Inventory model optimization revisited: Understanding...

Scientia Iranica E (2020) 27(3), 1572{1592

Sharif University of TechnologyScientia Iranica

Transactions E: Industrial Engineeringhttp://scientiairanica.sharif.edu

Inventory model optimization revisited: Understandingservice inventories to improve performance

L.E. C�ardenas-Barr�ona;�, J. Reynosob, B. Edvardssonc,and K. Cabrerab

a. Department of Industrial and Systems Engineering, School of Engineering and Sciences, Tecnologico de Monterrey E. GarzaSada 2501 Sur, Monterrey, N.L., Mexico, C.P. 64849.

b. EGADE Business School, Tecnologico de Monterrey Ru�no Tamayo y Eugenio Garza Lag�uera, San Pedro Garza Garc��a, N.L.,Mexico, C.P. 66269.

c. Karlstad University - Service Research Center Universitetsgatan 2, 651 88, Karlstad, Sweden.

Received 23 January 2018; received in revised form 7 August 2018; accepted 3 November 2018

KEYWORDSService;Inventory model;Optimization;Performance.

Abstract. Services are becoming increasingly important in the modern economy forboth service and manufacturing �rms; however, the inventory literature is focused mainlyon physical goods and, thus, only few studies have considered services in optimization.Further to that, the traditional service management literature relies on an extremely narrowde�nition of inventory that excludes services because they are perishable. Thus, there is alack of research on possible links between inventory optimization and service management.However, according to a new service inventory approach, business components such astasks or information, as di�erent from physical goods, can be performed and stored inanticipation of service demand as a form of service inventory, that is, inventorying byanticipation rather than delaying the service. This paper aims to contribute to this lackof research by proposing a service inventory optimization model that integrates a serviceorientation to optimize tasks and information to be performed in advance. In contrast withthe traditional inventory models where the objective is to optimize physical items, in thisapproach, physical products (whenever included) constitute only mechanisms for serviceprovision. This service inventory model contributes to the optimization of the quantityof tasks or information to be anticipated and, thus, provides customers with a number ofbene�ts.© 2020 Sharif University of Technology. All rights reserved.

1. Introduction

Inventory is widely researched in production andoperations literature [1] and deeply relevant to theperformance of any organization. More than a century

*. Corresponding author. Tel.: +52 81 83284235;Fax: +52 81 83284153E-mail addresses: [email protected] (L.E.C�ardenas-Barr�on); [email protected] (J. Reynoso);[email protected] (B. Edvardsson);[email protected] (K. Cabrera)

doi: 10.24200/sci.2018.50333.1639

ago, Harris [2] proposed the �rst inventory model, Eco-nomic Order Quantity (EOQ), which became a classicalreference due to its appealing simplicity, exibility, andfocus on optimization. Yet, this classical paradigmrefers solely to physical products: unitary items thatmanufacturing companies can order in lots, store in awarehouse, and ultimately use to maximize their pro-ductivity, from groceries in retailing to spare parts intransportation to food in restaurants to medical itemsin hospitals. With this view, inventory managementliterature largely ignores the storage and optimizationof non-physical components or service activities. Sev-eral inventory optimization models adopt an internal,

L.E. C�ardenas-Barr�on et al./Scientia Iranica, Transactions E: Industrial Engineering 27 (2020) 1572{1592 1573

goods-oriented logic with a focus on optimizing bene�tsfor the vendor, without addressing customers' needs.

For traditional manufacturing �rms, this perspec-tive makes sense. They frequently regard services asadded value or simply a sales tactic to remain com-petitive. However, modern manufacturing companiesare increasingly adopting service provision positions,transitioning from supplying goods to providing ser-vices that actually create value for customers [3{5].According to this logic, the reason for any company toexist is creating value for its customers and sharehold-ers; thus, it must integrate goods and services togetherto satisfy customers and stay competitive. Hence, tofoster pro�t maximization and improve service qualityon a long-term basis, companies need to strategicallyidentify and manage the optimal levels of serviceproductivity [6].

Even as services have become more importantthroughout modern economies, this topic continues tobe excluded from the inventory optimization literature.As a result of a systematic search performed to identifypapers featuring inventory models dealing with ser-vices, no inventory models addressing service activitieswere identi�ed, leading to a knowledge gap. Byexpanding the understanding of inventory optimizationissues in service businesses though, we could addressthis knowledge gap and maximize several service at-tributes including speed, quality, customization, andprice.

Speci�cally, it is noted that traditional servicemanagement literature asserts that services cannot beinventoried because they are perishable [7,8]. Forexample, an empty seat in a plane cannot be keptfor the next ight or a hotel room not sold tonight islost forever. This focus on the impossibility of storinga delivered service for later use re ects an extremelynarrow de�nition of inventory, in which services are�nished products instead of bundles of attributes thatare produced through sets of processes [9]. Yet, busi-ness components might also be performed and storedin anticipation of Service Demand (SD) as a form ofservice inventory [9,10]. In this form of inventorying,the central goal is anticipating, not delaying, theservice. Such a service inventory approach mightserve to expand a traditional optimization focus toinclude the non-physical components of the serviceorganization. From this perspective, the service in-ventory is a set of intangible components (e.g., tasks,information) that can be performed before customerdemand takes place. The goal is to identify whichtasks or information can be conducted in advance torender the core service and create value. This selectiondepends on the characteristics of each market, the costsassociated with creating the service inventory, and thedesired competitive position. By developing a ServiceInventory Optimization Model (SIOM) based on the

EOQ model, this study aims to expand a traditionalinventory and optimization focus to include services.

It is well known that traditional goods can al-ways be stored physically in designated places suchas warehouses, distribution centers, or other areas tohave them ready for use. An important question thusis whether it might be possible to use a traditionalEOQ inventory model to optimize services. From arigorous perspective, the answer is no; the traditionalEOQ inventory model has been developed to optimizeinventories of physical goods, and this study arguesthat it cannot be applied to optimize a number of intan-gible tasks and information required during a servicedelivery process. Additionally, traditional inventorymodels work on the assumption that physical goods arestandardized given their material attributes (e.g., size,weight, volume, etc.). In contrast, service processes,although following standardized protocols in manycases, experience a number of expected and unexpectedchanges and variations during their execution, andwe argue that they require a di�erent optimizationapproach from the products' perspective. Furthermore,a traditional EOQ inventory model calculates the costof inventory as the product of the per-unit inventorycost and the inventory average, re ecting the notionthat the inventory levels of physical units are constantlychanging over time. However, in the service inventorymodel proposed here, service tasks and informationthat can be prepared in anticipation are always readyon a particular time horizon, with the correspondingcosts. In turn, there is not necessarily an average useof tasks or information in calculating the inventorycost. This status justi�es the construction of a newservice inventory model; this paper proposes a serviceinventory model with a service orientation that seeksto optimize the tasks and information to be performedin advance, in which physical products function asmechanisms for service provision. Although di�erent infocus and orientation, this approach still aims to reducecosts by optimizing the costs related to anticipating theexecution of some portion of work that can improvecustomers' bene�ts in terms of service quality, speed,service variety (customization), or price. The challengeis to select and perform tasks or information that willbe truly valuable for customers and that competitorscannot imitate, such that the business operationsbecome unique. In this view, companies must rethinkhow and when they should use their resources, aswell as how suppliers and customers can participate inadvancing some tasks or information, to involve thesepartners in collaborative value co-creation roles.

