may indicate a trend.

Run of Seven on One Side:

A sequence of seven consecutive points on one side of the mean suggests a potential shift in the process mean.

Increasing or Decreasing Trend:

Six (or more) consecutive points continuously increasing or decreasing indicates a trend.

Cyclic Patterns:

Repeating patterns over a set of points may suggest a cyclical process influence.

Astronomical Point:

An extraordinarily high or low point, even if within control limits, could be significant and warrant investigation.

The Big Reveal: Spot Trends and Changes

Control Charts excel in revealing trends and shifts in your process over time. They’re like that friend who notices the slight change in your mood before anyone else does. Spotting these trends early on means you can tweak things before they spiral out of control.

The Fine Line Between Normal & Not-so-normal

Ever wonder if a change in your process is just a fluke or something to worry about? Control Charts help you distinguish between normal process variability and unusual occurrences that need your attention. It’s the difference between shrugging off a single cloudy day and preparing for a full-blown storm.

Continual Improvement Made Simple

By tracking how changes affect your process, Control Charts pave the way for continuous improvement. It’s about making informed decisions that lead to better, more efficient processes. Think of it as leveling up in a game, where each improvement gets you closer to your goal.

Communicate with Clarity

Imagine trying to explain how your project is doing without getting lost in technical jargon. Control Charts translate complex data into a language everyone can understand, making it easier to communicate status and needs with your team or stakeholders.

Understanding when to employ Control Charts can significantly boost your process management capabilities. Simply put, a Control Chart is a dynamic tool that tracks process performance over time, distinguishing between normal process variation and anomalies that require attention.

Choosing the Right Control Chart

The first step in harnessing the power of Control Charts is selecting the appropriate chart type based on your data type. Whether it’s measuring defects per unit with a U-chart or monitoring the mean and range of sample groups with an X-bar and R chart, picking the right chart ensures accurate monitoring.

Optimal Data Collection Period

Determining the appropriate timeframe for data collection and plotting is crucial. Typically, this involves capturing data that reflect normal operations but are sufficient to identify potential variations. The length of this period can vary, but it should be long enough to establish a reliable measure of process stability.

Step-by-Step Guide to Using Control Charts

1.    collect data:.

Start by gathering data in a sequential manner. For instance, if monitoring production quality, record the relevant metrics daily.

2.    Construct Your Chart:

With your data in hand, plot them on the chosen Control Chart format. Calculate and mark your control limits based on statistical methods (typically set at three standard deviations from the mean).

3.    Analyze the Data:

Look for patterns or points outside the control limits. These are signals that could indicate an out-of-control process needing investigation.

4.    Mark and Investigate Out-of-Control Signals:

Whenever a data point falls outside the upper or lower control limits, mark it and investigate the cause. This could involve a deep dive into production anomalies, a sudden change in materials, or an unexpected operational hiccup.

5.    Document Everything:

Record your findings and the steps taken to address any issues. This documentation is vital for tracing the root cause and validating process improvements.

6.    Continue Monitoring:

As new data points are generated, continue to plot them on the chart and check for new signals. This ongoing vigilance helps maintain process control and quality.

7.    Recalculate Control Limits:

If starting a new chart or after making significant changes to the process, recalculate your control limits using the new data, especially once you have at least 20 sequential points indicating stable process operation.

Imagine a bakery monitoring the weight of a batch of loaves. The baker uses an X-bar chart to track the average weight per batch and an R chart for the range. Over time, any data point that falls outside the calculated control limits may indicate a problem in the ingredient mixing process or oven performance. Investigating these anomalies ensures that each loaf meets the bakery’s standards for quality and consistency.

Each type of Control Chart has its advantages and is suitable for different types of processes and data distributions. Choosing the appropriate chart depends on the specific characteristics of the process being monitored and the objectives of the quality control program.

Here’s a list of different types of Control Charts, grouped into categories based on their applications and characteristics:

Variable Control Charts:

X-bar and r chart (mean and range chart):.

Monitors the process mean and variability by plotting the sample means (X-Bar) and ranges (R) from subgroup data.

X-Bar and S Chart (Mean and Standard Deviation Chart):

Similar to X-Bar and R chart, but it uses standard deviation (S) instead of range (R) to estimate process variability.

Individuals and Moving Range (I-MR) Chart:

Suitable for processes where it’s not practical to take multiple measurements per subgroup. It plots individual values and the moving range between consecutive points.

MA (Moving Average) Chart:

Plots the moving average of a process over time, smoothing out random variation to highlight trends.

MR (Moving Range) Chart:

Monitors the moving range of consecutive data points to detect shifts in process variability.

Attribute Control Charts:

P chart (proportion chart):.

Monitors the proportion of defective items in a sample.

NP Chart (Number of Defects Chart):

Tracks the number of defects per unit in a sample.

C Chart (Count of Defects Chart):

Used when the number of defects per unit can vary, but the size of the unit is constant.

U Chart (Defects per Unit Chart):

Tracks the average number of defects per unit of output.

Time-Weighted Control Charts:

Cusum (cumulative sum) chart:.

Tracks the cumulative sum of deviations from a target value, helping detect small shifts in the process mean.

EWMA (Exponentially Weighted Moving Average) Chart:

Combines information from all the data points in the process history, giving more weight to recent data. It’s sensitive to small shifts in process mean.

Multivariate Control Charts:

T^2 (hotelling’s t-squared) chart:.

Used when monitoring multiple correlated variables simultaneously. It detects shifts in the mean vector of the variables.

MEWMA (Multivariate Exponentially Weighted Moving Average) Chart:

Extension of EWMA for multivariate analysis processes is useful for monitoring shifts in mean vector and covariance matrix.

Other Specialized Control Charts:

G chart (gage chart):.

Used for monitoring the variability in measurement systems.

E Chart (Exponentially Weighted Moving Average Range Chart):

A variation of EWMA is used for monitoring process dispersion.

A Control Chart used in a subgroup of one to monitor process variability.

Levey-Jennings Chart:

Common in laboratory settings for monitoring instrument output.

General Structure and Setup

Diving into Control Charts, think of them as your process’s EKG—always monitoring the heartbeat of your operations:

Baseline Establishment:

First up, we need a baseline. Is your process stable? If it’s as steady as a surgeon’s hand, you’re good to go. Otherwise, stabilize before you analyze!

