GOALI: Sequencing the Assembly Line and Analyzing Manufacturing Complexity to Enhance Supply Chain Management

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This Grant Opportunities for Academic Liaison with Industry (GOALI) project's first major research objective is to effectively sequence mixed models under dynamic conditions in an assembly plant so that the sequence at the downstream assembly sections can be known in advance to the plant and suppliers. The second major objective is to develop a model and method in evaluating the manufacturing costs for having many choices in product options. This research effort can result in significant opportunities to enhance production control and supply chain management at the plant and potentially for similar production environments. Mixed-model assembly can provide a production environment with more even output of finished products, more even part usage rates on the assembly line, and potentially more even workloads among assembly stations. At the assembly plant, mixed models of pickup trucks are produced on a seven-mile long assembly line. Due to a block-painting practice and unexpected production problems in the upstream assembly sections, the intended mixed-model sequence is significantly revised prior to reaching the downstream assembly line sections. At the downstream sections, however, many suppliers need to know the model sequence in advance in order to achieve sequenced part delivery. This research will attempt to develop mixed-model sequencing under these dynamic conditions in order to achieve advance knowledge of the sequence. Development from this research is expected to be useful for similar industrial environments.Currently, customers are offered a vast number of choices in features such as cabs, box styles, engines, drives, tires, axles, doors, and colors. The vast number of option combinations has a significant impact on production, material, and inventory costs. This research attempts to develop a model for evaluating the impact of having various choices and options on manufacturing and material management. Also, this research attempts to more closely link production costs and product complexity in order to assist in sound decision making in this regard.