ABC analysis

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ABC analysis is an inventory categorization technique used in materials management. The method divides inventory into three categories (A, B, and C) in order of their estimated importance [1]. ABC analysis is based on the Pareto principle, sometimes referred as the 80/20 rule, developed by Italian economist Vilfredo Pareto who in 1890s discovered that approximately 80% of Italy's land was owned by 20% of the population. This observation leads to the general rule that a small number of items often account for the bulk of importance or value in many business situations.

Origins and development

The ABC analysis was first described by H. Ford Dickie, who was manager at General Electric Company, in 1951 in his article "ABC Inventory Analysis Shoots for Dollars, Not Pennies" published in Factory Management and Maintenance journal [2]. Dickie modified the Pareto chart which had been developed by Dr. Joseph Moses Juran and applied it for inventory management purposes. At that time he classified various items into three parts A, B, C and this was the origin of ABC analysis as we know it today.

The method gained popularity when quality management pioneers W. Edwards Deming and Joseph M. Juran brought these concepts to Japan in the post-war period. Pareto chart and ABC analysis were introduced to Japan by American QC gurus and became part of the 7 tools of QC movement. The technique spread to the world from Japan when the QC movement including Toyota production system was reexported [3]. Based on quality management concepts, total quality management employed the ABC concept and enjoyed widespread popularity during the late 1980s and early 90s.

Theoretical basis

The theoretical foundation of ABC analysis lies in the Pareto principle. Vilfredo Pareto (1848-1923) was Italian economist who observed in his garden that 20% of pea plants produced 80% of the healthy pea pods. This observation caused him to think about unequal allocation patterns everywhere [4]. In 1896 Pareto published "Cours d'économie politique" where he showed that approximately 80% of the land in Italy was owned by 20% of the population.

In the context of inventory management this principle translates to the observation that:

  • A small percentage of items (typically 10-20%) accounts for the majority of inventory value (70-80%)
  • A large percentage of items (typically 50-70%) accounts for only a small portion of inventory value (5-10%)

This unequal distribution of value is what enables managers to prioritize their efforts on the items that have greatest impact on business performance. The classification helps businesses to control high-value and important items more rigorously than items of lesser importance.

Categories of inventory

ABC analysis divides inventory into three distinct categories [5]:

Category A items

Category A items represents approximately 10-20% of total number of items but accounts for 70-80% of total inventory value. These items should receive most attention from management. They require tight control and accurate records. A items demands frequent review and monitoring, should have higher safety stock levels and warrants investment in demand forecasting. Because Class A inventory is directly linked to the success of the company, it is important to constantly monitor demand and ensure stock levels match.

Category B items

Category B items represents approximately 20-30% of total inventory items and accounts for 15-25% of total inventory value. They require moderate control and regular records and should be reviewed periodically. B items serves as a transition category between A and C. The cost of B items are moderate and the control requires a balance between too much and too little attention.

Category C items

Category C items represents approximately 50-70% of total inventory items but accounts for only 5-10% of total inventory value. They require simplest controls and minimal records. C items can be managed with periodic or automated reordering and may use larger order quantities to reduce ordering frequency. Just because B and C items do not have as high a value as Class A products does not mean they have no value. Inventory that habitually goes uncounted may be subject to theft or obsolescence.

Methodology

The fundamental formula for ABC analysis calculates the Annual Usage Value (also called Annual Consumption Value) for each inventory item [6]:

Annual Usage Value = Annual Demand (units) × Unit Cost

The main stages of ABC analysis include:

  1. List all inventory items and gather annual demand data for each item along with unit cost
  2. Calculate Annual Usage Value by multiplying annual demand by unit cost for each item
  3. Arrange items in descending order of Annual Usage Value with item having highest AUV ranking first
  4. Calculate cumulative AUV starting from highest-ranked item and compute cumulative percentage of total AUV
  5. Classify items where Category A includes items contributing to approximately 70-80% of cumulative value, Category B items contributing to next 15-20% and Category C the remaining items
  6. Establish appropriate inventory policies for each category including reorder points, safety stock and review frequencies

After calculating the AUV for each item the cumulative percentage is computed using formula:

Cumulative Percentage = (Cumulative AUV / Total AUV) × 100

Items are then ranked and classified based on their contribution to the total cumulative value. While there is no hard and fast rule for this, general guidelines suggests Class A for items accounting for 70-80% of inventory value, Class B for 15-25% and Class C for remaining 5% [7].

