Decision Policy Scenarios for Just-in-Sequence Deliveries

• Published in: Journal of industrial engineering and management 2017-01, Vol.10 (4), p.581-603
• Main Authors: Cedillo-Campos, Miguel Gastón, Morones Ruelas, Dario, Lizarraga Lizarraga, Giovanni, Gonzalez-Feliu, Jesus, Garza-Reyes, Jose Arturo
• Format: Article
• Language: English
• Subjects: assembly supply chain; Automobile industry; Automobiles; Automotive engineering; automotive industry; Case studies; Competition; Decision making; Disruption; Genetic algorithms; Inventory; inventory management; Just-in-sequence; Literature reviews; Logistics management; Manufacturers; Manufacturing; Markets; Replenishment; Safety stocks; Suppliers; Supply chain fluidity, Inventory management, Automotive industry, Manufacturing; Supply chains; Viscosity
• ISSN: 2013-8423; 2013-0953; 2013-0953
• DOI: 10.3926/jiem.2090

Purpose: The Just-in-Sequence (JIS) approach is evidencing advantages when controlling costs due to product variety management, and reducing the risk of disruption in sourcing, manufacturing companies and third-party logistics (3PL). This has increased its implementation in the manufacturing industry, especially in highly customized sectors such as the automotive industry. However, despite the growing interest from manufacturers, scholarly research focused on JIS still remains limited. In this context, little has been done to study the effect of JIS on the fluidity of supply chains and processes of logistics suppliers as well as providing them with a decision making tool to optimise the sequencing of their deliveries. Therefore, the aim of this paper is to propose a genetic algorithm to evaluate different decision policy scenarios to reduce risks of supply disruptions at assembly line of finished goods. Consequently, the proposed algorithm considers a periodic review of the inventory that assumes a steady demand and short response times is developed and applied. Design/methodology/approach: Based on a literature review and real-life information, an abductive reasoning was performed and a case study application of the proposed genetic algorithm conducted in the automotive industry. Findings: The results obtained from the case study indicate that the proposed genetic algorithm offers a reliable solution when facing variability in safety stocks that operate under assumptions such as: i) fixed costs; ii) high inventory turnover; iii) scarce previous information concerning material requirements; and iv) replenishment services as core business value. Although the results are based on an automotive industry case study, they are equally applicable to other assembly supply chains. Originality/value: This paper is of interest to practitioners and academicians alike as it complements and supports the very limited scholarly research on JIS by providing manufacturers and 3PL suppliers competing in mass customized industries and markets, a decision support system to help decision making. Implications for the design of modern assembly supply chains are also exposed and future research streams presented.