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FAQs
As discussed below, MRP is NOT scheduling. In the best of worlds, MRP would work out all of the operational details to have the most accurate estimate of capacity, and an exact determination of the time at which raw materials must be available. Enterprise Wide Manufacturing Data Systems do not perform this detailed calculation, because planning or scheduling to maximize capacity in the presence of finite resources is mathematically a very complex problem. You need an optimized planning/scheduling solution to precisely know the capability of your business, and to have the power to determine how to improve that capability. High level MRP based planning models simply are not capable of doing so.
For a summary of benefits, click here.
A schedule is an assignment of tasks for given resources (equipment or personnel), at given times.
Planning and scheduling both refer to the allocation of resources to meet product demands subject to process constraints. By convention, planning addresses a medium to long time scale of months to a few years, while scheduling addresses a shorter time scale of days to weeks.
A feasible schedule is one that satisfies manufacturing constraints, including material balances, hard demands, inventory and resource limitations and user defined criteria such as campaign length and minimum run length. A schedule is not feasible if even a single constraint is violated.
An optimal Plan or schedule is one that is feasible and has the best possible performance with respect to a desired performance criterion. Common performance criteria are maximizing profit, or minimizing production costs.
Basically, MRP is a calculation method geared toward determining how much of which raw materials are required and roughly when they should be ordered to fulfill a set of product orders. MRP generally consists of four steps: 1. Bill of Materials Explosion - looking backward from each product, determine which intermediates and raw materials are required, and in what quantities. 2. Netting - compare the above quantities against current inventory. 3. Lot Sizing - determine how the needed materials will be purchased or produced. 4. Start Date Determination - based on cycle time information, determine when each order should start production.
No. MRP, depending on the implementation, usually generates rough plans of which tasks will be done during a given planning horizon. However, the exact sequence of carrying out the plan, including the allocation of finite resources among the potentially many products, is not generated. Since MRP does not typically analyze the details of carrying out its plan, the feasibility of the plan is not necessarily guaranteed. In particular, capacity may actually be overestimated by an MRP system, which can result in unrealistic production goals.
To optimize process operations in order to improve customer service, lower production costs, reduce inventory and/or increase capacity. Another reason is to make the scheduling and planning process transparent, replicable, and institutionalized.
Effectiveness, extensibility, ease of use and cost. Other factors to consider are support and database integration.
Products are available costing anywhere from a few hundred dollars to hundreds of thousands of dollars. The less expensive products essentially replace a pencil and paper and allow manual construction of a Gantt Chart on a computer screen. The more expensive products contain algorithms to automatically generate plans and schedules. It should be noted that there is enormous variability in the capabilities of algorithms in high end packages. You should insist that an expensive tool be demonstrated on their process or one of similar complexity and structure.
It is the sum of the software cost, consulting fees, and indirect costs attributable to training, database integration, and maintenance of underlying models.
While there are a lot of products, services, and features in the market, the prospective client need not entirely base their choices on sales literature and sales representative claims. Asking for a demonstration is one way to assess how a product will perform in your application. Nothing is better than watching a system work on the real data.
Essentially, the huge number of equations and variables implied by industrial scale problems has been too large for the past combinations of solution algorithms, computer memory and processor speeds. The dramatic increase in computer memory and speed has finally reached the point where more sophisticated mathematical programming based approaches are tenable.
Many companies that offer scheduling and planning tools also offer tools that are geared for supply chain applications. Notice that these tools are almost always based on linear programming approaches and sometimes use mixed integer linear programs with a small number of integer variables. Given the number of similarities between supply chain and more detailed scheduling and planning applications, one would expect that mathematical programming would also be successful on the more detailed scheduling problems. The predominant reason that mathematical programming is not applied is that translating the successful solution methods from the supply chain to the more detailed level requires a significant skill base and investment of time on the part of a vendor.
The mathematical programming approach translates all of the decisions that must be made in a scheduling and planning problem into a set of variables whose values determine all the features of a solution. The physical constraints of the problem such as material balances, resource limitations, hard demand requirements and forced equipment outages are written in the form of equations using the variables. The goal guiding the solution of the scheduling and planning problem is supplied as an objective function which also uses the variables. Once defined from problem data, the mathematical program can be solved to determine feasibility and, in some cases, optimal solutions to the scheduling and planning problem.
The expertise of Data System vendors resides in Database and Transaction Systems, not planning and scheduling. If your Data System is capable of planning or scheduling, it may not be fully capable of generating operationally feasible schedules. Furthermore, if scheduling is not the main product focus of the vendor, its growth path is not assured. You may not have access to advanced capabilities such as cost optimization or improved capacity utilization as the scheduling software industry expands the technology envelope. If you perform production scheduling with less capable methods, you concede a valuable competitive advantage. The best way to remain competitive is to assemble a system composed of best-of-breed software for each aspect of your business. Before committing to the scheduling engine included in your Data System software, verify that it is capable of handling your real process constraints and that it can generate usable schedules.
Data integrity must be assured for each client of the data, whether the client is a human or a software package. As such, the responsibility for data integrity must be placed on the central repository. Other tools can operate on data and even cross-check data integrity. Although it is essential that software components communicate, the important factor to keep in mind is that projects succeed primarily because each component excels at performing its designed task.
Historically, plans and schedules have been generated for operations and the factory floor. Today, business changes are demanding more. In order to answer, "What if?" questions, data must be modified off-line. Clearly, it is impractical to make an entire copy of the Enterprise Wide data for each experiment. Rather, applications can extract a subset of data pertinent to the "What if?" and operate locally on that data. The central repository is left correct and intact.
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