SHIP FROM STORE OPTIMIZATION

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Ship from store optimization

The Client: One of the largest north-American clothing retailer. The retailer had brick and mortar stores across the country in addition to an online store

Business Problem: Shipping costs are a significant factor in impacting the profit margins of e-tailers.  Retailers who have a physical presence, as well as an online presence, have the option of shipping items from a store in addition to shipping from one of the central warehouses.  The decision of how to best fulfill an order is complex due to the number of considerations involved.

  • In clothing, it is common for as much as half the orders to have more than one item. Also given the variety of items available online, it is frequently the case that there may not be any store that has all the items in an order in stock.  In such a case, the order needs to be split across multiple stores.
  • Not all stores are equally punctual and prompt in delivering the products
  • Inventory optimization – Shipping items from stores that do not have sufficient inventory causes out-of-stock situations in stores.
  • Shipping costs, which is dependent on distance and package size for each shipment

There are naturally trade-offs, reducing transit time might imply shipping the order in 3 shipments from 3 nearby stores, but the shipping cost will be high and the customer may be annoyed by 3 separate deliveries for a single order. On the other extreme, shipping the entire set of goods in one shipment from a central warehouse may be ideal to reduce the number of shipments, but it does not allow for store inventory to be best utilized. 

Arriving at the best solution is requires allowing the business to decide the tradeoffs by setting weights for different objectives.

The existing solution was essentially a bunch of human-written rules.  They became complicated, incomprehensible and expert dependent.

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Our Approach: Our approach involved:

  1. Developing a suitable objective function via simulation. We developed a simulation framework that allowed the business to test-drive the optimization and experiment with different relative weights for the various objectives.
  2. An interface to allow the user to tune the weights of different components of the objective function.
  3. Application of business constraints and search for feasible solutions using evolutionary and other non-linear search strategies.

The business objective was a combination of the number of separate shipments,  shipping time, shipping cost, order margins, and store inventory levels

Deployment: The algorithm would process a batch of new orders every 30 mins to an hour. At any time, the orders processed were in 1000s.  No human decision making was involved.

Results: The client was able to save US$1M per year through a combination of improved order margin and shipping costs while minimizing the number of split orders (separate shipments to a customer).  In addition, of the hundreds of software that were evaluated by an external software auditing firm, this was the only one to get a 5-star rating.

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