Multi-criteria Classification for Spare Parts Management: A Case Study

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ScienceDirect Procedia Manufacturing 11 (2017) 1560 – 1567

27th International Conference on Flexible Automation and Intelligent Manufacturing, FAIM2017, 27-30 June 2017, Modena, Italy

Multi-criteria classification for spare parts management: a case study Catarina Teixeiraª, Isabel Lopesª*, Manuel Figueiredoª ªALGORITMI Research Centre, Department of Production and Systems, University of Minho, Guimarães 4800-058, Portugal

Abstract Inventory management of spare parts for production equipment is a process that affects the performance of maintenance management and therefore productivity. This paper presents an ongoing project aiming to develop a spare parts classification for integration in a computerized maintenance management system (CMMS) of a manufacturing company. The classification methodology should be able to define groups for which a stock management policy will be associated. Initially, a first classification is carried out to identify the necessity and importance of spare parts for maintenance. After that, a multi-criteria classification, including the previous maintenance classification, is used for defining the groups. ©©2017 Authors. Published by Elsevier B.V.B.V. This is an open access article under the CC BY-NC-ND license 2017The The Authors. Published by Elsevier (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the scientific committee of the 27th International Conference on Flexible Automation and Peer-review under responsibility of the scientific committee of the 27th International Conference on Flexible Automation and IntelligentManufacturing Manufacturing. Intelligent Keywords: Inventory management; maintenance management, multi-criteria classification, spare parts, spare parts management

1. Introduction Spare parts management is a specific branch of inventory management and it is characterized by a highly erratic and intermittent demand, and by parts costs of different magnitude. Whenever a component fails or requires replacement, a need for parts demand is created [1,2]. Spare parts inventories diverges from manufacturing inventory in many aspects [3]. The spare parts inventory is determined by the demand, instigated by preventive and corrective maintenance. The availability of spare parts should be directly

*

Corresponding author. Tel.: +351 253604762; fax: +351 253604741. E-mail address: [email protected]

2351-9789 © 2017 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the scientific committee of the 27th International Conference on Flexible Automation and Intelligent Manufacturing doi:10.1016/j.promfg.2017.07.295

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related to maintenance in order to reduce failure downtime and costs. Inventory and maintenance management must be seen as parts interconnected for optimizing company’s operations [4]. Spare parts stock management has the functionality of providing support to maintenance service, such as to ensure operability of the installed systems. According to Kennedy et al. [3] the stock management of spare parts has peculiars characteristics that make them different from product inventories, such as: The decision of whether to repair or replace has profound implications on maintenance inventory levels; The information about reliability is generally not available to the degree needed for the prediction of failure times; Obsolescence may be a problem as the machines for which the spare parts were designed become obsolete and are replaced; The costs associated with the lack of spare parts are difficult to quantify, since they include costs associated with quality and production losses. CMMS is now a central component of many companies’ maintenance departments, and it offers support on a variety of activities related to maintenance. Concerning spare parts, it can track the movement of spare parts and their requisition when necessary [5] and to facilitate the integration of logistics and maintenance perspectives [6]. The integrated logistics and maintenance decisions will allow a more efficient maintenance programming and its execution. The research work about spare parts management presented in this paper is included in the project of the development of a CMMS prototype based on an upgrade of the current system used by the company. This upgrade consists on software architecture redesign, adding a set of advanced management maintenance methodologies and functionalities [7]. Spare parts management is not currently included in the CMMS of the company. Management of spare parts is performed by a department that ensures all the plant's needs. To a more efficient and effective use of CMMS, it is intended that CMMS should be able to provide information relevant for the inventory management of spare parts. In order to improve inventory management of spare parts, it is intended to develop a classification method to evaluate spare parts according to their relevance, based on the information registered in the CMMS. Classification is required due to the high variety of spare parts and their characteristics. In this paper, a classification methodology able to support inventory management of spare parts is presented, creating groups of spare parts to assign an inventory management policy. The paper is organized as follows. In Section 2 a literature review is presented about spare parts classification. Section 3 defines the approach to perform classification. In Section 4, the methodology is presented through its application in a sample. Finally, in section 5 the main conclusions and further work are presented. 2. Literature Review Spare parts management has obtained great interest in literature, throughout the last decades. Various topics are addressed concerning spare parts, such as inventory control, demand forecasting and reliability, and supply chain management [8]. Spare parts classification is a relevant step to guide the whole management process. Many advantages can be achieved by proper classification. The demand forecasting process may be driven by data collected for different classes and performance improvement may focus on critical classes [9]. According to Cavalieri et al. [6], it is important to perform a categorization of the spare parts used in an industrial plant. This categorization is useful for filtering out those items where to put more attention. Huiskonen [10] states that spare parts classification allows the determination of an adequate spare parts management. It supports the choice of forecasting demand and inventory control methods, and establishes different performance targets in the levels of service and inventory turnover for each category. According to Huiskonen [10] and Molenaers et al. [8] there are two types of criteria to classify spare parts: process criticality, if its failure or malfunction results in severe consequences for the plant. For example, consequences related to loss of lives, environmental contamination or production loss; Control criticality, a spare part is considered critical if the possibility to ensure immediate availability of the part is difficult to control. To assess the criticality, spare parts classes should be created based on several quantitative and qualitative criteria. This section is divided into 2 topics, namely, classification techniques commonly used for spare parts and the main criteria for classification.