In sum, service is the result of the interactionstaking place between customers and suppliers throughpersonnel, physical resources, and processes, aimingto satisfy customer needs [11]. Such processes requirethe execution of tasks and information. Traditionally,

1574 L.E. C�ardenas-Barr�on et al./Scientia Iranica, Transactions E: Industrial Engineering 27 (2020) 1572{1592

due to their intangible and perishable nature, services,arguably, cannot be \inventoried"; thus, they happenduring the service delivery process. In this paper,a way to conceptualize service inventory is proposedby anticipating the execution of service tasks andinformation as a means to virtually \store" some partof the service to be performed, aiming to improvequality, customization, speed, and price. Based on thisproposition, the service inventory model determinesthe optimal quantity of service tasks and informationto be anticipated; therefore, they can be virtually\stored" to be ready for use before service is requiredby the customer. No existing service inventory modelfocuses on the optimization of anticipated intangiblecomponents (tasks and information) while integratinginternal (provider) and external (customer) perspec-tives. Therefore, a multi-disciplinary approach isadopted that integrates EOQ inventory contributionswith service management research to propose a newSIOM that contributes to the optimization of somepart of the service (tasks or information) prior tocustomer demand and, thereby, provides bene�ts forcustomers including higher service quality, greaterspeed, enhanced service customization, or lower prices.The proposed service inventory model contributes tonot only inventory research, but also service research,as this model is rooted in the notion that servicecomponents can be inventoried by incorporating thecustomer perspective; in addition, the model con-tributes to a better understanding of how anticipationand optimization might a�ect customer bene�ts.

The rest of the article is structured as follows.Section 2 begins with the proposed theoretical frame-work and a review of the existing EOQ inventorymodels. Section 3 presents a detailed derivation ofthe proposed service inventory model. Section 4contextualizes the service inventory model with someservice business examples. Finally, Section 5 detailsthe conclusions and Section 6 o�ers some suggestionsfor further research.

2. Theoretical framework

2.1. Existing work on EOQ inventory modelsSellers of goods must determine the quantity of prod-ucts to purchase from suppliers to satisfy customerdemand and still achieve pro�tability. Lot sizing andoptimization thus are critical issues for manufacturingcompanies, with direct impacts on the economic e�-ciency of their activities; therefore, various approachesin prior literature address these challenges (e.g., [12].The EOQ inventory model can determine optimalinventory levels while minimizing the total cost associ-ated with the purchase, delivery, and storage of prod-ucts [1,2,13]. A century after its initial publication,the EOQ has extended in various ways, as researchers

have added incremental conditions in an attempt tomodel real-world situations. Thus, the EOQ providesa foundation for a large number of papers that relaxdi�erent restrictions to build new inventory modelswith emphasis on cost and pro�t, which re ect speci�c,actual situations.

An early extension of the EOQ sought to resolvescheduling di�culties associated with lot sizes [2,14].Later research included quantity discounts [15], theproduction (or replenishment) rate, backorders withstockout penalties, and in ation [16,17]. A dynamicversion of the lot-sizing problem also appeared [18].The extensions of the EOQ grew exponentially, leadingto EOQ with multiple setups costs [19], a present valueformulation [20], a stochastic version [21], temporaryone-time discounts [22], an EOQ with learning [23],EOQ models with nonlinear holding costs [24], deteri-orating items [25{28], supplier credits [29], imperfectquality [30,31], recycling [32{34], EOQ for reverselogistics environments [35,36], and partial and fullbackordering [37,38] to mention just a few.

In several good reviews of EOQ inventory mod-eling approaches, Pentico and Drake [37] summarizedEOQ inventory models related with partial backorder-ing, while Khan et al. [39] o�ered an overview ofall extensions with imperfect quality, and Boucheryet al. [40] reviewed the integration of the EOQ inven-tory model into sustainability research. Andriolo etal. [41] explored the evolution of these extensions overa century and, also, provided a research agenda thatsuggests, among other things, developing integratedinventory models to include environmental aspectsfor sustainable supply chains and studying the socialimpact of inventory and purchasing decisions. Glocket al. [42] provided an excellent survey of literaturereviews on EOQ models to help primary researchersposition their own research, reveal the di�erent typesof EOQ inventory models, and identify new startingpoints for research. Janssen et al. [43] presented an up-to-date review of perishable inventory models, whichis a complement to the review of Bakker et al. [44].Shekarian et al. [45] presented an ample review in thearena of fuzzy inventory management with the aim toidentify and classify the main achievements obtained.Recently, Kok et al. [46] proposed a typology for themulti-echelon inventory �eld. This typology is appliedto categorize and review the widespread research ofmulti-echelon inventory models under uncertain de-mand.

2.2. Inventory models with service componentsDespite the extensive literature on inventory modelsand optimization, few studies have considered serviceas a core factor or component. Recognizing the evidentneed to develop a theoretically solid and manageriallyrelevant conceptualization of inventory models, we


reviewed existing research to build on those pieces thathave discussed service issues. Speci�cally, to identifypapers that feature inventory models dealing withservices, we searched for \service inventory model" inthe Scopus database, restricting our search to papertitles so that we could �nd publications whose primaryconsideration was service in inventory models. Nopublications used \service inventory model" as a com-pound term; thus, we modi�ed this search to identifypublications including any of these three words in theirtitles. As a result, 72 papers, from 1981 to 2018,were found, and both their topics and the componentsincluded in their proposed models were reviewed. Afterexcluding the articles that did not feature any servicenotions, 42 papers were obtained that could help usdetermine how the term \service" was conceptualized.According to this analysis, in existing inventory liter-ature, service refers to the service level and plays arole as an indicator of system performance, either asa conceptual notion or a component within inventorymodels.

For example, �ve articles that linked the ser-vice level, as a conceptual notion, to other perfor-mance measures such as the accomplishment of pre-established target goals, quality, or responsiveness werefound. Thiel et al. [47] showed that service-level qualitywas a non-monotone function of the inventory record'sinaccuracy rate; Samadi et al. [48] asserted that thequality of the services o�ered to customers was asigni�cant factor that directly a�ected demand and,therefore, must be considered for developing inventorymodels. With a Monte Carlo simulation model for alegal �rm, Swenseth and Olson [49] built a professionalservice inventory chain, taking into account the needto satisfy demand, minimize costs, and maintain agood quality level, such that professional servicespersonnel were managed as inventories. Gzara etal. [50] proposed two integrated network design andinventory control models for a service-part logisticssystem to ful�ll warehouses' service-level requirementsand, �nally, Byun et al. [51] derived a probabilisticinventory model for wireless service providers.

