Academic journal article Journal of Purchasing & Materials Management

Use of Price Indexes in MRO Buying

Academic journal article Journal of Purchasing & Materials Management

Use of Price Indexes in MRO Buying

Article excerpt

Use of Price Indexes in MRO Buying

Commodity price forecasts can be used as an effective tool in identifying potential "inflation offset" opportunities and in measuring purchasing performance for MRO materials. The use of such forecasts at the General Motors Corporation's regional purchasing operations in Detroit helps reduce cost increases by alerting buyers to potential price changes for indirect materials. The same forecasts are also used by management to provide an independent measurement of purchasing performance.

Most of the commodity price forecasts are based primarily on projections made by an independent econometric forecasting firm.1 This firm forecasts price changes for approximately 150 disaggregated producer price indexes (PPI) as well as consumer price indexes and other broad-based aggregate indexes. These PPI forecasts are generated by a complex set of mathematical models that reflect the composition and interaction of specific sectors of the economy.

PPI forecasts indicate anticipated price changes in the major commodity categories tracked by the U.S. Department of Labor, Bureau of Labor Statistics (BLS). Hence, since the index series used in the forecasting activity corresponds with the widely published BLS indexes, both the forecasted and historical data are compatible with numerous other data series published by the BLS and other governmental agencies. This continuity can be helpful when attempting to relate developments in indirect material price movements to other economic conditions and trends (e.g., industrial production and real growth in GNP).

In order to be useful in buying and purchasing management activities, the producer price indexes must be compared with internal commodity price data based on actual purchases. The relative movement of the two series provides a basis for forecasting and measuring purchasing performance.



Since it is impractical to track prices for each of the thousands of individual items purchased, a group of items must be selected carefully to represent a general commodity group. In actual practice, the nature and number of items selected to represent a commodity group should be based on dollar volume, consistency of item usage, and the ability to track price changes. In the example that follows, certain paper products have been selected because their dollar volumes are large, they are purchased repetitively, and their precise product specifications enable the buyer to track prices.

In order to track the prices paid by GM for paper products, the IPP shown in Exhibit 1 was constructed. A review of all paper purchases during a four-month test period identified sixty items which accounted for over two-thirds of the total dollars expended for paper products. January, 1979, was selected as the base month from which to measure price changes, because price data were readily available from that time forward and the data provided an adequate historical pattern for analytical purposes. Prices for each of the sixty items were tracked from January, 1979, through April, 1980.

The price for each item was then weighted to reflect the number of dollars expended for each one. This step was necessary since there was a wide variation in the amount of money spent by type of paper product. Thus, expenditures for one type of paper called "offset" accounted for .25 percent of the total sample, compared with 9.43 percent for "groundwood," another type of paper (see Exhibit 1). The weighted value was calculated by multiplying the unit purchase price by the appropriate percent of total dollar purchases. In the case of offset, the weighted value of 8.21 was found by multiplying $32.84 by .25. The example in Exhibit 1 shows only the weighted values for January, 1979, and April, 1980, although values were calculated for each interim month.

After the weighted values for each item were calculated, the values for a given month were totaled. …

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