Integration of Supply Chain Configuration Decision to the Preliminary Phase of Product Design from a Product Development Standpoint

Abstract

Supply Chain Management (SCM) is a necessary concept for manufacturing firms as it deals with the coordination and integration of interactions among collaborating suppliers to realise products. One crucial aspect of SCM is supplier selection. Organisations not only need to decide whether to keep the design and development of a product in-house or outsource, but they also need to be able to distinguish among potential suppliers in order to improve their Supply Chain performance. The Product’s Architecture is important to incorporating those interactions as it is typically established at the early stages of Product Development and impacts on the costs of collaboration within the Supply Chain. These decisions should be incorporated into the product design phases due to the two-way implications of the Product Architecture (PA) and its associated Supply Chain. However, despite the significance of this two-way interaction, previous research on this topic has focused mainly on the production stages of Product Development, and only a few studies have considered the supplier selection issue during the Product Design (PD) phases with the vast majority of those studies commencing at the later stages of PD (i.e. the detailed design phase of PD). Limited research exists at the early stages of PD and in particular the preliminary phase of PD where the PA is established. Moreover, this stage usually consists of uncertain data that managers have to work with and as such a method that can incorporate some element of uncertainty into the decision making is lacking. As a result, there is a pressing need to explore this issue further. In this thesis, a method was presented to integrate PA and Supply Chain Configuration (SCC) decisions. It begins with the PD aspect that analyses different chunk aggregation decisions of the PA, which then serves as a bridge connecting the different aggregated components and chunks to the suppliers. Then the supply chain aspect is analysed by looking at the performances of the different SCC under the different chunk aggregations. An Integer NonLinear Programming (INLP) model was then developed as it was deemed suitable to solve this. This INLP model was further extended to a stochastic model to incorporate for uncertainty in decision making in the early stages and explore if considering uncertainty early enough in decision making provided substantial benefits to the decision-makers. The developed INLP model was then evaluated by using a reverseengineered printer case study. The results obtained showed that it was beneficial for the Focal Company (FC) to incorporate SCC to the early design stages as this has implications on what PA the FC should select. The results also outlined what outsourcing strategy the FC should use when selecting suppliers. Also, the impact of different supplier characteristics on the decision of how the FC should aggregate the PA was tested. The results showed it is important for the FC to consider the different supplier characteristics when making PA decisions. Lastly, the INLP model was extended to a stochastic model to include uncertainty by varying the cost. The results were analysed using the concept of Expected Value of Perfect Information (EVPI), which is a well-established concept in tackling uncertainty. The results showed that it was beneficial for the FC to include uncertainty in decision making at the early stages of design. Finally, the model was tested via sensitivity analysis to validate the use of the model in this research—the sensitivity analysis aimed to test the behaviour of the model by varying different parameters in the model. After varying the data, the model produced the same results, which suggests the suitability of the use of the model. As a result, the optimisation of the Supply Chain (SC) and PA can be simultaneously accomplished during the preliminary design stages.