Control Limits:

These aren’t random boundaries; they are meticulously calculated at 3 sigma levels above and below your process’s average. Make sure these calculations are as precise as a clockmaker’s gears.

Identifying the Usual Suspects: Common and Special Cause Variations

Every process whispers its secrets through variations:

Common Cause Variations:

Think of these as your process’s personality—consistent, predictable quirks caused by the usual suspects like machine wear or environmental shifts.

Special Cause Variations:

These are the alarm bells. Something unusual happens, and it’s not part of the routine. A hiccup like a sudden machine breakdown or a material defect needs your immediate attention.

Spotting Patterns: What’s Your Chart Telling You

Patterns in your data can tell stories of underlying issues or changes:

Spotting regular up and down patterns? You might be looking at seasonal effects or predictable wear and tear.

Trends and Shifts:

Data trending upwards or downwards? Or perhaps a sudden jump in the average? Time to dig deeper and find out why.

Close Encounters with Control Limits

Keeping an eye on where your data points fall can save the day:

Out-of-Control Points:

These outliers are your red flags waving high. Something’s off, and it’s time to troubleshoot.

Near Limit Points:

Not quite out of bounds but too close for comfort. Keep a watchful eye here; trouble might be brewing.

Variability: The Devil’s in the Details

Consistency is key in any process:

Consistency of Variation:

Is the spread of data around your average increasing? That’s a sign of growing variability, which is as welcome as a bull in a china shop.

Clustering:

A lot of data points huddling together? It could mean your process variation is tightening up.

Rule-based Analysis: Following the Control Chart Commandments

Applying some tried-and-true rules can highlight issues that need your attention:

Western Electric and Nelson Rules:

These aren’t just guidelines; they are the guardians of your process stability. They help pinpoint non-random patterns that scream for your attention.

Stratification: Too Good to Be True?

Lack of dispersion:.

If everything’s too close to the average, you might be over-tuning your process or not capturing data variability effectively.

Collecting Data for Control Charts: Ensuring Precision

The foundation of any Control Chart lies in the data it represents. To ensure the data is accurate and useful, follow these detailed steps for optimal data collection:

Define Your Data Collection Criteria:

Select key variables:.

Identify which variables are critical to your process and need monitoring.

Determine Data Types:

Decide whether you need continuous (measurements) or attribute (count) data based on the process.

Utilize Appropriate Measurement Instruments:

Calibration:.

Regularly calibrate instruments to prevent drift and ensure consistent data quality.

Verification:

Use secondary methods to verify instrument readings periodically.

Establish a Data Collection Schedule:

Determine how frequently data should be collected to adequately monitor the process without overburdening the system.

Batch vs. Real-Time:

Decide whether to collect data in batches or in real-time, depending on process dynamics.

Educate and Train Data Collectors:

Consistency:.

Train all personnel on proper data collection techniques to maintain uniformity.

Documentation:

Create detailed protocols for data collection to serve as a reference.

Calculating Control Limits: A Step-by-Step Guide

Calculating control limits establishes the boundaries of expected variations in your process. Here’s a detailed method to accurately calculate these limits:

Calculate the Mean (X-bar):

Sum all the measurements and divide by the number of observations to find the process mean.

Determine the Average Range (R-bar):

Calculate the range (difference between the highest and lowest values) for each subgroup of data.

Average these ranges to find R-bar.

Apply the Appropriate Statistical Factors:

Factor selection:.

Depending on the sample size and distribution type, select the appropriate A2, D3, and D4 factors from standard SPC tables.

Calculation of Limits:

Use these formulas:

Upper Control Limit (UCL) = X-bar + (A2 * R-bar)

Lower Control Limit (LCL) = X-bar – (A2 * R-bar)

You can create a Control Chart in your favorite spreadsheet. Follow the steps below to create a Control Chart.

Steps to make a Control Chart in Microsoft Excel:

• Open your Excel Application.
• Install ChartExpo Add-in for Excel from Microsoft AppSource to create interactive visualizations.
• Select Control Chart from the list of charts.
• Select your data
• Click on the “Create Chart from Selection” button.
• Customize your chart properties to add header, axis, legends, and other required information.
• Export your chart and share it with your audience.

The following video will help you to create a Control Chart in Microsoft Excel.

Steps to make a Control Chart in Google Sheets:

• Open your Google Sheets Application.
• Install ChartExpo Add-in for Google Sheets from Google Workspace Marketplace.
• Fill in the necessary fields
• Click on the “Create Chart” button.

The following video will help you to create a Control Chart in Google Sheets.

Control Charts are more than just lines on a graph. They’re your guide to a smoother, smarter operation. Keep ’em close, and you’ll be on top of your game. Ready to chart a course to success? Let’s roll up our sleeves and get to it!

Using Control Charts for Process Monitoring: Key Strategies

Ever wondered how the pros keep an eye on manufacturing processes without breaking a sweat? Enter Control Charts. These handy tools aren’t just graphs; they’re the secret weapon for monitoring your processes and knowing exactly when to yell, “Hey, something’s fishy here!”

Here’s the scoop: keep your charts updated and watch for trends like a hawk. See a line creeping out of the normal zone? Time to jump in before things go haywire. Remember, consistency is your best friend when it comes to quality control.

Detecting Changes in Process Behavior: Early Warning Signs

Now, let’s talk about being a process detective. Changes in your process can be sneaky, but Control Charts are like having a magnifying glass. One popular trick is using Western Electric rules — think of them as the Sherlock Holmes of process monitoring. These rules help you spot the little changes before they turn into big problems. It’s all about catching those outliers and saying, “Aha, gotcha!” before they mess up your whole operation.

Action on Findings: Making Smart Decisions

Caught a red flag on your Control Chart? Don’t panic. It’s decision time, and here’s how you handle it: First, figure out if what you’re seeing is a fluke or a real trend. Next, dive into some root cause analysis — play detective and trace the issue to its source. Once you know the culprit, decide if you need a quick fix or a major overhaul. This isn’t just busywork; it’s about making your process leaner and meaner.

Using Process Control Charts for Monitoring

Regular monitoring:.

For real-time insight into process performance, Control Charts should be updated and reviewed regularly. This ensures any deviations are caught early and can be investigated promptly.

Training and Engagement:

Ensure that all team members understand how to read and interpret Control Charts. Engaged employees are more likely to take ownership of their processes and contribute to improvements.

