Design of Experiments and Statistical Process Control Help

Design Of Experiments And Statistical Process Control Assignment Help

Statistical concepts, techniques, tools, and experiments have been applied for many years in various industries such as computer chip manufacturing, automobile manufacturing, and pharmaceuticals. Statistical process control (SPC)has been used to control the processes used in the production methods in different manufacturing industries. SPC procedures and tools help the industries monitor the process behavior, find issues in the manufacturing systems, and come up with effective solutions to production problems. SPC is sometimes used interchangeably with the term statistics quality control (SQC).

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Statistical Process Control Charts

A control chart is an important tool in statistical process control, initially developed in the early 1920s by Walter Shewhart. It enables us  to record data and identify any unusual event in a dataset. A control chart comprises of a central line, upper line, and upper control limit. The central line is used to indicate the average, the upper line to indicate upper control limit and the lower line to indicate the lower control limit. By comparing current datasets to these lines, one can draw conclusions on whether the process variation is in control (consistent) or out of control (unpredictable). A control chart distinguishes between two categories of process variation:

  • Common cause variation, which is inherent to the entire process and is always present
  • Special cause variation, which comes from external causes and show that the statistical process is out of control

Conducting various process control tests can help identify when an ‘out of control’ event has occurred. Nevertheless, employing more tests increases the likelihood of a false alarm. To learn more about the two types of process variation, contact our design of experiments and statistical process control assignment helpers.

When To Use Statistical Process Control Charts

Statistical process control charts can be used in a wide range of industries and is considered an essential process control tool. Other tools used in conjunction with control charts include cause and effect diagrams, check sheets, histograms, Pareto chats, scatter diagrams, and stratifications. We will give a brief description of each of these tools in a few but first, let’s see find out when one can use a control chart. According to our design of experiments and statistical process control homework help experts, control charts are used when:

  • Controlling continuous processes by identifying and fixing problems as they occur
  • Predicting the expected range of results and outcomes from various processes
  • Determining whether a process is in control or out of control
  • Analyzing the patterns of process variation from non-routine events or ‘built into the process’ events
  • Determining whether a quality improvement project is able to fix the problems.It is developed to fix the problem or changes that need to be made to the control process

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Tools Used In Statistical Process Control

As we mentioned, there are various tools used in statistical process control. These include:

  • Cause and effect diagrams: Also called a fish bone diagram, a cause and effect diagram is used to identify many possible causes for a problem or an effect. It is mostly used to structure brainstorming sessions, as it helps sort out ideas into useful categories.
  • Check sheet: It is also known as a defect concentration diagram. A check sheet is usually a well-structured form used to collect and analyze data. It is mainly used when collecting data on the patterns or frequency of events, defects, problems, defect causes, defect locations, or similar issues.
  • Control chart: As stated, a control chart is used to analyze data to find out whether the process variation is in control or out of control.
  • Histogram:This is a graph used to indicate how often each value in a dataset occurs. It is commonly used when the data is numerical and one wants to see its distribution. A histogram can also be used by businesses to find out whether a certain process meets customers’ requirements, to analyze suppliers’ processes and to see the difference in various outputs. In addition, a histogram can be used to determine whether a change has occurred in a process over a given period of time or not.
  • Pareto chart: Also known as Pareto analysis or Pareto diagram, a Pareto chart is simply a bar graph. The bars are used to represent frequency or cost and are arranged such that the shortest bars are on the right and the longest on the left. This way, one is able to find out which events are more significant. A Pareto chart is used to analyze data about the occurrence or frequency of problems or when there are many issues and one wants to find out which one is the most significant.
  • Scatter diagram: This diagram is also called X-Y graph or scatter plot and is used to plot pairs of numerical data. It represents variables on the X and Y-axis to find out how they are related with each other. If the points fall along a curve or a straight line, it means that the variables are correlated.
  • Stratification: This can be defined as the process of sorting data, objects, or people into distinctive groups or layers. It is used in conjunction with other statistical process control tools to separate data that has been clustered together in a manner that is easy to understand. Stratification is commonly used before data collection, when the data is collected from different sources, or when data analysis requires separating different sources.

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Topics Covered Under Design Of Experiments And Statistical Process Control Homework Help

Our design of experiments and statistical process control homework helpers have assisted students on various assignment topics including:

  • ANOVA gauge R and R
  • Intra block analysis
  • Completely randomized design
  • Analysis of covariance
  • Split block
  • Treatment control designs
  • Tukey’s test for additivity
  • Estimation of process capability
  • Response surface

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