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2.1. Classification techniques Two types of methods can be applied to classify the criticality of spare parts: quantitative methods and qualitative methods [6]. In industry, the traditional classification method is ABC analysis, which is widely used to determine service requirements of spare parts [8]. The classification helps companies to simplify stock management. The objective of ABC analysis is to classify the inventory items or stock keeping units (SKUs) into three classes, namely: A (very important items); B (moderately important items) and C (relatively unimportant items) [11]. ABC analysis is easy to use and supports the inventory management of materials that are fairly homogenous in nature [12]. The criteria used to classify are product annual demand and average unit price [13]. Huiskonen [10] mentions that as the variety of control characteristics of items increases the one-dimensional ABC classification does not include all the control requirements of different types of items. In the literature, it has been generally recognized that a “classical” ABC analysis may not be able to provide a good classification in practice [14]. Another quantitative method is FSN, which classifies items in three categories: fast-moving, F, slow-moving, S and non-moving, N. The method is based on the analysis of the demand patterns and leads to a different kind of classification, that is focused on the moving rates of spare parts [6]. The qualitative methods normally used for spare parts classification are based in rough judgement or in scoring methods [6]. The VED (Vital, Essential, Desirable) classification is a qualitative method [15]. The VED classification system is based on the maintenance expert’s knowledge. Spare parts can be classified as vital, essential or desirable [6]. Although its apparent simplicity, the structuring can be a difficult task because its implementation can suffer from subjective judgments of users. Gajpal, Ganesh and Rajendran [16] suggested the application of VED classification with an Analytic Hierarchy Process (AHP) procedure to limit the problem of subjective judgments. The other qualitative method that is normally reported in the literature for spare parts classification is AHP. AHP has been considered as a leading and one of the most popular multi-criteria decision-making techniques. AHP attracts the attention of researchers due to the fact that normally the input data are easy to obtain [17]. It is used in a wide range of fields, especially in operations management, to solve complex decision problems by the prioritization of alternatives [19]. This technique can be used when it is required the consideration of qualitative and quantitative factors and it helps to define the critical factors through the definition of a hierarchical structure similar to a family tree [20]. In AHP, the relevant data is obtained from the use of a set of pairwise comparisons. The application of AHP helps to reduce the complex decisions to a series of simple comparisons and consequently it helps to synthetize results showing the best decision and the clear reason for the choice [20]. AHP uses a multi-level hierarchical structure of objectives, criteria, subcriteria, and alternatives. These comparisons are used to define the weight of each criterion and the relative performance measures of the alternatives for each criterion. This method also verifies the comparisons consistency and provides a mechanism to improve it in the cases where the comparisons are not consistent [17]. The Multi-Attribute Spare Tree Analysis (MASTA) proposed by Braglia, et al. [21] has a classification criteria based on the item criticality. The method provides two successive steps: the first step proposes the identification of four spare part criticality classes to analyze provisioning methods using a logic tree. The next step consists on getting suitable inventory management strategies to each one of the four defined classes. AHP is used to support the decision problem at each node of the logic tree. 2.2. Criteria selection The first stage of the multi-criteria classification method is the definition of significant criteria. The popular types of criteria used in four case studies are: lead time, probability of item failure, number of potential suppliers and price. In the case study proposed by Molenaers et al. [8] a multi-criteria classification method based on spare parts criticality is presented. The contribution of their research work was actual implementation of a classification method in an industrial environment. The approach developed by Stoll et al. [22] was intended to evaluate spare parts based in real inventory in cooperation with an industrial company. The goal was to solve the problem of stockage of spare parts. The method