Integration with Other Quality Tools:

Use Control Charts in conjunction with other tools such as Pareto charts and histograms . This integrated approach provides a deeper understanding of the data and facilitates effective decision-making.

Detecting Changes in Process Behavior

Western electric rules:.

These rules provide guidelines for detecting signs of out-of-control conditions. For instance, any single data point beyond the control limits, or two out of three successive points near the control limit, signals a potential issue.

Trend Analysis:

Regular analysis of the Control Charts can reveal trends that indicate process shifts or drifts before they reach critical limits. This proactive approach allows for adjustments before the process produces defects.

Action on Findings

Immediate response vs. further investigation:.

When Control Charts indicate an out-of-control process, determine if the cause is an inherent part of the process (common cause) or an external factor (special cause). Immediate adjustments are necessary for special causes, while common causes might require a deeper process analysis.

Root Cause Analysis:

Utilize tools like the fishbone diagram to delve deeper into underlying issues. This thorough investigation prevents recurrent problems and ensures sustainable process improvements.

Control Chart Challenges and Solutions: Navigating the Tides of Quality Management

Managing a process with precision requires a keen understanding of its variables, and a good deal of savvy problem-solving. Let’s break down these issues with the energy of a pep rally and the accuracy of a Swiss watchmaker (minus the watch, of course).

Dealing with Out-of-Control Points: A Detective’s Guide

When your Control Chart waves the red flag of an out-of-control point, don’t just stand there—investigate! Think of yourself as a quality control detective.

First, confirm if the chaos is real or just a false alarm—a statistical hiccup, so to speak. If it’s the real deal, dive into a root cause analysis. Was there a sudden material change? A new operator who’s still learning the ropes?

Or perhaps, it’s just Tuesday behaving like Tuesday. Whatever the case, identifying and addressing these causes promptly ensures that your process isn’t just running but galloping smoothly.

Adjusting Control Limits: When and How

Imagine you’ve fine-tuned your process, and things are looking up—quality is the best it’s been in years. Here’s where recalculating your control limits comes into play. If significant and sustained improvements are evident, it’s time to adjust these limits to reflect the new reality.

This isn’t just busywork; it ensures your Control Charts remain effective guardians of process stability. Keep in mind that recalculating without substantial reason can lead to confusion—a situation as unwelcome as soggy fries at a gourmet burger joint.

Addressing Non-Normal Data: Charting a New Course

Not all data plays nice. Non-normal data is like that one friend who never follows the movie plot. Here, traditional Control Charts might give you the slip.

Fear not! A transformation of your data might just bring it back in line. Whether it’s a logarithmic transformation or a square root adjustment, tweaking your data to fit the mold can work wonders.

If that sounds about as appealing as last year’s leftovers, alternative chart types like Individual-Moving Range (I-MR) charts or Cumulative Sum (CUSUM) charts might be your ticket to clarity.

In the end, mastering these challenges with Control Charts isn’t just about sticking to the rules—it’s about knowing when to bend them creatively and effectively. Keep these insights in your quality control toolkit, and you’ll not only maintain the upper hand over your process variability but maybe even add a little flair to the art of process control.

After all, who says quality management can’t have a bit of character?

Mastering Precision: Advanced Techniques in Control Charting

In the fast-paced world of manufacturing and quality control, traditional Control Charts have been the backbone of statistical process control (SPC). However, with evolving process demands and increasingly complex data, advanced Control Chart techniques have become essential. These techniques enable more precise monitoring and adjustment of processes, ensuring higher quality and efficiency.

Short Run Control Charts: Small Data, Big Insights

Ever tried to measure something scarce but vital? That’s where Short Run Control Charts shine. Ideal for small-scale productions or infrequent batches where data feels like gold dust, these charts help businesses make sense of limited information without losing their minds.

Imagine a boutique bakery specializing in custom wedding cakes. Each cake is unique—like snowflakes, but tastier. By using short run Control Charts, the bakery can ensure each batch of their limited-edition frosting meets quality standards without the need to produce large quantities that no one asked for.

Multivariate Control Charts: Watching Multiple Pots

Multitasking isn’t just a skill for the overly ambitious office worker; it’s also crucial in monitoring complex processes. Multivariate Control Charts are the unsung heroes here. They watch over multiple related quality characteristics simultaneously, ensuring that if something goes awry, it’s caught on the radar early.

Consider a high-tech company manufacturing smartphones. A multivariate Control Chart can track battery life, screen brightness, and button responsiveness in one go. If the screen starts dimming while the battery drains faster than a bathtub, the chart’s the first to shout, “Something’s wrong!”

Adaptive Control Charts: The Smart Adjusters

In a world where change is the only constant, Adaptive Control Charts are your best pals. These charts are like chameleons, adjusting their control limits based on real-time data to better reflect the current process behavior. They’re perfect for processes that evolve faster than a viral TikTok dance.

Picture a software development team rolling out updates faster than you can say “bug fix.” An adaptive Control Chart helps monitor the defect rates across versions, dynamically adjusting control limits as new updates are released and old bugs are squashed.

Integration with Improvement Methodologies: Leveraging Control Charts for Enhanced Quality

Each of the following examples underscores the adaptability of Control Charts in diverse environments, demonstrating their role in not only identifying and correcting outliers but also in driving continuous improvement.

Rev Up Your Six Sigma Engine with Control Charts

Six Sigma thrives on eliminating defects and variability in processes. Control Charts, or what the pros might call ‘process behavior charts’, serve as the backbone for this mission. By weaving Control Charts into the DMAIC (Define, Measure, Analyze, Improve, Control) phases, teams can literally watch variability squirm under the statistical spotlight. What’s the upshot? A data-driven path to process improvement that’s as clear as day.

Key Play: DMAIC Integration

What’s the problem? Control Charts kickstart the journey by highlighting process stability over time.

Crunch the numbers. With Control Charts, you spot trends and shifts faster than you can say ‘baseline’.

Seek and destroy. Identify causes of variations—Control Charts help pin them down.

Make your move. Implement changes and watch the Control Chart for signs of improvement.

Lock it down. Continuous monitoring with Control Charts ensures the process stays on its best behavior.

Creating Synergy: Combining Control Charts with Other Quality Tools

Control Charts are not used in isolation. Their integration with other quality tools such as fishbone diagrams, and 5 Whys analysis amplifies their effectiveness. This synergy allows for a more holistic approach to problem-solving:

Fishbone Diagrams:

These diagrams help in drilling down to the root causes of process variations highlighted by Control Charts. This combination is particularly powerful during the “Analyze” phase of DMAIC, as it ensures that solutions address the fundamental causes of process issues.

