Aligning classification and inventory control of spare parts                      in the frame of an integrated management...
the integrated management process of spare parts and its           Spares’ demand forecasting is then an important phase –...
2001) and the classification step aims at categorizing the                            literature to this end are often too...
(4) The calculation of the overall criticality index as the sum of     criticality index, as previously described. In this...
initiated from the inventory control models’ properties         and can be easily found in the market (i.e., mid to lowthe...
Starting from the criterion with the highest weight in the      capacity, European Journal of Operational Research, 97: 48...
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Spare parts final 27072012 - paper

  1. 1. Aligning classification and inventory control of spare parts in the frame of an integrated management process Marco Macchi*, Irene Roda*, Luca Fumagalli*, Flavio Beretta**, Sammy Saba** * Department of Management, Economics and Industrial Engineering, Politecnico di Milano, Piazza Leonardo Da Vinci 32, 20133 Milano, Italy, ** ABB Spa Process Automation Division, Via Vittor Pisani 16, 20124 Milano, Italy (email: marco.macchi@polimi.it; irene.roda@polimi.it; luca1.fumagalli@polimi.it; flavio.beretta@it.abb.com; sammy.saba@it.abb.com) Abstract: Spare parts management is an issue of outmost importance for effectiveness in manufacturing and service companies because of its impact on system availability and continuity of operations. On the other hand, spare parts are inherently difficult to manage in an efficient way. The present paper postulates that an integrated perspective for decision making in spare parts management provides potentials for positive results in both effectiveness and efficiency: a model for aligning classification and inventory control of spare parts is presented as a lever to this end. Keywords: Spare parts, integrated management process, classification, inventory control. hardly present in literature. Nonetheless, they think that1. Introduction the adoption of an integrated perspective is one of theEffective management of spare parts is essential to many main lever for improving the overall performance (inorganizations since they represent an important resource terms of effectiveness and efficiency) of spare partsto ensure system availability and continuity of operations. management in companies. This paper shares a similarBut spare parts are inherently difficult to manage in an mindset, therefore it aims at contributing to fulfill theefficient way: their inventories may consume a significant needs for an integrated spare parts management. A mainportion of the capital investment in a company. Hence, in assumption to this end is that a close attention should bemany organizations in the manufacturing, services and provided on the distinct characteristics pertaining to thedefense sectors, there are opportunities for cost savings by spare parts, different from other types of materials.engaging in more efficient management of spare parts The general function of spare parts is in fact to assist theinventories (Diaz and Fu, 1997). Indeed, the objective of a maintenance staff in keeping equipment in an operatingcompany should be to achieve effectiveness, without condition. This means that the policies that govern thelosing efficiency. How to deal with such an objective? spare parts dynamics are different from those that governThe advent of ERP (Enterprise Resource Planning) other types of materials such as the raw material, work-in-system, and more specifically of CMMS (Computerized process and finished goods from a manufacturing plant.Maintenance Management System), has favorably Spare parts are often characterized by stochastic demandcontributed to spare parts management, by supporting the with irregular patterns; further on, it is normally observedcreation, archive and share of structured information on that the rates of consumption for some parts are very highMROs (Maintenance, Repair and Operations) items and for some others are very low; some parts are(Labib, 1998). On the other hand, the knowledge behind characterized by low purchasing costs, while many otherstheir proper usage is still retained in the human mind. In have high or very high purchasing costs; last but not least,fact, managing spare parts is not just a technical problem procurement lead times are differentiated and they may bewhich can be resolved by installing a specific ICT tool. It high, especially in the case of specific or on order parts. Arather requires an ‘engineering’ way of managing better insight on peculiarities characterizing the sparemaintenance processes and of integrating the several parts can be found in several studies in the scientificactivities involved: this motivates the need for systemic literature such as, for example, Kennedy et al. (2001) andactions, to support an integrated perspective for decision Cavalieri et al. (2008).making in spare parts management (Cavalieri et al., 2008). The core objective of this paper is the definition of aNonetheless, as stated by Bacchetti and Saccani (2011), a model which allows aligning the spare parts characteristicsmajor problem talking about spare parts stands in the – analysed thanks to a spare parts classification – with thepaucity of research with such a systemic perspective. subsequent selection of the inventory control model. TheThese authors argue that an approach integrating spare alignment can be envisioned as a requirement for makingparts classification, demand management and forecasting, an integrated spare parts management system. The paperinventory management and performance measurement is presents a proposal to this regard. Section 2 firstly defines
