Total cost based decision support model for strategic sourcing – Case sourcing of MRO equipment in a process industry company

Abstract

In the globalized supply markets of today, industrial firms’ need to consider total cost of ownership (TCO) in their sourcing decisions is emphasized. The decision making involves consideration of many other costs than purchase price, such as transportation costs and quality costs. Common supplier selection methods may not be able to fully consider the actual costs of doing business with the suppliers and address the strategic considerations related to supplier selection, e.g. decisions on how many suppliers should be chosen and how much business should be awarded to them.

Research problem of this study is how to select sourcing strategies that minimize TCO of a particular commodity. The purpose of the study is to build a decision support model to support selection of sourcing strategies from total cost of ownership (TCO) perspective. The first objective is to construct a total cost of ownership based quantitative optimization model for supplier selection in a global sourcing environment. Another objective is to apply the model in the empirical part of this study for the sourcing of selected MRO product group in the case company to analyze current and optional sourcing strategies from TCO perspective and to identify savings opportunities. The data for the empirical part was collected from the case company’s databases.

The TCO based deterministic integer linear programming (ILP) model for supplier selection developed in this study enables the determination of TCO optimal sourcing strategies in a multi-product, multi-supplier, multi-period, multi-factory environment. The model provides a TCO-optimal choice of suppliers, amount of business awarded to them as well as cost-optimal inventory and ordering policies during the selected planning horizon at the product item –level. In contrast to most models presented in the literature, it covers sourcing of products to multiple facilities in different geographical locations, quantifying the potential cost trade-offs between shorter lead times and lower transportation/inventory costs through local sourcing and cost benefits e.g. through volume consolidation to global suppliers. Moreover, the model takes into account demand predictability by introduction of demand predictability parameters.

The empirical analysis showed a 19% TCO-savings potential in the current sourcing strategy of the studied MRO product group at the case company. The savings could be realized through detailed analysis of price/quality-optimal suppliers at item level, negotiating improved volume discount schemes, introduction of a selective quality inspection policy on supplier by supplier basis based on their quality record, and increasing direct sourcing from manufacturers while simultaneously optimizing inventory levels. Effect of demand predictability on the optimized TCO was shown to be at most 3%. The TCO optimal supplier selection was shown to be quite insensitive to both demand predictability and demand level of the studied product group.