Synthesis of activity-based costing and deep learning to support cost management: A case study in the automotive industry

Activity based costing (ABC) models for cost management and big data analytics are deeply rooted in academia and practice to efficiently manage operational costs. Inspired by cost management and deep learning (DL) theories we develop a multilayer perceptron (MLP) using a nested particle swarm optimized (PSO) neural network algorithm for self-controlled architecture and weight optimization. This innovative approach enables to mimic ABC while increasing prediction accuracy. In a real-world case study we use wheel manufacturing data provided by a large original equipment manufacturer (OEM) and follow a full factorial experimental design. Furthermore, we benchmark our novel DL approach with results derived from a traditional ABC analysis. We demonstrate that this intelligent cost estimation model can mimic ABC using few cost drivers contributing to efficient and transparent interorganizational cost management. Major quantitative findings within a conducted field experiment demonstrate a high forecast accuracy with low absolute cost percentage error (CPE) deviation of the novel cost estimation approach in industry. The results extend the scope of PSO for the simultaneous optimization of the architecture and weights of neural networks. Additionally, we prove that the PSO algorithm is a suitable alternative to traditional optimization methods in DL.