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Supplier selection is among the most critical in managing supply chains. Our discussion will guide us in developing tools which monitor the financial health of a supplier. Our text discusses the Z score in Chap 6. What does the Z score do? Please conduct some Internet research and support your comments with a brief overview of supplier bankruptcies in the news. Were there red flags that were not seen before a supplier went out of business?

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Final answer:

A Z-score is a statistical measure used to assess how far a data point is from the mean of a data set in terms of standard deviations. In supply chain management, it can indicate the financial health of a supplier and serve as an early warning for potential bankruptcy.

Step-by-step explanation:

The Z-score is a statistical measure that indicates the number of standard deviations a data point is from the mean of a set of data. It is particularly useful in the context of supply chain management for assessing the financial health of a supplier. The formula for calculating a Z-score is Z = (X - μ) / σ, where X is a single data point, μ is the mean, and σ is the standard deviation.

An example of this in a retail context could be the wait times at a supermarket checkout. If the mean wait time is five minutes with a standard deviation of two minutes, and a customer waits seven minutes, we can calculate their Z-score to determine how their wait time compares to the average customer's experience. A Z-score greater than 0 indicates the value is above the mean, and a negative Z-score signifies it is below the mean.

In the realm of supplier management, a low or negative Z-score could indicate financial distress or a higher risk of bankruptcy, serving as a red flag for companies relying on that supplier. Monitoring these scores helps companies preemptively identify and mitigate risks. Unfortunately, there have been instances where supplier bankruptcies occur, and the red flags, such as consistently low Z-scores, may have been overlooked or not monitored closely, leading to significant supply chain disruptions.

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