Simulation Tool for Inventory Models: SIMIN

Introduction

Inventory modeling is very important for optimization of ordering quantity and total cost associated with it. Due to randomly changing demand, effective management of production and inventory control operations in dynamic and stochastic environments, a lot of efforts are needed to improve existing inventory models. The subject of inventory control is a major consideration because of its practical and economical importance. A number of models have been defined on the basis on deterministic and stochastic demands (Kleinrock, 1964). Silver has introduced realistic inventory models (Silver, 1992; Silver, 1993). Several benefits of simulation and modeling over the traditional and analytical modeling have been listed (Browne, 1994). It is mentioned that simulation models could provide greater accuracy, flexibility and more informative inputs. Inventory control is the second most frequent area of application of simulation after queuing systems (Banks et al., 1984). Banks and Spoerer provided a simulation procedure to solve and analyze the classical continuous review (Q, r) inventory system (Banks et al., 1986). Based on their simulation outputs, it was found that the form of the demand and lead-time distributions can greatly affect the stockout performance of the inventory system. Haddock and Bengu proposed a decision support system composed of a simulation generator, output analyis techniques, and optimization procedures to solve the classical (Q, r) inventory model with lost sales (Haddock et al., 1987). The dynamic behavior of production inventory systems was well addressed by research (Wikner et al., 1991). It is shown by previous research that system dynamics (SD) simulations have a role to play in supply chain design (Towill, 1996). SD modeling techniques have been adopted in research for modeling supply chain systems (Arnold et al., 1996). The application of simulation optimization to complex design and control problems started at Chemnitz University of Technology with a problem, where optimal order decisions could be found for an horizontal structured MLIM with lateral transshipments by coupling simulation with a simple search method (Eugene et al., 2007).