Traditionally, when it came to supply chain planning, demand and supply streams have been projected as a specific deterministic value. For instance, 22 pallets of a specific item will flow through a given location on Monday.

But that single number is just a guess. It could be more or less, depending on how a host of assumptions and projections actually play out.

Yet despite the fact that this number is so uncertain and it’s not real – it’s only a guess –the whole supply chain is driven off this one number. And if it’s wrong, or should we say when it’s wrong, we’ve got all kind of problems. Review processes are set in motion. Costly expediting and reaction (or worse, overreaction) may begin. Up and down the supply chain. All because the number wasn’t the exactly the one we planned for – which was really just a guess to begin with.

A probabilistic supply chain model, like we use at ToolsGroup, uses a different approach. It assumes that this value is not a single number, but a probability function. So instead of saying there will be 22 pallets, the system says that there is a 50% chance that there will be 22 pallets, a 25% chance that there will be 21 pallets, a 15% chance that there will be 20 pallets, and so on. Rather than a single number, you get a range of values with different probabilities, also known as a probability distribution.

With this approach, you model the entire supply chain through this probabilistic distribution. And because it so much more mirrors the way the world really works, it has an inherently better chance of correctly describing the expected outcome and getting the planning process correct.

A simple example would be filling a truckload. If you have fixed numbers, you don’t know how best to fill the truck. When you have probabilistic requirements, you can use the natural uncertainty of your supply chain and the flows of materials to load the truck, and you will fill it in a way that optimizes your margin (or whatever goal you have set for yourself). And because you understand which outcomes are likely and which are not and can cause a problem, you can focus your review and expediting on those situations that truly deserve a reaction. Because you weren’t expecting an exact number, but a range of numbers with probabilities.

Labels: probabilistic modeling, probability distribution, supply chain planning