If a process is in control then most products produced by this process meet customer expectations

Summary

Statistical process control is a tool that emerged in America and migrated to Japan. It was ignored in America for many years while it helped Japan become a world quality leader. America re-embraced statistical process control in the last decade to help in the quest for continuous improvement.

One of statistical process control's key advantages is that it places the responsibility for quality squarely in the hands of the operator. Another key advantage is that it allows operators to determine if a process is drifting out of control before defective hardware is made, and in so doing, allows the prevention (rather than detection) of defects. The bottom line is that statistical process control allows the people doing the work to know they are producing conforming product, and to take preventive actions as processes show signs of drifting out of control.

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SPC and control charts are an excellent method to gain control of any plastics processing method. They allow the natural variation of the process (the control limits) to be clearly seen and accounted for, they also give early warning (via the alarms) of any special causes at an early stage. This early warning enables detection and rectification of any special causes before faulty products are produced.

All plastics processors should use control charting of some description to gain control of their process and improve the quality of their product.

SPC can be one of the most useful and vital tools in the drive to improve quality. Start using SPC now to gain essential insights into your plastics processing.

4.4.1.1 Statistical Process Control

Statistical process control (SPC) is the application of statistical methods to the monitoring and control of a manufacturing process to ensure that it operates at its full potential to produce a conforming product. Wiper manufacturers should employ SPC programs to control the physical, chemical, and contamination characteristics for each wiper lot that is manufactured.

Typically SPC data are plotted by sample number (as shown in Fig. 3.13). However, if multiple lots or wipers are to be compared, determining the best quality wiper can quickly become confusing and uninformative (as shown in Fig. 3.14). The downside is that with these data sets determining which wiper has the highest quality is often difficult.

(4) Statistical process controls

Statistical process control (SPC) is a control method for monitoring an industrial process through the use of a control chart. Much of its power lies in its ability to monitor both the process center and its variation about that center. By collecting data from samples at various temporal and spatial points within the process, variations in the process that may affect the quality of the end product or service can be detected and corrected, thus reducing waste and the likelihood that problems will be passed on to the customer. Process cycle-time reductions, coupled with improvements in yield, have made statistical process control a valuable tool from both a cost reduction and a customer satisfaction standpoint. With its emphasis on early detection and prevention of problems, statistical process control has a distinct advantage over quality methods, such as inspection, that apply resources to detecting and correcting problems in the end product or service. In addition to reducing waste, statistical process control can lead to a reduction in the time needed to produce the product or service from end to end. This is partly because the final product is less likely to need rework, but it also results from using statistical process control data to identify bottlenecks, wait times, and other sources of delays within the process.

8.5.3 Statistical process control

SPC is about the use of the control charts described in Section 8.5.2. The main objective of SPC is to prevent the special causes of variation occurring. If it achieves this objective, then the process remains statistically in control, that is, process variation is due to common causes only. The use of SPC should help determine two things. The first should be fairly obvious and that is when to adjust the process to get it back into control and to avoid manufacturing non-conforming products. Equally important is knowing when to leave the process alone. In fact, Oakland and Followell (1990) state that a major factor in the successful use of SPC is ‘the obligation to do nothing to a process unless and until there is clear evidence that change is required’. The fact that process adjustments are made when not required can actually lead to an increase in process variation. The reason this occurs is that process operators do not understand the nature of variation and, in particular, the difference between common and special causes of variation (Evans and Lindsay, 1993).

10.4.2 Statistical process control implementation

The first reported large-scale implementation of SPC in the apparel industry was carried out by Liz Claiborne Inc. The implementation was started by Liz Claiborne in 1997 on a pilot basis with a Florida-based supplier, with factories in Columbia. The garment rejects rate at the factories plummeted from over 20% in the mid-1990s to 3% in 1997, below 2% in 1998 and to 0.4% in the first half of 1999 as a result of the SPC implementation. This factory was used as a global showcase for SPC implementation, and important vendors were invited from across the globe to witness SPC in action. Liz Claiborne’s failed shipment index dropped by 33% between 1996 and 1999s because of the subsequent rollout of SPC implementation in Liz supplier factories across the globe (Bheda, 2004).

The production and quality staff in the apparel industry mainly rely on their technical expertise and experience to solve quality-related defects or variations and are relatively less exposed to the statistical concepts. Due to this, SPC implementation is likely to face resistance within the organization. Thus it is necessary to completely convince the top management of its implementation so that they support and ensure its implementation.

SPC implementation can be carried out using the following stages:

SPC briefing and training: The quality manager, supervisors, and operators on the critical operations should be briefed about the basic principles of SPC as well as the methodology of SPC implementation.

Identification of production line for pilot implementation and preparatory meeting: This shall involve a meeting between Production Manager, Pattern Master/Technical Supervisors, and Quality Manager/Supervisor. The objective of this meeting is to identify the critical operations of a garment style to be produced. These operations, if not controlled, are likely to contribute to a high rate of defects. The meeting also caters to the ways to minimize/eliminate defects by process modification and to take the desicion on the type of control chart that can be installed on these critical operations.