5 Whys Analysis:

This iterative interrogative technique complements the quantitative data from Control Charts with qualitative analysis. By asking “why” repeatedly, teams can uncover deeper insights into the reasons behind process variability or failures.

Real-World Applications: Demonstrating the Impact through Case Studies

Case studies across various industries illustrate the practical applications and benefits of Control Charts:

Healthcare:

Hospitals employ Control Charts to track patient wait times and treatment errors. These charts help maintain high standards of patient care and meet regulatory compliance.

Financial institutions use Control Charts to track transaction processing times and error rates, ensuring high efficiency and customer satisfaction .

Beyond Monitoring: Delving into Analysis and Prediction with Control Charts

Control Charts not only serve as tools for monitoring but also as pivotal instruments for deeper analysis and predictive measures. Here’s how you can transform ordinary monitoring into strategic foresight and proactive management.

Using Control Charts for Root Cause Analysis

Imagine you’re a detective, but instead of chasing crooks, you’re hunting down the reasons behind process variations or defects.

Enter the Control Chart, your trusty sidekick in this endeavor. By plotting data over time and marking out the highs and lows (upper and lower control limits), these charts spotlight the outliers in your process.

Let’s say you’re producing widgets, and suddenly, the defect rate spikes. A quick glance at your Control Chart shows several points outside the normal range. Digging deeper, you trace it back to a batch of subpar raw materials used one fine Tuesday afternoon. Bingo! You’ve found your culprit.

Predictive Capabilities of Control Charts

Now, what if you could see into the future?

With Control Charts, you kind of can. By analyzing patterns within the limits, you can forecast potential issues and nip them in the bud.

Consider a brewery monitoring the fermentation process. A Control Chart might reveal a gradual trend towards higher temperatures. Before your brew turns into a bitter disappointment, you adjust the cooling system, preventing a batch of bad beer and unhappy customers.

Statistical Process Control (SPC) for Process Improvement

Control Charts aren’t just for spotting trouble; they’re also about making good processes great. Statistical Process Control (SPC) uses these charts to fine-tune your operations systematically.

A car manufacturer tracks the alignment of headlights. Over time, the Control Chart reveals slight deviations that are still within limits but trending off-center. By recalibrating their equipment regularly, based on insights from the chart, they ensure every car meets their exacting standards right off the assembly line.

Control Charts for Non-Manufacturing Processes: A Practical Guide

Imagine you’re the manager of a bustling hotel, or at the helm of a busy customer service call center, or running a retail empire. No matter the setting, the quest for quality is universal. Enter Control Charts, not just a manufacturing mainstay but a versatile tool tailored for any process-oriented domain, be it hospitality, retail, or services.

Applying Control Charts Across Non-Manufacturing Sectors

Hospitality industry.

For hotels, the guest experience can be quantified and analyzed through various metrics such as check-in and check-out efficiency, room service speed, and cleanliness scores. Control Charts help in maintaining consistently high standards that keep guest complaints at bay and satisfaction scores on the rise.

Customer Service Call Centers

In the dynamic environment of a call center, Control Charts can be pivotal. They help monitor the average call handling time, ensuring efficiency without sacrificing customer satisfaction. Key performance metrics like call duration, resolution rate, and customer follow-up times can all be visualized and controlled, ensuring the service quality remains high and consistent.

Retail Sector

Retail managers can use Control Charts to track inventory levels, sales rates, and customer foot traffic. These charts assist in maintaining the delicate balance between overstock and stockouts, ensuring promotional campaigns are effective and the checkout process is swift, enhancing the overall customer shopping experience.

Customizing Control Charts for Unique Environments

Banking and insurance.

In banking, Control Charts can monitor transaction processing times and customer wait times, which is crucial for improving service delivery.

Insurance claim processing, another critical measure, can also be optimized by identifying bottlenecks and reducing variability in claim handling.

Logistics and Supply Chain

Supply chains benefit significantly from Control Charts by monitoring shipment times, reducing variability in delivery schedules, and ensuring consistency in product quality. These charts help logistics managers pinpoint process inefficiencies, leading to timely and cost-effective supply chain solutions.

Control Charts Frequently Asked Questions (FAQs)

What is the use of a control chart.

Ever wonder if your process is performing consistently or if those little hiccups are just flukes? That’s where a Control Chart comes into play. It’s a fantastic tool that lets you visualize the stability of your process over time. Whether you’re manufacturing widgets or processing paperwork, Control Charts help you see the story behind your process variations—pinpointing when things are just random noise or when something’s seriously off.

What is UCL and LCL in a Control Chart?

In Control Charts, UCL (Upper Control Limit) and LCL (Lower Control Limit) are like the boundaries of a playground. They define the limits of expected process variation. Stay within these lines, and everything’s peachy; stray outside them, and it’s a signal that you might need to take a closer look at your process. Think of UCL and LCL as your process’s cheerleaders, keeping everything in check.

How to interpret Control Charts?

Interpreting Control Charts is a bit like reading tea leaves, but with data. If your data points are randomly scattered within the control limits, your process is in control. But if you spot patterns like continuous points beyond the limits, or a run of points on one side of the centerline, it’s time to play detective—something’s influencing your process.

How to create a Control Chart?

Creating a Control Chart isn’t rocket science. Start with your data—measurements from your process. Plot these over time, calculate the average, and determine your control limits (UCL and LCL). Software tools such as ChartExpo can make this easier, but the gist is to map out your data, watch how it behaves, and establish the boundaries it typically operates within.

How to calculate control limits (upper and lower)?

Calculating control limits might sound daunting, but here’s a quick guide:

• Calculate the mean (average) of your dataset.
• Determine the standard deviation (a measure of variation).
• Set the UCL and LCL typically as the mean plus or minus three times the standard deviation. This formula might vary depending on the type of data and the specific Control Chart, but it’s a good starting point.

In wrapping up our journey through the intricacies of Control Charts, remember, these tools are not just about monitoring; they’re about empowering your continuous improvement processes. By integrating Control Charts effectively, you harness the ability to predict and pre-empt, turning potential pitfalls into powerful strides towards excellence.

Let your data speak, but ensure you’re fluent in its language. Control Charts are not merely tools; they are the translators of your process’s story.

