The supply chain of spare parts is the intersection between the supply chain, the after-sales
and the maintenance services. Some authors have tried to define improvement paths in terms
of models to satisfy the performance criteria. In addition, other authors are directed towards
the integration of risk management in the demand forecasting and the stock management
(performance evaluation) through probabilistic models. Among these models, the probabilistic
graphical models are the most used, for example, Bayesian networks and petri nets.
Performance evaluation is done through performance indicators.
To measure the appreciation of the supply of the spare parts stock, this paper focuses on the
performance evaluation of the system by petri nets. This evaluation will be done through
an analytical study. The purpose of this study is to evaluate and analyze the performance of
the system by proposed indicators. First, we present a literature review on Petri nets which
is the essential tool in our modeling. Secondly, we present in the third section the analytical
study of the model based on bath deterministic and stochastic petri networks. Finally, we
present an analysis of the proposed model compared to the existing ones.
The continuous improvement in the industries and organizations hinges upon the evaluation of their performance. In fact, the performance evaluation assists organizations to identify their strengths and weaknesses and, accordingly, enhance their efficiency. As soon as the concept of sustainability was propounded in the engineering based industries, the performance evaluation got more importance due to the environmental issues and social concerns along with the economical aspects. Therefore, this paper is an attempt to propose an approach based on fuzzy best-worst method (BWM) and fuzzy inference system (FIS) in order to evaluate the performance of an Iranian steel complex in terms of sustainability concept. In the proposed approach, the weights of some selected criteria were determined by fuzzy BWM method and, then, the score of the under study industry was calculated in terms of economic, environmental, and social aspects. At the end, an FIS was developed to calculate the final score of the intended industry. In order to check the efficiency of the proposed approach, its performance was measured using expert knowledge as well as real data of a steel complex in Iran. A moderate to high performance has been achieved for the understudy case through conducting the proposed approach. It was suggested that the industry should focus on the criteria with both high weights and low evaluated scores (for example the environmental management technologies and knowledge criterion) to increase its performance evaluation score. The obtained results were indicative of the efficiency of the proposed approach.
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