Improving Spare Parts Classification Using Multi-Criteria Approach
Enhance the method of inventory classification for spare parts by introducing a multi-criteria approach. This research challenges the traditional monetary value-based classification and explores new criteria for better inventory management in real companies. The study delves into the application of the Analytic Hierarchy Process (AHP) in spare parts classification, offering insights into effective inventory control strategies beyond monetary considerations.
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MULTI-CRITERIA CLASSIFICATION OF SPARE PARTS Henrique Kriguer and Valerio Salomon Sao Paulo State University Valerio.salomon@Hotmail.com Sao Paulo Research Foundation (FAPESP) Grant 2013/03525-7 and Grant 2016/04307-1
Multi-criteria classification of spare parts 1. Introduction 2. Objectives 3. Methodology 4. Analysis 5. Conclusions References and future submission Kriguer and Salomon, 2016 2
Introduction Spare parts = parts of an equipment those can be replaced when its performance decreases Policies for spare parts inventories maybe different from WIP inventories and inventories of final products to be sold to a customer ABC Classification is a common practice for inventory management Kriguer and Salomon, 2016 3
Introduction Inventories of Class A items, the more valuable items, may be better controlled, on a daily basis, for instance On the other hand, inventories of Class C items may not be not controlled, with stock out replenishments The classification of spare parts by monetary value alone could be dangerous Kriguer and Salomon, 2016 4
Introduction This work proposes a multi-criteria classification with the Analytic Hierarchy Process (AHP) However, according to international literature, AHP is not indicated to Classification Problems Still, an AHP-based model was successfully implemented by a plant of capital goods located in the Brazilian state of Sao Paulo Kriguer and Salomon, 2016 5
Objectives The main objective of the research is to improve the method of classification of inventoried parts in a real company A specific objective is to determine criteria for inventory classification other than the monetary value The challenge is the AHP application against MCDM literature Kriguer and Salomon, 2016 6
Methodology The classification of spare parts with multi-criteria approach is the main contribution of this work A plant of capital goods adopts a mono-criterion approach for its spare parts classification They based only on the monetary value of the inventory to classified it as A, B or C Kriguer and Salomon, 2016 7
Methodology From Kennedy et al. (2002) and Bacchetti and Saccani (2012): Classification of spare parts Monetary Value Ease of Obtainment Demand Criticality Life Span Spare Time Kriguer and Salomon, 2016 8
Methodology A Classification Problem involves a high number of alternatives that need to be grouped into classes Pairwise comparisons matrices are limited to order 9 (Saaty and Ozdemir, 2003) So, Ishizaka et al. (2012) proposed AHPSort But, what a about absolute measurement, also known as ratings (Saaty, 1986)? Kriguer and Salomon, 2016 9
Analysis Three senior managers were consulted, in a single session Criteria V D C L T S Priority Monetary value (V) 1 1/7 1 5 1/7 5 9% Demand (D) 7 1 7 7 1 7 38% Criticality (C) 1 1/7 1 5 1/7 5 9% Life Span (L) 5 1/7 1/5 1 1/7 1 3% Spare time (T) 7 1 7 7 1 7 38% Ease of obtainment (S) 1/5 1/7 1/5 1 1/7 1 3% Kriguer and Salomon, 2016 10
Analysis However, managers have not consensus on the ABC distribution. Then, they judgment were aggregated by geometrical mean (Saaty and Peniwati, 2005) Class A B C Priority 3 (105) 3 (567) A 1 73% 3 (1/105) 3 (75) B 1 21% 3 (1/567) 3 (1/75) C 1 6% Kriguer and Salomon, 2016 11
Analysis (original 5 Class A, 6 Class B, 21 Class C) Part Number Value [ ] Value [%] Cumulative [%] Class S220 5,412 18.9 18.9 A C365 3,629 12.7 31.6 A T001 3,526 12.3 43.9 A M444 3,269 11.4 55.3 A S665 3,205 11.2 66.5 A A124 2,543 8.9 75.4 B F254 2,489 8.7 84.1 B S126 236 0.8 96.4 C Kriguer and Salomon, 2016 12
Analysis (new 6 Class A, 21 Class B) Part Number V (9%) D (38%) C (9%) L (3%) T (38%) S (3%) Overall S220 A B C A A A C C365 A B C C A B C T001 A B C B A B C M444 A B C A A B C S665 A B C C A B C A124 A B C B B B C F254 B B C B B B C S126 A C A B C B C Kriguer and Salomon, 2016 13
Conclusions The empirical study presented AHP successfully applied to solve a multi-criteria classification problem, au contraire to international literature Plant managers validated the results AHP-based model was considered as a better practice for classification of spare parts AHP application begun with an expiring Expert Choice license, not renewed by EC, and not missed Kriguer and Salomon, 2016 14
Main references Bacchetti A, Saccani N (2012) Spare parts classification and demand forecasting for stock control, Omega 40, 722 737 Ishizaka A, Pearman C, Nemery P (2012) AHPSort: an AHP based method for sorting problems, Int J Prod Res 50, 4767 4784 Kennedy WJ, Patterson JW, Fredendall LD (2002) An overview of recent literature on spare parts inventories, Int J Prod Econ 76, 201 215 Saaty TL (1980) Absolute and relative measurement with AHP. The most livable city in the United States, Socio-Econ Plann Sci 20, 327 331 Saaty TL, Ozdemir M (2003) Why the magic number seven plus or minus two, Math Comp Model 38, 233 244 Kriguer and Salomon, 2016 15
Future submission Extended version of this work shall be submitted to Special Issue of J Multi-Criteria Decis Anal Kriguer and Salomon, 2016 16
