Plastics - delivery time


Our plastics manufacturer cannot distinguish himself from the competitor with arguments on price and quality. His competitors deliver at the same bottom price and with the same quality as himself. The only thing he can do better is to deliver on time, and eventually lower his delivery time.

So he invests in a complex planning system. But still, he is not able to get his actual delivery in a reasonable time frame from his scheduled delivery. The alternatives would be to loose market share or to increase stock so that the most popular products can be delivered of stock.


A planning system is based on a model. A model should reflect the way your manufacturing behaves for a specific product. However, if your model is based on average perceptions and the variance of its parameters is too big, then obviously the variance of the end result is a way out of bound.

As long we only talk about 1 order for 1 product, then the 'damage' will be limited to 1X the variance. However, with 100 orders for probably as many different products, your delivery time will be as close as 1 to 100 times the variance.

The reason for the variance is the lack of figures about the performance of the plant. The manager is unable to zoom in on the low performance element, and therefore unable to work out a strategy to solve the problem.

With PROMES software we are able to collect all events that lead to losses, and point to the basis of the problem. Even more, we are able to give a proposal for better parameters.

Bottom line?

Our plastics manufacturer was unable to bring his actual within 10 days of his estimated delivery time. Just imagine he can bring this to 2 days. This will gain him 8 days.

We could have solved this problem by a strategic increase of stock of finished goods. When the delivery time slips, we simply take from stock. When the order book is low again, we replenish the stock. It isn't perfect, but with some good statistics...

So, what's the value of 8 days stock?