A Vertical Software Prototype

Recommendation and Configuration of Value-Added Services for Repurposing Electric Vehicle Batteries A Vertical Software Prototype

Benjamin Klör

Markus Monhof

European Research Center for Information Systems University of Münster Münster, Germany [email protected]

European Research Center for Information Systems University of Münster Münster, Germany [email protected]

Daniel Beverungen

Sebastian Bräuer

Faculty of Business Administration and Economics University of Paderborn Paderborn, Germany [email protected]

Chair of Information Systems and Enterprise Modelling University of Hildesheim Foundation Hildesheim, Germany [email protected]

Abstract—Due to degradation effects, electric vehicle batteries (EVBs) have a lifetime of approximately ten years in electric vehicles (EVs). Recycling EVBs is inefficient and ecologically undesirable if the batteries can be repurposed to second life scenarios. Decision support systems (DSSs) can be implemented to identify the best second life scenario for which to repurpose individual batteries. However, the properties of EVBs as used products can cause information asymmetries that challenge or even disrupt the market for used EVBs. Providing value-added services with used EVBs is one strategy to mitigate the information asymmetry. Guided by seven design principles, we develop and demonstrate a vertical software prototype for configuring energy storage solutions (ESSs), consisting of used EVBs and value-added services. Core of the system is an item-based collaborative filtering recommender system. The prototype can serve as a blueprint for a class of information systems to repurpose other used goods as customer solutions. Keywords-Recommender System; Item-Based Collaborative Filtering; Decision Support; Electric Vehicle Battery; Design Science Research; Prototyping

I.

INTRODUCTION

Because of cell degradation effects in lithium ion batteries [1], [2], electric vehicle batteries (EVBs) are expected to reach their end of life in automotive applications after circa ten years of usage, since the batteries then do not any more provide enough capacity to drive sufficient distances with the electric vehicles (EVs) [3], [4]. Since the automotive original equipment manufacturers (OEMs) are required to take back used batteries and sales figures of EVs are continuously rising, the OEMs will have to deal with large numbers of used EVBs soon. Repurposing used EVBs is supposed to generate additional revenues for the OEMs [4], [5]. With raising numbers of used EVBs, a prospering

business might emerge in the near future. Furthermore, the additional revenues might lead to a reduction of the initial costs for EVs, since the battery is responsible for a major proportion of the EVs’ prices [5]. However, current research on repurposing EVBs is mainly focused on the technical feasibility of different second life applications, which can be classified as stationary (e.g., buffer storages in solar power farms), semi-stationary (e.g., partly mobile power storages on construction sites), and mobile (e.g., forklifts and light EVs) applications [6]. For instance, research initiatives on stationary applications are dealing with small-scale (e.g., Efficiency House Plus [7]), medium-scale (e.g., Hamburg Hafencity [8]), and large-scale (e.g., 2nd-use battery storage in Lünen [9]) second life battery energy storage systems. According to lemon market theory [10], customers face high uncertainty due to information asymmetries on markets for used products, such as used EVBs. Signaling the quality of a used EVB can help to mitigate this information asymmetry [11]. Providing value-added service that accompany an EVB, such as warranties (e.g., performance guarantees and revocation of the battery) and other services that document the involvement of the provider into a cocreation of value (e.g., transportation, installation, and maintenance) can be such signals. Beyond mitigating an information asymmetry, services might be needed to fit an EVB with the requirements of a second life scenario or to comply with legal regulations for handling EVBs as dangerous goods. Therefore, a provider of repurposed EVBs will likely supply customers with energy storage solutions (ESSs)—comprising used EVBs and value-added services— as bundled offers [12]. A mass customization strategy can be pursued to provide efficient, valuable, customer solutions (i.e., ESSs) that solve a problem faced by a customer. However, configuring customer solutions is a complex problem, since the number of alternatives increase exponentially with each additional

