In this article, we review the research state of the bullwhip effect in supply chains with

stochastic lead times. We analyze problems arising in a supply chain when lead times are

not deterministic. Using real data from a supply chain, we confirm that lead times are

stochastic and can be modeled by a sequence of independent identically distributed random

variables. This underlines the need to further study supply chains with stochastic lead times

and model the behavior of such chains.

Due to fast-paced technical development, companies are forced to modernise and update

their equipment, as well as production planning methods. In the ordering process, the customer

is interested not only in product specifications, but also in the manufacturing lead

time by which the product will be completed. Therefore, companies strive towards setting

an appealing but attainable manufacturing lead date.

Manufacturing lead time depends on many different factors; therefore, it is difficult to predict.

Estimation of manufacturing lead time is usually based on previous experience. In the

following research, manufacturing lead time for tools for aluminium extrusion was estimated

with Artificial Intelligence, more precisely, with Neural Networks.

The research is based on the following input data; number of cavities, tool type, tool category,

order type, number of orders in the last 3 days and tool diameter; while the only output

data are the number of working days that are needed to manufacture the tool. An Artificial

Neural Network (feed-forward neural network) was noted as a sufficiently accurate method

and, therefore, appropriate for implementation in the company.