Scenario n°1: Manual/Excel Planning

The first approach is manual planning, by hand or using Excel. It may involve human resources manually drawing vehicle routes on a map or organizing loading plans using Excel spreadsheets.

Manual planning implies no digitalization nor automation, thus the risk of errors highly increases with complexity. And it’s highly likely that you won’t ever get an optimal solution. Worst, quite often the solution is not even feasible (goods couldn’t be loaded, delays to customers, overstock/out-of-stock…) Most of the time, the expertise remains in the planner’s head which leads to bigger risks of losing valuable knowledge.

Scenario n°2: Supply Chain Management Softwares

Then, we find Supply Chain Management Softwares such as TMS, DMS, WMS, APS and OMS, to manage digitally, collaborate, and automate the flow of goods and services from suppliers to customers. They provide digitalization and visibility on the planning, tracking, procurement, inventory management, etc.

However, they lack the intelligence. Most decisions are still taken by hand, for example via drag-and-drop of tasks to resources in a planning. They provide a big step ahead in digital transformation of organizations, but don’t offer the engine capable of automating decision-making. This is where optimization modules can complement these Management Systems (Acteos, Klareo, Savoye, Urbantz…) by enhancing their business application via API with advanced mathematical optimization features.

Scenario n°3: Siloed Automation

Finally, some organizations start using optimization tools on the very last step of a decision process, once all structural and impactful decisions are made. This kind of automation is often isolated from corollary decisions, and thus applied to a small part of the whole problem. For example, they will automate the scheduling of deliveries once you have decided by hand the assignment to drivers and the slot of delivery.

Rather than a siloed and disconnected decision-making, a systemic optimization approach handling multiple dependencies between decisions offers much greater margins for improvement. For example, for a leader in the distribution of goods, Atoptima has provided a decision-support tool enhancing its TMS and WMS to automate and optimize in synergy palletization, loading and routing. Designing the pallets depending on how they will fit in the truck and taking into account the sequence of the delivery routing aftermath has significantly improved the quality of service thanks to better filling rates and reduced traveled routes. Savings are 2 to 10 times higher than optimizing these steps separately.