In the remaining 37 publications, the service levelas a component of the inventory model constitutes thepredominant approach. In these existing models, theservice level denotes the rate at which performancegoals are achieved (e.g., the percentage of calls an-swered by a call center); therefore, it mainly functionsas a constraint.

The service level constraint refers to the avail-ability of the stock needed to deliver the expectedservice level at a minimum cost, which may re ect threecommon service-level measures: the stockout or readyrate �, the �ll rate �, or the cumulative �ll rate service level. According to Schneider [52] and Chen andKrass [53], the � service level re ects the probability

that the inventory on hand does not fall below a criticallevel at the end of any period; the � service levelexpresses the expected fraction of demand covered frominventory on hand in any period; in addition, the service level expresses the expected ratio of cumulativedemand that can be covered from inventory to the totalcumulative demand during the lead time, plus a reviewperiod. This measure is thus equivalent to �, exceptthat the ratio spans multiple periods rather than justone unique period.

As is shown in Table 1, most inventory modelsinclude service level as a constraint using the �ll ratelevel. In all cases, the service level constraint is imposedwhile minimizing the total cost of an inventory system.

Inventory models are also traditionally focused onsystems optimization to accomplish performance goals;however, more recent research increasingly expandsthe model restrictions to include new components,as service levels, and thus better simulate real-worldsituations. Over time, inventory models have becomemore sophisticated, especially as supply chains grow toinvolve more actors and operations facing more com-plex circumstances. All these contributions allow forcost reductions and better performance, but they alsohave neglected intangible components and customerneeds. This knowledge gap strongly indicates the needfor a wider, multidisciplinary approach to integrateservice components in inventory models.

2.3. Service research with an inventorycomponent

Service research initially emphasized goods versus ser-vices dichotomy. Services were de�ned in relationto goods and how they could be produced and mar-keted. The distinctive characteristics of services suchas their Inseparability, Heterogeneity, Intangibility, andPerishability (IHIP) were considered unique to themand, in a traditional goods-oriented logic, value wasattached to the product or service from the provider'sperspective only [54]. However, IHIP characteristicsmight be neither exclusive nor generic to services, sincethey vary across di�erent situations and conditions.Therefore, IHIP characteristics require further explo-ration from a customer perspective, particularly inrelation to how to manage them to create memorablecustomer experiences [55].

The resulting paradigm change challenges the ideathat inventories are limited to material goods [10,56].Services might be \stored" in systems, machines,knowledge, people, and networks in line with ametaphor in which \the ATM is a store of standardizedcash withdrawals. The hotel is a store of rooms"([56], pp. 123{24). In other cases, services might beroutinely inventoried before \production," purchase, orconsumption, as the case of airlines or theaters, ratherthan solely after production. \When a university hires


Table 1. Existing inventory models integrating service level components.

Authors Service component Main contribution

Schneider [52]Service level as constraint(stockout level, �ll rate level,and cumulative �ll rate level)

Evaluation of three common service-level measures

Mehrez & Ben-Arieh [71]Service level as constraint(stockout level)

Extensive multi-item inventory model withprobabilistic demand and service level constraints

Lagodimos [72]Service level as constraint(stockout level, �ll rate level,and cumulative �ll rate level)

Multi-echelon models for evaluating the serviceperformance of two-echelon divergentnetworks operating under periodic reviewstatistical inventory control echelon-basedpolicies and using either a push ora pull rationing policy

Ouyang & Wu [73]Service level as constraint(�ll rate level)

Mixture inventory model with lead time andorder quantity as decision variables.

Hillier [74]Service level as constraint(stockout level)

Multiple-period model withservice level constraints tocompare the e�ect ofcommonality in single- andmultiple-period cases

Ouyang & Chuang [75]Service level as constraint(�ll rate level)

Mixture inventory model with backorders andlost sales, where the stockout costterm in the objective function isreplaced by a service level constraint

Ouyang & Chuang [76]Service level as constraint(�ll rate level)

Mixture of back-orders and lost sales periodicreview inventory model subject to aservice level constraint, whereboth the lead time and the reviewperiod are decision variables

Chen & Krass [53]Service level as constraint(stockout level, �ll rate level,and cumulative �ll rate level)

Inventory models in which the stockout cost isreplaced by a minimal service levelconstraint that requires a certainlevel of service to be met in every period

Xu et al. [77]Service level as constraint(�ll rate level)

Approximate analytical two-location inventorytransshipment model combining theorder-quantity, reorder-point (Q, R)continuous review ordering policy with thehold-back amount, which limits thelevel of outgoing transshipments.


Table 1. Existing inventory models integrating service level components (continued).


Lee et al. [78] Service level as constraint(�ll rate level)

Continuous review inventory model consideringthe mixtures of distribution of thelead time demand including a servicelevel constraint in which the leadtime, order quantity, and reorder point aredecision variables

Chu et al. [79] Service level as constraint(�ll rate level)

Revised algorithms for mixed inventory backorderand lost sales problem in which boththe lead time and order quantity aretreated as decision variables, asdeveloped by Ouyang and Wu [73]

Lee et al. [80] Service level as constraint(�ll rate level)

Continuous review inventory model consideringthe mixtures of distribution of the lead timedemand and controllable exponential backorderrate. Service level constraint is alsoincluded, considering lead time and theorder quantity as decision variables

Liang et al. [81] Service level as constraint(�ll rate level)

A simpli�ed and theoretically rigorous algorithmto improve the weaknesses and shortcomings ofOuyang and Chuang [76]

Hung et al. [82] Service level as constraint(�ll rate level)

Re�ning Ouyang and Chuang's [76] algorithmsto provide an optimal replenishmentsolution for decision-makers

Jha & Shanker [83] Service level as constraint(�ll rate level)

A model for an integrated vendor-buyer problemto jointly determine the optimal order quantity,lead time, and number of shipments from thevendor to the buyer during a production cyclewhile minimizing total expected costs of thevendor-buyer integrated system including servicelevel constraints

Jha & Shanker [84] Service level as constraint(�ll rate level)

Single-vendor, single-buyer integrated productioninventory problem for decaying items

Tajbakhsh [85] Service level as constraint(�ll rate level)

Continuous-review (Q, R) inventory model with a�ll rate service constraint, relaxing theassumption that the distribution of leadtime demand is known

Xu & Sun [86] Service level as constraint(�ll rate level)

A multi-item, multi-echelon inventory system thatallows lateral transshipments, direct delivery,and emergency ordering following stockouts




Jaggi & Arneja [87]Service level as constraint

(�ll rate level)

Exploration of the bene�ts of just-in-time philosophy

relative to reduced lead times and setup costs in a

periodic inventory model with service level constraint,

when the protection interval demand is

normally distributed.

