ABC analysis

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In materials management, ABC analysis is an inventory categorisation technique. ABC analysis divides an inventory into three categories—"A items" with very tight control and accurate records, "B items" with less tightly controlled and good records, and "C items" with the simplest controls possible and minimal records.

The ABC analysis provides a mechanism for identifying items that will have a significant impact on overall inventory cost,[1] while also providing a mechanism for identifying different categories of stock that will require different management and controls.

The ABC analysis suggests that inventories of an organization are not of equal value.[2] Thus, the inventory is grouped into three categories (A, B, and C) in order of their estimated importance.

'A' items are very important for an organization. Because of the high value of these 'A' items, frequent value analysis is required. In addition to that, an organization needs to choose an appropriate order pattern (e.g. 'just-in-time') to avoid excess capacity. 'B' items are important, but of course less important than 'A' items and more important than 'C' items. Therefore, 'B' items are intergroup items. 'C' items are marginally important.

ABC analysis categories[edit]

There are no fixed thresholds for each class, and different proportions can be applied based on objectives and criteria. ABC Analysis is similar to the Pareto principle in that the 'A' items will typically account for a large proportion of the overall value, but a small percentage of the number of items.[3]
Examples of ABC class are

  • 'A' items – 20% of the items accounts for 70% of the annual consumption value of the items
  • 'B' items – 30% of the items accounts for 25% of the annual consumption value of the items
  • 'C' items – 50% of the items accounts for 5% of the annual consumption value of the items

Another recommended breakdown of ABC classes:[4]

  1. "A" approximately 10% of items or 66.6% of value
  2. "B" approximately 20% of items or 23.3% of value
  3. "C" approximately 70% of items or 10.1% of value of the items

ABC analysis in ERP packages[edit]

Major ERP packages have built-in function of ABC analysis. User can execute ABC analysis based on user defined criteria and system apply ABC code to items (parts).

Mathematical calculation of ABC analysis[edit]

Computed (calculated) ABC analysis delivers a precise mathematical calculation of the limits for the ABC classes.[5] It uses an optimization of cost (i.e. number of items) versus yield (i.e. sum of their estimated importance). Computed ABC was, for example, applied to feature selection for biomedical data,[6] business process management[7] and bankruptcy prediction.[8]

Example of the application of weighed operation based on ABC class[edit]

Actual distribution of ABC class in the electronics manufacturing company with 4,051 active parts.

Distribution of ABC class
ABC class Number of items Total amount required
A 20% 60%
B 20% 20%
C 60% 20%
Total 100% 100%

Using this distribution of ABC class and change total number of the parts to 14,213.

  • Uniform purchase

When equal purchasing policy is applied to all 14,213 components, for example weekly delivery and re-order point (safety stock) of two weeks' supply, the factory will have 16,000 deliveries in four weeks and average inventory will be 2½ weeks' supply.

Application of weighed purchasing condition
Uniform condition Weighed condition
Items Conditions Items Conditions
 All items 14,213  Re-order point=2 weeks' supply
Delivery frequency=weekly
A-class items 200 Re-order point=1 week's supply
Delivery frequency=weekly
B-class items 400 Re-order point=2 weeks' supply
Delivery frequency=bi-weekly
C-class items 3,400 Re-order point=3 weeks' supply
Delivery frequency=every 4 weeks
  • Weighed purchase

In comparison, when weighed purchasing policy is applied based on ABC class, for example C class monthly (every four weeks) delivery with re-order point of three weeks' supply, B class bi-weekly delivery with re-order point of 2 weeks' supply, A class weekly delivery with re-order point of 1 week's supply, total number of delivery in 4 weeks will be (A 200×4=800)+(B 400×2=800)+(C 3,400×1=3,400)=5,000 and average inventory will be (A 75%×1.5weeks)+(B 15%x3 weeks)+(C 10%×3.5 weeks)=1.925 weeks' supply.