Listen closely, and lead your operations not just with insight, but with foresight.

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Control charts in healthcare quality improvement. A systematic review on adherence to methodological criteria

Affiliation.

• 1 Antonie Koetsier, MSc, Dept. of Medical Informatics, Academic Medical Center, Room J1b-115-2, P.O. Box 22700, 1100 DE Amsterdam, The Netherlands. [email protected]
• PMID: 22476327
• DOI: 10.3414/ME11-01-0055

Objectives: Use of Shewhart control charts in quality improvement (QI) initiatives is increasing. These charts are typically used in one or more phases of the Plan Do Study Act (PDSA) cycle to monitor summaries of process and outcome data, abstracted from clinical information systems, over time. We summarize methodological criteria of Shewhart control charts and investigate adherence of published QI studies to these criteria.

Methods: We searched Medline, Embase and CINAHL for studies using Shewhart control charts in QI processes in direct patient care. We extracted methodological criteria for Shewhart control charts, and for the use of these charts in PDSA cycles, from textbooks and methodological literature.

Results: We included 34 studies, presenting 64 control charts of which 40 control charts plotted two phases of the PDSA cycle. The criterion to use 10-35 data points in a control chart was least adhered to (48.4% non-adherence). Other criteria were: transformation of the data in case of a skewed distribution (43.7% non adherence), when comparing data from two phases of the PDSA cycle the Plan phase (the first phase) needs to be stable (40.0% non-adherence), using a maximum of four different rules to detect special cause variation (14.1% non-adherence), and setting control limits at three standard deviations from the mean (all control charts adhered).

Conclusion: There is room for improvement with regard to the methodological construction of Shewhart control charts used in QI processes. Higher adherence to all methodological criteria will decrease the risk of incorrect conclusions about the process being monitored.

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• Grand rounds in methodology: four critical decision points in statistical process control evaluations of quality improvement initiatives. Marang-van de Mheen PJ, Woodcock T. Marang-van de Mheen PJ, et al. BMJ Qual Saf. 2023 Jan;32(1):47-54. doi: 10.1136/bmjqs-2022-014870. Epub 2022 Sep 15. BMJ Qual Saf. 2023. PMID: 36109158
• Systematic review of the application of the plan-do-study-act method to improve quality in healthcare. Taylor MJ, McNicholas C, Nicolay C, Darzi A, Bell D, Reed JE. Taylor MJ, et al. BMJ Qual Saf. 2014 Apr;23(4):290-8. doi: 10.1136/bmjqs-2013-001862. Epub 2013 Sep 11. BMJ Qual Saf. 2014. PMID: 24025320 Free PMC article. Review.
• Caring for hearts and minds: a quality improvement approach to individualized developmental care in the cardiac intensive care unit. Butler SC, Rofeberg V, Smith-Parrish M, LaRonde M, Vittner DJ, Goldberg S, Bailey V, Weeks MM, McCowan S, Severtson K, Glowick K, Rachwal CM. Butler SC, et al. Front Pediatr. 2024 Apr 9;12:1384615. doi: 10.3389/fped.2024.1384615. eCollection 2024. Front Pediatr. 2024. PMID: 38655280 Free PMC article.
• Monitoring surgical quality: the cumulative sum (CUSUM) approach. Novoa NM, Varela G. Novoa NM, et al. Mediastinum. 2020 Mar 25;4:4. doi: 10.21037/med.2019.10.01. eCollection 2020. Mediastinum. 2020. PMID: 35118272 Free PMC article. Review.
• Country Characteristics and Variation in Diabetes Prevalence among Asian Countries - an Ecological Study. Widyahening I, Kayode G, Wangge G, Grobbee D. Widyahening I, et al. J ASEAN Fed Endocr Soc. 2019;34(1):80-86. doi: 10.15605/jafes.034.01.12. Epub 2019 May 7. J ASEAN Fed Endocr Soc. 2019. PMID: 33442140 Free PMC article.
• Effect of monitoring surgical outcomes using control charts to reduce major adverse events in patients: cluster randomised trial. Duclos A, Chollet F, Pascal L, Ormando H, Carty MJ, Polazzi S, Lifante JC; SHEWHART Trial Group. Duclos A, et al. BMJ. 2020 Nov 4;371:m3840. doi: 10.1136/bmj.m3840. BMJ. 2020. PMID: 33148601 Free PMC article. Clinical Trial.
• Effectiveness of a Daily Rounding Checklist on Processes of Care and Outcomes in Diverse Pediatric Intensive Care Units Across the World. Kashyap R, Murthy S, Arteaga GM, Dong Y, Cooper L, Kovacevic T, Basavaraja C, Ren H, Qiao L, Zhang G, Sridharan K, Jin P, Wang T, Tuibeqa I, Kang A, Ravi MD, Ongun E, Gajic O, Tripathi S; SCCM Discovery CERTAINp Collaborative Investigators. Kashyap R, et al. J Trop Pediatr. 2021 Jul 2;67(3):fmaa058. doi: 10.1093/tropej/fmaa058. J Trop Pediatr. 2021. PMID: 32853362 Free PMC article.

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7 QC Tools for Process Improvement | PDF | Case Study

• What are 7 QC Tools?

→ 7 QC tools are systematic and scientific methods for problem-solving.

→ Also, they are used for product and process improvement.

→ They are used to solve almost 90% of shop floor problems very easily.

Table of Contents:

• History of 7 QC Tools

When to Use the Basic 7 QC Tools?

Why to use the 7 qc tools.

• The Basic 7 QC Tools For Process Improvement
• Cause and Effect Diagram (Fishbone or Ishikawa) 
• Pareto Chart
• Scatter Diagram
• Control Chart
• Benefits of 7 QC Tools
• Limitations of 7 QC Tools

History of 7 QC Tools:

→ The Basic 7 Quality Control Tools originated after World War II in Japan.

→ Dr. Edwards Deming has played an important role in introducing statistical quality control methods.

→ He recommends the use of statistical methods to improve manufacturing quality.

→ After his work, Japanese industries have improved a lot in quality and processes in manufacturing.

→ Primarily Kaoru Ishikawa introduced the 7 QC Tools.

→ Dr. Kaoru Ishikawa was a professor at the engineering college at Tokyo University.

→ Ishikawa is known for the “Democratizing (Visual Aids/Symbols) Statistics” .

→ Good visual representation makes statistical and quality control more comprehensive.

