4.5 Article

Design and Development of Best Class Discrete Production Model for Distributed Manufacturing under Industry 4.0

期刊

ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING
卷 47, 期 12, 页码 16485-16504

出版社

SPRINGER HEIDELBERG
DOI: 10.1007/s13369-022-07061-4

关键词

Distributed manufacturing; Discrete production model; Production line; Supply chain; International load sharing (ILS) system

资金

  1. King Fahd University of Petroleum Minerals
  2. DROC
  3. IRC-IMR

向作者/读者索取更多资源

Global competitiveness presents challenges for manufacturing companies to meet dynamic customer demands and maintain market share. Distributed manufacturing is adopted to increase facility utilization by decentralizing production, allowing customers to choose the best manufacturers based on their track record. Industry 4.0 and IIoT enable online monitoring of production key parameters for manufacturers in a DM network.
Global competitiveness creates a challenge for manufacturing companies to maintain their market share with dynamic customer requirements. Capital investment in machinery does not allow facility expansion to accommodate large orders from customers but to reconfigure the manufacturing enterprise. Distributed manufacturing (DM) is embraced in order to increase facility utilization by decentralizing production. An enterprise in charge of a DM network allows customers to choose the best manufacturers available for their order based on their track record, which is available through historical and online performance data. Furthermore, manufacturers as members of this network may receive orders based on their past performance. Industry 4.0 with all necessary Industrial Internet of Things (IIoT) enables the online monitoring of production key parameters of manufacturers subscribed to a DM network. We develop a new network model of manufacturers teamed under specific terms and conditions to support a group of customers who have specific needs. The proposed model, known as the continuous supervised model, is created with the ARENA simulation software. We demonstrate the effectiveness of our model by contrasting it with the standard practice approach. To ensure the best possible performance, we continuously monitor the cost, quality, delivery time, and production rate indicators of the various manufacturers and update their performance ranking for current and future orders. Furthermore, using the analytic hierarchy process (AHP) approach, a single performance measure based on the four indicators is developed. Implementing the proposed model showed an improvement in the average performance by 51.3%.

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