Statistical Process Control in Operation Mnagement
- 1. PRESENTED BY GROUP 11 P. ASHWIN AYAPPADAS-231250092 UTTKARSH YADAV-231250160 VIVEK KUMAR-231250165 TARUN KUMAR-231250151 TUSHAR SINGH-231250154 STATISTICAL PROCESS CONTROL Submitted to:- Proof. Suvendu Naskar
- 2. INTRODUCTION SPC is method used in quality control and management to monitor, control, and improve processes. It involves the use of statistical techniques to analyze and understand process variability and to ensure that processes operates efficiently and consistently, producing products and services that meet predefined quality standards. Statistical Process Control It is caused by factors that are inherent to the processes and affect all outcomes to some extent. Common cause variations are predictable and can be characterized by statistical distribution. Examples of common cause include minor fluctuations in temperatures, variations in raw material quality, or small change in operator’s technique. It is caused by specific, identifiable factors that are not inherent to the process under normal operating conditions. Special causes are often sporadic and unpredictable. Examples of special cause include equipment malfunctions, material defects or environmental disturbances Natural cause Special cause V A R I A B I L I T Y
- 3. Steps involved in statistical process Data Collection: Data is collected regularly from the production process. This data could include measurements of dimensions, weights, or other relevant variables. Data Analytics: Statistical tools and techniques are applied to analyze the collected data. This involves calculating measures of central tendency (median or mode) and measures of dispersion (such as standard deviation). Control Charts: Control charts are graphical tools used in SPC to monitor the stability and performance of a process over time. Common types of control charts include the X-bar chart for monitoring the process mean and the R-chart for monitoring the process variation. Process Capability Analysis: SPC also involves evaluating the capability of a process to meet customer requirements. This is done by comparing the process variability to the tolerance limits specified by the customers.
- 4. Steps for implementing Statistical Process Control (SPC) Define the Process and its Objectives Select Appropriate Control Charts and Data Collection Methods Analyze Data Using Control Charts Collect Data and Establish Baseline Performance Take Action Based on the Analysis Continuousl y Monitor and Adjust the Process
- 5. Understanding Central Tendency and Dispersion in Statistical Process Control (SPC) Central tendency refers to the tendency of data points to cluster around a central value. Dispersion measures the spread or variability of data points around the central tendency. Central Tendency Dispersion Central tendency and dispersion are crucial for • Monitoring process variability • Identifying sources of variation • Ensuring quality and process stability Central tendency and dispersion are key metrics monitored in SPC to • Detect shifts or trends in process performance • Distinguish between common cause and special cause variation
- 6. The control chart is a graph used to study how processes changes over time. Data are plotted in time order. A control chart always has a central line for average, upper line for the upper control limit, and a lower line for the lower control limit. These lines are determined from the historical data. By comparing current data to these lines, you can draw conclusions about whether the process variation is due to natural cause or due to special cause. CONTROL CHARTS
- 7. Process Control Chart Plots the process mean over time. Use to track the process level and detect the presence of special causes affecting the mean. Plots the process range over time. Use to track process variation and detect unexpected variation. Plots the process standard deviation over time. Use to track the process variation and detect unexpected variation. Plots the cumulative scores based on "zones" at 1, 2, and 3 standard deviations from the center line. Use to detect unexpected variation. Variable Control Chart Variables control charts plot continuous measurement process data, such as length or pressure, in a time-ordered sequence. There are two main types of variables control charts: charts for data collected in subgroups and charts for individual measurements. Variables control charts for subgroups include Xbar, R, S, and Zone.
- 8. Process Control Chart P chart is one of the quality control charts used to assess trends and patterns in counts of binary events (e.g., pass, fail) over time. p charts are used when the subgroups are not equal in size and compute control limits based on the binomial distribution. C chart is also known as the control chart for defects (i.e., counting the number of defects). It generally monitors the number of defects in consistently sized units. Attribute Control Chart In contrast, attribute control charts plot count data, such as the number of defects or defective units. If your process can be measured in attribute data, then attribute charts can show you exactly where in the process you’re having problems, if any. Attribute Control Charts include p-charts and c-charts.
- 9. Process Variation Process variation (also known as process variance or process variability) can be defined as the numerical value indicating how far processes vary from their expected performance. This is a leading cause of quality issues in production and transactional processes. Many times, product quality issues are not identified until it has turned into a large-scale disaster. two types of process variation Common cause variation Special cause variation
- 10. Common cause variation, also referred to as “noise” results from things that may or may not be known. The causes for this variance are usually quantifiable and natural in the system. Common cause variation usually lies within three standard deviations from the mean where 99.73% of values are expected to be found. If you are plotting on a control chart this variation is indicated by a few random points that are within the control limit. The impact common cause variation has on the process outputs is controllable and predictable resulting in a statistically stable process. Special cause variation refers to unexpected things that affect a process. These variations were not observed, are unusual and non- quantifiable. These sporadic causes are the result of a change introduced in the process resulting in a problem. Things can get more challenging when special cause variation is introduced because they occur out of the blue. This failure can be corrected by making changes in the methods, material, or processes. Process Variation
- 11. Enhancing Process Stability and Performance with SPC Early problem detection by Real-time monitoring Improved product quality,Consistent processes within specifications ensure higher quality products Reduction of variation Identify and eliminate sources of unwanted variation Waste and rework minimization By preventing defects upfront Data-driven decision making by objective, quantifiable data that facilitates informed process improvement choices.
- 12. Cost of implementation Complexity Data collection Misinterpretation of results Sustaining usage Challenges of SPC Implementati on