Joshi & Soni [88]Service level as constraint

(�ll rate level)

A model with service level constraint and controllable

lead time in a fuzzy stochastic environment in which

expected shortages are calculated using the credibility

distribution, treating lead time demand as

fuzzy stochastic.

Cheng et al. [89]Service level as constraint

(stockout level)

Proposition of two variants of a production planning

problem in the hybrid push-pull systems.

Lin [90]Service level as constraint

(�ll rate level)

A mixed inventory policy for a controlled setup

cost in the stochastic continuous review model,

involving controllable backorder rate and variable

lead time in which the stockout cost is replaced

with a service level constraint

Ma & Qiu [91]Service level as constraint

(�ll rate level)

A distribution-free continuous review inventory

model in the presence of a service level constraint

Shahpouri et al. [92]Service level as constraint

(�ll rate level)

An integrated vendor-buyer inventory model

considering the lead time and ordering cost

as decision variables. To avoid imprecision

in estimating shortage costs, the service

level constraint is considered

Jha & Shanker [93]Service level as constraint

(�ll rate level)

An integrated production-inventory model in a

batch production environment for supplying a

set of buyers, with a service level constraint

for each buyer

Hidayat et al. [94]Service level as constraint

(�ll rate level)

A two-echelon supply chain inventory model for a

single supplier and single buyer of one product,

facing a stochastic demand condition with the

reorder point as a decision variable, solved

simultaneously with the buyer's order quantity,

length of lead time for the buyer, and number of

shipments using the service level constraint in

place of the shortage cost.




Bieniek [95]Service level as constraint(stockout level)

A model considering inventory location, where thedecision is made to minimize the holding costsand supply costs of safety stock from the centralwarehouse to the customer.

Jiang et al. [96]Service level as constraint(�ll rate level)

A two-echelon inventory model with one supplier andseveral retailers to satisfy certain service levelsand minimize total inventory cost

Cheng et al. [97]Service level as constraint(�ll rate level)

Two bi-objective inventory models to minimize inventorycosts while maximizing customer service using cycleservice level and �ll rate.

Sarkar et al. [98]Service level as constraint(�ll rate level)

Extension of the model of Moon and Choi [99] withthe consideration of setup cost reductions andquality improvement. Initial investments and theservice level constraint are used to obtain theoptimal result.

Yilmaz et al. [100]Service level as constraint(�ll rate level)

Service level equation with a di�erent approach inthe forward-reserve models

Annadurai [101]Service level as constraint(�ll rate level)

A distribution-free continuous review model in thepresence of a service level constraint foroptimizing lead time by considering an extracrashing cost and ordering cost

Jaggi et al. [102]Service level as constraint(�ll rate level)

Revision of Ouyang and Chuang [76] and Lianget al. [81] for a wide range of the levels ofservice when demand during the protectioninterval (T + L) is normally distributed

Kurdhi et al. [103]Service level as constraint(�ll rate level)

An integrated production-inventory model for asingle-vendor, two-buyer problem with partialbackorder and controllable lead time underindependent, normally distributed demand amongbuyers. Service level constraint correspondingto each buyer is included to limit shortages.

Kurdhi et al. [104]Service level as constraint(�ll rate level)

Two fuzzy continuous review inventory models underservice level constraint in the case of partialbackorder and when the quantity received isuncertain, where order quantity, reorder point,lead time, and ordering cost are decision variables




Hemapriya andUthayakumar [105]

Service level as constraint(�ll rate level)

Addressing the feasibility of decreasing the orderingcost and the lost sales due to stockout in a continuousreview inventory model with shortages considering thesituation when the quantity received is uncertain. Thisinventory model considers service level constraint. Here,the lead time crashing cost is a function of negativeexponential lead time.

Jauhari and Saga [106]Service level as constraint(�ll rate level)

A joint economic lot-sizing problem under stochasticdemand for a vendor-buyer system is proposed. Here,the demand and the buyer's ordering cost are consideredto be fuzzy. The vendor's manufacturing process isimperfect and the buyer's screening process is imperfecttoo. Additionally, the vendor has the opportunity to makean investment to reduce the setup cost.

a new professor, tapes a lecture, or assigns resources fora course in a future semester, in e�ect, it is inventoryingpart of an educational service, and when studentsinternalize the values of university education, they haveinventoried the knowledge and skill base for lifelonglearning. In short, they have created human capitalthat they can draw on for their bene�t over many yearsor decades" ([54], p. 331).

Accordingly, service inventory alters the way ser-vice is conceptualized and operationalized. Followingthe de�nition o�ered by Vargo and Lusch ([54], p. 334),services are the \application of specialized competences(knowledge and skills) through deeds, processes, andperformances for the bene�t of another entity or theentity itself." Therefore, they might be considered setsof interactions that take place before, during, and afterservice delivery, through which service providers andcustomers co-create value. The interactions involvetask performance, information, and other resource ex-changes and a�ect how customers perceive the servicesas bene�ts [57].

Due to the dynamic nature of services, manyresearchers have attempted to �nd new ways to manageservice capacity, increase productivity, and enhanceservice quality while still maximizing pro�ts. Diversetools and techniques have been developed to addressthe main issues of service operations management [58].For example, queue and bottleneck management toolsfunction to manage demand, especially in settingswhere capacity is a constraint, based on the premisethat queues are inevitable. Because perceived waitingtime tends to be greater than actual waiting time, thesetechniques attempt to reduce perceived waiting time atde�ned levels of resource utilization [59]. Thus, they

emphasized the relevance of identifying key tasks thatrepresent bottlenecks. Even when queuing and bottle-neck management do not address service inventory, theidenti�cation of key tasks can highlight areas where aservice component can be inventoried.

Operations planning and scheduling techniquesfocus on �nding a balance between demand and supplyplans at all levels by forecasting demand and organiz-ing operations to match it, integrating overbooking,or stabilizing revenues to counteract customer no-shows [60]. By planning operations, the �rm canmaximize its resource utilization and minimize itscosts. Optimization is a key component, though evenif some tasks or complete services could be scheduledand performed in advance, no formal model details aprocess for inventorying tasks to increase customers'bene�ts.

Another common concept in service operationsmanagement centers on service capacity and demandusing Service Level Agreements (SLA) to lay out theterms, conditions, and penalties for the delivery ofservices between a service provider and customers. AnSLA speci�es the services to be delivered in a busi-ness relationship and exclusions; it describes how theservice provider's performance will be measured andprovides a legal structure for relationship managementincluding contract monitoring and dispute resolution.It also speci�es the remuneration for core services andexpenses incurred and o�ers a means to calculate thecost of additional (non-core) services [61,62]. A keyadvantage of SLA is that it prepares both provider andcustomer to co-create the service, with the potentialfor identifying tasks and information that need tobe performed or obtained before the service can be


delivered. However, their application has been limitedto speci�c industries.