Comparison of "equal" and "weighed" purchase (4 weeks span)
ABC class No of items % of total value Equal purchase Weighed purchase note
No of delivery in 4 weeks average supply level No of delivery in 4 weeks average supply level
A 200 75% 800 2.5 weeks 800 1.5 weeksa same delivery frequency, safety stock reduced from 2.5 to 1.5 weeksa, require tighter control with more man-hours.
B 400 15% 1600 2.5 weeks 800 3 weeks increased safety stock level by 20%, delivery frequency reduced to half. Fewer man-hours required.
C 3400 10% 13,600 2.5 weeks 3,400 3.5 weeks increased safety stock from 2.5 to 3.5 weeks' supply, delivery frequency is one quarter. Drastically reduced man-hour requirement.
Total 4,000 100% 16,000 2.5 weeks 5,000 1.925 weeks average inventory value reduced by 23%, delivery frequency reduced by 69%. Overall reduction of man-hour requirement.

a) A class item can be applied much tighter control like JIT daily delivery. If daily delivery with one day stock is applied, delivery frequency will be 4,000 and average inventory level of A class item will be 1.5 days' supply and total inventory level will be 1.025 weeks' supply, a reduction of inventory by 59%. Total delivery frequency is also reduced to half from 16,000 to 8,200.

  • Result

By applying weighed control based on ABC classification, required man-hours and inventory level are drastically reduced.

  • Alternate way of finding ABC analysis:-

The ABC concept is based on Pareto's law.[9] If too much inventory is kept, the ABC analysis can be performed on a sample. After obtaining the random sample, the following steps are carried out for the ABC analysis.

  • Step 1: Compute the annual usage value for every item in the sample by multiplying the annual requirements by the cost per unit.
  • Step 2: Arrange the items in descending order of the usage value calculated above.
  • Step 3: Make a cumulative total of the number of items and the usage value.
  • Step 4: Convert the cumulative total of the number of items and usage values into a percentage of their grand totals.
  • Step 5: Draw a graph connecting cumulative % items and cumulative % usage value. The graph is divided approximately into three segments, where the curve sharply changes its shape. This indicates the three segments A, B and C.

Benefits of ABC Analysis[edit]

Selective Inventory Control better known as ABC analysis has great advantages to businesses and among the benefits of this inventory management tool includes:

  • Improved inventory control– By categorizing items based on their value and importance, the management can focus their resources on the most important items and ensure that they are appropriately managed.[10]
  • Correct sales forecasting– By analyzing sales data and inventory levels, the management improves sales forecasting.
  • Cost reduction– ABC analysis allows the management to focus on products A which are more important. By doing so, the cost which could be spent on managing other items i.e B and C are saved.[11]
  • Increased cash flow– ABC gets rid of excess unnecessary inventory and reduces the chances of stockout. Holding cost and stockout are the main dictators of cash flow and avoiding them improves cash flow.

See also[edit]


  1. ^ Manufacturing planning and control systems for supply chain management By Thomas E. Vollmann
  2. ^ Lun, Lai, Cheng (2010) Shipping and Logistics Management, p. 158
  3. ^ Purchasing and Supply Chain Management By Kenneth Lysons, Brian Farrington
  4. ^ Best Practice in Inventory Management, by Tony Wild (2nd Ed., p. 40)
  5. ^ Ultsch, Alfred, Jörn Lötsch. "Computed ABC analysis for rational selection of most informative variables in multivariate data." PLOS One 10.6 (2015): e0129767.
  6. ^ Kringel, D., Ultsch, A., Zimmermann, M., Jansen, J. P., Ilias, W., Freynhagen, R., ... & Resch, E. (2016). Emergent biomarker derived from next-generation sequencing to identify pain patients requiring uncommonly high opioid doses. The pharmacogenomics journal.
  7. ^ Iovanella, A.: Vital few e trivial many, Il Punto, pp 10-13,July, 2017.
  8. ^ Barbara Pawelek, Jozef Pociecha, Mateusz Baryla,ABC Anal-ysis in Corporate Bankruptcy Prediction, Abstracts of the IFCS Conference,p 17, Tokyo,Japan,2017
  9. ^ Pareto's law in this example is that a few high usage value items constitute a major part of the capital invested in inventories whereas a large number of items having low usage value constitute an insignificant part of the capital.
  10. ^ Production Management: Latest Edition. SBPD Publications. 2016-12-14. ISBN 978-93-83697-83-0.
  11. ^ "Effective Techniques for Inventory Stock Control - SIPMM Publications". 2018-10-14. Retrieved 2023-03-24.

External links[edit]