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OMP uses forecast items to establish a common reference between commercial and production organizations. Based on this common reference, the commercial organization can aggregate the items into any segmentation-fitting commercial dimensions such as geographical division, customer type segments, and end-use application. Similarly, the production organization can aggregate the forecast items into a segmentation suiting production constraints such as product type and finishing, product routing, and product dimension range. This allows the automatic conversion of a commercial forecast into a production forecast.

The OMP solver is configured to optimize the sales and operations plan, while balancing service level, operational costs and inventory levels, and considering finite machine calendar and capacity constraints, material availability constraints, campaigns, etc. You can run several what-if scenarios and compare the results using easy-to-read management graphs, views and pivot tables, with drill-down functionality for deeper analysis when required.

In a make-to-order context, the object is to devise an ‘on-the-fly’ plan for any inquiry or booked order (Capable-to-Promise or CTP). While doing this, OMP takes into account a wide range of constraints such as material availability, finite machine capacity, machine and campaign constraints, and resource availability. It includes a solver which aims to accede to the requested service level while respecting the existing plan and all the other relevant constraints. Based on this, it can calculate a realistic and reliable promise date.

In OMP, you can compare the plan established by the planner with the schedule made by the scheduler. You can also compare it with the actual schedule processed on the shop floor the next day, using data from the MES.

OMP takes into account the needs of the new requirement (inquiry or booked order) while respecting the existing plan. Once the CTP solver has created the plan for the new inquiry or booked order, the related items and capacities are automatically reserved to avoid conflicts and overpromising.

Depending on inspection results, additional or alternative operations might be required to correct the product characteristics. If rerouting is required, the scheduler can assign production to an available alternative route or even create a new routing. The impact on the final delivery date and on the scheduling of other orders is immediately made apparent.

OMP includes a material allocator tool which will suggest using stock items to supply orders or find orders that can be supplied using the stocks available. Allocation of stock items to orders is based on matching material and order attributes, which need to be identical or close to identical. Some additional operations on materials may be required to fit the order characteristics. The material allocator tool is typically run every week or every few days. The proposed combinations can either be accepted or refused. Systematic use of this system will gradually reduce the dead stock and keep it low, because you will be able to seize every opportunity to consume stock items instead of producing new material.

Any material and order attribute available in the system can be used to define the allocation rules. The allocation criteria are defined using logical comparisons (<, =, etc.) or formulae. A comprehensive set of allocation rules is preconfigured based on our sector experience and can be supplemented by customer experience. Key users can activate or deactivate rules and modify rule groups or individual rules, as defined in the access rights.

OMP includes a bill of materials and routing generator which generates standard product routes as well as alternative routes and reprocessing routes. The complex routings generated can be used to plan operations and logistics (in OMP), to steer operations on the shop floor (in MES) and to carry out cost precalculations (in ERP).

The system can work with an exhaustive list of predefined master data or it can work like a variant configurator, taking into account order attributes to dynamically determine the routing and the routing parameters. When needed, it can also handle and even dynamically determine the bill of materials based on varying order quantities and process yields. In such cases, the system can create order-specific bills of materials and routings.

The OMP cutting system seeks the optimal geometrical combination of open orders and the material physically available in stock. It minimizes material losses and other cost factors such as knife set-up costs and too early/too late delivery, while respecting geometrical and cutting constraints, as well as quality defects and non-quality utilization rules.

All external drivers are integrated into the OMP forecast statistics model, ensuring that the forecast is automatically adapted to them. Extra demand, such as volumes generated by projects, can be manually added to the forecast.

Sales turnover and sales margin are calculated from the forecast using standard pricing or standard margins. The forecast calculation does not consider cost factors such as machine load, so the margin computed with the forecast model is a standard margin. When adding the Sales & Operations Planning model, detailed production and transport cost modeling can be carried out and margins can be computed as the difference between price and cost.

In OMP, the user can always override the solution provided by the solver. However, it is recommended to limit the number of interactive planning actions, given the high number of variables at play. For this reason, it is essential to carefully configure the solvers and their rules and parameters. This configuration is initialized during implementation and can be modified by dedicated key users.