Calculation example

Consider a company with 10 inventory items. After collecting data and calculations:

Item Annual Demand Unit Cost AUV % of Total Cumulative % Class
P1 1,000 $50.00 $50,000 35.7% 35.7% A
P2 2,000 $20.00 $40,000 28.6% 64.3% A
P3 500 $30.00 $15,000 10.7% 75.0% A
P4 800 $12.50 $10,000 7.1% 82.1% B
P5 1,500 $5.00 $7,500 5.4% 87.5% B
P6 600 $10.00 $6,000 4.3% 91.8% B
P7 2,500 $2.00 $5,000 3.6% 95.4% C
P8 1,000 $3.00 $3,000 2.1% 97.5% C
P9 400 $5.00 $2,000 1.4% 98.9% C
P10 300 $5.00 $1,500 1.1% 100.0% C
Total $140,000 100%

In this example Class A (3 items, 30%) accounts for 75% of total value. Class B (3 items, 30%) accounts for 16.8% of total value. Class C (4 items, 40%) accounts for 8.2% of total value. This example shows typical distribution where small number of items contribute majority of value.

Applications in different industries

ABC analysis is applied across various industries and business functions:

Manufacturing and production

In manufacturing ABC analysis is used for raw material management, work-in-progress inventory control, spare parts classification and production planning optimization. The technique helps identify which materials require tighter controls and which can be managed with simpler systems [8].

Retail and distribution

Retailers use ABC analysis to identify the products most profitable to the business. They can then use the data to promote those products across retail locations and ensure there is adequate stock on hand. Applications include product assortment decisions, shelf space allocation, warehouse layout optimization and promotional strategy development.

Healthcare and pharmacy

ABC analysis is extensively used in hospital and pharmacy inventory management. According to studies introducing ABC-based inventory control measures in hospitals can reduce costs by up to 20% [9]. Research on pharmacy stores showed that ABC analysis revealed 13.78% of items as A category, 21.85% as B category and 64.37% as C category items, accounting for 69.97%, 19.95% and 10.08% of annual drug expenditure respectively [10]. Hospitals use ABC analysis to manage supplies of medication, equipment and consumables keeping critical life-saving items stocked without tying up resources in less essential supplies.

Supply chain management

In supply chain management the technique is used for supplier relationship prioritization, logistics optimization and procurement strategy development.

ABC-XYZ analysis

ABC-XYZ analysis is an enhanced version that combines value-based classification (ABC) with demand variability classification (XYZ) [11]. Given the fact that there are limitations to the ABC classification particularly the limitation to one single criterion and the non-existence of a demand analysis, the problem is overcome by the introduction of the XYZ classification.

XYZ Classification divides items based on demand predictability:

  • X items have stable demand with high forecast accuracy (coefficient of variation less than 10%)
  • Y items have variable demand with moderate forecast accuracy (CV 10-25%)
  • Z items have irregular demand with low forecast accuracy (CV greater than 25%)

The combined matrix creates 9 categories (AX, AY, AZ, BX, BY, BZ, CX, CY, CZ) each requiring different inventory strategies. For example AX items are high priority with stable demand where replenishment should be optimized. AZ items are high priority but irregular requiring careful monitoring. CZ items might be candidates for elimination from inventory. The application of the ABC-XYZ analysis improves the decision-making process with respect to inventories which consequently contributes to a reduction in costs.

