Probabilistic learning curve and real options approach to the valuation of cross-training with product life cycle

Thanasiri Muttulingam, Ryerson University

Date of Award

2011

Degree Type

Thesis

Degree Name

Master of Applied Science (MASc)

Department

Mechanical Engineering

First Advisor

Mohamed W. M. Ismail

Second Advisor

Mohamed Y. Jabar

Abstract

Learning and forgetting are two important characteristics in manufacturing environments where workers are cross-trained to increase their flexibility of adapting to different tasks. Cross-training is introduced by industries so that one worker can work on multiple stations. This thesis develops two models: (i) a probabilistic learning curve approach to the production lot size problem to determine the economic manufactured quantity (EMQ); (ii) a real options approach to the valuation of cross-training with product life cycle. Different workers perceive the complexity of a certain task differently and each worker will have his/her learning curve with its individual characteristics. So, it is more realistic to assume that the learning curve characteristics are random variables with given probability density functions. Furthermore, for the second model, the demand of the product follows three-regime product life cycle. Each regime is modeled by a geometric Brownian motion. The net present value (NPV) is calculated using the real options. The results show that there is a significant change in the NPV compared to standard model with simplified assumption