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Synergy Between Smart Manufacturing and the Secure Supply Chain (Part 2)
June 5, 2019 | Michael Ford, Aegis Software Corp.Estimated reading time: 17 minutes
Adaptive planning is a term that has recently been associated with smart factories, but in fact it represents the evolution of the actual practical planning method that has been in use on most shop-floors for many years. The legacy adaptive planning tool of choice is simply Excel. Other planning tools, of which there are a great variety available, whether based on infinite, finite or APS models, are focused on mid-range or long-term planning tasks, meeting customer deadlines agreed far in advance, with little actual regard for execution level optimization of the operation. This has been left to production engineers to figure out for themselves, based on their knowledge of the processes, with the aid of Excel to help plan out the actual optimized sequence of work. With Industry 4.0, this model is pushed to the extreme, as continuous assessment of work allocation needs to be done in line with almost daily demand changes from customers. Excel is a great tool, but is simply not capable of understanding and representing all of the live planning constraints needed to avoid risk of execution or on-time delivery failure, such as with consideration of the actually available materials. The smart adaptive planning solution builds on the experience of those who have depended on Excel for so long, to bring visibility and understanding of constraints into focus, allowing planning engineers to make quick and accurate allocation and sequencing decisions whilst retaining the maximum possible operational efficiency. This is achieved through the live sharing of information from both automated and manual operations, in terms of products completed and materials consumed, as well as transactional processes such as material logistics.
Having reliable data about material consumption, including usage and spoilage, is another key element of the smart factory. A lean “pull” signal for materials can be generated based on actual material consumption rates, together with details of follow-on scheduled work orders, which can then be used to create a live logistics plan for the transport of materials from the warehouse, or local point of use stores to the machines, only as and when needed, in other words, just in time (JIT). Live data collection from assembly processes ensures that every material is accounted for, creating a near-perfect record of inventory, which can subsequently be shared with ERP and MRP. The need for the creation of pushed kits can be completely avoided. The accurate visibility of material inventory with which to execute work-orders in the foreseeable future is accurately determined by the use of adaptive planning, so the build-up of unnecessary physical material buffer stock, both on the shop-floor and in the warehouse, can be completely avoided, without any risk of unexpected internal material shortages. With MRP now able to know exactly what materials are in the factory, an accurate and less urgent series of material orders can be placed, with more flexibility.
When a modern smart digital MES system, specifically designed for the IIoT environment, is providing support for manufacturing execution—lean supply chain management on the shop floor in the smart factory—full traceability occurs as a natural outcome, being essentially “free” from an operational cost perspective. With lean materials, all carriers of materials, as well as individually marked key components and sub-assemblies, are uniquely labelled and tracked from their original packaging. As each instance of a material is used in production, there is traceability of the exact origin of the material used, whether an expensive IC or an apparently inconsequential passive component. If any material quality error should occur, such as the detection of a counterfeit material, the exact incoming material packaging can be identified, as well as all of the uses of material from that packaging, including the location of any remaining material.
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