System Dynamics Modeling and Multicriteria Analysis Methods for Selecting Scenarios in a Harness Assembling Plant

The global automotive industry faces significant challenges with respect to its supply chain, particularly component scarcity and the increasing complexity of modern vehicles, which have severely impacted the production of high-tech harnesses. This study addresses the issues faced by a leading automotive harness manufacturing company in Ciudad Obregón, Mexico (an international company that requested confidentiality), which has suffered considerable economic losses (over USD 2870) and production downtime due to component scarcity and delivery delays in component deliveries, affecting “Crew Grande” harness production. This proposal aims to develop a technological solution with a graphical interface to support decision-making in the face of this scarcity. The methodology employed system dynamics to model the supply chain’s complexity, using software such as Stella® Architect for Forrester diagrams and equations and Vensim® PLE for causal diagrams. The model was validated with a relative error, confirming its reliability. Multicriteria decision-making (MCDM) was performed using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Faire Un Choix Adequate (FUCA) methods to evaluate 15 scenarios (normal, pessimistic, and optimistic), identifying the four most favorable scenarios for optimizing operational performance. The results demonstrate these solutions’ potential to mitigate losses, improve operational efficiency, and strengthen the company’s position against market and demand fluctuations, especially for its main client, Ford Motor Company, using a graphical user interface (GUI) to support analysis and decision-making.