  2. 2. the integrated management process of spare parts and its Spares’ demand forecasting is then an important phase –objectives, it then focuses on the importance of aligning phase 2 of the process – to proceed with the next phases.classification and inventory control of spare parts. An Special techniques are generally required for MROs sinceanalysis of the state of the art is undertaken in Section 3 a large portion of them are characterized by eitherby focusing both on the existing classification methods intermittent or lumpy demand. The outcome of this phaseand on the inventory control models abundantly discussed is the achievement of demand information (demand sizein literature. Section 4 presents the model proposed in and pattern), a relevant input for inventory control.order to support the alignment: the model is developed Thereafter, the inventory analyses, carried out on the basisand then clarified by a short example. Section 5 finally of different peculiar characteristics of the spare parts, suchdiscusses an agenda for future researches fulfilling the as annual consumption value, procurement lead time, unitgaps still existent in this proposal. cost and frequency of use, help a company in establishing2. Problem statement suitable policies for selective control – step 1 of phase 3 –. This also helps in focusing further efforts on the operative2.1 Integrated spare parts management process layer, to design the suited inventory control model – stepAn integrated management process can be defined as an 2 of phase 3 –, thus systemizing the ordering procedureapproach that considers the different phases involved in and also achieving, as a managerial goal, an optimum levelthe process in order to get a holistic vision of the whole of the inventory, with adequate balance of the trade-offsstream of activities, with the objective of controlling them in the target set of performances.globally for granting the achievement of the desired final The application of computer-based applications (e.g., aresults. This is quite a general definition. Accordingly, the CMMS) for processing spare parts information and formanagement process of spare parts can be organized as an operating spare parts control system is then needed forintegrated stream of activities leading to the management the organization and ensures timely operational actionsdecisions (e.g. how many spares to keep in stock, how for an efficient and effective spare parts management –many and when to re-order, etc…), with the objective of phase 4 of the process –.granting the achievement of a target set of performances.The integrated process, as given in Figure 1, is derived Last but not least, policy test and validation is intended asfrom Cavalieri et al. (2008) and consists of five phases. the last phase – phase 5 of the process –. This should help orienteering toward a continuous improvement approach: each selected policy should be tested, reviewed and, if not valid, modified, going back to previous decision making phases. 2.3 On the alignment between classification and inventory control As it emerges from the previous section, spare parts classification and demand forecasting can (and should) be Figure 1. Integrated spare parts management process related to inventory control (Boylan, Syntetos, 2008).Figure 1 envisions a continuous improvement approach, This work focuses on the potentialities for improvementsstarting from phase 1 to 5 and back, as well as a number which may be reached not only through a dedicatedof sub-steps required along the process in some phases. inventory control policy implementation, but thanks to a better alignment between the spare parts classification andAccording to the best practices cited in literature, the the selection of the most adequate inventory controlmanagement decisions in all the phases should be made model. The basic assumption is that the spare partshaving in mind a systemic perspective, and adopting a classification has to respond to the need of consideringdifferentiated approach, where different kinds of spare the spare parts characteristics in order to define theparts are treated with different demand and inventory criticality of each item / class of items. This phase allowsmanagement techniques (Boylan, Syntetos, 2008). to consequentially define which is the best inventory2.2 Objectives of each phase of the integrated process control model to be used in order to manage the specific item / class of items. The idea is then to create aPart codification and classification – steps of phase 1 of mechanism that helps mapping the spares’ characteristicsthe integrated process – help the organization minimizing with the most proper inventory model applicable for theirthe duplication of spare parts stocking thereby reducing management. As far as the demand forecasting phaseinventories, aid the accounting process and facilitate the regards, this work does not consider this specific aspect.computerization of spare parts inventory control systems To those interested, Syntetos and Keyes (2009) carried outand, most of all, allow identifying different classes of spare a recent review of spare parts forecasting literature.parts basing on their peculiar characteristics. In particular,the identification of peculiarities is an important task since 3. State of the Artit enables to differentiate the spare items, with the goal to 3.1. Classification of spare partscharacterize the so called ‘criticality’: this is a leading welldiscussed concept in spare parts management to drive Criticality of spare parts is probably the first feature that issubsequent decisions and, in fact, the inventory control pronounced by the spare part logistics practitioners whilemay be decided driven by it. enquired about specific item characteristics (Huiskonen,