Installation of control charts at the critical operations: This stage revolves around training the operators/inspectors at the critical operation and the method to construct control charts and how to interpret them.

Interpretation and corrective action: Installation of the control chart is not enough at it only tells you the status of the process. It is important to have procedures in place for initiating corrective action should the process go out of control. Cause-and-effect diagrams can be drawn here as they are quite useful for analyzing the root causes of defects and initiating corrective action.

Continuous monitoring of progress and reviewing the improvement: The process is likely to get stabilized, and the defect rate is likely to drop with the implementation of control charts. It is important to monitor the progress and ensure that the implementation efforts do not hit roadblocks.

Deciding on implementing SPC in other areas of production: Organizations can now decide on the future of the SPC implementation and rollout the same throughout manufacturing processes using the experience of the pilot phase.

In the past decades, quality management practices in the apparel industry have undergone a significant change in factories across the globe. There is also no doubt that the quality of apparel produced has improved significantly in recent times. However, still, there is a large group of factories that struggle to produce quality right the first time. This is mainly because their processes are not under control and variation is high and unpredictable. A prominent example of the implementation of SPC in the Apparel industry is cited below.

SPC methods are widely implemented across different factories. In one of the factories, SPC was implemented in Trouser sewing lines for process performance improvement. The project included theoretical and on-job training schemes for different quality team members to understand the SPC concept and its implementation procedure. Significant improvements in the sewing section were achieved post implementation. The four months analysis before the implementation of the SPC tools showed that the average alternation percentage was 9.14%.

After implementing the SPC tools, the average alternation percentage reduced from 9.14% to 6.4%, a 30% reduction in the alteration rates (Abtew, Kropi, Hong, & Pu, 2018).

2.2 Statistical Process Control

Statistical process control is a methodology to monitor and analyze process inputs parameters and outputs characteristics, take corrective actions if the process is out of control limits which is updated periodically based on the process data, so as to continually reduce variation in processes and products. The process control limits are tighter than process specification limits. The goal is to prevent defects from occurring in order to provide cost-effectively products that meet customer requirements.

Statistical process control involves conducting measurements on process parameters of the products. Normally control charts are plotted, and upper and lower process control limits are established. The control charts reveal when the process is drifting out of control, and steps are taken to bring the process within control limits before it drifts out to some unacceptable value. The control parameter can be either a variable or an attribute. Accordingly, there are two basic types of control charts: control charts for variables (X-bar charts, range charts) and control charts for attributes (p charts, c charts). The generic steps in statistical process control include evaluation of process behavior by means of control charts, determination of process variability, and corrective actions to ensure that the process is under control. This real time feedback can be used to stop a process before a defect occurs. Defects that can be introduced by means other than systematic drifts in process are not the target of statistical process control.

18.1 Statistical Process Control

Statistical process control (SPC) is a statistical method of quality control for monitoring and controlling a process to ensure that it operates at its full potential. It determines the stability and predictability of a process. It can be applied to any process where the output of the product conforming to specifications can be measured. Control charts, continuous improvement, and the design of experiments are some of the key tools, which are further explained in Chapters 20, 22, and 31, respectively. Of these, control charts are most significant to SPC. The superiority of SPC over other TQM tools such as inspection, is that it emphasizes early detection and prevention of problems, rather than the correction of problems after they have occurred.

The Awarding Committee of Deming Application Prizes defined Statistical Quality Control (SQC) as “the integrated activity of designing, manufacturing and supplying the manufactured goods and services at a quality demanded by the customer at an economic cost.” The committee also added that “the customer-oriented principle is the basis, in addition to paying keen attention to public welfare. The company’s aim should be to succeed through the repetition of planning, execution, evaluation, and corrective action by applying the statistical concepts of activities of survey, research, design, procurement, manufacture, inspection, sales, etc., both inside and outside the company.”

1.4 Future trends

SPC has become one of the most commonly used tools for maintaining acceptable and stable levels of quality in modern manufacturing. The modern manufacturing environment is focused on computer integrated manufacturing and the challenges lie in developing advanced computer algorithms and process controls to implement the SPC tasks automatically.

Currently, the focus is on unit process-control methods such as run-2-run (R2R), unit process development and transfer and improvements in the methods to ensure component functionality and reliability. Considerable potential has been identified in the manufacturing of health-related systems and various health-monitoring systems have been developed or are in the development stages.

Much work is being done on the process of prediction and the improvement of product parameters and yield. New methods which help in process improvement, such as virtual metrology have been developed, incorporating control density improvement and the reduction of measurement operations. Models for data visualisation and analysis are in progress and still more effective models related to process improvement are to be developed.

The modern manufacturing world is demanding more precise and accurate methods for meeting industrial expectations. Advanced process control methods are always necessary across a variety of applications. More sophisticated methods of fault diagnosis are therefore being developed by researchers. Sensor implementation and integration with numerically controlled machines are developing rapidly. Investment in sensor technology that provides real time information for modern computer integrated manufacturing is increasing and more research is under way to meet the requirements of industries worldwide.

22.2 Statistical Process (Quality) Control