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In the past, Original Equipment Manufacturers (OEMs) were vertically integrated. They manufactured all the elements of their products, from integrated circuits, to component packages, to Printed Circuit Boards (PCBs), to box units. In today's rapidly changing business environment, there is a paradigm shift in this historical business model. The OEMs are outsourcing most of their previous design and assembly activity to the rapidly growing Electronics Manufacturing Service (EMS) industry. Spurred by the increased competition among the EMS providers and in response to the needs of the OEMs, a new model of manufacturing that epitomizes an integrated and predictive manufacturing environment is emerging. The dynamic nature of the EMS environment mandates the need for a manufacturing schema that is both integrated and predictive (rather than reactive). An integrated predictive manufacturing environment demands the need for establishing efficient automation and knowledge management from all functional departments of manufacturing. Such a model of manufacturing will eliminate manufacturing losses through a series of paradigm shifts in manufacturing. The implementation of techniques like Kaizen (or 'Continuous Improvement') help in maximizing efficiency by identifying the wastes and losses in a manufacturing environment. The primary objective of this research was to establish an integrated predictive manufacturing environment through 'continuous improvement' techniques. The research area was identified as the manufacturing wastes or losses that existed due to the non-availability of a real time integrated information model. After analyzing these myriad issues/concerns, it was felt that all the issues could be solved through a series of paradigm shifts in manufacturing. The first step was to perform a general analysis of the operational structure and losses in the manufacturing environment. Once the losses were identified, they were grouped into equipment, production, process, operational, and material related losses. The key performance indicators for monitoring the variety of losses were identified. Based on this understanding, a formal requirement specification was established. Once the requirement specifications were defined, a framework of the information model was designed. Based on the framework of the model, different entities were implemented through the adoption of Kaizen techniques. Since a major portion of electronics manufacturing at an EMS provider is performed by capital intensive Surface Mount Technology (SMT) equipment, the performance of this equipment plays a major role in deciding the performance of the line, which in turn impacts the performance of the plant. Therefore, an integrated system with the capability to collect real-time data from production equipment and provide information on critical performance indicators (like machine/line/plant utilization, downtime and component placement reports) was evaluated. This system enhances the operational efficiency of the plant. Process and production monitoring tools were researched, designed and developed based on these performance indicators. Shop floor control systems that provide on-line information on Work In Progress (WIP) status, defect-data, finished goods ¡°genealogy¡± and unit-level traceability through data captured throughout the product lifecycle are being developed and implemented. Process metrics like First Pass Yield (FPY), Defects Per Unit (DPU), and Defects Per Million Opportunities (DPMO) are shared with the shop floor personnel in real time through the application of proven internet technologies. Real time Statistical Process Control (SPC) charts and process triggers, which serve as proactive tools in predicting 'out-of-control' situations, were also developed. Process metrology tools were developed through the interfaces of inspection equipment like Automated Optical Inspection (AOI) and X-ray laminography. These predictive tools provide new understanding and data for process characterization that can, in turn, develop and improve the process specification, and the manufacturing process itself. The modular and uncomplicated design of the manufacturing model helped in providing better user interfaces and reduction in development and 'roll out' time. The proposed solution provides the tools and utilities for identifying, isolating, and eliminating the most prevalent manufacturing wastes/losses by providing real time monitoring and feedback tools.


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