good or service, which can overstrain the mental information processing capabilities of human decision-makers. Collaborative recommender systems [13]–[15] can be used as interactive decision aids to handle this complexity. The research objective of this paper is to design, implement, and demonstrate a vertical software prototype [16], [17] for configuring ESSs. Core of the system is an item-based collaborative filtering recommender system that compares ensembles of EVBs and second life scenarios with similar ensembles that have been sold before. Based on a similarity measure, the system then proposes value-added services that a decision-maker should offer together with an EVB. The prototype goes beyond previous implementations of information systems that were designed for configuring customer solutions in a mass customization approach. Other researchers can use the software prototype as a blueprint for designing their own recommender systems for value-added services to accompany used goods in second life scenarios. The remainder of the paper is structured as follows. In Section 2, we review information asymmetries related to the repurposing of used EVBs, existing solutions for configuring and pricing value-added services, and recommender systems as interactive online decision aids. In Section 3, we present and justify our research method. After designing a domainspecific service configuration process in Section 4 and developing seven design principles for a configuration software to create ESSs in Section 5, the vertical prototype is presented in Section 6. Because a proper evaluation of the prototype requires empirical service purchase data that is presently not available but will be generated by a conjoint analysis soon, Section 7 conceptualizes two evaluation strategies, which are going to be executed in summer 2017. Section 8 concludes the paper. II.

RELATED WORK

A. Asymmetric Information in Repurposing Used Electric Vehicle Batteries Even if a used electric vehicle battery might no longer meet the requirements of an automotive application, researchers and practitioners agree that EVBs can be repurposed in less demanding second life scenarios [18]– [22]. Since the used EVBs are supposed to still have 70%80% of their original capacity left, they can, for example, be repurposed as stationary energy storage systems to limit the load on the public power grid caused by charging infrastructure [23], to support residential services such as load-shifting [24], or to increase the local consumption of renewable energy [19]. The EVB’s final end-of-life is currently expected when the battery reaches around 50% of its original capacity. In between, second life scenarios bear the potential of generating additional revenues and thus might contribute to lowering an EV’s total cost of ownership [5], [25] and environmental footprint. Even if mature business models or a market for trading used EVBs are all but emerging, in the future there will likely be specialized second life manufacturers to repurpose and distribute used EVBs in a close cooperation with an automotive OEM [26].

However, as used goods, EVBs can be subject to hidden characteristics that require establishing mechanisms for overcoming an asymmetric distribution of information between sellers and buyers of used batteries. Without mechanisms for reducing uncertainty about the quality of used goods, lemon market theory [10] predicts market failure. The market for used EVBs might evolve into a lemon market, since potential customers cannot fully comprehend the quality of a used EVB and, hence, if it is applicable to the customers’ second life scenarios. Because of individual aging effects during the automotive application like specific driving habits or varying environmental conditions (e.g., temperature and humidity), each EVB degrades differently [4], [27]. Although the seller may acquire and disseminate data on an EVB’s condition (e.g., by manual tests or recording during the automotive application) to the customers, these data are likely to be too opaque for them. Consequently, the seller has an information advantage over potential customers, which is why the seller must provide transparency to reduce existing information asymmetries and to avoid market failure. Configuring customer solutions consisting of used EVBs and value-added services, like performance guarantees or revocations, might be a way out of this dilemma, since such services signal a high battery quality and a low purchase risk to the customers [6], [26]. Further to be offered services strongly depend on the used EVBs’ particular applications and the targeted services. As depicted for residential battery energy storage systems that are used in combination with rooftop photovoltaic panel installations on private homes, also services that build on the data generated by the customers’ usage of the battery system might be offered [28], such as the hosting of online dashboards for self-monitoring or consultancy services for optimizing the customers’ energy use. B. IT Artifacts for Modeling, Configuring, and Pricing Customer Solutions Mass customization is a strategy for efficiently dealing with diverse customer demands based on bundling re-usable modules that can be supplied at almost the cost of standardized offerings [29]. In their basic form, mass customization strategies involve two activities: modular design and configuration. In modular design, reusable goods and service components need to be engineered and specified [30], [31]. In customization, the pre-defined modules are bundled to form customized solutions that fit the idiosyncratic needs, wants, and demands [32], [33] of particular customers. While mass customization can take different forms [34], IT artifacts have been designed and implemented to support users with the modular design and configuration of customer solutions. H2-ServPay [35]–[37] supports modeling goods and service modules, bundling them into individual offerings that fit particular customers’ demands, and finding a price that exceeds the provider’s cost and is lower than a customer’s willingness-to-pay. Moreover, a collaborative filtering recommender system for value-added services was designed—however, not implemented—to incorporate data from a conjoint analysis [38]. E³service is an ontology-based

