A Multi-Perspective Approach to Support Collaborative Cost Management in Supplier-Buyer Dyads

Joint cost management is a decisive factor for sustainable collaboration in supplier-buyer dyads. For both parties the establishment of an accurate cost estimation (CE) framework supports managing suppliers’ costs as well as manufacturer’s quotation costing. Grounded on resource dependence theory and following a design science research approach, this study aims to develop a multi-perspective CE system inspired by statistical learning , deep learning- decision-making and multi-agent theory. We evaluate our system in a single case and computer simulation study using empirical data coming from observations and archives at a large Bavarian original equipment manufacturer (OEM). The results indicate that our CE approach can select the most significant cost-drivers and predict total costs of parts and assemblies with high accuracy to support the supplier in efficiently managing its costs. In making the CE blackbox model transparent using a combination of model agnostic post-hoc explainable artificial intelligence approaches we create user acceptance for both suppliers and OEMs. All CE-artifacts are ensembled in a multi-agent system being able to automatically manage costs with suppliers and furthermore, as a model extension, can lead to a collaborative price agreement. Our system supports supply chain managers on both sides in entering into a sustainable long partnership