EFFECTIVENESS OF DYNAMIC SIMULATIONSUPPORTING AND OPTIMIZING DESIGN AND MANAGEMENTOF WAREHOUSE FACILITIES

This paper presents some industrial applications of dynamic simulation techniques in order to show their effectiveness in supporting management decisions and to solve strategic and operative logistic problems. Several significant real cases belonging to different business sectors, but always concerning the optimization of material handling system and manufacturing and distribution warehouse facilities’ design and management, are described. In the first one Visual Interactive Simulation (VIS) is used in order to design a physical temporary and distributive buffer warehouse; next an example of Montecarlo Simulation (MS) to support management and design of Automatic Storage & Retrievial System (AS/RS) is described. In conclusions two applications of VIS’s use to choose materials storage in different warehouse systems are presented and the impact of class based storage policy is emphasized. Aim of this study is to show simulation approach’s effectiveness in order to optimize the Supply Chain (SC) according to the absolutely necessary need of integrating all business processes across intra and inter organizational boundaries and according to a competitive context where Customer wants more differentiated products in lower volumes and within shorter delivery times. The dynamic modeling approach based on simulation istruments passes through a hierarchical route of investigation which is made of some suboptimizations and involves simultaneously different entities of the integrated logistic channel. These relationships and influences are traditionally neglected in an ineffective modeling of a functional system. The efficiency and efficacy of simulation tools rise above static approaches, which are ineffective to consider all variables and constraints involved in real istances whose computational complexity is very hard. After a qualitative description, the dynamic modeling of each industrial problem is illustrated thus justifying more significant and critical choices; hereafter all the results from the optimized process, which is based on simulative runs, are described.