→ The Basic 7 QC Tools gained popularity for their simplicity and effectiveness across the world.

→ The Basic 7 Quality Control Tools are necessary for problem-solving and process improvement.

→ Each tool has its own specific applications and benefits.

⏩ Refer to the below-mentioned key points when we can use 7 QC tools:

• For identifying potential causes of a problem
• Useful during brainstorming sessions
• When collecting data in a structured manner
• For monitoring process trends or patterns over time
• During the identification of the distribution of data
• Prioritization of defects, causes, efforts, etc
• Identify or validate the correlation between two variable
• Process flow documentation, analysis, and improvement
• A graphical technique that is easily understood by all
• Most helpful in troubleshooting quality-related issues
• They are fundamental tools for process and product quality improvement
• This facilitates the organization to resolve basic problems
• The 7 QC tools are easy to understand and implement
• They do not require complex analytics and statistical skills
• The basic 7 QC tools are simple yet powerful
• We can get an 80% result by applying 20% of the effort

The Basic 7 QC Tools For Process Improvement:

➝ Now we will understand the Basic 7 QC Tools in detail.

⏩ The 7 QC Tools are:

Note: We are considering the Flow Chart as a part of the 7 Basic QC Tools.

Also, you can consider stratification as a part of this tool.

➡️  Sample Presentation File

➡️  WhatsApp Us to Get this Presentation Bundle

➡️  Mail us to Get this Presentation Bundle

(1) Flow Chart:

→ "Flow Chart is a diagrammatic representation that illustrates the sequence of operation to be performed to get the desired result."

→ It is a visual representation tool for a sequence of operations, therefore, we can easily illustrate the Internal and external operational boundaries.

→ A flow chart includes the steps involved in a process, the decision points, and the flow of control from one step to the next.

→ A flow chart is also called a "Process Flow Diagram."

⏩ Purpose of Flowcharts:

• Visualize Processes
• Identify Inefficiencies
• Standardize Procedures

⏩ Basic Elements of the Process Flow Diagram are:

⏩ Basic Symbols Used in Flowcharts:

• Parallelogram

⏩ Steps to Create a Flowchart:

• Define the Process
• Select Symbols
• Draw the Flowchart
• Review and Revise

⏩ Benefits of Using Flowcharts:

• Improvement
• Communication

⏩ Use Cases:

• Process mapping in manufacturing and service industries
• Identifying areas for quality improvement in workflows
• Documenting procedures for training and compliance

👉 Read our detailed article on Different Types of Flow Charts Explained with Examples

(2) Cause and Effect Diagram (Fishbone or Ishikawa):

→ "Cause and Effect Diagram is a meaningful relationship between an effect and its causes."

→ It guides concrete action and tracks the potential causes during an investigation of the effort to determine whether the item significantly contributes to the problem or not.

→ The cause and effect tool is a very popular root cause analysis tool.

⏩ The Different Names of Cause and Effect Diagram are:

• Ishikawa Diagram
• Fishbone Diagram
• Herringbone Diagrams

→ A fishbone diagram can identify all possible cause(s) for the problem.

→ In this tool, we can find possible causes with the help of the 6M concept those are man, machine, material, method, measurement, and mother nature.

→ Also, we can use this tool in marketing as 8P analysis and service industry as 4S analysis.

⏩ Steps to Create a Cause and Effect Diagram:

• Define the Problem
• Identify Main Categories
• Brainstorm Causes
• Analyze and Prioritize

⏩ Benefits of Using a Cause and Effect Diagram:

• Visual Representation
• Team Collaboration
• Problem-Solving

⏩ Use Cases of Cause and Effect Diagram:

• Quality improvement projects
• Problem-solving in manufacturing
• Analyzing service delivery issues

👉 Check our detailed article on Cause and Effect Diagram Explained with Case Study

(3) Check Sheet:

→ "Check Sheet is a well-structured data sheet that is used for collecting and analyzing data."  

→ Data collected by the check sheet is used as input for the other tool and data can be collected based on asking a question by what, when, where, who, why, and how.

→ The purpose of a check sheet is to summarize the data and a tally count of event occurrences.

→ A check sheet counts the number of occurrences of an event, such as defects or non-conformance.

→ Hence the Check Sheet is also called a "tally sheet".

→ In many cases, a checklist will summarize countable data related to certain types of defects and will provide a rough graphical representation of wherein a part of the process, defects occurred.

⏩ Key Aspects of a Check Sheet:

• Structured Form
• Real-Time Data Collection
• Ease of Use
• Data Visualization

⏩ Uses of a Check Sheet:

• Defect Tracking
• Data Collection
• Process Improvement
• Quality Control

👉 Read our detailed article on Different Types of Check Sheets Explained with Example

(4) Histogram:

→ "Histogram is a type of bar graph representing the frequency distribution of the data."

→ Karl Pearson introduced the Histogram which is a bar graph representing the frequency distribution on every bar.

→ Histograms are used to show whether the output of our process matches the customer's requirements or not?

→ Also, we can make the decision based on the data patterns plotted on the Histogram.

→ With the help of the graph we can analyze whether the process is capable of meeting customer requirements or not?

→ A histogram is a pictorial representation of a set of data.

⏩ Key Aspects of a Histogram:

• Frequency Distribution
• Intervals (Bins)

⏩ Steps to Create a Histogram:

• Collect Data
• Determine the Range
• Choose Intervals (Bins)
• Count Frequencies
• Draw the Bars
• Interpret the Patterns

⏩ Uses of a Histogram:

• Understanding Data Distribution
• Identifying Patterns

⏩ Different Types of Histogram are:

• Normal Distribution
• Skewed Distribution
• Double-peaked or Bimodal
• Multipeaked or Multimodal
• Edge Peaked Histogram
• Truncated or Heart-cut histogram

👉 Also read a detailed article on Different Types of Histograms Explained with Case Study

(5) Pareto Chart:

→ "Pareto Chart is a bar graph arranged in descending order of height from left to right."

→ Pareto chart shows the order of the largest number of occurrences by item or by classes and the cumulative sum total.

→ The Pareto analysis helps us to prioritize where we can get more benefits by applying fewer efforts.

→ It is also very popular as a prioritization tool.

→ It communicates the principle of 80:20.

→ The Pareto Principle gives us information about the Vital few from Trivial many.

→ Hence,  It is known as the "Vital few from Trivial many tool".

