ANLY482 AY2016-17 T2 Group21 : Finals

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Exploratory Mid-Term Finals

Survival Analysis

Due to the censored demand identified during our exploratory analysis, using survival analysis provides a way for us to handle such hidden values. Survival analysis will be performed using the JMP built-in survival functions. We will be using two features:

1. Basic survival function

  • Applies Kaplan-Meier estimator to account for censored values

2. (Cox) proportional hazards fit

  • Fits a linear model between predictors (explanatory variables) and the hazard function.
  • Parameters estimates show how predictors affect the hazard function.

Product Stock-Out Time

To account for non-stockout products, we perform our survival analysis of product stock-out-time with the following definition:

  • Subject: A product identified by name and size
  • Time to event: Time in days for a product to stockout
  • Censor: 0 if product stockout, 1 otherwise

Figure 8 Here

The above analysis shows a more accurate median time of stockout of 18 days, which is longer than our sponsor’s target of achieving a stockout period of 7 days. From the survival plot, we can also see that 68% of all products still remains on the shelf after 7 days after launch. It is, therefore, useful to understand which products groups have a longer stockout periods. We further add groupings by category to our analysis.

Inventory Performance Grid

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