approach for automatically bundling multi-supplier eservices, by comparing the needs, wants and demands of customers with the benefits and added-value offered by service modules [39], [40]. Although e³service is a sophisticated tool, its designers have identified substantial potential for its further improvement [41]. Further research in the area has also been called for, “[…] to enhance the possibilities for modularization, standardization, contextualization, and re-configuration of service components and resources” [42]. C. Recommender Systems as Interactive Decision Aids Recommender systems are heavily used as interactive online decision aids [43] in e-commerce applications. Interactive online decision aids provide customers with recommendations and enable them to deal with “[…] multialternative, multi-attribute preferential choice [tasks]” [43], citing [44]. Recommender systems can be classified by their applied technique into collaborative filtering, content-based filtering, and hybrid [45] recommender systems. Additionally, there are less common types, like demographic, utility-based, and knowledge-based recommender systems [46]. Recommendations derived from collaborative filtering are based on preferences other customers have already expressed for certain items. Customers with similar preferences on one or more items are likely to have similar preferences on other items, too [14]. Thus, items liked by similar customers (neighbors) are recommended. In collaborative filtering, the similarity between customers is solely based on preferences on items rather than on certain properties of an item. Preferences can be expressed explicitly (e.g., by ratings) or implicitly (e.g., by previous purchases) [47]. Since user-to-user (user-based) collaborative filtering tends to have scalability problems with a growing customer base [14], a common variant is item-to-item (item-based) collaborative filtering that uses similarities between items instead of customers [15], [48] and allows a pre-computation of similarities [14]. However, both approaches suffer from the so-called “cold-start problem” [47] if not enough contextual information about users or items, such as historical purchases or ratings, is available. In contrast, content-based recommender systems are less prone to missing data by providing data on the items’ properties through descriptions. Thus, the disadvantage of content-based techniques refers to the provided items’ descriptions that must be available and must reveal the similarity between items. Usually, (standardized) keywords or tags are used to describe the items’ contents [45]. The items are recommended with regard to the preferences expressed by the user. These preferences can either be expressed by the users directly or can be derived from their previous behavior (e.g., from historical purchases) indirectly. Hybrid recommender systems combine collaborative filtering and content-based filtering to avoid the limitations of each individual technique. Their results are based on combining results from separate systems, including contentbased approaches in collaborative systems (or vice versa) or building recommendation models using both techniques [45].

III.

RESEARCH METHOD

Design science research [49], [50] contemplates the iterative design and evaluation of IT artifacts [51], such as software implementations. In line with literature on software prototyping, we set out to develop a configuration software for ESSs, which is supposed to be used by a second life manufacturer, as a vertical software prototype. Such prototypes provide a crosscut through all architectural layers (including user interface, business logic, and business objects) and present the implemented core functionalities [16], [17]. An important rationale behind vertical prototyping is to develop knowledge on a software system’s objectives, requirements, and usability early in a project. To purposefully design the prototype, we reviewed literature on repurposing used EVBs, configuring customer solutions, and recommender systems, culminating in seven design principles (DPs). Moreover, in line with decision theory [52], we conceptualized a service configuration process on which the prototype is based. Developed in C#, the prototype was iteratively designed and implemented in an agile software development project. While we designed a first instance of the system for configuring ESSs, the software component can be used as a blueprint to design systems for repurposing other used goods, too. Please contact the authors for accessing the software prototype. IV.

PROCESS FOR CONFIGURING ESSS

Configuring adequate ESSs—consisting of used EVBs and value-added services—is the purpose and scope of the system. Since the related decision problem involves creative solution strategies and—in a short-term to mid-term perspective—its recurring frequency is expected to be low, the problem is a non-programmed decision task [52]. Subsequently, the decision task is conceptualized in line with the four generic phases of a decision process: (1) intelligence (e.g., gather information), (2) design (create decision alternatives), and (3) choice (pick decision alternative) to reach an (4) implementation (realize the selected decision alternative) of a used EVB in a second life scenario (Fig. 1). Since we focus on identifying value-added services that complement a used EVB in its second life, we assume that other software components—such as a decision support system (DSS)—are in place to identify the set of all second life scenarios in which a used EVB can be repurposed for technical reasons (step i). For starting the configuration of the ESS, mandatory services for repurposing EVBs must be selected (step ii). For instance, EVBs are dangerous goods so that their transportation must be conducted by logistics specialists. Based on historical data on services that have been sold with similar EVBs fitting to similar second life scenarios, value-added services that complement an EVB are recommended to the seller (decision-maker) of the ESS (step iii). On top of mandatory services, the decision-maker then considers including recommended and optional services to configure variants of ESSs (step iv). Each of these solutions represents an offer made to a customer. After receiving an order from a customer (step v), the selected ESS is implemented at the customer’s site (step vi).

