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Manufacturing resource planning (MRP II) [1] is a method for the effective planning of all resources of a manufacturing company. Ideally, it addresses operational planning in units, financial planning, and has a simulation capability to answer " what-if " questions and is an extension of closed-loop MRP (Material Requirements Planning).
An MRP II system can include finite or infinite capacity planning. But, to be considered a true MRP II system must also include financials. In the MRP II (or MRP2) concept, fluctuations in forecast data are taken into account by including simulation of the master production schedule, thus creating a long-term control. [8]
Manufacturing resource planning, (MRP II), derived from/a followup to MRP/Material requirements planning; Material requirements planning; Maximum retail price, in India and Bangladesh; Marginal revenue product, in the marginal revenue productivity theory of wages; Market risk premium, a risk premium
BPCS includes MRP logic to manufacturing operations, provided there are high standards of data validity such as engineering specifications and inventory accuracy. It runs on several systems, with IBM I, being the most popular. It is written in AS/SET CASE tool, RPG, SQL and other languages supported on IBM I. [3]
Manufacturing resource planning (MRP2 or MRP II Topics referred to by the same term This disambiguation page lists articles associated with the same title formed as a letter–number combination.
Aggregate data are applied in statistics, data warehouses, and in economics. There is a distinction between aggregate data and individual data. Aggregate data refers to individual data that are averaged by geographic area, by year, by service agency, or by other means. [ 2 ]
One example of PBR that conforms to this definition is an MRP with an index-based ARM that is calibrated so that utilities earn superior (inferior) returns for productivity growth exceeding (falling short of) the industry norm. Another example is an APM for reliability that uses a benchmark reflecting industry norms.
The GAISE College Report begins by synthesizing the history and current understanding of introductory statistics courses and then lists goals for students based on statistical literacy. [13] Six recommendations for introductory statistics courses are given, namely: [14] Emphasize statistical thinking and literacy over other outcomes