How Advanced Analytics & AI Can Transform Inventory Management in Retail

Retailers can avoid over- and out-of-stock situations by automatically replenishing warehouses through demand forecasting and pro-active redistribution. Continue reading and discover how Advanced Analytics and AI can help you make more data-driven decisions & automate their inventory management.

The growth of research in the domains of data science, machine learning and AI enables data-driven organisations to take things even further. These new technologies and techniques enable a new way of extracting patterns from data, of deriving insights from data.

Insights that help data-driven organisations to better understand what is happening and why it happened. Insights that permit a retailer to predict what will happen, anticipate and prescribe actions, applying ever more advanced analytics.

This article focuses on three jobs-to-be-done in Inventory Management where Advanced Analytics & AI can create the most impact with your data.

1. Use Advanced Analytics and AI to forecast the most optimal inventory levels and appropriate distribution of items.

Make improved inventory management decisions and reach optimal inventory levels.

Getting inventory levels right has always been on top of mind of retail executives. Each executive understands the impact of the significant amount of working capital that is tied up in stock, each season again. Yet, with the stellar increase in the number of sales channels, managing own inventory levels as well as assessing the impact of increased competition has become utterly challenging.

The related decision-making includes the difficult task of understanding customer needs - how well will the product be selling - and should take into account supply chain uncertainties. By applying advanced analytics and AI retailers can optimize their spending behaviour with regard to stock, as the organization is presented with better insights with regard to those uncertainties.

Retailers should focus on bringing data together that includes demand forecasts, production lead times, supplier constraints, inter-warehouse distribution times, as well as relevant publicly available data on f.i. weather. It will enable them to make better decisions, for instance with regard to the safety and cycle stocks that should be maintained in order to cope with uncertainties.

Improved management of different types of stock enables a retailer to better understand the economics that underpins his supply-chain challenge, quantify the impact of uncertainties and make better decisions accordingly.

Getting the distribution of items over various shops right, the first time

For certain retail organisations, getting the initial distribution of items right is crucial as the cost of redistribution is just too high or lead times in replenishment are too long. Especially when selling seasonal items, there is most often only one shot at getting inventory levels right.

If not, a retailer is confronted with missed sales opportunities or required to introduce additional promotions in order to eliminate stock. Either way, having to do so cuts right to a retailer’s bottom line.

Gather and make data available to understand the factors that drove or hindered sales in the past. Allow the application of data-science and machine-learning techniques in order to simulate sales and predict what inventory levels should be maintained, at what location and at what cost.

With the use of advanced analytics & AI, a retailer can obtain insights into what is likely to sell where, and in what quantities. Retailers can develop a unique inventory profile for items allowing them to calculate the ideal distribution of items and predict possible fluctuations.

2. Apply Advanced Analytics and AI to timely replenish inventory, avoiding out-of-stock situations

The improved approaches to gathering data and making them available, are most welcomed in retail environments where demand is highly volatile and lead times are often uncertain. For instance, when it comes to preventing out-of-stock situations.

In retail, the customer has become unforgiving. Marketing and sales may have gone out their way to make a customer aware, nurture and lead him or her towards a purchase, only to find the desired item out-of-stock. In such a case, a few strokes on a smart device suffice to lose the purchase to an online competitor.

Avoiding out-of-stock situations has, therefore, become essential. Traditional supply-chain planning was designed around a fixed, rule-based approach with regard to the replenishment of items. Future-proof planning builds on readily-made available data and the application of advanced analytics.

A variety of influencing parameters can be brought together to allow a machine-learning algorithm to produce demand forecasts with regard to specific items. It can lever company-owned data in a combination with supplier data (batch size, lead times) and publicly available data such as online competitor data, public holidays or local weather.

By embracing advanced analytics today, a retailer can set himself on a path towards automated replenishment. Still assisting a decision-maker in the replenishment decision today but gradually erasing human error from the equation. As such, a retailer can ‘train a system’ that will at some point be able to autonomously take each replenishment-related decision.

+ Blog - Long Read: Smart Algorithms in Retail: More Bounce to the Ounce by Investing in Operations!

+ Client Case: How an online beer retailer benefits from AI-driven replenishment

3. Let Advanced Analytics and AI guide the redistribution of inventory when items threaten to pile up

Walking the fine line between under- and overstock situations often prompts retail procurement managers to generously procuring items. Why? Well, an overstock situation is often perceived as less threatening than an out-of-stock situation.

Yet the impact on available working capital can, however, be substantial, let alone the risk of markdowns, waste and loss in brand value.

Retailers can harness advanced analytics & artificial intelligence to automate the cumbersome process of periodically analyzing the inventory level of each store, both online and offline, for each product. It allows them to make timely decisions and push the appropriate instructions through the supply chain.

Driven by a custom-made redistribution algorithm and dashboard, the person managing inventory can query a tool to see whether inventory shifts are needed, their impact on possible revenue and their expected costs. This redistribution tool can furthermore take into account additional parameters such as the timing of planned intra-store transport.

The aforementioned has a significant impact on the time spent on this process by the inventory manager. With the click of a button, appropriate orders can be sent in the enterprise resource planning tool. It allows the tool to learn with each decision, ultimately gaining the level of intelligence that allows it to conduct the process autonomously.

+ Client Case: How a predictive algorithm enables an omnichannel retailer to timely redistribute items