|Ph.D Student||Eklin Mark|
|Subject||Cost Estimation in a Finite-Capacity Stochastic|
|Department||Department of Industrial Engineering and Management||Supervisors||Professor Avraham Shtub|
|Professor Yohanan Arzi|
|Full Thesis text|
As the world has turned into a global village and firms compete in the worldwide market, survival and success strongly depends on the ability to accurately estimate product costs. In order to analyze the affect of shop workload, machine loading, and outsourcing decisions on the product unit costs estimation, a deterministic model for rough-cut cost estimation in a capacitated made-to-order environment was developed. Existing cost estimation model in deterministic environment, based on marginal analysis - the difference between the total cost without the new order and the total cost with the new order, was improved. Next a cost estimation model that takes into account the stochastic environment was developed. Linear regression-based predictive models were generated for estimating the mean (MT) and the coefficient of variation (CV) of the total cost. A comparative study of five alternative rough-cut cost estimation methods designed to replace the simulation was done. An activity based cost estimation model, that takes into account the stochastic process characteristics as well as setup time, machine failures and yields of products was developed. The activity based cost estimation was found better than the traditional cost estimation. It was found that by taking into account the capacity and stochastic nature of parameters the cost estimation accuracy is improved significantly.