A Predictive Model for an Optimal Industrial Chain Using Expert Choice: A Case Study

Abstract

This research was prepared in the context of preparing for a doctoral thesis with the aim of finding a predictive model for an optimal industrial supply chain in an industrial plant (the case under study), by making a multi-criteria decision using the Expert choice program.

An electronic questionnaire was developed on the kobo toolbox to determine and prioritize the criteria (main and sub) and then determine the specific weight of these criteria using Expert choice.

The predictive model has been divided into four levels to facilitate finding a solution to the problem of specific weights for these criteria. The first level is the problem to be solved, which is to reach the optimize state in terms of shortening the length of the supply chain. The second level consists of six main criteria, and the third level consists of 19 sub-criteria that are divided on the six main criteria and the fourth level is the alternatives.

The rational double comparison method (Sati) was used in the electronic questionnaire that was distributed to managers and their assistants in the supply chain departments in the studied case, because they have a vision of the optimize situation that the factory seeks to reach, then the data was entered into the Expert choice program to reach the qualitative weight of the criteria and alternatives.

It was concluded that the predictive model of the industrial supply chain is agile, and by comparing the predictive situation with the current situation, it was noted that the agile strategy decreased by 6.7%, the lean strategy decreased by 11.8%, while the mixed (leagile) strategy increased by 18.9%.

It was found that the agile strategy achieved the highest percentage after conducting the questionnaire for the optimal vision for shortening the length of the supply chain by entering the results into the Expert choice program, where it reached 52.5%, followed by the mixed (leagile) strategy with a rate of 31.1%, followed by the lean with a rate of 16.8%.