An adaptive network-based fuzzy inference system to supply chain performance evaluation based on SCOR® metrics. (January 2020) - Lima-Junior, Francisco Rodrigues; Carpinetti, Luiz Cesar Ribeiro
Highlights: It proposes a performance evaluation system based on ANFIS and SCOR® metrics. The ANFIS tunes the topological parameters using historical performance data. It helps managers to identify gaps between expected and achieved performance. It is appropriate to deal with imprecise values and uncertain variables. Abstract: Evaluating the performance of supply chains (SC) is a critical activity to enhance the outcomes of operations along the SC tiers. In order to support this evaluation process, several studies have proposed the application of artificial intelligence techniques combined with the performance metrics suggested by the SCOR® model (Supply Chain Operations Reference). However these propositions present some limitations. While the systems based on Mamdani fuzzy inference do not allow adaptation to the environment of use based on historical performance data, the systems based on artificial neural networks are not adequate to deal with imprecise data and qualitative metrics. In order to overcome these limitations, this paper presents a new approach to support SC performance evaluation based on the combination between the SCOR® metrics with an adaptive network-based fuzzy inference systems (ANFIS). In total, 56 candidate topologies were implemented and assessed using MATLAB. The random subsampling cross-validation method was applied to select the most appropriate topological parameters for each ANFIS model. The mean square error between the target values and the values estimated by each ANFIS model demonstrate its greater accuracy of prediction. In addition, results of the hypothesis tests based on paired samples indicate that the proposed ANFIS models are adequate to support SC performance evaluation. The proposed system can help managers to develop improvement actions plans based on the outcomes of the evaluation process. When compared to previous approaches, it presents advantages such as greater accuracy of prediction, learning ability based on historical data, suitability to support decision making under uncertainty, better interpretability of results, among others.