In contrast, the yield management approach,originally used by airlines, is widely adopted to helpservice �rms in various industries manage their ser-vice capacity more pro�tably. This technique appliesinformation systems and price strategies to maximizecompany revenues and pro�t. Prices are set accordingto predicted demand levels, allowing price-sensitivecustomers who are willing to purchase at o�-peaktimes to obtain favorable prices, while price-insensitivecustomers who want to purchase at peak times alsocan do so [63,64]. Yield management uses reservationsystems to distribute demand [65]. These practices rep-resent a type of service inventory, from the provider'sperspective, because they help minimize costs andmaximize pro�ts. However, yield management does notaddress customers' needs. With focus on pro�ts, it isfrequently perceived by customers as unfair, especiallyin industries where the practices are not common [66{68]; therefore, it a�ects perceived service quality.

The increasing use of information systems alsohas made data analysis more relevant across all thepreviously mentioned techniques. Data mining, asan interdisciplinary approach, supports the discoveryof patterns in large datasets by leveraging arti�cialintelligence, machine learning, statistics, and databasesystems. The main goal is to extract informationand turn it into valuable knowledge about businessactivities (markets, employees, customer habits andpreferences, patterns, and trends); therefore, it rep-resents a very useful tool to manage capacity anddemand [69].

The contributions discussed in this section havebeen made mainly by service academics and practition-ers, looking for innovative ways to maximize servicee�ciency, quality, and pro�ts. However, in this goods-dominant logic, strategies focused on internal criteriaand customers play passive roles, mainly as recipientsand not as participants, such that their needs are rarelyeven considered. Optimization has a role, but notnecessarily focused on customer bene�ts. Despite someof these approaches that are based on the notion ofplanning or forecasting service operations, we foundno evidence that they conceptualized anticipation as aservice component to be optimized. Thus, we arguefor the need of developing a service inventory modelthat addresses service from a wider perspective as acollection of processes including intangible components(tasks and information) that can be anticipated and op-timized to maximize both organizational and customerbene�ts.

2.4. Service inventoriesAs discussed previously, the dominant view in inven-tory research implies that only physical products can

be inventoried. The lack of research on non-physicalcomponents in inventory models may be due to thedi�culty associated with modeling inventory servicetasks or information. Service inventory based on tasksor information di�ers completely from product inven-tory; therefore, the considerations in managing serviceinventory must be distinct from those in managingphysical products. Many service textbooks promotethe notion that intangibles such as services cannot bestored [70]. However, the previously noted changein the way that service characteristics (Inseparabil-ity, Heterogeneity, Intangibility, and Perishability -IHIP) have been conceptualized emphasizes the needfor an extended service inventory de�nition. Fromthis perspective, some service operations managementconcepts might be considered precedents of serviceinventories; however, no integral approach exists thattranslates these e�orts into customer bene�ts.

According to Chopra and Lariviere [9], a serviceinventory includes the processes performed before theclients' arrival, to request a service. Thus, a kind ofbu�er of service tasks or information arises to helpdeal with variability in demand and establish a quickerresponse time to clients' requests. In other words,service tasks or information might be completed bythe anticipation of demands so that organizations cancreate customer value through better response times,quality, customization, and prices. In this process,customers engage in di�erent levels of participationeither as part of the anticipation, by providing infor-mation or performing a task, or as a bene�ciary of theservice anticipation [9,10]. These arguments suggestthe potential for a new SIOM, as detailed next.

3. Towards developing a Service InventoryOptimization Model (SIOM)

The proposed service inventory model is based onthe analysis of the traditional EOQ inventory modelcomponents and key concepts from service research. Inbuilding this model, we consider anticipation, of tasksor information, as a cost; this anticipation cost is themain unit of analysis and the variable to be optimized.Thus, the selection of key tasks and information tobe anticipated represents a signi�cant decision, de-termined by the provider, depending on its industry,performance goals, and expected impact in terms ofcustomer bene�ts (quality, speed, customization, andprice). In line with the traditional EOQ model,di�erent cost categories are also considered to buildthis service inventory model, namely Service InventoryOrganization Costs (SIOC), Service Inventory Execu-tion Costs (SIEO), and Service Inventory Costs (SIC).However, as di�erent from inventorying manufacturedgoods, these sets of costs are developed to re ect theneed for organizing, executing, and preparing the use


of those anticipated tasks and information as a way ofservice inventory.

In contrast with the traditional EOQ model wherethe inventory level is changing over time requiring anaverage inventory level to be calculated, in the ServiceInventory Optimization Model (SIOM), there is noneed for \storing" tasks and information limited tophysical space; they could be virtually \stored" almostin�nitely. Therefore, there is no need to establish aparameter to determine the moment at which servicetasks and information should be anticipated again inthe service process. This always allows keeping the op-timal level of tasks and information virtually \stored",guaranteeing the appropriate service provision to cus-tomers and improving quality, customization, speed, orprice.

In this section, the arguments for identifying eachof the cost components of the SIOM are detailed next;in general, service dynamics across pre- and productionstages is captured and the operationalization of serviceinventories is enabled [9].

3.1. Service Inventory Organization Cost(SIOC)

The anticipation of tasks or information to ensure thatthe service is ready to be provided when the client needsit is crucial in competitive markets, especially becauseperceived waiting time has such a notable in uence.Long waits might even cause customers to decline tobuy the service, leading to lost sales. Identifyingwhich relevant tasks and information to anticipate andgetting organized to anticipate them can create costs,or service inventory organization costs (SIOC), whichcan be expressed in dollars per anticipation. Thatis, SIOC represents the cost of getting organized toperform each anticipation of tasks or information to beready to serve customers such as gathering resourcesrequired to ensure the tasks or information are readybefore they are required as part of the service.

3.2. Service Inventory Execution Cost (SIEC)The cost of actually performing tasks or information inadvance can be expressed in dollars per anticipated taskor information. It constitutes the Service InventoryExecution Cost (SIEC).

3.3. Service Inventory Cost (SIC)When a service is delivered, those service tasks andinformation anticipated before are used (in that ser-vice) and, thus, they are anticipated again to keepthe optimal number (of tasks and information). Thus,following the anticipation, tasks or information are\waiting" for some time before being used; then,once used, they are immediately \replaced" by newones so that they can always be ready for the nextcustomer. In this case, there is a related ServiceInventory Cost (SIC), accounted for in dollars per task

or information and per time unit. That is, SIC isthe cost incurred for having the anticipated task orinformation ready and waiting to be used as part ofthe service. As explained earlier, virtually \storing"these anticipated tasks or information always allowskeeping their optimal level needed to be ready foruse without waiting for a \reorder" level, as there isno need to establish a parameter to determine themoment when service tasks and information should beanticipated again in the service process, guaranteeingthe appropriate service provision for customers andimproving quality, customization, speed, or price.

This conceptualization represents a key di�erencefrom the traditional EOQ model, where the inventorylevel is changing over time requiring an average inven-tory to be calculated due to physical space limitationsrelated to tangible goods, thus requiring the de�nitionof a reorder parameter.

