PROCESS MONITORINGBindutesh Saner

Instrumentation and Control Engg.Pravara Rural Engineering College, Loni

India

Abstract—Statistical Process Control (SPC) is an industrystandard methodology for measuring and controlling quality dur-ing the manufacturing process. Attribute data (measurements)is collected from products as they are being produced. Byestablishing upper and lower control limits, variations in theprocess can be detected before they result in defective product,entirely eliminating the need for final inspection.

I. I NTRODUCTION

Statistical Process Control is an analytical decision makingtool which allows you to see when a process is workingcorrectly and when it is not. Variation is present in any process,deciding when the variation is natural and when it needscorrection is the key to quality control. Statistical ProcessControl (SPC) charts offer users the chance to monitor thevery heartbeat of their processes. By collecting data they canpredict performance. Taking sample readings from a processseems straightforward. Of does it? Look more closely. Do weunderstand our process fully?In manufacturing areas we probably do. In non-manufacturingareas we may be less confident. And who collects the data?What sample size is required? How often are samples taken?These are vital questions to those intending to daily use thecontrol chart with a view to improving process performance,particularly in nonmanufacturing, or service, areas wherethetechniques are new.The control chart has been with us since 1924. It has beentried and proven, and accepted as a highly effective tool inimproving processes. In view of the fact that there is currentlyrenewed interest in Shewhart’s work, it is important to considerhow the control limits were originally set up. However, at theend of the day, it is the logic and rules of collecting dataand interpreting the pattern of points on the chart that is theimportant issue in understanding process behaviour and thediscovery of insights for process improvement.

II. PURPOSE OF CHART

Control charts are an essential tool of continuous qualitycontrol. Control charts monitor processes to show how theprocess is performing and how the process and capabilitiesare affected by changes to the process. This information isthen used to make quality improvements.Control charts are also used to determine the capability of theprocess. They can help identify special or assignable causesfor factors that impede peak performance.How they work?Control charts show if a process is in control or out ofcontrol. They show the variance of the output of a process over

time, such as a measurement of width, length or temperature.Control charts compare this variance against upper and lowerlimits to see if it fits within the expected, specific, predictableand normal variation levels.If so, the process is considered in control and the variancebetween measurements is considered normal random variationthat is inherent in the process. If, however, the variance fallsoutside the limits, or has a run of non-natural points, theprocess is considered out of control.

III. C ONTROL CHARTS ARE KEY TOQUALITY

IMPROVEMENT

There are a handful of control charts which are commonlyused. They vary slightly depending on their data, but all havethe same general fundamentals

Control charts have four key features:-

• Data points are either averages of subgroup measurementsor individual measurements plotted on the x/y axis andjoined by a line. Time is always on the x-axis.

• The Average or Center Line is the average or mean ofthe data points and is drawn across the middle section ofthe graph, usually as a heavy or solid line.

• The Upper Control Limit (UCL) is drawn above thecenterline and often annotated as ”UCL”. This is oftencalled the + 3 sigma line. This is explained in more detailin Control Limits on page 16.

• The Lower Control Limit (LCL) is drawn below thecenterline and often annotated as ”LCL”. This is calledthe - 3 sigma line.The x and y axes should be labeled and a title specifiedfor the chart.

IV. SPECIFICSPCTOOLS AND PROCEDURES

The preparatory phases of SPC involve several steps usinga number of different tools. These tools are described belowand most are available in Statit QC. Eight quality toolsare available to help organizations to better understand andimprove their processes. The essential tools for the discoveryprocess are:

• Check Sheet• Cause-and-Effect Sheet• Flow Chart• Pareto Chart• Scatter Diagram• Probability Plot

• Histogram• Control Charts• Brainstorming

There are numerousSPC software programsavailable suchas SPC forMS Excel, Minitab andSPC X from Air AcademyAssociates.

V. STEPSINVOLVED IN STATISTICAL PROCESSCONTROL

Proper Statistical Process Control starts with planning anddata collection. Statistical analysis on the wrong or incorrectdata is rubbish, the analysis must be appropriate for the datacollected.Be sure to PLAN, then constantly re-evaluate your situationto make sure the plan is correct.The key to any process improvement program is the PDSAcycle described by Walter Shewart.

• PLAN :Identify the problem and the possible causes. TheQC tools described in this manual can help organizationsidentify problems and possible causes, and to prioritizecorrective actions.

• DO:Make changes designed to correct or improve thesituation.

• STUDY:Study the effect of these changes on the situa-tion. This is where control charts are used they show theeffects of changes on a process over time. Evaluate theresults and then replicate the change or abandon it andtry something different.