  3. 3. 2001) and the classification step aims at categorizing the literature to this end are often too complicated and limitedspare parts basing on it. Since both quantitative and due to computational efforts (e.g. the use of a linearqualitative factors (e.g., price, lead time, specificity, stock optimization model): limits have to be considered whenout costs, severity, etc.) may be considered as criteria for implementing the method in industry. Among the severalassessing criticality, the methodology to be followed for methods from literature, the AHP is proposed in thisimplementing such a phase deserves special attention. work as a proper method for spare parts classification and, looking ahead along the integrated managementIf the traditional methods for classification were based on process, for aiding the alignment of classification andsingle classification criterion (see traditional quantitative inventory control. AHP is one of the most quoted MCICABC-Pareto analysis) or on pure qualitative considerations method when referring to spare parts, since many years:(see VED analysis, categorizing spares into Vital, Essential Gajpal and Ganesh, 1994 is one of the first example ofor Desirable items), more recently there has been a trend use of AHP for spare parts classification, to characterizeshowing that researchers started to assess the necessity of their criticality. A part from its consolidated use, twoconsidering more than one criterion and both quantitative aspects were taken into account to further motivate itsand qualitative factors. Indeed, the discussion in literature selection: (i) the simplicity, in terms of computationalis bringing to the actual remark that a well-structured applicability of the method; (ii) the adaptability to theclassification of spare parts cannot be based neither just needs to assess criticality, by taking into account severalon one single criterion nor on qualitative judgments only. factors, both quantitative and qualitative ones.The need for a multi-criteria inventory classification(MCIC) started to be disclosed in the research literature. Briefly, the AHP consists in a decision-support procedureIn fact, only a proper multi-criteria classification can lead for dealing with complex, unstructured, and multiple-to the definition of critical spare items which reflects the attribute decisions. It is a powerful tool to find out thereal priorities, given to different criteria. A selection of relative priorities or weights to be assigned to differentmeaningful research works about MCIC classification criteria which characterize a decision (Saaty, 1990).methods is proposed in Table 1. The table summarizes an Focusing on the scope of classifying spare parts basing onin-depth analysis of literature done as a preparation of this several criticality criteria, three basic steps are needed forpaper. The reader may consider, for details on different using AHP.classification methods, the study by Molenaers et all. (1) The description of the decision problem with a hierarchical(2011) as a general review. structure. Within this context the problem’s structure can MODEL ADVANTAGES DRAWBACKS be presented as it is in Figure 2. The main element is the Bi-criteria It introduces more than It is considered too approach one criterion, hence it much demanding if final objective of the problem, at the top level of the enlarges the capability more than two criteria hierarchy, which is the calculation of an overall criticality of traditional methods are adopted (this is due index for each spare item. The criteria to be considered (e.g. by a joint ABC + to high computational VED analysis) complexity) for calculating the criticality index itself are defined at the AHP-Analytic It is a multi-criteria It requires judgments to middle level and finally the modes, which each criterion Hierarchy Process decision supporting a be expressed by an method for calculating expert (i.e. subjective can assume depending on its value (high, medium, low), an overall criticality judgments) are set by the definition of proper ranges (for the values). index ABC-fuzzy It allows considering A practical applicability classification nominal and non- is difficult (this is due to nominal attributes high computational complexity) DEA (Data It computes the weights It is considered too envelopment for the criteria by an much demanding if the Approach) – optimization method; number of spare parts Weighted Linear this allows to overcome to classify is high (due Optimization problems connected to high computational method with subjective complexity) judgments Single overall score It is simple to use When the number of of an inventory criteria is large, it is not item –calculation an easy task for a by skipping decision maker to rank Figure 2. Structure of the classification problem optimization all the criteria as it is required. (2) The use of pair-wise comparisons is carried on to estimate: i) It is only applicable for continuous criteria the relative weights (reflecting the given importance) of measures each criterion comparing to the others and ii) the relative importance of