How machine learning and optimization can be applied to Omnichannel Retailing?

  • 30 July 2019
  • 2 replies

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Today, the challenge for the companies is providing consistent, yet unique and contextual brand experiences across multiple customer-aware touchpoints, including brick and mortar, marketplaces, web, mobile and social. In response to these new customer preferences, the Omnichannel Strategy has been created to be a fully-integrated approach to coordinate different channels using just one strategy, marketing, price, database, assortment and goals.

In this context, optimization models, commonly used for Network Design, Inventory and Fulfillment, may not be the best alternative. I would like to discuss some topics:

  1. What are the new business requirement for Omnichannel Retailing?
  2. How machine learning, optimization and analytics can be applied to Omnichannel Retailing?
  3. What are the biggest challenges for optimization?
Thank you in advance

2 replies

Hi Luis - you have a very interesting point, thanks for sharing. It's clear that omnichannel adds a great degree of complexity and the power of optimization and prescriptive analytics allows something of a 'taming' of the complexity into a series of scenarios and executable options. Omnichannel also brings the option of focusing precious resources on particular segments with optimization and machine learning being powerful tools to enable this. Happy to discuss.
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Thank you Chris !!! In a multichannel environment, the customer accesses the information and always buy in the same channel. In Omnichannel, the customer can buy the product online and pick-up at the physical store. These models can be called (click and collect) or (ship from store). However, such a purchase must be considered as an online sale or a physical sale. If the store can also sell for ecommerce, the whole strategy of Network Design must change. This crossfilling changes the dynamics of the operation and should be considered when you a building a long term strategy. If the store is able to fulfill orders, we can have more safety stock and fixed cost. There are many trade-offs that we need to understand due to the new fulfillment process. How to address these challenges with Network Design and Multiechelon Inventory ?


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