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(MASTA) developed by Braglia, et al. [21] presents a model for inventory management of spare parts. The model takes into account a set of attributes for the classification of the parts in the paper industry. In the study of Cakir and Canbolat [23] a multi-criteria inventory classification applied in a small electrical company is proposed. The classification combines the potency of recent information technologies such as Java Servlets, MySql database and the modeling principles of the fuzzy AHP methodology. 3. Multi-criteria classification steps The company where this study is being carried out defines the quantity to order of spare parts and the safety stock value based on the information provided by the suppliers of the machines and the experience of the maintenance department. Currently, the spare parts are classified through the ABC-classification based on one parameter (consumption) and through the FSN classification which takes into consideration the moving frequency. At the moment, 18% of the items in stock are classified as non-moving (FSN Classification). This classification put in evidence the spare parts that have not been moved for many years. The classification based in ABC and FSN only considers a single criterion. Nevertheless, a single classifying criterion cannot generally represent the whole criticality of an item. For instance, the lack of a slow-moving item can have a major impact on productivity. Therefore, in the context of spare parts logistics, an approach that includes criticality as a fundamental criterion should be developed. Spare parts management is not currently supported by the CMMS of the company. The inventory management of spare parts is done by performing the record in the ERP system and also in a "supermarket" of material that is used for maintenance actions. In this paper, a multi-criteria classification of spare parts is developed in order to assign an adequate inventory policy to each spare part, combining a set of criteria. Contrarily to what currently happens with the use of ABC and FSN analysis. In the first step, the criticality of spare parts is defined, identifying the importance and need of the spare part for production. The main goal is to assign to each spare part a level of criticality using the designation VED (different from the VED concept presented in the literature) which divides the spare parts in three criticality categories: Vital, Essential and Desirable. • Vital: Part failure have great impact on production processes; • Essential: Part failure have middle impact on production processes; • Desirable: Part failure pose no risk to the production processes. In the literature the VED concept appears generally combined with the AHP method. In this case, two criteria and the respective levels in a matrix are conjugated, since both criteria have the same degree of importance. The result of the criticality of spare parts will be used as input of a multi-criteria classification whose objective is to group spare parts that use the same inventory policy. Fig. 1 shows the two steps involved in the proposed spare parts classification methodology. The purpose of the first step of this classification is to classify spare parts as to their necessity and importance for maintenance. Therefore, the result of this step is to assign the spare parts into one of three levels of criticality. This will be used in a second classification that aims to create groups of spare parts sharing the same stock management policy. In the second step a multi-criteria classification that combines the criticality of the spare parts with criteria related to the inventory management is performed.

Fig. 1. Inputs and outputs of the classification methodology

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4. An application example In this section an application example of the classification methodology is presented. The first step of the classification, criticality of spare parts, is validated using a set of spare parts. In the second step of the classification, the criteria and methodology are presented. The selection is based in both the literature analysis and the industrial assessment presented in the remainder of this paper and will help identifying the most proper criteria to be considered when classifying spare parts. 4.1. Criticality of spare parts The objective of the first step in spare part classification is to evaluate the criticality of spare parts in an efficient and detailed way. Before defining the rules to criticality assignment, the most appropriate criteria have to be selected. Two criteria, namely Function and Impact on Production, were defined. The Function criterion is divided into 3 levels and the criterion Impact on Production in 4 levels. Table 1 presents the description of the criteria and respective levels. Table 1. List e description of criteria Criteria

Description

Function

The function performed by the spare part in the production process

1.

Auxiliary function

The spare part function consists on supporting the equipment operation, it does not interfere directly on production (e.g. control, comfort, structural integrity, economy, prevent misuse).

2.

Safety function

The spare parts function is to preserve operator safety, it may not directly interfere on production

3.

Indispensable function

The spare part is involved in a primary function of the equipment

Production Impact

Impact of the spare part failure on the production process

0.

No Impact

The spare part failure has no impact on production.

1.

Quality losses

The spare part failure causes defective products.

2.

Productivity reduction

The spare part failure causes a reduction in production rate.

3.

Sudden stop

The spare part failure causes an immediate stop of the machine, causing the total equipment shutdown.

