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Data-driven decision making enhances your supply chain resilience


Which technological developments energise service chains? This was the central question at SLF's Service Leadership Summit, held on 2 June. Jack Pool and Olaf Witsen from Districon shared inspiring cases in which data-driven decision making played a key role.

The aim of their contribution? To show companies how to optimally prepare your logistics organisation for unexpected developments. How do you create supply chain resilience and what role can AI play in this? In a carrousel with small groups (and therefore much interaction), they discussed this with companies.

Supply chain disruptions

Questions in the beginning took up a lot of time in each session. The companies present shared their cases about disruptions in their supply chains. And there were many. Like a shocking case of respiratory equipment that did not make it through customs while pressure from ministries to deliver was increasing. Or a company that produces tractors and, due to a shortage of parts, bought up refrigerators to be able to keep producing. And deliveries that were delayed due to the closure of the Shanghai port.

"These examples confirm the need for a solution to deal with uncertain factors that lead to disruptions in the supply chain," explains Olaf. "We distinguish 3 types of disruptions:

  1. normal uncertainties, such as delays
  2. incident-driven uncertainties, such as the blocked Suez Canal
  3. disruptions that affect you in the longer term, such as the consequences of the war in Ukraine and developments due to corona.

AI can limit the damage

The time of designing logistics processes as lean as possible seems to have come to a turning point. But do you have to build up extra stocks as a company? And how do you calculate the right quantities? What do you do with your contracts? These are questions that many of our clients struggle with. With AI we try to map this out better for them."

Jack: "What effects do disruptions have on your supply chain? With data-driven decision making, we show variations in what can happen. Whereas this is often done manually in case of panic, you can make a much more realistic estimate via the app. You can substantiate it better because so many scenarios are calculated. This makes it less error-sensitive. Besides, it is too much work to do manually. With AI, you can calculate these scenarios on a daily basis, fed from databases.

Supply chain resilience

We are currently further developing the method of data-driven decision making. It gives our clients insight into the correct organisation of their supply chain. For example, should they conclude additional contracts? Or buy on a spot market? These are decisions that they can take properly with this method. This method allows you to quantify risks. This enables you to make a better estimate of how great the chance is that you will be able to run production or not in the event of a certain disruption. This in turn gives companies more certainty".


Data-driven disruption making thus helps companies to be well prepared for possible disruptions. For the cases mentioned, a certain structure in their supply chain would certainly have helped these companies. It provides better insight into the situation as a company and allows you to anticipate. Data-driven decision making increases your supply chain resilience.

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