Ops 571 Statistical Process Control

Statistical process control (SPC) is the application of statistical techniques to determine whether a quantity of material should be accepted or rejected.

This journal is an insight on my thoughts and experiences and my own reflection of operations management and the topics in which we have looked at in both lectures and my interactive workshops. In this journal I will cover the following:

Automating process controls to ensure systems correctly process and record information. (FFIEC IT Examination Handbook InfoBase - Design Phase., n.d.). Quality management techniques that improve quality assessments include error detection, process control, and process design. These processes detect missing values, improve recurring errors, and help optimize effeciency. (Even, A., & Shankaranarayanan, G., 2009).

1. Christensen, E., Coombes-Betz, K. and Stein, M., (2007) The Certified Quality Process Analyst Handbook. Wisconsin: ASQ Quality Press

This term paper examines a case study with Hot Shot Plastics company in which statistical process control (SPC) with variable measurements using X bar and R control charts is used to determine whether the process variability is in control. Sample data are utilized to demonstrate how to use X bar and R control charts to check if all the sample points are within the control limits. Patterns on the control charts are analyzed to understand the possible reasons that the process is out of control.

For the remainder of the project we should ensure we are using the process adjustment that uses the quality control measurements to ensure that the

The three categories of SQC include the traditional statistical tools, acceptance sampling, and statistical process control (SPC). Traditional statistical tools are descriptive statistics like the mean and range, used to describe qualitative characteristics. Acceptance sampling is a process of taking a random sample or portion of a batch and deciding whether to accept or reject the whole batch. SPC is a process that uses samples to determine whether a process is functioning normally or not (Ryan, 2011). Traditional statistical tools describe the quality characteristics although they do not elucidate the extent of the quality (whether good or bad). Acceptance sampling determines whether an entire batch produced should be accepted or rejected after the goods have been produced. However, SPC tracks the process to ensure it is functioning properly for a given period. These tools can be used together effectively. Traditional statistical tools are used as inputs into SPC, which is updated frequently enough to ensure that quality problems are identified in good time. Finally, after a batch has been produced, acceptance sampling is used to determine whether the batch is of good quality or not.

Critical success factors like Return On Investment, on-time deliveries, throughput, customer lead-time and headcount productivity can all be monitored using statistical techniques (i.e. control charts). Thus, the natural variations in the critical success factors become important. Moreover, control charts create performance “bands,” which symbolize the natural variation of a process that is under control. Anything outside the band indicates an out-of-control process and may need further investigation if it is a frequent event.

Statistical Process Control is an analytical decision making tools which allow us to see when a process is working correctly and when it is not. Variation is present in any process, deciding when the variation is natural and when it needs correction is the key to quality control.

The problem is simple and fundamental. Human error is an inevitable part of the process of collecting statistical data. This is consistently overlooked in companies that utilize manual SPC[1] (statistical process control) for their manufactured goods. To ensure the

Slack et al (1995) postulates that operations management if effectively implemented and managed can be a crucial factor for the company development. According to them operations management is an essential catalyst for an organisation to grow. They  believe that operation management is essential for ;

3. Operation Management 7th Edition book (International Student version) written by Russell & Taylor. Copyright@ 2011 John Wiley & sons (Asia) Pte

Statistical process control refers to a statistical method used to separate variation produced b y special causes and varation produced by natural causes. This is done so that it is possible to eliminate the special causes and to establish and maintain consistency in the process, allowing the process to be improved.

Modeling data with Six Sigma helps us to understand the internal workings of an organization. In the article "Simulation Analysis Of A Production Process With Selected Six Sigma Ratios." Dobrzyński, Michał, and Piotr Waszczur (2012) simulate the application of statistical methods to monitor processes and parameters for hidden factors (49). Standard SS ratios are applied to a model of an automobile production line to show the proper analyses of individual process parameters. Their models show that “so called Total Quality Management” practices of simply placing quality inspections between each process are blind to hidden factors. By executing a six sigma quality initiative to analyze the individual operation parameters and processes the number of irreparable defects decreased significantly (50). This study is a reminder of the continued need for companies to gather concentrated statistical data and use models to examine processes for hidden factors and continuously improve organizational functionality.

Control process is the strategy or policy and procedure to define the risk in business activities and helps to manage and control the risk for a long period. Control process helps to achieve the predefined goals of the organization.