Chapter 18 Optimizing and Controlling Processes through Statistical Process Control

Objectives After reading the chapter and reviewing the materials presented the students will be able to: Explain the rationale for Statistical Process Control (SPC) Understand Control Chart Development Understand Management’s Role in SPC Understand Implementation and Deployment of SPC Understand the Inhibitors of SPC

Statistical Process Control Defined Statistical Process Control (SPC) is a statistical method of separating variation resulting from special causes from variation resulting from natural causes in order to eliminate the special causes and to establish and maintain consistency in the process, enabling process improvement.

Rationale for SPC The rationale for SPC is to improve product quality and simultaneously reduce costs, and to improve product image in order to successfully compete in world markets.

Variation in Process The output of a process that is operating properly can be graphed as a bell shaped curve. The x-axis represents some measurement such as weight or dimension and the y-axis represents the frequency count of the measurements. The desired measurement value is at the center of the curve, and any variation from the desired value results in displacement to the right or left of the center of the bell. 99.73% of the process outputs will be between the +- 3 σ (sigma) limits (fig 18-1, page 314). When special causes are present the curve will take a new shape and variation can be expected to increase, lowering output quality (fig 18.2, page 314). The result is more scrap, higher cost, and inconsistent product quality.

Rationale for SPC Continual Improvement: It is usually the improvement of processes that yield improved products and services. Before a process is improved it is necessary to understand it, identify causes that may generate variation, and eliminate them. Predictability of Process: Manufacturers have to know that their process is under control, repeatable, and predictable in order to meet customer requirements. This can be achieved and maintained through SPC. They should look at their worst production month and base their commitments on that. Elimination of Waste: When waste is eliminated cost of goods produced is reduced, and the quality of the product is enhanced. Statistical process control (SPC) is the key to eliminating waste in the production process. Sampling: It is less costly to inspect only 1 piece out of 10 (10% sampling) or 5 out of 100 (5% sampling). Sampling is accepted by critical customers like the U.S. government. The condition for sampling is that the process must be under control. Auditing: When a company’s processes are in control using SPC, the internal quality assurance organization can reduce its inspection, relying on audits. This reduces quality assurance costs and with it the cost of quality.

Management’s Role in SPC Only management can establish the production quality level. SPC and the continual improvement that results from it will transcend department lines, making it necessary for top management involvement. Budgets must be established and spent this can be done only by management.

Management Commitment SPC and continual improvement represent a new and different way of doing business, and management commitment is absolutely necessary. No one in any organization, except the management, can mandate such fundamental changes. A department will be prevented from reaping all the benefits if other departments are working to a different agenda.

Authority over Processes and Production Operators who use SPC to keep track of their processes must have the authority to stop the production process when SPC tells them something is wrong. As long as the plots vary about the process average but do not penetrate a control limit, the process is in control and is being influenced by common causes of variation only. Stopping the line prevents waste and defective products.

Steps in Implementing SPC – The Preparation Phase The three phases in implementing SPC are preparation, planning and execution. The preparation phase has 3 steps: 1. Commit to SPC – top management must be committed. It requires spending money, utilizing human resources, changing the organization’s culture, hiring employees with new skills, or retaining consultants. 2. Form a SPC Committee – SPC can be delegated to a cross functional team that is tasked to oversee implementation and execution. A typical team will be composed of representatives from manufacturing, quality assurance, engineering, finance, and statistics. In a manufacturing plant, the manufacturing member should be the team leader. The function of the team will be to plan and organize the implementation for its unique application, to provide training for the operators, and to monitor and guide the execution phase. Forming the committee is top management’s responsibility. 3. Train the SPC Committee: The training must be done by an expert. The members will then know enough to set objectives and to determine which process should be targeted first. Continued help from a statistics expert remains critical.

Steps in Implementing SPC – The Planning Phase The planning phase includes the next 5 steps: 4. Set SPC Objectives: How will we measure success (balance sheet, customer feedback, reduction in scrap, lower cost of quality). Objectives may be added, eliminated, or changed, but they must be in place and understood by all. 5. Identify Target Processes: Select a few processes for pilot implementation. With some initial successes under its belt, the organization can go with confidence to the processes that are the most critical. Start implementation at the front of a series of processes. 6. Train Appropriate Operators and Teams: The operators and teams who will be directly involved with the collection, plotting, and interpretation of SPC data, and those who will be involved in getting the targeted processes under control will require training in the use of quality tools. 7. Ensure Repeatability and Reproducibility of Gauges and Methods: All measuring instruments from simple calipers and micrometers to coordinate measuring machines must be calibrated and certified for acceptable performance. 8. Delegate Responsibility for Operators to Play a Key Role: Operators need to be delegated the responsibility for collecting and plotting the data, maintaining the SPC control charts, and taking appropriate action.