• ACT :If the result is successful, standardize the changesand then work on further improvements or the nextprioritized problem. If the outcome is not yet successful,look for other ways to change the process or identifydifferent causes for the problem.

Control charting is one of a number of steps involved in Sta-tistical Process Control. The steps include discovery, analysis,prioritization, clarification, and then charting. Before usingStatit QC software, appropriate data must be collected foranalysis. Then, you need to begin again and do it over andover and over. Remember, quality is a CYCLE of continuousimprovement.

VI. H ISTOGRAM AND CHART

Below Figure shows a typical set of readings obtainedby collecting samples from a process. Control chartingrequires the mean of each sample to be used, rather than theindividuals. Figure 4 also shows the calculated values of themean X and range R.

The first stage in constructing a control chart for X requiresX, the mean, to be plotted against time as shown in belowFigure

The histogram corresponding to these X values is alsoshown to the right of the chart. This pattem of points overtime results in a unique profile of points or histogram.However, the reverse is not true. For example, if all that isknown is the histogram, what can we determine about the

Fig. 1. CHART

Fig. 2. control chart ploting

process? Very little, in fact. below figure illustrates justthreeof the many patterns which all give the same histogram.What the histogram does not tell us, therefore, is the mannerin which it was built up. We need a graph over time todetermine this, and these graphs, commonly called run charts,are a first step in generating control charts.

Fig. 3. Histogram

VII. C OMMON AND SPECIAL CAUSES

The form of control chart which we use today was firstgenerated by Shewhart. He recognised that if a process wasstable it was also predictable. i.e., once the natural variation ofthe process has been determined, it is then possible to predictfuture performance. This natural variation of the process doesnot alter over time unless action is taken to change the system.A process is defined as being stable if its natural variation isdue to common causes. The process is then said to be understatistical control. If a process is unstable, that is because

unusual factors are operating on the process. These factors,known as special causes, result in the process being out ofstatistical control. Shewhart recognised that we make mistakesat times, in that we take action when we should not do so.Equally.we sometimes let things drift, assuming the process will rightitself, when in fact we should react at the first sign of trouble.Shewhart was therefore aiming to devise a rule which wouldbe sensitive enough to pick up a special cause, but not sosensitive as to react to extremes in terms of common causes.Take the figures plotted in Figure 5 as an example. It makessense to use a central value as a reference point. The bestmeasure of central location is the mean value, that is theaverage obtained by adding all one hundred readings anddividing the total by 100, giving:

733/100 = 7.33

In fact, this mean can be obtained much more directly. Wealready have the values of the 20 sample means. Hence theoverall mean is given by the following calculation:

∑x/20 = 146.6/20 = 7.33

This mean of the sample means is known as the grand mean,or x (X double bar),thus

x = 7.33

Figure 7 shows our run chart ofx values together with a linefor x conventionally drawn as a broken line and often calledthe central line. The question is now where do we draw lineson the chart which will sensibly indicate the presence ofspecial causes.

Fig. 4.

VIII. COLLECTING THE DATA

Whatever the organisation, manufacturing or otherwise,personnel should be responsible for monitoring theirown processes. Hence, in the same way as operators inmanufacturing industry collect data, their equivalents innon-manufacturing - clerical assistants, clerks, technicalsupport staff, managers-should all collect sampled data fortheir administrative processes.This may cause problems initially since it is a change fromthe tradition of looking for trends in data or comparingthe data of one period with another. Administrative people

recognise that data has traditionally been collected in orderto measure levels of productivity, rather than the inherentcapability of their processes.There may be a natural reluctance to assist in an activitywhich may have repercussions on their own employment.This is understandable but management has the duty tore-assure those who may be confused if process change is tobe achieved by the informed analysis of available data withthe intention of improvement action.

Fig. 5.

Properly handled, however, and in an environment of trustand co-operation, many difficulties should be surmountable.The insights for improvement available from the analysis ofsuch data are considerable and should not be overlooked byany organisation determined to improve its performance andcustomer service levels.

IX. CONCLUSION

You now have the basic fundamentals of statistical processcontrol. You should understand that it measures variance, andis used to determine if a process is in control. You shouldunderstand the different steps necessary in performing SPC,including problem identification, data gathering, prioritizationand analysis. You should understand the key concepts and vo-cabulary for using SPC charts. Finally you should understandthe different types of data and be able to select which controlchart to use, depending on the data you have gathered.

REFERENCES

[1] JOSE A ROMAGNOLI and AHMET PALAZOGLU,INTRODUCTIONTO PROCESS CONTROL, 3rd ed. Harlow, England: CRC Taylor andFrancis Group.

[2] DAVID HOWARD, THE BASICS OF STATISTICAL PROCESS CON-TROL PROCESS BEHAVIOUR CHARTING