After selecting the criteria and levels it was verified that both criteria have the same importance. Therefore, the levels of the Function criterion were ordered from 1 to 3, as indicated in Table 1, and the levels of the Impact on Production criterion were ordered from 0 to 3. In both cases, the smaller number represents the less relevant level. The result of this first step is the attribution of 3 levels of criticality to spare parts: Vital, Essential and Desirable. For this purpose, a matrix of combinations of criteria and levels was used (Fig. 2).

Fig. 2. The combinations matrix

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The combinations matrix has been validated, with the help of the company, by analyzing several different parts. This study reveals that among all the 12 possible combinations, 3 never occur. Table 2 presents a sample of the analyzed spare parts, each one corresponding to the 9 existing combinations, as well as the level of criticality for each spare part. Table 2- Spare parts classification example Description

Function

Production Impact

Sum

Classification

Cable M12

3

2

5

Essential

Grippe finger - milling cutters

3

3

6

Vital

Vacuum cleaner bag Ringler

1

0

1

Desirable

Chuck for motor Spindle

3

3

6

Essential

Light bulb 24V/2.6W

1

0

1

Desirable

Port Ethercat Junction-EK1122

3

3

6

Vital

Cable encoder

3

2

5

Essential

Ultrasonic sensor microsonic

3

3

6

Vital

Emergency button - ASI

2

3

5

Vital

ET-30 Emergency (stop)

2

0

2

Desirable

Interlock switch

2

2

4

Essential

Needles ICT

3

1

4

Essential

Protection bellows

1

0

1

Desirable

Brush

3

1

4

Essential

Support Scanner

1

2

3

Essential

Glass selective

1

1

2

Desirable

4.2. Multi-criteria classification In the second step, the criticality level is used as criterion in multi-criteria classification of spare parts. Two other criteria were added: Lead Time and Price. Next, ranges for the possible quantitative and qualitative results of each criterion are defined in Table 3. The intervals were defined with the company to cover all spare parts. Table 3 – Criteria levels Criteria /Levels

Criticality

Lead time

Price

High

Vital

> 3 weeks

> 1500 €

Medium

Essential

> 5 days and ≤ 3weeks

> 300 € and ≤ 1500€

Low

Desirable

≤ 5 days

≤ 300 €

After validation of criteria and respective levels, it is necessary to select the method for their comparison. Thus, the use of a decision tree is proposed. Fig. 3 shows an example. In this case the first criterion to be taken into account will be criticality, in second the lead time and the third the price of the spare parts. Finally, it is important to define the most appropriate stock management policyfor each spare part. In this case, four classes of stock management policies are proposed, as explained in Table 4.

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Fig. 3. Example of multi-criteria classification Table 4 - Inventory policies Inventory policy

Description

No stock

Unavailability of a spare part is a conscious decision

One spare part in stock

This management policy implies ordering just in time when spare part is taken from stock.

Multi spare part inventory

It implies stocking more than one piece of a particular item. Inventory level, safety stock, the type of inventory control, and the order replenishment have to be calculated. More than a model can be settled down.

•

Model 1

•

Model 2

In defining the stock management policy, it will be important to take into account two aspects: the number of machines for which the spare parts are used and the demand for spare parts. To determine spare parts demand, reliability studies will be performed. In the case of parts that are periodically replaced (preventive interventions) it will be necessary to know the probability of failure for the item during the preventive maintenance interval. 5. Conclusions Spare parts inventory management represents a complex problem due to the difficulties concerning data collection, the quantity of information to be considered, and the large amount of items involved. Spare parts are important for maintaining production process operating efficiently, thus avoiding production and quality losses. On the other hand, high inventory levels are expensive, due to both capital immobilization and storage space. Thus, many studies are carried out in an attempt to optimize the quantity of spare parts stored. For this, the factors that influence the decisions about the acquisition and storage of spare parts must be known. This paper presents a study that aims to develop a multi-criteria classification for the definition of a stock management policy for each spare part. The development of this multi-criteria classification tool will help the organization making decisions to hold spare parts in stock based on quantitative and objective information.

