Handout MK. Pengawasan Mutu 2011/2012 STATISTICAL PROCESS CONTROL SPC Statistical studies can be classified into two types: enumerative and analytic. An analytic study considers the population in a dynamic sense, and its objective is to predict or improve a process or product in the future. In the quality management field, statistical methods can be used for analyzing numerical data focusing on results. “One picture is worth a thousand words”. The primary goals of SPC Minimize production costs. Attain a consistency of products and services that will meet production specifications and customer expectations. Create opportunities for all members of the organization to contribute to quality improvement. Help both management and employees make economically sound decision about actions affecting the process. Statistical tools used in QC applications Seven basic tools that have been used successfully in food industries for decades: Flow chart Cause and effect diagram Control chart Histogram Check sheet Pareto chart Scatter diagram Flow Chart In a flowchart, the description A series of blocks with each of each process is written block representing one major process, that inside the blocks. Any other describes an operation that significant information is is studied or is used to plan usually written outside stages of a project. blocks. Each block is connected with an arrow to Flowcharts provide an show where that process excellent form of documentation for a leads. process operation, and often are useful when examining how various steps in an operation work together. Flow Chart A flowchart is important project development and documentation tool. It visually records the steps, decisions, and actions of any manufacturing or service operation and defines the system, its key points, activities and role performances. Histogram A histogram is used to graphically summarize and display the distribution of a process dataset. It can be constructed by segmenting the range of the data into equal-sized bins (segments, groups, or classes). The vertical axis of the histogram is the frequency (the number of counts for each bin), and the horizontal axis is labeled with the range of the response variable. The number of data points in each bin is determined and the histogram constructed. The user defines the bin size. Histogram Cause and Effect Diagram A problem is A similar diagram can systematically tracked be used to back to possible causes. systematically search The diagram organizes for solutions to a the search for the root problem. cause of a problem. Cause and Effect Diagram This diagram is created by Kaoru Ishikawa, one of the founding fathers of modern management Ishikawa diagram/ fishbone diagram. Causes are arranged according to their level of importance or detail, resulting in a depiction of relationships and hierarchy of events. Cause-and-effect diagrams are typically constructed through brainstorming techniques. The diagram are frequently arranged into the four most common major categories: – Manpower, methods, materials, and machinery (for manufacturing) – Equipment, policies, procedures, and people (for administration and planning) Cause and Effect Diagram Example Cause-Effect Diagram Scatter Diagram It is similar to a line graph except that the data point are plotted without a connecting line drawn between them. Scatter charts are suitable for showing how data points compare to each other. At least 2 measured objects are needed for the query (one for x-axis and one for y-axis) Scatter Diagram Scatter diagrams are used to study possible relationships between 2 variables. Although these diagrams can’t prove that one variable causes the other, they do indicate the existence of a relationship. More than one measure object can be used for the y-axis as long as the objects are of the same type and scale. The purpose of scatter diagram is to display what happens to one variable when the other variable is changed. The diagram is to test the theory that the two variables are related. The slope of the diagram indicates the type of relationship that exist. Pareto Charts The Pareto Principle is used Pareto charts are used to by business and industry to decide what steps need to work to continually improve be taken for quality quality. improvement. Quality improvement involves tackling one issue at a time. By addressing the ones causing the most difficulty, improvements can be made & monitored for continuous progress. Pareto Charts The number of occurrences or the costs of occurrences of specific problems are charted on a bar graph. The largest bars indicate the major problems and are used to determine the priorities for problem solving. A Pareto chart graphically summarizes and displays the relative importance of the differences between groups of data. A Pareto chart can be constructed by segmenting the range of the data into groups. The number of data points in each group is determined and the Pareto chart constructed; however, unlike the bar chart, the Pareto chart is ordered in descending frequency magnitude. Control Charts A broken line graph proportion of defective illustrates how a process pieces changes over time. behaves over time. Samples are periodically taken, checked, or measured, and the results are plotted on the chart. The charts can show how the specific measurement changes, how the variation in measurement changes, or how the Control Charts A control chart is a graphic display of the actual quality performance judged against a reference frame showing a central line representing the average quality value and upper and lower lines called the upper control limit (UCL) and lower control limit (LCL). Control Charts Control charts are used to: • find sources of special-cause variation (variation that is caused by specific, fixable occurrences) • measure the extent of common-cause variation (variation that inherent in the process) • maintain control of a process that is operating effectively. Control Charts Types of control charts: Control variable charts: X-bar and R charts Attribute charts: p, np, c, and u charts X-Bar and R-Charts The most commonly used of They can be used for the control charts and the controlling every step of most valuable. production process, for the They are ideal tools to acceptance/ rejection of improve product quality lots, and for early detection and process control and of equipment or process help to drastically reduce failure. scrap and rework while assuring the production of only satisfactory products. X-Bar and R Charts The X-bar and the R charts are used for control variables that are expresses in the discrete numbers such as inches, pounds, pH units, angstrom, percent solids, or degree of temperature and so on. The R chart is developed from the ranges of each subgroup data, which are calculated by subtracting the maximum and the minimum value in each subgroup. Since the R chart indicates that the process variability is in control, the X-bar chart can then be constructed. The center line is mean of the sample means. Example of X-Bar and R Charts The chart of a X-bar and R should not be used with samples ≥10 or when the sample size is not constant. Attribute Charts They are also used for control of defect analysis. They are particularly useful for controlling raw material and finished product quality and for analyzing quality comments in consumer letters. Attributes control charts are used when measurements are too difficult to take, when measurements do not apply to the situation (such as visual checks for flaws), or when they are too costly to take because of time lost. Attribute Charts p chart • It is the most commonly used attributes chart. • The value p is the fraction, or percentage, of the number items checked that are defective (unacceptable). • Large samples of 50 or more are needed. np chart • The np chart is sometimes used instead of the p chart because it is easier; np is simply the number rather than the fraction, of defective items in the sample. • The p and np charts differ by that constant divisor, so they do the same job with respect to control, process analysis, etc. Attribute Charts c chart The c chart tracks the number of defects in constant size units. There may be a single type of defect or different types, but c chart tracks the total number of defects in each unit. u chart • When samples of different size are taken, u is the average number of defects per unit. • The u chart is quite similar to the c hart in function. Example p chart: Example np chart Example c chart References Hubbard, M.R. 2003. Statistical Quality Control for the Food Industry, 3rd Ed. Kluwer Academic/ Plenum Publisher. New York. Vosconcellos, A. 2004. Quality Assurance For The Food Industry. CRC Press. Boca Raton.