TCO-modell som beslutsstöd vid backsourcing

Sammanfattning

In a globalized and competitive market, strategic decisions regarding, among other things, production and sourcing are crucial for companies’ competitiveness. Make-or-buy decisions, where companies choose between insourcing or outsourcing, are significant for the company’s strategic and economic consequences. Backsourcing, where previously outsourced production is brought back in-house, is often caused by various factors such as the increased costs. Total Cost of Ownership (TCO), as a tool to identify direct and indirect costs, can be used as decision-making support within manufacturing. However, the models usually use deterministic factors that should be stochastic and dynamic, and their usefulness is limited in backsourcing of locally outsourced production. The purpose of this study is to develop a TCO model that can handle constant, dynamic and stochastic factors to support make-or-buy decisions. The study was conducted as a case study with an abductive approach. The case study primarily used qualitative methods with elements of quantitative methods. The qualitative methods were used to create understanding, while the quantitative methods were used to enable a structured and objective assessment of the economic consequences of backsourcing. Data collection consisted of observations, document studies, and written communication. Based on the collected data and the literature, a comprehensive TCO model including constant, dynamic and stochastic cost factors was developed in Excel. The model has been tested in an empirical case to examine its potential to improve the basis for decision-making in backsourcing of locally outsourced production. The results show that backsourcing is the most cost-effective decision for the case company, mainly due to the supplier’s service cost being higher than the internal labour cost, as well as the elimination of transport costs. The study shows that a TCO model can be designed by identifying relevant cost factors and then adapting the model to the company. Furthermore, the study shows that uncertainty in future costs affects the results, and therefore the model should be used as a decision-support tool rather than a definitive basis for decision-making. The study contributes theoretically by showing how TCO models can be combined with Monte Carlo simulations based on historical data, enabling results to be presented as a cost interval based on probability distributions. The practical contribution includes an Excel sheet with a complete model structure. Further research can examine how results from TCO models are used in decision-making processes and analyze their utility.