VINCENT SYSTEM

Vincent System



Vincent System

A.Please copy and paste the “model” for this point 4 from Vensim (in this space) and submit the model in Vensim too (with the email submitting the homework - please name the model simulation2)

This research presents a System Dynamics SD approach to model and analyze a single stage scalable manufacturing system for LifeAfterLife Inc. The system is exposed to a random demand that is assumed to follow a normal distribution pattern. The main contribution in this paper, is adding new modules to the existing state of the art of capacity scalability management in order to bring it near to reality. The proposed modules allow for costs evaluation, scaling capacity on seasonal basis, and applying system breakdowns.

A full-fledged simulation model (attached as supplementary material) was developed and tested using Vensim Program. Two capacity scaling policies are presented and used to study the effect of the new modules on the system's performance - where Capacity level, Inventory level, Backlog level and Costs are the measures of the system's performance. The results show System Dynamics ability to model real conditions that face capacity scaling planners, and present the actual effect of system breakdowns on facility performance. Moreover, this study investigates the impacts of applying seasonal capacity scaling on scalable systems to the given question.

Capacity scaling is considered a classical problem in many industries, and it was known as the capacity expansion problem to satisfy increasing demand in a cost effective way. The first study of the capacity expansion problem was conducted by Manne (1967, 12). Representative review of the classical capacity expansion problem can be found in Luss (1982, 85). Since demand uncertainty increases and technological advancement are faster, the need to address the capacity scaling problem from a dynamic view point where capacity can be increased and decreased becomes an essential requirement.

Recently, multiple modelling techniques are used to assist the capacity scalability planner to determine the best scalability policy based on different performance measures. Kim and Duffie (2005, 23) presented a multi work station production system model that is based on control theory.

A proportional control policy with a control gain Kb delayed by period Dk was applied, and it represents realities of hiring and firing labor force and other issues that prevent instantaneous adjustment of capacity to specify the new capacity. Deif & H. ElMaraghy (2007, 548) proposed a system dynamics approach for a single stage scalable capacity model. The model objective was to examine different scalable policies using multiple performance measures like inventory, backlog and capacity levels.

Further analysis for the same capacity scalable model was presented on Deif & H. ElMaraghy (2011) focusing on a market-capacity integration policy. Spicer (2007, 741) developed an integer programming optimization tool to investigate the optimal configuration plan of a scalable RMS. His objective was based on minimizing investment cost and reconfiguration costs over a finite horizon with known demand. Also he presented an optimal solution model for the multi period scalable-RMS using dynamic ...