→ It states that 80% of an effect comes from 20% of the causes.

⏩ Key Aspects of a Pareto Chart:

• Cumulative Line

⏩ Steps to Create a Pareto Chart:

• Identify Problems/Causes
• Measure Frequency or Impact
• Calculate Cumulative Percentages
• Draw the Chart

⏩ Uses of a Pareto Chart:

• Prioritizing Problems
• Resource Allocation

👉 Read our detailed article on Pareto Chart Explained with Case Study

[6] Scatter Diagram:

→ "Scatter Diagram is used to study and identify the possible relationship between two variables."

→ It is used to identify and visualize the relationship between two variables.

→ Mostly the scatter diagram is used to validate the cause-and-effect relationships between two variables.

→ This tool helps in decision-making during the problem-solving process.

→ Also it helps to determine the correlation between two variables.

→ Scatter Diagram is the best validation tool.

⏩ Different names of the Scatter Diagram:

• Scatter Plot
• Scatter Graph
• Correlation Graph
• Scatter Gram

⏩ Key Aspects of a Scatter Diagram:

• Data Points
• Correlation

⏩ Steps to Create a Scatter Diagram:

• Identify Variables
• Plot Data Points
• Analyze the Pattern

⏩ Types of Correlation:

• Positive Correlation
• Negative Correlation
• No Correlation

⏩ Uses of a Scatter Diagram:

• Identifying Relationships
• Predicting Trends

👉 Also visit our detailed article on Scatter Diagram Explained with Example

[7] Control Chart:

→ "Control Charts are used for studying the process variation over time."  

→ The control chart was invented by Walter A. Shewhart working for Bell Labs in the 1920s.

→ A control chart is also known as a Shewhart chart or process-behavior chart.

→ With the help of this tool, we can determine whether a manufacturing process or a business process is in control or not?

→ The control chart is a graph which is used to study process changes over time

→ Comparing the above tool this is the best forecasting tool.

⏩ Key Aspects of a Control Chart:

• Center Line (CL)
• Upper Control Limit (UCL)
• Lower Control Limit (LCL)
• Control Limits

⏩ Steps to Create a Control Chart:

• Calculate the Mean
• Calculate Control Limits
• Plot the Data
• Analyze the Chart

⏩ Types of Control Charts:

• X-bar Chart

⏩ Uses of a Control Chart:

• Monitoring Processes
• Identifying Variation

👉 Read our detailed article on Control Chart Explained with Case Study

Benefits of 7 QC Tools:

• Provides a structured approach for problem-solving
• Easy to understand
• Easy to implement
• A scientific and logical approach
• Improve the quality of products and services
• Identifying and analyzing problems
• Used for root cause analysis
• Enhance customer satisfaction

Limitations of 7 QC Tools:

• The accuracy of data collection depends on a person's skills
• Statistical interpretation requires highly skilled persons
• They are focused on identifying problems rather than preventing
• Reactive approach
• Focus on symptoms, not on root causes

Conclusion:

→ Seven QC tools are most helpful in troubleshooting issues related to quality

→ Different factors cause different effects on the process and make them unstable.

→ Those parameters cause variation in the process.

→ These tools are the most helpful for improving the process.

→ We can improve the efficiency and effectiveness of processes by using these tools.

Related Posts

23 comments.

very good presentation skill and to the point explaination

Thanks for your feedback and kind comment!!!

How to download???

You can check the individual articles!!!

Best in short... Great work Nikunj

Thank you very much for your kind comment!!!

Simply wonderful. Thanks very much!

this is a great initiative , well done

Thank you for your kind words!!!

Thanks and happy learning!!!

Nice teaching

Thank you and Happy Learning!!!

Great good initiative 👍 a How to Download

Thank you for your kind word!!

This is so helpful

Thanks for your feedback

HOW MAKE PARETO

You can go through with our article on pareto.

Sir can you please share process audit checklist

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Control chart: A key tool for ensuring quality and minimizing variation

Reading time: about 6 min

• Process improvement

You will need to correct variations that have a negative effect on your business, and that’s where a control chart can be beneficial for your company. Learn more about control charts and get started with a template now.

What is a control chart?

A control chart—sometimes called a Shewhart chart, a statistical process control chart, or an SPC chart—is one of several graphical tools typically used in quality control analysis to understand how a process changes over time. 

The main elements of a control chart include:

• A visual time series graph that illustrates data points collected at specific time intervals.
• A horizontal control line to more easily visualize variations and trends.
• Horizontal lines, representing upper and lower control limits, placed at equal distances above and below the control line. These upper and lower limits are calculated from the data that is recorded on the time series graph over a specified period of time.

Benefits of using a control chart

Control charts can help you:

• Understand the variations that are always present in processes: Variations within your control limits indicate that the process is working. Variations that spike outside of your control limits indicate problems that you need to correct.  
• See when something is going wrong or may go wrong: These problem indicators let you know that you need to take corrective action.
• Notice patterns within plotted points: The patterns indicate possible causes, which can help you find possible solutions.
• Predict future performance: By closely examining your process’s variations, you’re more likely to understand how it will perform in the future.
• Generate new ideas for improving quality: You can find new ways to improve your process based on your analysis.

Understanding process variation

Before you can build your control chart, you will need to understand different types of process variation so you can monitor whether your process is stable. Variations can come from common causes and special causes.

Common cause variations

Common cause variations are predictable and always present in your processes. 

As an example, consider how long it takes you to commute to work every morning. You may drive the same route every morning, but the drive is never the same. Perhaps it takes you an average of 20 minutes from the time you leave your house until you pull into the parking lot. Due to common cause variations—such as stop lights and traffic congestion—some days it will take less time and other days it will take more time. 

Even though you don’t know exactly when you will get to work tomorrow, you know that it will fall within an acceptable time frame and you will arrive on time.

When variations stay within your upper and lower limits, there is no urgent need to change your process because everything is working within predictable parameters.

Special cause variations

Special cause variations are usually sporadic and unpredictable. For example, running out of gas, engine failure, or a flat tire could extend your commute by an hour or more, but these types of special causes will not happen every day.

When special cause variations occur, it’s still a good idea to analyze what went wrong to see if these anomalies can be prevented in the future. In our commuting example, you could make sure you stop at a gas station when you’re running low on gas and make sure your vehicle is well maintained to ensure proper operation. 

How to make a control chart

Control charts are a great way to separate common cause variations from special cause variations. With a control chart, you can monitor a process variable over time. 