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Acknowledgements This research is sponsored by the Portugal Incentive System for Research and Technological Development. Project in co-promotion nº 002814/2015 (iFACTORY 2015-2018) and has been partially supported by COMPETE: POCI-01- 0145-FEDER-007043 and FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013. References [1] Boylan JEJ, Syntetos AAA. Forecasting for inventory management of service parts. Complex Syst Maint Handb 2008:479–506. [2] Fortuin L, Martin H. Control of service parts. Int J Oper Prod Manag 1999;19:950–71. [3] Kennedy WJ, Wayne Patterson J, Fredendall LD. An overview of recent literature on spare parts inventories. Int J Prod Econ 2002;76:201– 15. doi:10.1016/S0925-5273(01)00174-8. [4] Van Horenbeek A, Buré J, Cattrysse D, Pintelon L, Vansteenwegen P. Joint maintenance and inventory optimization systems: A review. Int J Prod Econ 2013;143:499–508. doi:10.1016/j.ijpe.2012.04.001. [5] Labib AW. A decision analysis model for maintenance policy selection using a CMMS. J Qual Maint Eng 2004;10:191–202. doi:10.1108/13552510410553244. [6] Cavalieri S, Garetti M, Macchi M, Pinto R. A decision-making framework for managing maintenance spare parts. Prod Plan Control 2008;19:379–96. doi:10.1080/09537280802034471. [7] Lopes I, Senra P, Vilarinho S, Sá V, Teixeira C, Lopes J, et al. Requirements Specification of a Computerized Maintenance Management System – A Case Study. Procedia CIRP 2016;52:268–73. doi:10.1016/j.procir.2016.07.047. [8] Molenaers A, Baets H, Pintelon L, Waeyenbergh G. Criticality classification of spare parts: A case study. Int J Prod Econ 2012;140:570–8. doi:10.1016/j.ijpe.2011.08.013. [9] Márquez AC, Iung B t. A review of multi-criteria classification of spare parts: From literature analysis to industrial evidences. J Manuf Technol Manag 2014;25:528–49. [10] Huiskonen J. Maintenance spare parts logistics: Special characteristics and strategic choices. Int J Prod Econ 2001. [11] Hatefi S, Torabi S, Bagheri P. Multi-criteria ABC inventory classification with mixed quantitative and qualitative criteria. Int J Prod Res 2014;52:776–86. doi:10.1080/00207543.2013.838328. [12] Flores B, Whybark DC. Multiple criteria ABC analysis. Int J 1986. [13] Ramanathan R. ABC inventory classification with multiple-criteria using weighted linear optimization. Comput Oper Res 2006;33:695–700. doi:10.1016/j.cor.2004.07.014. [14] Altay Guvenir H, Erel E. Multicriteria inventory classification using a genetic algorithm. Eur J Oper Res 1998;105:29–37. doi:10.1016/S0377-2217(97)00039-8. [15] Mukhopadhyay SK, Pathak K, Guddu K. Development of decision support system for stock control at area level in mines. IE Journal-MN 2003;84:11–6. [16] Gajpal PP, Ganesh LS, Rajendran C. Criticality analysis of spare parts using the analytic hierarchy process. Int J Prod Econ 1994;35:293–7. doi:10.1016/0925-5273(94)90095-7. [17] Triantaphyllou E, Mann SH. Using the Analytic Hierarchy Process for Decision Making in Engineering Applications : Some Challenges. Int J Ind Eng Theory, Appl Pract 1995;2:35–44. [18] Gass SI, Rapcsák T. Singular value decomposition in AHP. Eur J Oper Res 2004;154:573–84. doi:10.1016/S0377-2217(02)00755-5. [19] Subramanian N, Ramanathan R. A review of applications of Analytic Hierarchy Process in operations management. Int J Prod Econ 2012;138:215–41. doi:10.1016/j.ijpe.2012.03.036. [20] Bevilacqua M, Braglia M. The analytic hierarchy process applied to maintenance strategy selection. Reliab Eng Syst Saf 2000;70:71–83. doi:10.1016/S0951-8320(00)00047-8. [21] Braglia M, Grassi A, Montanari R. Multi-attribute classification method for spare parts inventory management. J Qual Maint Eng 2004;10:55–65. doi:10.1108/13552510410526875. [22] Stoll J, Kopf R, Schneider J, Lanza G. Criticality analysis of spare parts management: a multi-criteria classification regarding a cross-plant central warehouse strategy. Prod Eng 2015;9:225–35. doi:10.1007/s11740-015-0602-2. [23] Cakir O, Canbolat MS. A web-based decision support system for multi-criteria inventory classification using fuzzy AHP methodology. Expert Syst Appl 2008;35:1367–78. doi:10.1016/j.eswa.2007.08.041.