the modes, given each criterion, with Table 1. MCIC methods: advantages and drawbacks respect to the overall criticality (e.g. the higher the price ofIt is important to underline that a common issue of spare the item is, the higher its overall criticality will be).parts classification with a multi-criteria perspective is the (3) The integration of weights in order to obtain an overallidentification of the relative importance of criteria: hence, importance (weight) for each criterion reflects its contribute overthe MCIC methods comprise either algorithms, models, the overall criticality definition of an item, both in term ofapproaches or also rules of thumb for deciding the relative relative importance with respect to the other criteria andweights (measuring the relative importance) of the criteria of its mode implications.defining the overall criticality. The proposals from
  4. 4. (4) The calculation of the overall criticality index as the sum of criticality index, as previously described. In this way, notthe criteria’s weights obtained in the previous step. only a proper classification based on the overall criticality index can be implemented, but also a useful input for theThe use of AHP in order to classify spare parts and to aid next step of inventory control is available: that is what wethe alignment of classification and inventory control is call the ‘criticality profile’ of the spare item / class ofpresented further in this paper. items which, according to the AHP procedure, simply3.2 Inventory control models results from the hierarchy reflecting the problem under analysis and, in particular, it is the decomposition of theWhen one considers inventory control models, he/she overall criticality index into its component criteria takingnaturally associates them to the stock sizing problem. An into account their overall weights which comprehendinventory model has in fact a clear impact on the stock both the importance and mode’s implications. In the radarlevel reached during plant operation. The general scope of chart of Figure 3, the criticality profile of an item isstock sizing is: ‘to find the smallest integer number of represented, where Ci are the decision criteria.spare parts which must be stocked, so that requirement 0,05 C1for parts from the installed base, during a cumulative 0,04operating time T, is met with a given probability R’ 0,03(Birolini, 2004). To this end, different alternatives of 0,02 C5 C2 0,01inventory control model can be decided. 0,00An inventory model is usually characterized according totwo main parameters, used for controlling the stock level(Cavalieri, 2008): (i) a criterion (x) that specifies the C4 C3conditions under which a new order of spare parts should Figure 3. Example of a criticality profile of an itembe issued; (ii) a reference point (y) for the quantity to be As far as the criteria to be considered, given an in-depthordered. Inventory models can be classified with the taken analysis from both literature and industrial surveycouple (x, y), where both x and y can assume different (Roda, 2012), five criteria have been selected as the mostvalues depending upon the type of inventory review (i.e. proper ones for MROs classification: Lead Time, Price,periodic or continuous review) and the optimal order size. Turnover Rate, Stock out cost and Specificity.Literature is full of proposed inventory control models After the implementation of the AHP methodology, thebut each of them seems to be presented, in most of the spare items are then classified with respect to an overallcases, without an explicit connection with the peculiarities index, which takes into account more than one criteria,of the spare parts for which it is recommended to be and the items’ criticality profiles are also available. It isapplied to. No or few works in fact speak about the now possible to carry on with the next step dealing withapplicability of the inventory control models they present, the selection of inventory control models: the idea is toreferring to spare parts different characteristics. For this assign the proper control model to criticality profiles; towork concern, 4 inventory control models are considered this end, the applicability conditions for each controland studied in their applicability: (i) the continuous review, model are analysed, in order to fit the inventory modelswith fixed reorder point (r) and fixed order quantity (Q), features with the criticality profiles.referred to as (Q, r); (ii) the continuous review, with fixedre-order point (s) and order-up-to level (S), referred to as 4.2 Applicability of inventory control models(s, S); (iii) the periodic review, with fixed ordering interval(T) and order-up-to level (R), referred to as (T,R); (iv) the The idea at the basis of this paper is to find proper modelscontinuous review and order-up-to level (S) in a one-for- to manage stocking and purchasing of maintenance items,one replenishment mode, referred to as (S-1, S). considering that within a stock there are several different classes of spares, each one with their own peculiarities. InSpecifics about the algorithms on which the models base particular, by referring to the categorization of the actualon are not presented in this paper: plenty of literature stock of a company, resulting from the classification step,works can be found about this issue. On the other hand, a this step now aims at the selection of the best model todiscussion is herein presented for what concern their manage each class of spares / each critical spare item. Aapplicability: this aims at providing