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December 02, 2015
How Retail Supply Chain Analytics Will Set You Apart
By: Dinesh Natarajan, Jeff Keene & Haresh Gopalan

Retail is an industry that is in a constant state of entropy. Increasing competition, high customer expectations, multiple channels and a wide array of product offerings are just a few of many examples that constitute a dynamic environment in the retail industry. As the population and the choices they are given continues to grow, the retailer will continue to see more complex challenges. To stay competitive, retailers must take an analytical approach to better understand their business and anticipate customer behavior.


Every retailer has its own processes to plan and sell products in its supply chain. Typically, there are four main activities: Planning, Buying, Procurement/Production & Allocation.

The planning team is responsible for reviewing past season performance and creating a merchandize plan for the next season based on a fixed budget. The team decides the style count by store, identifies top products and creates an assortment plan for the season. The buying team works closely with the planners, designers and the production team to determine the right mix of color and size for each style. The production team (along with the buying team) is responsible for cutting Purchase Orders (POs) by monitoring fabric inventory, assessing supplier capability and determining factory capacity.

Typical lead time in retail for production and shipping is 60-90 days. If a product (with good sales/margins) requires replenishment during a season, the production team is tasked to “chase” the product and bring it in within 10-20 days. The final step is allocation – the process of allocating the products to all the stores in the network. The allocators look at store level performance and determine the right mix of products that should go in to a store.

There are several decision points in the retail process. In the current retail supply chain, these decisions are driven by the instincts of an experienced person in the organization; the huge dataset that retailers possess is seldom used in the decision making process. We have seen that leveraging this data through analytics is a potent tool to make smart business decisions at various points in the retail process.

Here are few examples:

  • Advanced Forecasting – Any forecasts built on historical data assume that all the information contained in that historical data is representative of the future. Forecasting is difficult at best in most organizations; in a fast paced setting like retail, it can be even more challenging due to constantly changing tastes/styles of the consumer. Planning and buying functions can benefit greatly from improvements in forecasting. This is where predictive analytics comes in. Predictive analytics encompasses a variety of techniques from data mining, statistics, and machine learning that analyze current and historical facts to make predictions about future events. In addition to the historical data, external datasets such as weather data or demographics can be used to build sophisticated forecasting models.


  • Assortment Planning Optimization – Product assortment is the different types of products that a retailer offers for sale. An assortment plan is always a trade-off between the breadth and depth of products that a retailer wishes to carry. Assortment optimization can help in this decision making process. Assortment optimization is essentially a variant of the bin packing problem, in which your objective is to maximize the revenue or profit margin. Retailers can gain competitive advantage by monitoring, analyzing and optimizing assortments at both micro (store) and macro (company) levels.

  • Price Optimization – The question of how much to charge is of paramount importance to a retailer. There is always a trade-off between price and how many units will be sold, and the goal can differ from case to case. If the product can be easily sourced and the customer doesn’t require immediate delivery, it may make sense to maximize profit margin. Alternatively, if a product must be stocked on shelves and has a short expiration period, the price has to be set low enough to keep it moving. Price optimization analytics can be designed around these goals (and many others) and can take past history and future expectations into account to determine the best possible price. And they can be updated in real time, so the product is always priced correctly.

  • Markdown Analytics – Markdown analytics is the process of finding a way to help retailers to maximize revenues during the end of the product lifecycle. Markdowns are a complex decision making process for retail managers as ‘too much too soon’ markdowns reduce the revenue potential and ‘too little too late’ markdowns increase the risk of ending the season with high levels of inventory. As an example, the price optimization study with Rue La La[Name Redacted] uses complex machine learning and optimization techniques to address this issue. The underlying procedure to optimize markdowns will provide confidence to the managers about the timing and pricing of the products, thereby relieving them of the apprehensions about “too soon” and “too late” scenarios.


It is clear that advanced analytics in retail can improve operations in a number of ways. Perhaps the best part is that analytics is constantly improving; the more historical data available, the better the forward-looking analysis will be. How can it help your business?


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