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SEPTEMBER 2018 I SMT007 MAGAZINE 53 manufacturing. Once the work order demand is generated in the ERP system, much diligent work is put into developing a production plan to satisfy the orders. Complicated spreadsheets and workbooks are used to model the manu- facturing flow and to manage constraints that are external to the ERP system. Unexpected changes in the customer demand or the man- ufacturing constraints are difficult to integrate into the existing plan. Optimization of product groupings happens infrequently outside of the day-to-day planning activity. Considering the smart factory topology defined above, the automation of the finite planning process would function at each layer of the factory. At the enterprise layer, the ERP system will manage the customer requirements and the high-level site calendar. At the site lay- er, a digital model of the production process needs to be generated based on all the con- straints in the factory. All of lines, machines, processes, materials, transactions, and resourc- es must be considered in the model to create a simulation of the manufacturing process. The process specific layer must perform two impor- tant functions. First, it must supply real-time performance information from the manufac- turing equipment through the IoT infrastruc- ture. Second, it must supply the means to sim- ulate production for the given process. When all layers are working together, a ful- ly optimized production plan can be devel- oped. Demand from the ERP system is decon- structed into the individual manufacturing pr ocesses. Iterative simulations find the ideal manufacturing sequence using the static site constraints and the live performance data from the factory. A feedback mechanism between the planning application and the equipment processes provides optimized programs and product groups based on the discrete demand. Changes in the demand or the constraints can be continuously accounted for in the produc - tion schedule. Lean Material Management Application For many manufacturers of electronic assem- blies, maintaining an efficient supply chain is key to success. Significant investments in ERP systems and automation ensure that there are materials in the warehouse to satisfy the cus- tomer demand but managing the movement of material from the warehouse to the machine is often burdened by many manual processes. Large line-side buffer stocks and lack of vis- ibility into individual packages (reels, sticks, trays) of components contribute to a discrep- ancy between the real-world stock and the system inventory. With rich, detailed infor- mation available in the smart factory, a lean material engine can bridge the gap between the ERP inventory and the shop floor to pro- vide just-in-time (JIT) material logistics to the machines. The first step to developing the lean material management engine is accessing information held in various systems. The ERP system will provide the work order demand which defines the sequence and schedule of products to run. The warehouse management system provides the detail of individual components that are available for production. At the process spe- cific layer, the equipment system will provide the machine program information, perfor- mance information, and material consumption details. Next, using a production schedule, the cur- rent machine setup, and the live IoT data stream from the equipment, the lean materi- al engine can determine when individual com- ponents will need to be replenished, either on the current order as reels are exhausted or on an upcoming order during a changeover. With the connection to warehouse management, the At the process specific layer, the equipment system will provide the machine program information, performance information, and material consumption details.