MRP here is Materials Requirement Planning and not Maximum retail price. When we are formulating chemical or pharmaceuticals products, or when we are making assemblies out of various components (manufactured or bought-out) we are dealing with bunched requirement of materials. The requirements of raw materials then depend upon the requirement of production of the finished product (assembly, or a shampoo or a medical formulation). It seems, it would be better if we know the production plan/schedule for the assembly of the finished product and accordingly arrange or all the raw materials that go into the finished products, rather than depending upon statistics and probabilities. This is precisely what MRP attempts to do. It is a simple system of calculating (arithmetically) the requirement of the input materials at different points of time based on the plan or schedule for production of the finished good.

There are no probabilities involved anywhere, only the derivation of the requirements of input materials based on the requirement or plan for production of the final products. Such a system will work well for materials that have no direct demand of their own, but have only a derived demand. These materials can be called dependent demand items. The finished assembly has a direct demand of its own and therefore it is an independent demand item.

Let us also review the Rconomic Order Quantity (EOQ) model/s. All of them assume a uniform or a more or less uniform pattern of consumption of material. Based on averaged consumption, the EOQ model answers the ‘how much’ and ‘when’ questions for optimal cost considerations. Optimal cost or otherwise, the basic difficulty in some peculiar production situations arises because of the averaging of the consumption of materials.

When we are dealing with five different varieties of shampoo, five varieties of soaps, another five of cleaning powders or solutions the requirement for many raw materials over time for these formulations does not fall in the smooth average consumption pattern.

It is interesting to note that if the material is stocked as per EOQ, we may find excess material in inventory during February, March, May and September month when we do not need the materials at all. Also, in April and August we fall terribly short of the required material. In all good faith, the EOQ model tries to answers the questions of ‘how much’? and ‘when to stock’? but fails miserably when encountered with an erratic (seemingly) requirements pattern for the materials. But, in many industries particularly for dependent demand items such seemingly erratic requirement patterns are common.

Statistics is the science of averages. And precisely because of this characteristic, the statistical or averaging methods fail in situations such as the above.


People have been calculating requirements of materials prior to the advent of statistics. As industry became more complex, models such as EOQ and the science of statistics became available and offered relief from cumbersome and detailed calculations for planning. The trouble was that even in those situations where arithmetical computations would have done a better job, people used statistical and averaging methods.

But, with the advent and proliferation of computers things changed. The cumbersome arithmetical computations could be done in no time at all. Naturally since the last two decades, the MRP system is being once again, seriously considered. Several decades ago this was the only planning system available and had no special name to it. Today it has come to be known as MRP.

With the modern day works using thousands of different materials, and manufacturing a variety of products, a computerized system for MRP becomes essential. In the Western countries and also in India many organizations have installed an MRP system.

How successful has the experiment of installing the MRP system, particularly in India been? The degree of success of any system depends upon the proper consideration of the prerequisites of the system. This is true with MRP system as well. —