For each assignment (i) Identify set of EVBs matching to scenarios

(ii) Select mandatory services

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(v) Invite customer’s selection of the var. offered (order)

(vi) Implement the selected customer solution

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Figure 1. Configuration process of ESSs in line with Simon’s [52] decision process.

V.

DESIGN PRINCIPLES FOR AN ESSS’ CONFIGURATION SOFTWARE

Based on the previously conceptualized configuration process, we derive seven design principles that must be implemented by a configuration software for creating ESSs. The design principles are introduced by successively elucidating their function and contribution to the abstract phases of the decision process. A. Intelligence Phase Design Principle 1. The configuration software is part of a model-driven DSS [53] that also supplies a set of feasible assignments of batteries and second life scenarios [54]. These assignments are identified by a matching component, which utilizes binary integer programming and technically constrains the matching of batteries to scenarios. Since used EVBs must comprise certain technical characteristics in terms of voltage, current, power, and capacity levels to make them work in specific scenarios, it is a prerequisite to solve the matching problem and to provide a set of technically feasible assignments for configuring any ESS [55]. Design Principle 2. Repurposing an EVB might require value-added services. Reasons include complying with legal regulations or reducing the information asymmetry between providers and customers. Such mandatory services are defined by configuration rules and are identified by a configuration rules engine. For instance, every battery must be installed at the customer’s site, which clearly exceeds most of the customer’s capabilities. Hence, a professional installation service must be provided. Moreover, the rules engine is able to identify if services exclude each other. The simplest case of such an exclusion is that only one service per category can be selected, i.e., “fast delivery” and “standard delivery” arguably exclude each other. Consequently, the configuration rules engine returns a list of value-added services that must be supplied with a used EVB. Design Principle 3. To cope with the user’s information overload that is inherent to the service configuration process, a service recommender system—as the core component of the ESSs’ configuration software—automatically recommends value-added services that are likely to be purchased by individual customers. The recommender system should implement item-based collaborative filtering to recommend services that have been bought in similar assignments of EVBs to second life scenarios (items) in the past. However, there are several problems in identifying such similar assignments, since each used EVB is arguably an individual product in terms of its aged technical characteristics and its entire aging history. Furthermore, assigning (selling) an individual EVB to a second life scenario with its individual scope (e.g., home buffer storage

vs. emergency power supply) and individual technical requirements makes the whole assignment (recall, this is what we consider to be an item) highly individual, too. To identify similar items anyway, the service recommender system should utilize a cluster-based approach, since cluster analysis promises grouping similar objects [56].1 The system must perform two successive steps: (1) preprocessing (identifying) similar items and (2) computing (highly) likely service recommendations to a new item (Fig. 2). First, the preprocessing step identifies historical items (sold in the past), which comprise batteries and scenarios similar to the assignment for which recommendations are required. For that to happen, a cluster analysis (based on the k-means algorithm [57] using the squared Euclidian distance over the technical deviation) is conducted to identify clusters of similar (historical) items, each residing in a threedimensional clustering space (clustering 3-space). A clustering 3-space is defined by the relative technical deviation of the batteries’ characteristics from the scenarios’ optimal requirements in terms of deviating voltage (U), current (I), and capacity (C). For instance, an assignment containing a battery (U=316.8V; I=172.5A; C=11.4kWh), which feasibly matches to a scenario (U=331.8V; I=105.49A; C=16kWh), deviates from the scenario’s requirements by (∆U=-5%; ∆I=39%; ∆C=-40%). This assignment is recorded in the clustering 3-space with the coordinates (-5, 39, -40). All historical items together shape the respective clustering 3-spaces (Fig. 3). Since a new assignment cannot be compared to historical items comprising a different battery type (BT={BT1,BT2,…,BTn}) or scenario type (ST={ST1,ST2,…,STn}), there are n=|BT|*|ST| different clustering 3-spaces required. For each clustering 3space the number of clusters (k) is determined by counting the peaks (local optima) of the clustering 3-space’s frequency scale of the contained residuals (assignments). 2 Finally, for each clustering 3-space, all k clusters (now containing similar assignments) and their respective cluster centers can be determined. Second, for a new assignment, the most similar cluster (comprising similar assignments) is determined by revealing the minimum distance to all cluster centers inside a clustering 3-space. All items, which have been identified to 1