Steps in Implementing SPC – The Execution Phase The execution phase includes 9 steps: 9. Flowchart the Process: Flowcharting will reveal process features or factors that were not known to everyone. The development of the process flowcharts should be the responsibility of special teams composed of the process operators, their internal suppliers and consumers, and appropriate support members. 10. Eliminate the Causes of Special Variation: The cause and effect diagram is then used to list all the factors (causes) that might impact the output (effect). Then by applying other tools such as Pareto Charts, histograms, and stratification, the special causes can be identified and eliminated. Elimination of special causes should be a team effort. 11. Develop Control Charts: The statistics expert or consultant can help develop the appropriate control charts and calculate valid upper and lower limits and process averages. 12. Collect and Plot SPC Data & Monitor: The process operator takes the sample data and plots it on the control chart at regular intervals. The operator carefully observes the location of the plots, knowing they should be inside the control limits. 13. Determine Process Capability: When a process is in control and is still not capable of meeting the customer specifications, it is up to management to upgrade the process capability, which may require the purchase of new equipment. 14. Respond to Trends and Out of Limits Data: With experience, operators may be able to handle many of these situations on their own, but if they cannot, it is important they summon help immediately. The process should be stopped till the cause is identified and removed. Prevent the production of defective products that must be scrapped or reworked. 15. Track SPC Data: The SPC committee and management should see where they should concentrate resources for improvement. 16. Eliminate the Root Cause of Any New Special Cause of Variation: For example, it is possible that the material from a new vendor for raw material may cause the process to shift the process average one way or the other. Eliminating the root cause may require management approved procedure mandating the use of preferred suppliers. 17. Narrow the Limits for Continual Improvement: Narrowing the limits will result in fewer parts failing to meet the specifications. Quality will improve, and costs will decrease. The key is finding ways to improve the process.

Inhibitors of SPC The most common inhibitor of SPC is lack of resources. Capability in Statistics: Many organizations do not have the in house expertise in statistics that is necessary for SPC. Misdirected Responsibility for SPC: The process operators will require help from the statistician and others from time to time, but they are the appropriate owners of SPC for their processes. Failure to Understand the Target Process: A good SPC system cannot be designed for a process that is not fully understood. Failure to Have Process Under Control: Before SPC can be effective, any special cause of variation must be removed. Inadequate Training and Discipline: Everyone who will be involved in the SPC program must be trained. Measurement Repeatability and Reproducibility: Before a gauge is used for SPC it should be calibrated and its repeatability certified. Low Production Rates: Low rates of production offers an opportunity for taking a 100% sample.

Summary Statistical Process Control (SPC) is a statistical method of separating variation resulting from special causes from variation resulting from natural causes in order to eliminate the special causes and to establish and maintain consistency in the process, enabling process improvement. The rationale for SPC is to improve product quality and simultaneously reduce costs, and to improve product image in order to successfully compete in world markets. 99.73% of the process outputs will be between the +- 3 σ (sigma) limits. When special causes are present the curve will take a new shape and variation can be expected to increase, lowering output quality. The result is more scrap, higher cost, and inconsistent product quality. Sampling: It is less costly to inspect only 1 piece out of 10 (10% sampling) or 5 out of 100 (5% sampling). Sampling is accepted by critical customers like the U.S. government. The condition for sampling is that the process must be under control. Operators who use SPC to keep track of their processes must have the authority to stop the production process when SPC tells them something is wrong. The three phases in implementing SPC are preparation, planning and execution. The preparation phase has 3 steps: 1. Commit to SPC 2. Form a SPC Committee 3. Train the SPC Committee. The planning phase includes the next 5 steps: 4. Set SPC Objectives 5. Identify Target Processes 6. Train Appropriate Operators and Teams 7. Ensure Repeatability and Reproducibility of Gauges and Methods 8. Delegate Responsibility for Operators to Play a Key Role. The execution phase includes 9 steps: 9. Flowchart the Process 10. Eliminate the Causes of Special Variation 11. Develop Control Charts 12. Collect and Plot SPC Data & Monitor 13. Determine Process Capability 14. Respond to Trends and Out of Limits Data 15. Track SPC Data 16. Eliminate the Root Cause of Any New Special Cause of Variation 17. Narrow the Limits for Continual Improvement. The most common inhibitor of SPC: Capability in Statistics, Misdirected Responsibility for SPC, Failure to Understand the Target Process, Failure to Have Process Under Control, Inadequate Training and Discipline, Measurement Repeatability and Reproducibility, Low Production Rates.

Home Work Answer Questions 1,2 on page 336. 1. Define the concept of statistical quality control. 2. Explain briefly the rationale for SPC.