This paper presents a new methodology to predict behavioural changes in
manufacturing supply chains due to endogenous and/or exogenous influences in
the short and long term horizons. Additionally, the methodology permits the
identification of the causes that may induce a negative behaviour when predicted.
Initially, a dynamic model of the supply chain is developed using system dynamics
simulation. Using this model, a neural network is trained to make online
predictions of behavioural changes at a very early decision making stage so that
an enterprise would have enough time to respond and counteract any unwanted
situations. Eigenvalue analysis is used to investigate any undesired foreseen
behaviour, and principles of stability and controllability are used to study several
decision configurations that eliminate or mitigate such behaviour. A case study of
an actual electronics manufacturing company demonstrates how to apply this
methodology and its real benefits for enterprises. |
