Artificial Intelligence in Retail Planning
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Today retailers have more than intuition and experience to put their retail plan into action. They can leverage Artificial Intelligence / Machine Learning (AI/ML), and Predictive Analysis as part of their supply chain management.
Using AI/ML in retail planning, retailers have the advantageous capability to plan for what's lying in the future. They can avoid risks, make the best of the current trends, and generate higher revenue.
AI in retail planning
Adopting these technologies would be the first step to digital transformation that is required to overcome the following challenges of supply chain management:
Anticipate Potential Out-of-Stock Scenario
Out of stocks are costing retailers dearly and it is causing them to lose potential sale opportunities. Surprisingly there is still a lack of urgency to address this problem. The ability to proactively know when and where a surge in demand could occur, is part of the solution to this problem.
To gauge this manually is a near-impossible task. The brands not just need to forecast effectively, they also need to have a thorough understanding of every tiny bit of data to know how these trends would vary with region and customers to prevent stock-outs. Machine Learning with its data analytics prowess could be the game-changer for retail business. The technology can turn the spreadsheet data into actionable insights, find patterns in the underlying data w.r.t demography, social situation, etc. Retailers can use this knowledge to mobilize their supply chain movement to avoid overstocks and stockouts.
Optimized distribution is the goal of every retail planning. Retailers would be offering products for a lower price to gauge the customer interest in the product, and how much it is selling across regions. Based on the results he will replenish the stocks as per the demand in a particular store or region. Firstly it's not possible manually to evaluate these wide-spread trends, also retailers can't channelize their manpower utilization only to this task. Artificial Intelligence and Machine Learning can automate the evaluation of data and carry out the auto-replenishment of stocks based on where it is going to get maximum sales and revenue. This frees up the employees to cater to other important aspects of the retail business such as product sales, marketing, etc. This also prevents unsold inventory from fallings into discount racks, the technology ensures that the product reaches where the demand exists.
Dynamic Inventory Allocation
Getting products to the right place where they are needed is the aim of every supply chain network. Most of the supply chain network functions centrally, where a central warehouse will be catering to the need of all the retail stores. Technology can unify these scattered inventories together. In the case of a stockout, a store can request movement of stocks from the nearest store rather than the central warehouse. This saves time, cost, and ensures that product distribution is managed round the clock.
Ensure Inventory to Meet Demand Spikes
Demand spikes are always difficult to anticipate, especially those motivated by promotions and new product launches. These are very challenging scenarios to assess and forecast the right amount of inventory to be stocked. The failure to tap on the opportunity causes immense losses to retailers on multiple levels such as revenue loss due to stockouts, marketing costs, etc.
Lack of data is not the problem, but the inability of humans to infer insights from it is. Using AI, retailers can harvest the information of pinpoint accuracy on how and where to navigate inventory to make the best of the demand spikes.
AI/ML upgrades the entire supply chain movement forecast. The lack of critical analysis that was required is now possible due to the AI/ML and data analytics. It makes sure that the retailers have the right information at the right time when it is needed most to make the best of an opportunity.
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