A problem has been identified regarding bottlenecks within the SMF lab, necessitating an analysis for line balancing. The organization of parts is also under review, including improvements in warehouse management, as the current bin storage system is being removed. Stock inventory strategies are being assessed, with comparisons between Just-in-Time (JIT) and Just-in-Case (JIC) approaches, alongside evaluations of make-versus-buy decisions for inventory and the Haas machine. The process for purchasing parts is being examined, including the creation of part numbers and Bills of Materials (BOMs). Additionally, the labeling of parts at workstations is being addressed. A bottleneck analysis is being conducted on the autonomous work cell, while work instructions at each station are being reviewed for clarity and effectiveness. Finally, a Product Data Management (PDM) system is being considered, with potential integration into a platform such as Oracle.
A significant challenge has been identified within the Purdue SMART Manufacturing Lab, where inefficiencies in production processes, data integration, and supply chain management have been observed. The lab's current manufacturing system is not fully optimized, limiting its ability to meet the goal of producing 12 skateboards per hour. Issues related to hardware and software integration have been noted, preventing seamless data collection and real-time analytics, which are essential for process optimization. Additionally, bottlenecks in material handling, warehousing, and workflow efficiency have been detected, creating disruptions in production flow and increasing operational costs [1].
The implementation of PTC software has been planned; however, its full integration into the lab’s processes remains a challenge. Furthermore, continuous flow manufacturing (CFM) has been adopted, yet inefficiencies in execution have been recognized, leading to potential inventory excess and production slowdowns. Supply chain resilience has also been assessed, revealing vulnerabilities in risk management strategies that could impact on the overall stability of operations. The integration of additive manufacturing (AM) technologies into the existing framework poses further complexities, requiring an adaptive approach to ensure flexibility in production and supply chain coordination [1].
To achieve the desired operational efficiency, the current workflow must be thoroughly analyzed, and strategic improvements must be implemented. The lack of a fully integrated, data-driven decision- making system has hindered the lab’s progress toward becoming a model for smart manufacturing.
Without a structured approach to supply chain optimization, risk mitigation, and process streamlining, the lab remains susceptible to inefficiencies that limit productivity. Therefore, a comprehensive review and redesign of the manufacturing system, including technology integration, workflow optimization, and supply chain resilience strategies, is necessary to meet the lab’s production goals and establish a sustainable, efficient, and data-driven manufacturing environment [1].