Date of Award


Publication Type


Degree Name



Product Platform;Safety Stock;Stochastic Demand;Variety Management


Ahmed Azab


Fazle Baki



Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.


During the era of digitalization, customer demand has become more customized than before. For this reason, the variety of products is increasing rapidly. Manufacturing industries are experiencing extraordinary challenges due to frequent updates of customer requirements. Now the manufacturers are focusing more on mass customization than mass production to keep pace with this situation, however, managing variety using the make-to-stock strategy increases holding costs, and the make-to-order strategy increases lead time. Moreover, uncertainty in demand is making mass customization more challenging. Though hybrid manufacturing is a promising concept for variety management, very few works consider stochastic demand. The concept of incorporating additive and subtractive manufacturing is known as hybrid manufacturing. Both the additive and the subtractive processes have some limitations when used separately; however, combining these methods can suppress the limitation and maximize the strength. Thus, this study aims to integrate the concept of product platform (considering delayed product differentiation technique) and hybrid manufacturing process to deal with product variety and stochastic demand. This research presents an optimal mixed integer programming (MIP) model to minimize manufacturing and holding costs. While developing the MIP model, multi-period stochastic demand is considered. According to the developed model, the platform consists of some common features, and a new variant will be produced by adding and/or removing features. This model's novelty is satisfying stochastic demand while maintaining a certain service level. The mathematical model is solved using the exact optimization solvers such as Gurobi through the AMPL programming language. Finally, four case studies are performed to showcase the strengths of the developed novel mathematical model.