Diagram 1 – A stochastic optimization procedure
Stochastics in practice
In practice, stochastics is fast gaining ground in optimization procedures. A typical challenge concerns decisions on production capacity in network planning. When should I build up capacities? How many machines should I procure? What if the new machine stands idle because there is a temporary lull in orders? Or is it advisable to outsource orders – but then that means making a low profit?
And the good thing about it is…
With stochastic optimization, you can make decisions which, on average, are highly successful. In this way, business success is not about gambling on a forecast; it’s about guarding against many possible future scenarios – and that pays off.
OPTANO and stochastic optimization
Scenarios form the basis for stochastic optimization. OPTANO offers extensive possibilities here; planners can create scenarios easily and without any technical restrictions. OPTANO differentiates here between the data used to make calculations, between master data and scenario-dependent data. Scenario dependent data is, above all, data which forms the basis of uncertainties and fluctuations. In the scenarios, data can be changed easily, the consequences which are caused as a result can be analyzed and stored separately.
After scenarios have been created for various data, it is interesting to compare these in order to establish which variant is more feasible. Defined key figures (e.g. KPIs – key performance indicators) can be used here.
OPTANO, with the possibilities it provides for scenario generation, makes a convincing impression! Read our blog entry with more details on this topic: What-if scenarios in production planning