Difference between revisions of "ANLY482 AY2016-17 T2 Group21 : Finals"
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With the above metrics for each product, we can now visualize each product’s inventory performance on an inventory performance grid. Sell-through rate and the average rate of sales combine to produce an inventory performance grid that helps us understand different aspects of merchandising. <b>Sell-through rate</b> depicts stock sufficiency while the <b>average rate of sales</b> is a measure of the popularity of the product. With that, each product can be classified into different quadrants, with each quadrant representing different business meaning. The figures below depicts an inventory performance grid of the <b>products A, B, C, and D</b> in a 2 days and 7 days time window respectively. | With the above metrics for each product, we can now visualize each product’s inventory performance on an inventory performance grid. Sell-through rate and the average rate of sales combine to produce an inventory performance grid that helps us understand different aspects of merchandising. <b>Sell-through rate</b> depicts stock sufficiency while the <b>average rate of sales</b> is a measure of the popularity of the product. With that, each product can be classified into different quadrants, with each quadrant representing different business meaning. The figures below depicts an inventory performance grid of the <b>products A, B, C, and D</b> in a 2 days and 7 days time window respectively. | ||
| − | [[File:Str2day.png| | + | [[File:Str2day.png|550px]] |
| − | [[File:Str7day.png| | + | [[File:Str7day.png|550px]] |
The inventory performance grids of 2 days and 7 days have different business meaning and interpretation. For 2 days, products should have an optimal sell-through rate of between 0.3 to 0.6 and a rate of sales of above 1.50 pieces per day. This means that the product is popular and that the stock quantity is optimal. If the sell-through rate of the product is above 0.6 with a high rate of sales, it signals that the product is under stocked and the actual demand is higher than predicted. | The inventory performance grids of 2 days and 7 days have different business meaning and interpretation. For 2 days, products should have an optimal sell-through rate of between 0.3 to 0.6 and a rate of sales of above 1.50 pieces per day. This means that the product is popular and that the stock quantity is optimal. If the sell-through rate of the product is above 0.6 with a high rate of sales, it signals that the product is under stocked and the actual demand is higher than predicted. | ||
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The different grids provide businesses with a snapshot of product performance over different time windows. For example, by simply looking at the 7 day grid, it can be said that product A is a optimal performer. However, when you combine the 7 day grid with the 2 day grid, it can be concluded that product A is extremely popular and achieved a sell-through of 0.8 within 2 days. As such, product A can be classified as “under stocked”. | The different grids provide businesses with a snapshot of product performance over different time windows. For example, by simply looking at the 7 day grid, it can be said that product A is a optimal performer. However, when you combine the 7 day grid with the 2 day grid, it can be concluded that product A is extremely popular and achieved a sell-through of 0.8 within 2 days. As such, product A can be classified as “under stocked”. | ||
| − | [[File:totalgrid.png| | + | [[File:totalgrid.png|550px]] |
In the above figure, we plot the 7 day average figures of collections 760 - 769. In the figure, collection 761 and 768 falls into the bottom left quadrant. With a relatively low rate of sales and low sell-through rate. It can be said that the popularity of the products in the collection was misjudged and brought in a disproportionate amount of stock. On the flip slide, collection 763, 765 and 767 falls into the top right quadrant, indicating optimal stock amounts and high popularity amongst users. | In the above figure, we plot the 7 day average figures of collections 760 - 769. In the figure, collection 761 and 768 falls into the bottom left quadrant. With a relatively low rate of sales and low sell-through rate. It can be said that the popularity of the products in the collection was misjudged and brought in a disproportionate amount of stock. On the flip slide, collection 763, 765 and 767 falls into the top right quadrant, indicating optimal stock amounts and high popularity amongst users. | ||
Revision as of 18:20, 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 our sponsor to utilize SA in their decision making process. To achieve this, a clear data representation is needed to help our sponsor understand their product demand. Our group came up with a visual decision tool called the Inventory Performance Grid which relies on both Sell-Through Rate and Average Rate of Sales on a product to produce actionable insights for our sponsor.
We created an example based on 4 different products which we sold in a 2 days, 7 days, and 30 days time window.
With the above metrics for each product, we can now visualize each product’s inventory performance on an inventory performance grid. Sell-through rate and the average rate of sales combine to produce an inventory performance grid that helps us understand different aspects of merchandising. Sell-through rate depicts stock sufficiency while the average rate of sales is a measure of the popularity of the product. With that, each product can be classified into different quadrants, with each quadrant representing different business meaning. The figures below depicts an inventory performance grid of the products A, B, C, and D in a 2 days and 7 days time window respectively.
The inventory performance grids of 2 days and 7 days have different business meaning and interpretation. For 2 days, products should have an optimal sell-through rate of between 0.3 to 0.6 and a rate of sales of above 1.50 pieces per day. This means that the product is popular and that the stock quantity is optimal. If the sell-through rate of the product is above 0.6 with a high rate of sales, it signals that the product is under stocked and the actual demand is higher than predicted.
The ideal stock-out time of each product is aimed to be 7 days. As such, products with less than 0.7 sell-through rate after 7 days can be deemed to be not ideal. Of course, these numbers can be tweaked as deem fit. The main idea behind the inventory performance grid is to give businesses a simple assessment of the performance of their inventory decisions. For example, it is apparent that product B is a poor performer. Products similar to product B can be avoided in the future.
The different grids provide businesses with a snapshot of product performance over different time windows. For example, by simply looking at the 7 day grid, it can be said that product A is a optimal performer. However, when you combine the 7 day grid with the 2 day grid, it can be concluded that product A is extremely popular and achieved a sell-through of 0.8 within 2 days. As such, product A can be classified as “under stocked”.
In the above figure, we plot the 7 day average figures of collections 760 - 769. In the figure, collection 761 and 768 falls into the bottom left quadrant. With a relatively low rate of sales and low sell-through rate. It can be said that the popularity of the products in the collection was misjudged and brought in a disproportionate amount of stock. On the flip slide, collection 763, 765 and 767 falls into the top right quadrant, indicating optimal stock amounts and high popularity amongst users.