Follow these steps to get started:

• Decide on a time period, typically noted on the X-axis of the control chart, to collect the necessary data and establish your control limits.
• Collect your data and plot it on the control chart.
• Calculate the average of your data and add a control line.
• Calculate upper and lower control limits and add these lines in your chart, ideally in a different color or style.
• Note any “out-of-control signals,” or places where your data falls outside of your control limits. Investigate the cause and adjust your process to minimize risk of these abnormalities.
• With your control limits in mind, continue to track your process.

Don’t worry—we’ll walk through all of these steps with our commute example.

For example, let’s say you want to record the amount of time it takes to commute to work every day for a set number of days. Every day you measure the amount of time it takes from the moment you leave your house until you pull into the parking lot. After the data is plotted on a control chart, you can calculate the average time it takes to complete the commute.

The control chart below is a simple visual aid for plotting the amount of time your commute takes over 25 days.

In our example, you collected data for 25 consecutive days. The calculated average indicates that it takes 24.9 minutes on average to make the trip each day. This average becomes your control line (CL), shown in green.

How to calculate upper and lower control limits

After you have calculated the average, you can calculate your control limits. The upper control limit (UCL) is the longest amount of time you would expect the commute to take when common causes are present. The lower control limit (LCL) is the smallest value you would expect the commute to take with common causes of variation.

To calculate control limits, follow these steps:

• Subtract the average number from the number you recorded for each day and square the result. (For example, our Day 1 calculation would be 23 - 24.9 = -1.9 x -1.9 = 3.61.)
• Find the average of all the squared results.
• Find the square root of that result. The square root is the standard deviation. 
• Determine how many standard deviations you want to fall within your controlled process. The upper and lower limits in a well-controlled process are equal to +3 and -3 standard deviations from the average.

In the example, we end up with a standard deviation of 6.9. Our upper control limit is 45.6 minutes (24.9 + 6.9 + 6.9 + 6.9), and the lower control limit is 4.2 minutes (24.9 - 6.9 - 6.9 - 6.9), shown in red on the control chart example.

As long as all of the points plotted on the chart are within the control limits, the process is considered to be in statistical control. That’s great news for your business—there is no urgent need for change. You can always make improvements, but operating within the control limits is an admirable goal.

The points that fall outside of your control limits indicate the times that the process was out of control. If these out-of-control points happen rarely, you need to look at them to analyze what went wrong and to plan for fixing them in the future. If the process hits out-of-control points often, this could indicate a pattern you need to address.

Now you’re ready to optimize processes, increase quality, and stop variation in its tracks. Get started with our control chart template.

Explore 7 other basic quality tools that can help you improve your processes.

About Lucidchart

Lucidchart, a cloud-based intelligent diagramming application, is a core component of Lucid Software's Visual Collaboration Suite. This intuitive, cloud-based solution empowers teams to collaborate in real-time to build flowcharts, mockups, UML diagrams, customer journey maps, and more. Lucidchart propels teams forward to build the future faster. Lucid is proud to serve top businesses around the world, including customers such as Google, GE, and NBC Universal, and 99% of the Fortune 500. Lucid partners with industry leaders, including Google, Atlassian, and Microsoft. Since its founding, Lucid has received numerous awards for its products, business, and workplace culture. For more information, visit lucidchart.com.

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Use control charts to correct the variations that have a negative effect on your business. Learn more about control charts and get started with a template now.

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POPULATION HEALTH ANALYTICS

Using control charts to measure performance, august 10th, 2021  |   emergency department classification  |   population health management & improvement.

In some of our recent posts, we’ve been taking a look at how the ACG System’s suite of tools can be used to understand emergency department (ED) visits, which helps users optimize health care utilization and reduce potential costs.

If you read our earlier posts , you know that the ACG System can reveal specific trends in ED visits for a certain population, specifically, patients who visited for non-emergent care or primary care (PCP) treatable conditions. By drilling down into this data, ACG System users can understand root causes of ED use, segment patients into actionable groups and develop an effective strategy to reduce potentially-avoidable visits.

This week, we’ll discuss a method for tracking those ED reduction strategies using control charts.

How Control Charts Work

Control charts are a user-friendly tool that differentiate true change in a metric from random variation that occurs naturally. Control charts help identify meaningful change early and are an engaging visualization for different types of stakeholders. Supplementing the control chart with markers of key intervention dates can help leaders understand the relationship of intervention timing to outcomes. These advantages make control charts the ideal tool to monitor changes in ED utilization. In fact, the ACG System’s granular ED visit export file can help develop effective control charts for internal monitoring purposes.

Using Control Charts to Monitor ED Visits

All this makes sense in theory, but let’s look at a real-world example of how control charts can be used to monitor ED usage in a population. In the below example, the user became concerned about an increasing trend in avoidable ED visits starting in Month 16. The trend was identified via overall increase in ED visits/1000, and once the analytic team drilled down into the trend, it revealed growth in avoidable ED visits as an impactable cost-driver. A suite of interventions to reduce avoidable ED visits was implemented in Month 19.

The analytic team used historic ED visit data, organized by the ACG System’s ED Classification algorithm’s category and month , to generate the control chart with historic mean and measures of variation. The horizontal blue line represents historic average monthly rate of avoidable ED visits. The two red lines represent upper and lower control limits.

Interpreting this graph, the peaks in Month 17 and 19 represent significant variation above historic means, supporting the organization’s interpretation that avoidable ED visits were increasing. Once the intervention was implemented in Month 19, utilization returned to post-intervention means in months 22 and 23. However, had the data points continued near the upper control limit, the organization would have an early-stage indicator that the intervention was not achieving the desired outcome.

Looking out to month 25, utilization of avoidable ED visits crosses the lower control limit, indicating significant variation from the historic mean – in this case, for the better.

The Value of Control Charts

The above example demonstrates how the ACG System’s unique tools and granular visit-level data can help an organization use control charts to monitor ED visits in near-time, creating a strong business-level understanding of intervention impact. Ultimately, these control chart tools give users clear, specific data to indicate whether or not a specific intervention is achieving the desired goal. The result? Users have the information and tools they need to make changes that optimize health care resources and reduce costs.

*The ACG team would like to thank Shannon Murphy, MA for concept and development of this ED monitoring application. More details on using Statistical Process Control and Control Charts to monitor health interventions can be found here .

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