The purpose of this paper is to develop an Internet of Things (IoT)-based framework for supply chain (SC) performance measurement and real-time decision alignment. The aims of the proposed model are to optimize the performance indicator based on integrated supply chain operations reference metrics.
The SC multi-dimensional structure is modeled by multi-objective optimization methods. The operational presented model considers important SC features thoroughly such as multi-echelons, several suppliers, several manufacturers and several products during multiple periods. A multi-objective mathematical programming model is then developed to yield the operational decisions with Pareto efficient performance values and solved using a well-known meta-heuristic algorithm, i.e., non-dominated sorting genetic algorithm II. Afterward, Technique for Order of Preference by Similarity to Ideal Solution method is used to determine the best operational solution based on the strategic decision maker’s idea.
This paper proposes a dynamic integrated solution for three main problems: strategic decisions in high level, operational decisions in low level and alignment of these two decision levels.
The authors propose a human intelligence-based process for high level decision and machine intelligence-based decision support systems for low level decision using a novel approach. High level and low level decisions are aligned by a machine intelligence model as well. The presented framework is based on change detection, event driven planning and real-time decision alignment.
Rezaei, M., Akbarpour Shirazi, M. and Karimi, B. (2017), "IoT-based framework for performance measurement: A real-time supply chain decision alignment", Industrial Management & Data Systems, Vol. 117 No. 4, pp. 688-712. https://doi.org/10.1108/IMDS-08-2016-0331
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