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| Some of the fundamental challenges of process manufacturing are: |
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Unit of Measure Conversions |
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Product Attributes |
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Generation of By-products |
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Many Ways to Make a Product |
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No Fixed Cost Analysis |
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High capacity cost |
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Many outputs from same input |
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A-shaped or X-shaped product evolution |
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Recipe or formula instead of Bill of Materials |
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High seasonality of raw materials |
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| Infynita Inc. has come up to address these challenges for the businesses in the process industries for their production planning and scheduling. Optimization Engine for Process Industries not only addresses these challenges but allows businesses to operate smoothly in face of fluctuating customer demands, decreasing margins, high customer service level expectations etc. |
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| In creating a production plan, Optimization Engine for Process Industries optimizes setups, batch sizes, and run frequencies at a daily or weekly level of detail by taking into account manufacturing restrictions such as: |
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Finite line capacity |
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Sequence dependent setups |
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Minimum and incremental lot sizes |
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Limited raw materials |
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Rate balancing between stages of production |
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| By rationalizing production versus inventory trade offs, Optimization Engine for Process Industries determines how to satisfy customer demands and achieve inventory targets while maximizing manufacturing efficiency. |
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| Optimization Engine for Process Industries provides functionality that allows companies to: |
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Improve customer service |
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Increase manufacturing efficiency |
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Successfully handle seasonal demand |
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| Improve customer service |
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| In today's business environment, demand can change quickly. Manufacturers can see their safety stock depleted overnight. The key to delivering high customer service is in knowing how to work within current manufacturing and material constraints to best meet demands and maintain safety stocks. This is often easier said than done, given the multitude of options for constructing a production plan over an extended horizon of days and weeks. Optimization Engine for Process Industries uses a unique combination of optimization to search through the maze of options to enable manufacturers to achieve the highest possible customer service levels. |
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| Increase manufacturing efficiency |
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| Companies that have succeeded in attaining high customer service often achieve it at the expense of higher inventories or increased changeovers. Higher safety stock levels can absorb demand fluctuations and changeovers that are more frequent enable more items to be made in a shorter period. However, being responsive to customer demand does not necessarily mean that manufacturing efficiency has to be forsaken. The key to competitive advantage is to achieve high customer service in the most efficient way possible. |
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| Optimization Engine for Process Industries employs a unique approach to multi objective planning. It allows companies to optimize manufacturing efficiency within the context of achieving high customer service. Increased manufacturing efficiency translates into one or more of the following benefits: reduced finished goods and material inventories, reduced manufacturing costs, and increased asset utilization. |
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| Successfully handle seasonal demand |
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| Seasonality of demand is a reality that almost all companies in process industry must address. Given that very few businesses can afford the luxury of abundant excess production capacity, it is certain that demand will outstrip production capacity during peak periods. Under these circumstances, Optimization Engine for Process Industries helps planners determine the optimum manufacturing strategy. It allows planners to explore options such as: |
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Pre-building inventory |
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Campaigning production of product groups |
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Running overtime shifts and sub-contracting |
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| The optimum manufacturing strategy depends on the relative magnitude of setup costs, inventory costs, overtime costs, and contract manufacturing costs, as well as the effect of production sequences on the time lost to changeovers. In addition, only an extended planning horizon of days and weeks will provide the required visibility for making decisions about pre-building and dynamically lot-sized campaigns. |
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| Optimization Engine for Process Industries optimizes true manufacturing costs in the context of determining production sequences within and across daily and weekly periods. |
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| Modeling |
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Multi-stage flow-process manufacturing |
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Lot-sizing (minimum and incremental run quantities) |
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Hierarchical campaign sequencing (minor changeovers sequenced within major changeover sequences) |
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Rate balancing between manufacturing stages |
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Prioritized demands |
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Multiple levels of inventory requirements (minimum, target, maximum, limit) |
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Finite line capacity |
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Minimum line utilization requirements |
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Frozen material receipts that constrain the production plan |
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Material requirements generation |
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Frozen production horizon |
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In-process manufacturing lead time |
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By-product production |
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Limitation in number of changeovers |
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| Optimization |
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Considers multiple objectives in a hierarchy that automatically resolves conflicts in a prioritized manner |
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Uses real manufacturing costs including sequence-dependent setup costs, inventory carrying costs, variable manufacturing costs by line, and overtime costs |
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Simultaneously considers finite capacity and material availability |
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Leverages a unique combination of proven including mathematical programming and combinatorial heuristics |
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