3.4. Service Demand (SD)Service Demand (SD) re ects the total number ofservices performed to satisfy customers' requirements.Every time a customer requests a service, some tasksmust be performed, and information must be used tocover the SD. Based on the goal to be achieved in termsof customer bene�ts, SD might include a predeterminednumber of anticipated tasks or information. Thus, SDis expressed as the total tasks or information per timeunit.

3.5. Quantity of Service Inventory (QSI)The decision variable in the service inventory model isthe quantity of service inventory (QSI) that is equalto the quantity of tasks or information required to beconducted in advance to be ready when the service isrequested by the customer so that the service providercan co-create value with customers.

3.6. Total Service Inventory Cost (TSIC)The Total Service Inventory Cost (TSIC) comprisesthree category costs. First, the total SIOC of allanticipations during a planning horizon (e.g., a year)is the product of the cost of getting organized toperform one anticipation (SIOC) and the number ofanticipations required on that time horizon. Thenumber of anticipations is equal to the total SD dividedby the QSI ( SD

QSI ). Therefore, total SIOC can beexpressed as follows:

[SIOC][SD]QSI

:

Second, this paper includes the TSIC of tasks orinformation performed in anticipation, which is theproduct of the SIC and the QSI. Each anticipationis conducted and \stored," waiting to be used aspart of the service and, then, is immediately replaced;


therefore, this cost is always recurring. The TSIC oftasks or information is therefore determined as follows:

[SIC][QSI]:

Third, the total SIEC of all anticipations is cal-culated by multiplying the SIEC associated with eachanticipated task or information to satisfy customers'SDs. Formally,

[SIEC][SD]:

Combining these three cost categories, we canderive the TSIC function to be optimized (TSIC) asfollows:

TSIC(QSI) =[SIOC][SD]

QSI+ [SIC][QSI]

+[SIEC][SD]:

3.7. Service Inventory Optimization Model(SIOM)

The preceding function contains a unique decisionvariable, namely the QSI. Simplifying the SIOM withdi�erential calculus, we obtain:

QSI =r

[SIOC][SD]SIC

:

Therefore, the SIOM indicates that the optimal quan-tity of service tasks or information to be performed inanticipation depends on the square root of the productof the cost of anticipating tasks or information and thetasks or information required to satisfy SD, divided bythe SIC.

4. Contextualization of the SIOM

To highlight the relevance of the proposed new SIOM,contextualization is provided using general examples ofdi�erent service tasks: preparing content for streaming,enabling mobile banking transactions, preparing prac-tices and tools to provide business consultancy, andcustomizing high-end hotel room facilities accordingto customer needs, preferences, and habits. Servicesare becoming increasingly reliant on information and,due to technological advances, they include tasks thatcan be performed in advance to improve quality,reduce price, increase customization, and speed upservice provision. Di�erent inventory costs and modelcomponents for each of the examples are shown inTable 2 in detail. Each contextualization case begins bydetailing the internal criteria used to select the tasksor information to be anticipated. Then, each modelcomponent is contextualized for a range of selectedcases. The external criteria for optimization (quality,customization, speed, and price) then lead to the briefdescriptions of the functions to be optimized. Finally,in Table 2, how customers participate in advancing

tasks or information, as well as in the service itself,is explained.

4.1. Anticipating part of the serviceThe selection of tasks and information to be anticipateddepends on the characteristics of the industry, the costsassociated with anticipation, expected business perfor-mance, and the expected customer bene�ts (quality,price, customization, and speed). This process isrelevant, in that the anticipation of selected tasks orinformation represents the service components to beoptimized.

For example, for content streaming, the selectedanticipation is a speci�c content (movie, song) that isready to be delivered, 24/7. In this industry, havingsongs, movies, and other digital contents ready priorto customer demand is an absolute necessity, becausecustomers expect a quick response and the exibilityto customize their content selection according to theirpreferences. For mobile banking, anticipation entailsbanking operations that are ready to be performedthrough mobile devices. Similar to content stream-ing, this anticipation re ects industry practices andcustomer expectations of availability, security, andcustomization.

In a business consultancy setting, the tools andpractices must be ready to be used before the customerrequires them, which o�ers a representative example ofhow work can be performed in advance and stored, evenwhen customers have completely di�erent pro�les andneeds. Finally, in the high-end hotel example, the costsare associated with creating customized rooms thatmatch speci�c customer preferences, needs, and habits,even before customers' arrivals. All these practices arerequired, because they largely determine customer sat-isfaction, o�ering a representative example of customerparticipation relevance in service anticipation. In all ofthese cases, information technology plays a key role asan enabler of service anticipation.

4.2. Service Inventory Organization Cost(SIOC)

The SIOC, as de�ned previously, is the cost of havingall the resources required to perform each anticipationof task or information and, thus, advance the serviceto be delivered to customers. In content streamingservices, this cost may entail paying for the rightsto the movies or songs to be streamed, includingcareful selections of the premiere and classical movies,songs, or videos. The cost of these rights likely variesaccording to factors such as novelty and demand.The cost of being prepared for customers' contentselections also involves developing robust informationtechnology infrastructure (e.g., servers, software, andinformation systems) that can store and manage vastamounts of digital content and enable customers to


Table 2. Service inventory model: components, criteria, contexts, and functions.

Model components/case (optimizationinternal criterion)

SIOC SIEC SIC SD QSI

Optimizationof externalcriterion

(customer)

Functionoptimized

with model(internal +

externalcriteria)

Customerparticipation

Content streaming

(adding/eliminating,

accessing, streaming)

Process to deliver

speci�c content to

watch or hear

through streaming,

24/7 (e.g., Net ix,

Spotify)

Cost of paying for

movie/series/song

rights to stream

content

Cost of developing

a system to select

and customize

preferences

Cost of developing

an ICT

infrastructure to

store and deliver

high-quality digital

content 24/7

Cost of

uploading

content for

streaming

Cost of

categorizing

uploaded

content

Cost of

maintaining

content to be

available

online

Type and

amount of

content

available at

any time for

customer

access

Optimal

amount of

content to

buy and keep

available

Lower price

Greater

customization

Anticipating

content

availability to

allow

customers to

select it freely

Customer

provides

information

about

preferences

and habits.

Customer

selects content,

place, time,

and frequency

of use,

according to

suggestions.

Mobile banking

(accessing,

validating, operating,

and securing)

Process to deliver

banking operations

(consultations,

transfers, and

payments) through

mobile devices 24/7

Cost of developing

banking policies

for mobile devices.