a better focus on the framework is required to this end which, acting as a meta-alignment of the outcomes of spare part classification and model, allows to relate different inventory control modelsthe selection of inventory control. to different characteristics of the spare parts. The framework considers, according to the proposal of4. Model Definition this paper, the ‘criticality profiles’ of the classes of spares /spares as to drive the selection. Hence, an analysis of4.1. Using AHP in spare parts classification as a tool for alignment applicability conditions of each inventory model has beenAs far as the classification step concerns, the objective is undertaken to build the framework, driven by thethe definition of a criticality index for each spare item / elements measured in the ‘criticality profiles’. In particular,class of items. Once the criteria to be considered in the the aim has been to identify the peculiarities of the itemsdecision process have been defined, by using the AHP it is to which an inventory model best fits, based on the theoryeasy to determine the weight that each of them has on the already presented in literature references plus additionalfinal assessment of criticality and to calculate the overall considerations logically derived by a deductive process
  5. 5. initiated from the inventory control models’ properties and can be easily found in the market (i.e., mid to lowthemselves. specificity).For what concern the (Q, r) models, it is widely stated and 4.3 Model developmentapproved in literature that they are implementing policies By structuring the reasoning presented in the previousfor fast or normal moving spare items: Gelders and Loony subsection it is possible to finally define the framework:(1978), for example, empirically demonstrate the classical this is materialized as a matrix able to guide the decisionEOQ model as appropriate for normal and fast moving about which inventory control model to be used for aitems; Porras and Dekker (2008) also present the (Q,r) particular item’s criticality profile, by referring to themodel with normal approximation of the demand as a mode assumed by each criterion for that item (Table 2).suitable model for items with high turnover rate. As far asthe lead time concerns, these models are usually applied in C1 C2 C3 C4 C5 Lead Price Turnover Stock Out Specificitycase of long lead times (Fera, Lambiase, 2010). Since they Time Rate Costare based on the continuous review, these models enable (Q,r) Mid/long High Mid/high Mid/low Mid/lowto keep the average stock level lower than in the periodic (s,S) Short High Low Mid/high Mid/highreview: they are then preferable for high price spare items, (T,R) Mid/short Low Mid/low Low Mid/lowbecause of the resulting lower holding costs. Thereafter, inpractice, the (Q,r) models are normally mentioned for (S-1,S) Long High Low Mid Mid/highmanaging either consumables or generic materials (that is,materials characterized by low or, at most, mid specificity). Table 2. The framework to select inventory controlFurther on, no particular concern is given to the stock out models starting from the classification outcomescosts: it can be deduced that low/mid stock out costs canbe considered as the applicable characteristics. The selection will be guided by the criticality profile which identifies the most critical criteria for a certain item andThe (s, S) model is based on the assumption that demand which are the ones to be considered first in the selectionbehaves as a Poisson distribution; the (S-1, S) model is a of the model. The following steps may be considered: (1)particular case of (s,S) model, with an underlying Poisson consider the criterion with the highest value in thedemand distribution as well (Feeney and Sherbrooke, criticality profile composition; (2.) look at its mode (high,1966). These models are globally proven in literature as medium or low); (3.) by using the framework, select thethe best choice for slow moving items. Moreover, the models which can be used for such criterion’s modemodels are usually preferred for spares with high price. In (scroll the criterion column and select the rows where theparticular, (S-1, S) model is the most appropriate when value in the cells corresponds to the mode assumed by thedemands and order costs are relatively low, and the spares’ criterion); if the models (rows) selected are more than 1,prices are high (Rose, 1972), especially when the lead time go to step 1, else stop.is long. On the other hand, the continuous review of the(s, S) model fits well with items characterized by a short 4.4 An examplelead time (Duchessi et al., 1988). In practice, these models A short example in order to clarify the usage of the modelare usually mentioned for stocking specific materials (that developed in the paper is herein given. After using AHPis, materials with a mid/high specificity) and in case of for classifying the components of the combustion systemmid/high stock out costs (this is mentioned especially for of a mining truck’s engine, the overall criticality indexthe case of (s,S)). obtained for each of them allows to identify the mostThe (T,R) model is run in the common use particularly in critical one, that is the fuel tank. The radar chart in Figurecompanies not utilizing computerized control. This is also 4 shows the criticality profile of that item.frequently seen when the spare items are ordered from the C1: LEAD TIME 0,4same supplier, or require resource sharing. Because of the 0,3periodic review, this model leads to higher stocks, then 0,2 C5: SPECIFICITY C2: