Supply Chain

Predictive Forecasting

Increase EBIT in retail with AI-supported demand planning

29. November 2021

7 Minutes

Increase EBIT in retail with AI-supported demand planning

In the last two years, uncertainty has increased significantly for the trade: Supply chains are not robust enough or keep breaking down, employees in logistics drop out. Ultimately, goods are not available and turnover is lost. At the same time, consumers increasingly value security of supply, coupled with flexibility and a favourable price. This is reflected in their shopping behaviour: new business models such as “home delivery” within ten minutes or click-and-collect solutions are putting pressure on the market. AI-based solutions help companies to forecast demand more accurately, have goods available exactly and thereby increase EBIT.

The most important points of the blog

Respond now to stay competitive

Companies must react to the current changes now in order to remain competitive. New, AI-supported concepts for retail, such as predictive forecasting & demand balancing, start from the online shop or stationary shop, but also encompass the logistics and distribution networks all the way back to the suppliers.

Traditional demand planning and models reach their limits

To be prepared for short-term fluctuations, retailers must be able to predict current and future demand more accurately. Ideally, this forecast should be accurate to the hour and to the product – right down to product criteria such as the size and colour of a winter jacket. This is the only way to ensure product availability. With AI-supported forecasting solutions, the demand of each shop and online shop can be precisely predicted and thus planned. If an accurate demand estimate is available, the upstream stages along the supply chain should be optimised at the same time. In this way, the availability of products can be reliably ensured in the long term.

Predictive Forecasting: Advantages of AI-supported demand planning

AI-supported demand planning offers great potential for companies. Thanks to AI solutions, specific customer requirements, general conditions or purchasing behaviour can be taken into account. In this way, additional efficiency can be increased along the supply chain:

Increase sales

If highly fluctuating products such as seasonal, weather-dependent or event-driven goods are available in the right quantity, this generates sales for the company – more than, for example, with a competitor who did not anticipate the demand.

Reduce costs

Accurate forecasting of required demand does not only mean that the right products are available for customers. It also means that there are no unnecessary quantities in the shops that cannot be sold. Costs for “sell-off” or even “discard” are eliminated.

Reduce inventory along the supply chain

Stocks along the supply chain are necessary in the right quantity, too high stocks tie up resources and generate expenses. Flexibilisation and on-demand models based on AI-supported demand planning can reduce these inventories. This helps to optimise working capital and generate a positive cash flow.

Higher utilisation of logistics

Through better, AI-supported forecasts, transports can also be better planned and empty runs can be avoided, for example. In addition to reducing costs, this also contributes to a reduction in CO2 emissions.

Optimised customer satisfaction

The customer receives his products quickly, uncomplicatedly and the way he wants them. And above all: he receives the desired product, which is no longer a matter of course. This increases customer satisfaction and loyalty.

How does AI-supported demand planning work?

An AI-supported solution for demand planning takes into account external parameters such as the weather, the course of the week or seasonal effects. Other factors also play a role, such as promotions, competitor campaigns, social media activities or the shift from bricks-and-mortar retail to home delivery. On this basis, the expected demand for the products is calculated.

In addition, the AI solution subsequently checks the effect of every action: Was the forecast correct? Which factors should be weighted “better”? In this way, decisions become more and more accurate and match the demand. This “learning” often does not take place in classic processes, as the results of decisions are not checked and thus no sustainable improvement occurs. Another advantage of AI solutions compared to classic demand planning is that existing information such as sales figures per store are immediately passed on to the AI-supported solution via digital systems. This creates a closed loop – a closed circle from the forecast to the sale.

Increase customer satisfaction and EBIT

AI can contribute to a sustainable increase in EBIT through accurate product availability. With AI-based solutions, companies can increase their performance, productivity and turnover while reducing costs. All in all, these factors have a positive effect on EBIT. The positive effect is not limited to the material: The satisfaction of the end customer also increases. And satisfied customers come back.

Questions? Fell free to contact us!

E-Mail: [email protected]

Michael Schweikl

Managing Partner