Dynamic replenishment instead of impoverishment


Budget cuts have become the main weapon in the arsenal of today’s retail supply chain managers. As a result, innovation, and social innovation in particular, are under threat. This is a real shame since innovation can actually enable companies to serve their customers better, keep their inventories small and make the best use of their human resources. Most importantly, innovation can also ensure that your company earns more. Forecasting demand and predicting your workforce needs are crucial steps that you can take to start innovating with relative ease.

Static retail versus dynamic replenishment

Many retailers are working with a fixed and predefined delivery schedule for the coming period, regardless of any change in their marketing activities, good or bad weather, and other dynamic aspects. Inevitably, this leads to unwanted spikes in logistics. This is why the Dutch supermarket chain Albert Heijn decided to adapt its transportation schedule to operational needs, adding dynamism to the process by demand forecasting. Similarly, Walmart, Tesco and Carrefour have been pioneering in this field, regularly adapting their replenishment and delivery schedule and avoiding a fixed composition for daily transport journeys. This has improved their loading and logistics efficiency without the need for workforce reductions or major investments in mechanization. Other retailers have gone one step further, matching utilization and delivery with demand forecasting based on point-of-sale data. With this approach, retailers can not only keep their stocks replenished, but also mobilize the right number of employees to deliver any given service.

“Prediction is very difficult, especially about the future

Although most people don’t disagree with Niels Bohr very often, the examples above illustrate that using historical data to forecast demand is possible in practice. Airliners, tour operators and hotel chains have been doing this for years, adjusting their pricing every day based on predictions made with historical data. Is it possible to embrace demand forecasting in retail and logistics? The answer lies in your company’s ability to leverage data. According to DHL’s recent report on Big Data in Logistics, increasing efficiency with predictions is one of the key benefits of using big data in logistics. The European e-commerce company Otto, for instance, was able to arrive at a much more accurate forecast at the Stock Keeping Unit level using big data.

Anticipate the prediction

Predictive analytics is a must-have for retail companies. If you see something coming, you ought to anticipate it. This can be done in several ways. Dynamic pricing, as discussed above, has been a popular practice in a variety of industries. It won’t be long before retailers start adopting dynamic pricing as the standard practice for influencing demand as well. If you’re looking for a starting point, ask the right questions first. If your expectations are uncertain, it is advisable to remain flexible. As Walther Ploos van Amstel elegantly states: Predict & Prepare and Sense & Respond. If we all get more insights from our data, maybe our strategies can also become more robust.

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Goos Kant
Bio: Goos Kant (1967) is a full-time professor of Logistic Optimization at Tilburg University. He is involved in the master program of Business Analytics and Operations Research, as well as in the master program Data Science & Entrepreneurship. He is the project leader of a large R&D project on horizontal collaboration in logistics. Goos is also a managing partner at ORTEC, with global responsibility for all solutions in the logistics industry. His primary area of interest lies in the 3PL-industry in optimizing their planning processes in the end-to-end supply chain. Goos is involved in courses from MBA-schools TIAS and Nyenrode, and member of ORTEC’s supervisory board. He holds both an MSc and a PhD in Computer Science.

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