Where supply chain management and data science meet, and where theory and practice meet, that’s where Data2Move meets. At intersections like these, interesting questions arise. In our Data2Move Research Stories, you’ll find out how students have answered them.

This time, we look into Nazli Akgül’s master thesis: a study of the relationship between a company’s demand planning process and its supply chain performance.

Where it started

This international manufacturer of pharmaceutical products aimed to improve its performance regarding inventory management and planning – and save costs thanks to this improvement.

Where did this objective come from? Well: the company noticed its demand planning took to much time. As a consequence, their decision-making process was often hurried, and led to suboptimal outcomes. This problem was found throughout the company across multiple countries. It affected the inventory, which was often inaccurate, and raised costs unnecessarily.

Akgül took to the problem with an in-depth analysis of the company’s planning activities and the way they related to inventory levels and performance. She found that some activities held back performance – and others turned out to be more important than initially thought.

Findings and advice

By using simulation techniques, Akgül investigated possible changes to see what effect they would have. Of course, reality may differ from the simulations, but they generally prove to be valuable. It was concluded that the company should try to reduce reporting tasks for planners and, instead, allocate more time for backorder analysis and demand consensus meetings with supply chain partners.

By doing so, predicting demand would no longer be an issue, the company’s inventory would be managed accurately from now on, and performance would increase significantly.

Furthermore, thorough data analyses showed that demand planning performance significantly decreased when planners had to spend time on assuring the data are of good quality. Therefore, it is strongly recommended that the manufacturer adopts a more accurate data system.

The new approach Akgül recommends, including the new data system to support it, would require change on a large scale. An investment at first, which makes for a major time-saver later on.