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Statistical Process Control

“Properly applied, S.P.C. is virtually foolproof. It is simple to use, involves little or no complicated mathematics, and almost guarantees to pay for itself in the saved effort.” (Wilson) Definition Outputs from a manufacturing process will vary – perhaps minutely – from the exact specification, and deliveries from service will differ in quality and substance.

These inconsistencies in quality require constant monitoring to see if they are random, regular, haphazard, important, or evidence of a problem. The monitoring and controlling should be applied to the process, not the product and can be greatly facilitated by S.P.C.

Process control means controlling the production by checking its quality while the work is still in process. Implementing S.P.C. means applying statistical techniques and analysis to that control function. As S.P.C. is about measuring the quality of work in process, its implementation is usually allied to techniques related to quality systems management.

Advantages of implementing S.P.C.

Chaudhry and Higbie report the following benefits from applying S.P.C. in a chemical plant: •improved production efficiency •a more consistent product •greater reliability – shifting control efforts from product to process •greater ease in pinpointing problem occurrences •provision of a usable measure of performance •clearer communication of objectives •improved customer relations.

Disadvantages of implementing S.P.C.

S.P.C. can take time to apply rigorously but applications do show that there are few, if any, disadvantages to S.P.C. Its application must remain relevant and useful, rather than becoming a system ‘for its own sake.’ Action checklist 1. Plan the programme and communicate organization-wide Firstly, you need to get the overall project context right with those over-arching elements essential to all successful change programmes:

  • securing proactive and continuous top management commitment
  • appointing the right project leader and obtaining the right expertise
  • establishing flexible time-frames and broad resource requirements
  • communicating regularly with the implementation teams and with everyone else who has involvement
  • preparing an effective and continuous training programme.

Secondly, you need to adopt a specific operational plan or process to follow, such as:

  • locate the process to be tackled
  • research the extent of the problem to be controlled
  • specify objectives, and resources, data and training needed
  • select the appropriate technique(s) to control the problem
  • plan the equipment, materials and expertise for the technique(s) chosen
  • ensure the steps of the techniques will be adhered to to identify possible causes of the problem test possible solutions.

2. Identify which tools and techniques may be the most appropriate

There is a tool kit of techniques and each has a particular application. Choosing the right technique for the right process is important and should be properly researched.

The tools and techniques include:

  • Checklists provide established best practice in simple sequence so that a process may be checked and controlled for doing the right thing at the right time.
  • Flow charts show the sequential steps in a process, how work flows from one area to another and the when, how, where (and where not) of activities.
  • Cause and effect (fishbone) diagrams attempt to relate effects to causes. They examine all related possibilities of a process which is going wrong, or a product or service that is not satisfactory.
  • Scatter diagrams plot the occurrence(s) of failure, or deviations from the norm enabling confirmation or denial of a suspected relationship, between, for example, supply and production, or production and delivery.
  • Histograms, pie charts and bar charts show how a process is performing at a moment in time. They are useful for showing the impact of one factor against another, such as faults against a process, or customer calls against sales.
  • Run charts show the same data as a histogram but plot the values over time.
  • Pareto analysis – according to Pareto, relatively few failure reasons are responsible for the many failures in a system; hence the 80-20 rule. A Pareto chart – a vertical bar chart with the bars representing complaints or defects ranked in descending order – shows the relative importance of a set of measurements and therefore enables focus on the most pressing problems.
  • Process performance checks focus on the most recent observations from the process: a glance at a given moment in time.
  • Process performance evaluations focus on past performance, using all available historical data to see how a process has been operating and suggest improvements.
  • Process capability studies focus on current observations of the process, using control charts to determine the variability and thus capability of a specific process under statistical control.
  • Control charts identify continuing and special causes of variation in a process.

They can demonstrate that a process is (not) currently in control, and can warn of causes of variation and signal the need for correction or improvement. Samples of data are needed to calculate limits, expectations and norms.

There are different kinds of control chart such as:

  • attributes charts – with only two values such as right/wrong, pass/fail. Here you need to ask what is being counted, for example defective items against total items.
  • variables charts – average, range (limited or unlimited), and median (the number in the middle of the set). Here you need to ask how many types of measures are to be controlled.
  • Correlation and regression analysis are methods for determining the type of cause and effect relationship between variables.

3. Establish norms and indicators You won’t know if something is wrong unless you know what to expect when things are going right. You need to establish – from (successful) experience or from an information-gathering exercise – a sample of data from which you can determine acceptable limits, norms or indicators of performance.

4. Resource support and prepare procedures Consult the hands-on practitioner and technical and procedural manuals to ensure the fit of the techniques selected and to make sure that the appropriate technical support is in place, not only for designing and constructing the control charts but also for analyzing and adjusting the applications.

5. Integrate S.P.C. into the quality management system Integration is vital for S.P.C. to succeed. While the tools and techniques used must be perceived to be of practical, operational value and not mere number-crunchers, the management of S.P.C must ensure that it integrates into the overall quality management system.

6. Select a winning pilot and don’t rush Choosing the first process for S.P.C. implementation can be critical for success. Start S.P.C. in a process with the most glaring quality problems and the best-anticipated outcome. Poor production records, high costs, high complaints and high failure levels will tell you where to look. Identify the critical variables to be the subject of the control. Exercise patience in following through the implementation programme. Trying to get results too quickly usually means the wrong results. Gathering specific detail meticulously and paying attention to detail will bring its rewards.

7. Use the charts for improvement not just control Assuming that your initial state was problem-free, solving a problem means getting back to where you were; control does not necessarily mean improvement. The evidence which S.P.C. produces can indicate what improvements can be made where, and – with systematic selection of processes – how.

Dos and don’ts for implementing S.P.C.


  • Define clear objectives and pinpoint which process – or part of a process – you are to tackle.
  • Monitor the process – not the product.


  • Take short cuts – expertise in the statistical method used is vital.
  • Fix the blame once you’ve solved the problem – fix the process.

Reference Statistical process control textbook, 4th ed, John S Oakland Oxford: Butterworth Heinemann, 1999 SPC essentials and productivity improvement: a manufacturing approach, William A Levinson and Frank Tumbelty Milwaukee Wisconsin: ASQC Quality Press, 1997 Measuring process capability: techniques and calculations for quality and manufacturing engineers, Davis R Bothe New York: McGraw Hill, 1997 SPC and business improvement, Mal Owen Bedford: IFS Publications, 1993

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Statistical Process Control. (2021, Jan 31). Retrieved September 17, 2021, from

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