Advantages of ABC analysis

ABC analysis provides several benefits to organizations [12]:

  • ABC analysis places tighter and more frequent controls on high-priority inventory. High-priority inventory or class A inventory is the class that customers request most often
  • You can allocate resources more efficiently during cycle counts. The ABC analysis method saves time and labor counting only the inventory required by the cycle for the class of inventory versus counting all inventory items each cycle
  • One of the primary advantages is cost reduction. Businesses can optimize inventory levels for each category. A items being most critical might warrant higher safety stock levels while C items may be kept at minimal levels reducing carrying costs and storage expenses
  • Better forecasting becomes possible because investment in demand forecasting for critical A items improves overall supply chain performance
  • Strategic procurement helps identify items where price negotiations and supplier relationships can yield greatest savings
  • By maintaining appropriate safety stock levels for A items companies can reduce costly stockouts of critical products

Limitations of ABC analysis

Despite its widespread use ABC analysis has several limitations [13]:

  • ABC analysis classifies products based on annual usage value alone ignoring demand volatility, lead time and product significance. ABC analysis considers only one dimension of inventory performance either value or frequency. However inventory planning involves multiple dimensions such as lead time, variability, criticality, substitutability and profitability
  • One of the main limitations is that it does not take into account seasonal demand. Items may be classified as A even if they only have high demand during certain times of the year
  • The ABC inventory analysis does not meet Generally Accepted Accounting Principles (GAAP) requirements and also conflicts with traditional costing systems. Companies using ABC analysis need a second costing system for GAAP purposes
  • One of the main challenges is that it relies on a static classification based on historical data. Product values can change due to demand or cost fluctuations necessitating continuous monitoring and updates
  • While the focus is often on A items there is a risk of neglecting C items. These items though individually less valuable can collectively represent a significant portion of inventory costs. Excess stocks are always in jeopardy of obsolescence or damage
  • The choice of criteria and the categorization process can be somewhat subjective. Different individuals within the organization may have varying opinions on how to classify items

Other approaches related to ABC analysis

Several complementary and alternative methods exist for inventory classification [14]:

  • VED Analysis - Classifies items as Vital, Essential or Desirable based on criticality to operations. Particularly useful in healthcare and manufacturing where some items are critical for patient safety or production continuity
  • FSN Analysis - Categorizes items as Fast-moving, Slow-moving or Non-moving based on consumption patterns and turnover rates. Helps identify obsolete stock
  • HML Analysis - Classifies items as High, Medium or Low cost per unit. Useful for prioritizing price negotiations with suppliers
  • SDE Analysis - Groups items as Scarce, Difficult or Easy to obtain focusing on procurement complexity and supply chain risks
  • ABC-VED Matrix - Combines value-based (ABC) and criticality-based (VED) classifications. Particularly popular in hospital inventory management where both cost and patient safety are important considerations

Multi-criteria inventory classification (MCIC) considers additional factors such as lead time, criticality, substitutability, obsolescence risk, durability and supplier reliability. Methods used for MCIC include Analytic Hierarchy Process (AHP), cluster analysis, fuzzy logic approaches and ELECTRE method [15].


ABC analysis — recommended articles
Supply chain management — Safety stock — Quality management — Logistics — Strategy — Production planning — Forecasting — Cost — Efficiency

References

Footnotes

  1. Ravinder H., Misra R.B. (2014), pp. 257-264
  2. Dickie H.F. (1951), pp. 92-94
  3. Stojanović M., Regodić D. (2017), pp. 29-48
  4. Ravinder H., Misra R.B. (2014), pp. 257-264
  5. Teunter R.H., Babai M.Z., Syntetos A.A. (2010), pp. 343-352
  6. Ravinder H., Misra R.B. (2014), pp. 257-264
  7. Flores B.E., Whybark D.C. (1986), pp. 38-46
  8. Ramanathan R. (2006), pp. 695-700
  9. Gupta R., Gupta K.K., Jain B.R., Garg R.K. (2007), pp. 325-327
  10. Wandalkar P., Pandit P.T., Zite A.R. (2013), pp. 126-131
  11. Stojanović M., Regodić D. (2017), pp. 29-48
  12. Teunter R.H., Babai M.Z., Syntetos A.A. (2010), pp. 343-352
  13. Flores B.E., Whybark D.C. (1986), pp. 38-46
  14. Wandalkar P., Pandit P.T., Zite A.R. (2013), pp. 126-131
  15. Ramanathan R. (2006), pp. 695-700

Author: Sławomir Wawak

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