PRICEhigher holding costs, than the continuous review: this is a 0,1reason why the (T,R) model can be better justified for low 0,0price items. With higher stocks, mid to low turnover ratesare expected for the model: this depends on the demandrate, not high with maintenance materials. Differently to C4: STOCK OUT COST C3: TRthe ones above, in this case the order quantity is not fixed Figure 4. Fuel Tank’s criticality profilebut the inventory level is restored to its maximum valueeach replenishment period: this approach is simple andusually applied for not very critical parts in term of stock From the criticality profile (see Figure 4) and the criteriaout costs. In practice, the periodic review can be justified modes assumed by the item (see Table 3), it is thenalso for what concern the lead time: with mid/short lead possible to select the inventory control model, by usingtime, the periodic review is acceptable, while not being the framework (of Table 2).forced to implement a computerized control to keep C1 C2 C3 C4 C5stocks under a continuous review. This is often Lead Price Turnover Stock Out Specificity Time Rate Costaccompanied by the fact that materials are quite generic Long High Low High Mid/High Table 3. Fuel Tank’s criteria modes
  6. 6. Starting from the criterion with the highest weight in the capacity, European Journal of Operational Research, 97: 480-overall criticality index (i.e. the lead time) and following 492.the algorithm presented in the previous section, the (S- Duchessi P., Tayi G. K., Levy J. B, A (1988) ;Conceptual1,S) model is selected as the most proper one. Approach for Managing of Spare Parts, International Journal5. Conclusions of Physical Distribution & Logistics Management, Vol. 18 Iss: 5, pp.8 – 15The major problem, for the framework developed in thispaper to be more consistent and solid, stands in the Feeney G. J., Sherbrooke C. (1966), The (S-1,S) Inventorypaucity of research with an integrated perspective on spare Policy under Compound Poisson Demand, Managementparts management: as evidenced from literature review, an Science, Vol. 12, No. 5, A ,Sciences, 391-411approach integrating spare parts classification, demand Fera, Lambiase Nenni (2010), A Proposal for Estimatingforecasting, inventory control models and performance the Order Level for Slow Moving Spare Parts Subject tomeasurement is hardly present in literature. In order to fill Obsolescence, iBusiness, 2, 232-237this gap, in this work a deductive process has been usedwith the aim of handling the few information from Gajpal P. P., Ganesh L. S. (1994), Criticality analysis ofliterature about differentiated ways of managing spare spare parts using the analytic hierarchy process,parts in the most consistent way in order to obtain the International Journal of Production Economics, Vol. 35, Iss 1-framework. Of course, this methodology is useful to 3,pp 293-297create the basis for the definition of a model; building ofmore theory is necessary to define a complete and robust Gelders L.F., Van Looy P.M. (1978), An Inventory Policyframework. In particular, detailed and focused empirical for Slow and Fast Movers in a Petrochemical Plant: Ainvestigations are required in the next future for validation Case Study, The Journal of the Operational Research Society,of the framework: case studies and proper testing, such as Vol. 29, No. 9, pp. 867-874with simulation, to verify the adaptation to a real setting Huiskonen (2001), Maintenance spare parts logistics:of the framework, will be required. This will help studying Special characteristics and strategic choices, Internationaldifferent scenarios and validating models, considering also Journal of Production Economics, Vol71, Iss1-3, 6, pp125-133the dynamics in the application in a real industrial world. Kennedy, WJ, Patterson, JW, Fredendall, LD. (2002) , AnAcknowledgments overview of recent literature on spare parts inventories, International Journal of Production Economics, 76(2): 201–215.This paper grounds on a research supported with theproject “Dote Ricerca applicata: MeS-MaM - Metodi e Labib A.W., (1998) World-class maintenance using aStrumenti per la riduzione dei costi di gestione dei computerised maintenance management system, Journal ofMateriali di Manutenzione” (in English: Methods and Quality in Maintenance Engineering, Vol. 4 Iss: 1, pp.66 - 75tools for the reduction of cost of spare parts). The project Molenaers A., Baets H., Pintelon L., Waeyenberghwas funded by Regione Lombardia (Lombardy Region) G.(2011), Criticality classification of spare parts: A caseand ABB Spa Process Automation Division. The authors study, International Journal of Production Economics (to bewould like to thank the other persons that contributed to published).the project, namely Eng. Federico Curcio formerly M.Scstudent from Politecnico di Milano and Eng. Salvatore De Porras, Dekker (2008), An inventory control system forAngelis project manager from ABB Spa Process spare parts at a refinery: An empirical comparison ofAutomation Division. different re-order point methods, European Journal of Operational Research 184 pp.101–132References Roda I. (2012), An integrated decision framework forBacchetti A., Saccani N. (2011), Spare parts classification Spare parts classification and Inventory control policyand demand forecasting for stock control: Investigating selection: Conceptualization, development and validationthe gap between research and practice, Omega, Vol40, Iss6, in industrial contexts; MSc Thesis (Politecnico di Milano)722-737 Rose M. 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