2

Our cluster analysis implements k-means algorithm through the machine learning framework “Accord.NET” [62]. For determining the number of clusters k, we apply the R package “flacco” [63] to count the local optima of a clustering 3-space’s frequency scale. Because of its four-dimensionality (∆U, ∆I, ∆C, and frequency), the frequency scale cannot be plotted. For this reason, Fig. 3 colors the clusters in each exemplary scatterplot.

Preprocessing similar items

Computing and recommending value-added services

For each clustering 3-space, determine its clusters of similar items (assignments) and their respective cluster centers

Determine most similar cluster to the new assignment (minimum distance to all cluster centers)

Calculate purchase propability for each distinct service instance of all items in the cluster

Remove irrelvant services with purchase probabilities below a cut-off score (e.g., 0.7)

Figure 2. Sequence plan of the service recommender system.

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belong to the same cluster, are now considered to be same items by the recommender system. For calculating the related services’ purchase probabilities, all historical service instances are aggregated to reveal the number of distinct service instances, which have been bought in the past for all items in the cluster. Subsequently, for each service instance the purchase probability is calculated (e.g., “five-year warranty” was selected by 87% of similar assignments). Finally, all service instances with a purchase probability of less than a preset cut-off score (e.g., 0.7) are removed from the set of service recommendations.

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Figure 3. Two clustering 3-spaces with k clusters each (colored)—10.000 pseudo-items for BMW i3 EVBs in smart homes (upper, k=4) and Nissan Leaf EVBs in solar plants (lower, k=7)

The recommender system supplies a list of services that are (highly) likely to be bought by an individual customer, for whose scenario requirements a specific battery system has been selected. Moreover, this list is used for the configuration of distinct customer offers. B. Design Phase Design Principle 4. A variants configurator supports a seller (decision-maker) in the design and configuration of distinct ESSs (customer offers). While a manual mode supports the decision-maker in the design of creative solutions by configuration rules and service recommendations, an automatic mode configures three different solutions, which structurally realize the concept of (bounded) rationality [58]: satisficing solution (“basic solution”) comprising necessary services identified by the rules engine; best practice solution (“reasonable solution”) comprising necessary services identified by the rules engine and additional services identified by the recommender system according to high purchase probabilities of [0.9, 1.0]; and maximizing solution (“premium solution”) comprising necessary services identified by the rules engine and additional services identified by the recommender system having both likely and highly likely purchase probabilities of [0.7, 1.0]. As a result, the variants configurator supplies a set of distinct ESSs (offers) that can be offered to a customer. This design principle is intended for supporting mass customization [29]. Hence, the variants configurator provides programmed decision support to enable sellers to easily configure various offers that could satisfy individual customer demands more adequately. Design Principle 5. Applying the matching component might likely generate a large set of feasible assignments that must be supplemented with suitable value-added services to create feasible ESSs. The resulting task of configuring distinct variants is a considerable challenge, since each assignment necessitates, at least, three distinct offers. Hence, a task list is required to break down the decision problem into smaller pieces, which could be processed by several decision-makers (e.g., sales engineers) and thus enables collaborative task processing. The task list and the variants configurator are closely integrated, since the former supplies the latter with input objects (assignments) for which the configuration of distinct ESSs (offers) is required. C. Choice and Implementation Phase Design Principle 6. In the choice phase, the configured ESSs are offered to a customer. A customer decision routine prompts a customer for choosing one of the offered solutions. The customer must be able to efficiently compare

