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Given the constant striving of economic organizations to succeed in the global competitive environment, companies emphasize the need for operational efficiency and process optimization. Technology progresses at a rapid pace to meet these demands, and supply-chain strategies change to offer growing opportunities in the global economy.

Manufacturers invest considerable resources in planning, analyzing and predicting the optimal timing and quantities to produce the variety of company goods, and new technological tools enable high-quality data analysis to support these growing decision-making needs. However, some traditional decision making systems may still be useful for both high and low budget firms unable to invest in costly tools. These include inventory classification systems, frequently referred to as ABC analyses, which provide an important analytical framework for inventory management [1].

ABC categorization is based on Pareto’s Law. Pareto was an Italian economist and sociologist who found that 20% of the people control 80% of the wealth [2]. This principle, also called the 80/20 rule, may be tapplied to various areas. For example, 20% of the purchases are from 80% of the suppliers, 20% of the sales lines provide 80% of turnover, and 20% of the production lines occupy 80% of warehouse space. All these form the basis of inventory control research [3].
The ABC categorization is quite simple. Inventory items are usually divided into three classes according to their turnover ranking, and labeled A, B and C [2,4]  (although in some situation the number of classes may be higher [4]) . The A class usually includes from 10 to 15 percent of inventory items that represent 60 to 80 percent of the total inventory value; the B class includes about 25 percent of inventory items with 15 to 20 percent of the total value; and the C items include the largest portion – around 50 to 55 percent, representing 15 percent of inventory value [2].

Since the current stock level doesn’t provide any indication as to which items are more important than others – for example, there may be high-value items that are sold rapidly, leaving low inventory levels, or low-value items with high inventory levels because they are not sold – item ranking is set according to annual turnover, given by: Annual usage × Unit cost [3].

Steps in ABC Analysis

   1. Determine the annual usage and dollar value of inventory items, as illustrated in Table 1.

        Table 1: Setting input data for ABC analysis

Item SKU	Unit cost ($)	Annual usage (thousands)	Annual turnover
Item1	10	190	1900
Item2	40	25	1000
Item3	33	10	330
Item4	5	30	150
Item5	14	95	1330
Item6	20	9	180
			Total: 4890

  2.  Determine the aggregate percentage of annual turnover, as illustrated in Table 2.

        Table 2: Determining aggregate percentage

Item SKU	Unit cost ($)	Annual usage (thousands)	Annual turnover	Percentage of total	Aggregate percentage
Item1	10	190	1900	38.85%	38.85
Item2	40	25	1000	20.45%	86.5
Item3	33	10	330	6.75%	93.25
Item4	5	30	150	3.07%	100
Item5	14	95	1330	27.20%	66.05
Item6	20	9	180	3.68%	96.93
			Total: 4890

  3. Classify the items. Table 3 illustrates the classification according to the policy where A items represent 65 percent of total value, B items represent 20, and C items represent the remainder.

        Table 3: ABC classification

Item SKU	Unit cost ($)	Annual usage (thousands)	Annual turnover	Percentage of total	Aggregate percentage	Classification
Item1	10	190	1900	38.85%	38.85	A
Item2	40	25	1000	20.45%	86.5	B
Item3	33	10	330	6.75%	93.25	C
Item4	5	30	150	3.07%	100	C
Item5	14	95	1330	27.20%	66.05	A
Item6	20	9	180	3.68%	96.93	C
			Total: 4890

Figure 1 illustrates the items as a percentage of the total, before ABC classification. Figure 2 illustrates them after the classification.

        Figure 1: Before ABC classification


        Figure 2: After ABC classification

*Note: you can easily generate your own ABC charts online here.

After applying the analysis, you may choose among several inventory control methods. For example, a Fortune 500 company may choose to monitor A items daily, controlling B items using an economic order quantity model, and maintaining C items as a minimum safety stock, perhaps even without monitoring them at all [2]. Another example is distribution redesign: A items may be stocked in all warehouses, B items in a few regional warehouses, and C items only at the plant [5].

It is easy to see how purchase order patterns, restructured based on ABC analysis, can mean considerable savings. If a firm unjustifiably attributes similar importance to all items, and therefore its policy is to purchase all items once a month, this may naturally result in redundant stock or even worse –shortage in high-demand items.
ABC analysis thus ensures significant cost savings, representing an invaluable  tool in the management tactical arsenal.

Bibliography

1. Stanford, R.E. and W. Martin, Towards a normative model for inventory cost management in a generalized ABC classification system. Journal of the Operational Research Society, 2007. 58(7): p. 922-928.
2. Berniker, E. and D.E. McNabb, Applying matrixed Pareto analysis with activity based costing for operations and cost management. Journal of Business and Management, 2005. 11(1): p. 73-88.
3. Wild, T., Best Practices in Inventory Management, 1997, (chapter 3): Wiley publication.
4. Fuerst, W.L., Small businesses get a new look at ABC Analysis for inventory control. Small Business Management (pre-1986), 1981. 19(000003): p. 39.
5. Lambert, D.M., J.R. Stock, and L.M. Ellram, Fundamentals of Logistics Management. 1998: McGraw-Hill.

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