Excel Logistics Services - Case Solution
Excel Logistics Services case study analyzes the introduction of statistical process control (SPC) into the distribution hub servicing a department store chain. The case looks at the receiving progress in the hub and describes the implementation of the SPC method. Additionally, run charts, control limits, and Pareto diagrams are discussed.
Case Questions Answered
- Help Robin Stalk organize the data by preparing a run chart. Set up the appropriate control limits. Is the process in control?
- How can you prioritize the areas for improvement? (Use a Pareto chart to justify your approach.)
- Set out an action plan for improving the process.
Q1: Help Robin Stalk organize the data by preparing a run chart. Set up
the appropriate control limits. Is the process in control?
A run chart is used to monitor the behavior of a variable over time for a process or system. It graphically displays cycles, trends, shifts, or non- random patterns in behavior over time.
Since it gives a holistic snapshot of the system in one step, it can help us identify problems, provide insights on the time when a problem occurred, and help us monitor the progress when solutions are implemented. Some of the benefits of the run chart are:
- Simple to create and maintain
- Does not require in-depth statistical training to use
- Displays data in a straightforward, analyzed manner
- Identify patterns that can provide some indication of where the quality problem
In the case of Excel Logistics Services, we need to understand the variation or change in the proportion of defectives over time. The error has been captured for the following: Slotter Errors, Keying Errors, Letdown Errors, ITR ADJ Errors, Putaway Errors, and Other Errors.
We then try to see calculate the “Total Error,” which is the sum of all the above-mentioned errors. The next step is to calculate the error rate, i.e., how many defectives are being detected for our sample used for testing.
We have used Exhibit 3 to make the run chart to show the proportional variation of defective varies by day in the receiving area.
Now, plotting this across the time…
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