Cost of developing

a mobile

operations

portfolio

Cost of developing

ICT infrastructure

to provide safe,

fast, reliable

mobile banking

operations 24/7,

including security

elements

(�rewalls, tokens,

and cards)

Cost of

making

banking

information

available

through

mobile

devices

Cost of

maintaining

banking

information

available

online

Amount of

information

available at

any time to

perform

customers'

mobile

transactions

Optimal

amount of

information

available at

any time to

perform

mobile

transactions

Faster speed

Anticipating

information

availability to

allow

customers to

transact with

mobile

devices

Customer

downloads and

con�gures

mobile

banking apps

Customer

decides to

access and

exchange

information to

perform

transactions at

chosen place,

time, and

frequency

Notes: ICT = Information and Communication Technology.

create digital accounts and customize their preferences.These aspects must be ready in anticipation of the tasksand information associated with streaming services.Similarly, for mobile banking, the cost of preparationfor customers' selection of operations includes thedevelopment of robust information technology infras-tructure; however, it features the costs of adequatesecurity policies for banking transactions.

In business consultancy services, the cost of or-ganizing the service inventory may involve the iden-ti�cation and selection of relevant tools and practicessuch as simulators, surveys, or business analytics toolsthat might facilitate the consultancy process, which

have costs to acquire. In this case, an importantanticipation cost relates to the selection and hiring ofspecialized sta�, who can make an adequate balancebetween business customer needs and selected toolsand practices. The creation of a mechanism to enabledata transfers and sharing is another important costfor these services.

Finally, for high-end hotel services, preparationfor customizing room facilities involves the cost ofdeveloping a set of personalization options and policiesfor customers, re ecting the elements most valuedby customers and, also, a careful evaluation of thelimits of what a hotel can o�er without a�ecting other


Table 2. Service inventory model: components, criteria, contexts, and functions (continued).

Model components/case (optimizationinternal criterion)

SIOC SIEC SIC SD QSI

Optimizationof externalcriterion

(customer)

Functionoptimized

with model(internal +

externalcriteria)

Customerparticipation

Businessconsultancy(diagnosing,advising, andassessing)

Process of preparingtools and practices toprovide businessconsultancy servicesto corporate clients

To identify andselect standardtools and practicesfor businessconsultancy

Cost of creating abusinessinformationdatabase (marketsshare, trends, etc.)

Cost of creatingdata sharinginfrastructure

Cost of gettingspecialized sta�

Cost ofselection,purchase, andstorage ofbusinessinformation

Cost ofdeveloping acatalogue ofstandardtools andpractices fordiagnosing,advising, andassessingcompanies

Cost oftrainingconsultants tousestandardizedtools andpractices

Cost ofspecializedsta�

Cost ofmaintainingbusinessinformationdatabase

Cost ofmaintaininga sharinginformationplatform

Type andamount oftools andpracticesrequired tosatisfybusinesscustomers'demands

Optimalnumber oftools andpracticesavailable toconductconsultancy

Higher quality

Faster speed

Anticipatingsta� andresource(information,practices, andtools)availability toprovidebusinessconsultancyservices

Businesscustomer doesnot participatein anticipation

Businesscustomerde�nes goalsto be achievedduring theconsultancyprocess.

High-end hotels(booking, registering,hosting)

Process ofcustomizing roomsaccording tocustomerpreferences, specialneeds, and habits(e.g., Ritz-Carlton,Marriot)

Cost of developingcustomizationoptions andpolicies forcustomers.

Cost of developingICT infrastructureto support onlineinformationexchange, storage,and transactions

Cost of developinga exibleoperation policythat allows forspecial requestsand empowersemployees

Cost ofpreparinghotel roomsaccording tocustomerpreferences,specialneeds, andhabits

Cost ofmaintainingcustomizedrooms readybut not beingused

Number ofcustomizedrooms readyto be used

Optimalnumber ofcustomizedrooms readyto be used

Highercustomization

Higher quality

Anticipatingroomavailabilitywith speci�crequirements

Customerprovides dataabout the stay(dates, numberof guests),personal roompreferences,special needs,and habits.

Note: ICT = Information and Communication Technology.

customers. It also might entail costs to acquire speci�cproducts and items (e.g., pillows, cradles, wheelchairs,and sheets). An information system is also required tosupport the registration and track customers' requestsand preferences.

4.3. Service Inventory Execution Cost (SIEC)The SIEC is the actual cost of performing the selectedservice anticipation. In the case of content streamingservices, it may include tasks related to categorizingand uploading digital content. Even if this process


can be automatized, it still involves some cost, whichis likely the key to making customer selection moree�cient. Even if mobile banking transactions can beautomated, the cost of executing the de�ned policiesand creating an operations portfolio for each customersimilarly must be considered. For business consultancyservices, the cost relates to the development of stan-dardized processes to apply previously selected toolsand practices to di�erent business scenarios, includingcosts to train consultants in the use of standard toolsand processes. Finally, high-end hotel services incurcosts to adapt room facilities to special needs andcustomer requests, such as adding pillows, adaptingthe temperature, changing the sheets, or installing acradle before a customer's arrival. It may also involvethe cost of employees who are required to make theseadaptations.

4.4. Service Inventory Cost (SIC)Once the task or information has been anticipated, the\waiting" period begins before delivering the service.For digital content streaming and mobile bankingservices, it likely includes the cost of maintainingcontent or information online. Technology costs havesteadily decreased; however, this cost might still entailthe potential risk that customers do not select somecontents for which the streaming service has paid ordo not need extreme security policies that the bankhas established. In the business consultancy setting,the cost instead refers to maintaining business toolsand practices (e.g., licenses, memberships, and access)and keeping highly specialized sta� \on call" to o�erdiagnosis and advice to customers. For high-end hotelservices, these costs entail customizing room facilitiesaccording to the needs and preferences of speci�ccustomers, ready to be used. The cost of not havingthat room available for other customers is also featuredin the SIC.

4.5. Service Demand (SD)In this context, SD is the amount of tasks or informa-tion required to satisfy customer requests. Consideringstreaming content services, SD is not only the quantitybut also the type of content that must be availablefor customers to select among, which in turn requiresa deep analysis of customer demand patterns andexternal information such as new contents and formatsthat are likely to be released in the future. In mobilebanking, it represents the mobile operations and datarequired to ful�ll customers' requests. For the businessconsultancy, SD represents the number of consultants,tools, and practices needed (tools con�gured and em-ployees trained) to provide service for customers. Forexample, if a business customer requires advice ontalent management, the consulting company needs tohave a human resources specialist available. Finally,

SD relates to the number of rooms that needs to beavailable for customers' use in the hotel example.

4.6. Customer participation and impact ofanticipation

Anticipation should have e�ects on quality, speed,customization, and price; in the contextualizationpresented here, customers can function as either par-ticipants or bene�ciaries. In the content streamingsetting, customers participate by providing informationabout their preferences and habits and, as a result,they bene�t from paying lower prices for contentsdue to economies of scale and from receiving greatercustomization related to when, where, and how toaccess the selected contents. In mobile banking,customer participation is mainly a bene�ciary and ableto access and exchange information to perform quickmobile transactions anytime and anywhere. Customersof the business consultancy service do not participatein anticipation; however, as bene�ciaries, they receivehigher service quality and speed, because the con-sultants have been previously trained and preparedwith the most relevant tools and practices to respondto these customers' demands. When the high-endhotels prepare room facilities according to customers'needs and preferences, they provide the customers withhigher quality and customization, contributing to thecreation of memorable experiences, especially if thosecustomers have already provided information abouttheir preferences in previous visits so that the hotelcan anticipate some tasks without any explicit requestfrom customers.