In the S&OP model, you have a lot of possibilities for model price and cost modeling, both at aggregated and at detailed product and machine level. When making a plan, you can either use these financial parameters to valorize the plan (EBITDA and cash simulation once plan volumes and mix are determined), or you can decide to use these financial parameters to drive the plan (e.g. determine plan volumes and mix for EBITDA maximization under cash constraints). This will allow you to optimize the plan for best financial value.

The system includes to-do panels, emphasizing major plan conflicts such as machine overloads and too low/high inventory levels. Users will only see conflict types that are relevant to their role. For each conflict, hyperlinks point directly to the root of the conflict, guiding the user to a quick resolution.

The planning and promising system needs to be fed with a set of consistent data by-products (materials) such as processes (routings), operations and machines, bills of materials and yields, and interoperation times, with a level of detail that depends on the model objectives and the level of accuracy required. These data can either be generated using the OMP master data generator, using formulae or derived from historical data, or provided by an external system through an integration.

Consumables needed for production and the resulting scrap are automatically calculated from the plan through the bills of materials associated with each order. Based on these data, OMP allows you to simulate stock evolution over time and to provide the purchase department with accurate information on the quantity of consumables and external scrap to be purchased. The same model can also be used for repairables and tools, allowing activities in the maintenance workshops to be steered and prioritized.

It is essential to share a single common production model and schedule among the different actors in the supply chain, allowing them to organize accordingly. For example, the production schedule allows the warehouse to anticipate any extra required volumes, as well as maintenance activities to be organized during planned downtimes or delivery transport to be scheduled based on the date of entry into stock, etc. It is extremely beneficial to set up a full business process organization around the scheduling system.

You can check adherence to the schedule by comparing a given day’s OMP schedule with the next day’s MES data. To ensure adherence on the shop floor during execution, it is essential for the scheduler to build a schedule that is technically feasible. For this reason, OMP allows you to model constraints relevant to shop floor execution. Alerts and graphs are used to indicate whether a schedule complies or conflicts with these constraints, and confirms whether a schedule is executable or needs to be modified.

With OMP, you can reduce the impact of unplanned downtimes proactively and reactively. The proactive approach involves using the simulation tool to assess the impact of possible downtimes on a given schedule, allowing you to modify the schedule and make it more robust. Whenever an unplanned outage does occur, the scheduler can easily adapt the plan by assigning alternative machines, choosing alternative routing, or reorganizing campaigns, always using the simulation capabilities to reduce any negative impact as much as possible.

Having real-time production data available is not mandatory but is very helpful to keep the schedule on track. For example, when a given operation on the shop floor takes longer than expected, an automatic real-time data feed ensures that the schedule of subsequent operations on that machine adapts automatically. Where real-time data are not continuously sent back to the scheduling system, the system can also deal with a batched update process. In between the batch feeds, the system will assume that the shop floor continues to execute the schedule.

OMP allows advanced planning and scheduling activities to be split by time horizon, machine responsibilities, product groups and plan zones. This allows schedulers and planners to work simultaneously without interfering with each other’s work.

In addition to the material allocation rules, you can define a cost for each possible combination between a stock item and an order to be supplied. You may either assign relative costs (prioritizing preferred combinations) or financial costs (considering the real cost of the combination, for example its impact on reducing stock or on additional rework or yield losses). The allocations are then optimized by minimizing the total cost of combinations.

Based on the order book forecast, OMP can predict the need for raw materials. It can optimize the blending of raw materials based on the purchase prices of the individual raw materials and on the alternative raw material mixes that can be used to fulfil projected orders. The purchasing department can then negotiate deals with providers in terms of quantity and price and use the blending system to simulate alternatives in mix and/or pricing.

The OMP blending model balances the consumption of materials available in the yard among the production orders planned on a short-term horizon (1-2 weeks). The raw materials are shared fairly between the different grades, taking into account total planning for the immediate future. The blending system can also optimize the blend of materials by heat. In optimizing the blend for a single heat, it is recommended to set a minimum level of low-quality raw material consumption to avoid cherry-picking.

The OMP blending system considers the worst-case scenario in terms of the chemical composition of scrap material. As a result, the optimized blending always remains on the safe side, avoiding costly correction in subsequent steps. The required correction can be computed based on a chemical sample taken during the process. The blending system suggests the correction that would have the lowest cost considering the sample result, the target chemical composition, and the available alloys and their cost (purchase price and processing cost).