Assortment Planning with the Self-Organizing Map
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README.md

Assortment Planning with the Self-Organizing Map

We applied the self-organizing map (SOM) to solve an assortment planning problem for Solitas customer. We introduce a case study in a still unpublished paper where data science approach is used to optimize a supply chain of product. The initial results of a still unpublished academic paper show that the sales is improved by 29% (http://www.solita.fi/ajankohtaista/solita-science-edellakavijyytta-tutkimuksen-kautta/).

Need 4 Speed program (http://n4s.fi) has helped a lot in the experimentation and academic work so far. Tampere University of Technology has participated in development and research of the method.

Empirical real world experimentation with 8 products and some hundred sales outlets together with a simulation showed resulted to an improvement of 45%. It is still unkown how the solution applies to the total yearly sales of products.

The method is simple and straight-forward:

Neural Networks

  1. Export sales data of previous period
  2. Organize the sales data with the SOM
  3. Count SSI and SCO in the SOM neighborhood and median in the real-world neighborhood.
  4. An SSI value > median and SCO > median predicts good sales

The same expressed as an experiment with K-means clustering of three control variables and Whitney-Mann U test:

Hypothesis:

Higher SOM-sales index (SSI) and SOM-coverage (SCO) values together with target outlet SOM-coverage (TOC) help to prevent zero sales in a highly optimized environment. Helper variables: number of products with similar sales profile Nspp and sales outlets Nsop

Independent variable: a value of 0 or 1 indicating SSI > median of real-world neighborhood (assortment size and total sales), SCO > median of real-world neighborhood and TOC > 2/Nsp , where Nsp = number of outlets with similar sales profile on

Control variables: Assortment size (as) Total sales (ts) Sales per place (spp = ts/as)

Experiment:

We applied K-means clustering for the control variables (as, ts & spp) with N of 9 clusters in order to acquire comparable product-outlet-pairs Clusters with at least 30 additions were investigated in detail.

Whitney Mann U-test for Control variables were: P-values 0.8, 0.4 and 0.3

Result for Whitney Mann U-test for sales in currency for groups A and B was P-value of 0.06

Simulation:

In addition to this we created a simulation In April 2014 products A, B, C, D, E, F, G and H were added to several outlets. An algorithm removed the same products from different outlets with the following logic:

  1. Select outlets with same size (assortment size and sales +-30%)
  2. Filter product-outlets to outletSales < q1 and productSales < median
  3. Filter product-outlets with smaller SSI and SCO values
  4. Remove the product-outlet with lowest SSI

The simulation resulted to 45% increase in sales.

Limitations: The experiment was done in a small scale. It is unknown how e.g. product cannibalism affects the total change or how the other 1500 products and 4000 sales outlets behave in a similar setting.

In the best case, this approach could work in any domain with thousands of products and sales outlets:

SOM

In practice, the method works as follows:

SOM-practice