5. Limitations and further research

With its theoretical nature, this research o�ers the�rst step toward a service inventory model that canoptimize speci�c tasks or information to be executedin advance to enable the provision of highly valuableservices for customers. Further research can continuethese e�orts, and we suggest several avenues here.First, it would be interesting to empirically studywhether any signi�cant di�erences arise in the actualperformance of companies that apply traditional inven-tory models versus those that shift and start using theproposed SIOM.

Second, further research could measure the im-pacts of the services SIOM for customers, as well ashow they in uence the value co-creation process.

Third, it would be interesting to develop SIOMsfor di�erent settings, with varying constraints in termsof time, service quality, and human and technicalresources.

Fourth, research should apply the model in di�er-ent contexts and conduct empirical studies in variousareas (e.g., hospitality, healthcare, and transporta-


tion). These empirical results can form a basis for there�nement of the model.

Fifth, the proposed inventory model is developedbased on the total cost function minimization. Min-imizing these costs does not always produce higherpro�ts though; therefore, further research might ex-plore the optimization of service inventory models byconsidering the maximization of the pro�t function.

Sixth, in addressing the optimization of serviceoperations, organizations are considered as isolatedunits here. However, companies are part of theirsurrounding business context, in which they interactwith other entities. Therefore, an integrated approachmight support the development of the service inventorymodel for an integrated supply chain, with severalmembers in each stage of the chain.

Seventh, as a natural next step, it is suggestedthat the service-based inventory model be integratedwith a traditional, goods-based inventory model. Most�rms rely on both goods and services; thus, an inte-grated inventory optimization model is greatly requiredand can even transcend the goods versus services divideto focus instead on value and value creation for di�erentstakeholders. In this case, researchers might includenot only �rms and their customers but also corporatesocial responsibility questions, which are becomingincreasingly important for all businesses.

Eighth, research should zoom in on speci�c com-ponents in the model such as the SIC and quality ofservice inventory.

Ninth, another interesting topic for future re-search is making speci�c optimization e�orts for di�er-ent stakeholders such as customers, employees, owners,or shareholders, as well as service inventory optimiza-tion over di�erent time horizons and across disparatedemand situations.

Finally, future research might focus on the devel-opment of service design/redesign tools to integrate thenotion of service inventory optimization into di�erentstages of the whole customer experience journey.

Overall, we acknowledge that this manuscriptaims to break new ground in the inventory theory. Weintend to expand current knowledge so far limited to in-ventorying physical goods by setting a new frameworkfor services. This is only the �rst step towards manyother future contributions. It is expected that theproposed conceptual model be re�ned, enriched, andempirically tested for fueling additional publications.

6. Conclusions

This study developed a Service Inventory OptimizationModel (SIOM) based on the Economic Order Quantity(EOQ) model with a focus on optimizing anticipatedintangible components (tasks and information) whileintegrating internal (provider) and external (customer)

perspectives. Therefore, this paper presented a mul-tidisciplinary approach that integrated EOQ inven-tory contributions and service management researchto propose a new service inventory model to optimizesome part of the service (tasks or information) thatcan be anticipated prior to customer demand. Theexamples we o�er on the contextualization of themodel also reveal how the proposed service inventorymodel might contribute to the optimization of serviceperformance, with impacts on the customer's bene-�ts (i.e., service quality, speed, customization, andprice). The application of the proposed model toidentify those parts of the service to be anticipatedand optimized provides companies with good insightsto develop and deploy the optimal level of resources torespond to customers' demands, including technology,highly-specialized employees or redesigned processesenabling customer participation, to name a few. Akey, distinctive characteristic of the proposed serviceinventory model is its focus on optimization as aprocess to provide bene�ts for both the �rm and thecustomer based on a paradigm that goes beyond thetraditional goods/services divide.

Finally, this service inventory optimization modelcan help practitioners and managers who seek toimprove their business operations by reducing costs,increasing pro�ts, and enhancing service levels. Withthis proposed service inventory optimization model,decision-makers can increase the customization of theirorganization o�ers and provide additional services,having an impact on customer satisfaction. Serviceinventory optimization might also be considered as asource of di�erentiation for companies as they cannotbe easily imitated. In addition, by applying this model,�rms can provide reliable services more rapidly, therebyenhancing the productivity of their skilled workers andthe value of their services.

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Biographies

Leopoldo Eduardo C�ardenas-Barr�on is currentlya Professor at School of Engineering and Sciences atTecnol�ogico de Monterrey, Campus Monterrey, M�exico.He is also a faculty member at the Department ofIndustrial and Systems Engineering at Tecnol�ogico deMonterrey. He was the Associate Director of theIndustrial and Systems Engineering programme from1999 to 2005. Moreover, he was also the AssociateDirector of the Department of Industrial and SystemsEngineering from 2005 to 2009. His research areasinclude primarily related to inventory planning andcontrol, logistics, and supply chain. He has publishedpapers and technical notes in several internationaljournals. He has co-authored one book in the �eld ofsimulation in Spanish.

Javier Reynoso is a Professor of Service Managementat EGADE Business School, Monterrey, Mexico. Hismain interest is to promote and develop research and


academic activities in the service �eld in Mexico andLatin America. Javier is the co-author of the �rsttextbook on Service Management written in Spanish,used in 20 countries. Listed in the Top 15 MBA Profes-sors in Latin America, by Revista America Economiain 2012, he has received the ITESM's Teaching andResearch Faculty Award for his contributions to theservice sector on three occasions: 1999, 2005, and 2013.He received the Christian Gr�onroos Service ResearchAward 2013 in recognition of his career achievementsand signi�cant originality in service research. In 2016,Javier Reynoso was awarded International Fellow ofCTF -Service Research Center- at Karlstad Universityin Sweden for his signi�cant career contributions inservice research.

Bo Edvardsson is a Professor and Founder, CTF-Service Research Center and Vice Rector, KarlstadUniversity, Sweden. In 2008, he received the RESER

Award \Commendation for lifetime achievement toscholarship" by The European Association for ServiceResearch and, in 2004, The AMA Career Contributionsto the Services Discipline Award. In 2013, Bo was ap-pointed Distinguished Faculty Fellow of the Center forExcellence in Service, University of Maryland and Hon-orary Distinguished Professor of Service Management,EGADE Business School, Monterrey Tech, Mexico. Hisresearch includes new service development and inno-vation, customer experience, complaint management,service eco-systems, and transition from product toservice in manufacturing.

Karla Cabrera is a PhD Student at EGADE BusinessSchool, Monterrey, Mexico. She has contributed tomore than 10 academic publications in service re-search. Her main interests are service management,service ecosystems, and services at the base of thepyramid.

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