Difference between revisions of "ANLY482 AY2016-17 T2 Group21 : Finals"
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==<div style="background: #404041;font-weight: light; padding:0.3em; text-transform:uppercase;letter-spacing:0.1em;font-size:18px; font-family: 'Century Gothic'"><font color=#ffffff><center>Inventory Performance Grid</center></font></div>== | ==<div style="background: #404041;font-weight: light; padding:0.3em; text-transform:uppercase;letter-spacing:0.1em;font-size:18px; font-family: 'Century Gothic'"><font color=#ffffff><center>Inventory Performance Grid</center></font></div>== | ||
| − | [[File:Tableabc.png| | + | A goal of our project is to make it accessible for retailers to utilize SA in their decision making process. To achieve this, a clear data representation is needed to help retailers understand their product demand. Our group came up with a visual decision tool for retailers to easily derive actionable insights. |
| + | [[File:Tableabc.png|800px]] | ||
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Revision as of 17:50, 23 April 2017
| 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
A goal of our project is to make it accessible for retailers to utilize SA in their decision making process. To achieve this, a clear data representation is needed to help retailers understand their product demand. Our group came up with a visual decision tool for retailers to easily derive actionable insights.