different offers that vary because of the included battery system and the configured and selected services. An additional feedback mechanism is beneficial, such that the customers can indicate if no offer fits their requirements. To easily interface with the customers, the routine should be implemented as a web portal that does not require the customers to use any special software than a browser. Enabling customers to compare different variants in a welldesigned cockpit might allow them to make efficient and suitable buying decisions. Design Principle 7. A buying decision then creates an order recorded in the database. Thus, the configuration software needs to interface with enterprise resource planning (ERP) systems to inform the outbound logistics about the delivery of the selected solution. Furthermore, the ERP systems of external service providers can be triggered to start the provision of (external) services, such as installation, maintenance, etc. Consequently, the configuration process is concluded by supplying the ESS at

the customers’ sites, which realizes the implementation of the selected solution. VI.

DEMONSTRATION OF THE VERTICAL SOFTWARE PROTOTYPE

The service configuration software has been implemented as one component of a more complex DSS [54] that supports the entire process of repurposing used EVBs, from returns management to application in second life scenarios. Based on assessing the feasibility of matching all EVBs with all second life scenarios (DP1), mandatory and optional services are recommended by applying configuration rules (DP2) and item-based collaborative filtering (DP3), culminating in designing different ESSs as offers (DP4; DP5). The current prototype cannot acquire customer decisions (DP6), yet, and does not interface with ERP systems (DP7). Subsequently, the software prototype’s functionality is demonstrated vertically with regards to DP2DP5.

Figure 4. Vertical prototype of the service configuration software, demonstrating DP2-DP5.

Fig. 4 shows the variants configurator (lower screen), which integrates the results of the configuration rules engine and the recommender system on the user interface. The configuration rules engine analyses each feasible assignment,

which is made by the matching component (DP1, omitted), in terms of mandatory value-added services. For that to happen, it is possible to define configuration rules for services by a rule chain (DP2, upper screen), which

indicates, (i) if the selection of a specific service for a specific type of battery or scenario is allowed or not and (ii) if the selection of a specific service implies the inclusion or exclusion of other services. The example shows a configuration rule that applies to a specific battery type, for which a specific service is excluded. Creating a configuration rule for a mandatory service implies its selection by the variants configurator and is indicated by a red color-coding (DP2, lower screen) in the service selection list. Moreover, a color-coding is also applied for the services suggested by the recommender system, classified by their purchase probability to be likely [0.7, 0.89] (blue) and highly likely [0.9, 1.0] (green) (DP3; DP4, lower screen). For instance, the service “Upgrade Battery Management System Home” has a probability of 70% (blue) and the service “Training (≤ 10 Participants)” was bought at the likelihood of 93% (green) of similar items. The task list manager (DP5, lower screen) has been implemented and is realized as a sidebar on the left-hand side and contains assignments, which necessitate service configurations. On the left side of the lower exemplary screen (DP5), there are five configuration tasks for different assignments. One of which is currently in configuration (yellow). The remaining tasks are still non-configured items and have to be completed by the user later on. VII. EVALUATION CONCEPT As indicated, the business for repurposing batteries is not existing, which implies that nobody currently owns data on (historical) service purchases, yet (“cold-start problem”). Since item-based collaborative filtering recommender systems require historical data to generate any recommendations [14], the prototype’s evaluation is challenging. Because of the non-existing application domain, we are going to generate required data on service purchases for repurposed batteries by a conjoint analysis [59]. The conjoint analysis is planned to be conducted with real owners of smart homes (i.e., a house equipped with photovoltaic panels) to assess their willingness-to-pay towards diversely constituted service bundles that could accompany a used battery system in their scenarios (smart homes). Furthermore, each individual preference of a smart home owner towards one of these service bundles is interrogated (i.e., poll the individual purchase preference), which consequently generates empirical service purchase data to solve and remove the inherent “cold-start problem” from our recommender system. Based on this data, we outline two possible strategies for evaluating the recommender system in the following. The evaluation is planned to be conducted in summer 2017, such that first results might be available for presentation at the conference. To evaluate the decision quality, the recommender system provides in terms of precision, recall, and accuracy [60], one possible evaluation strategy could be to split the generated data from the conjoint analysis into two datasets for training and testing the recommender system. As a starting point, the training dataset is fed into the recommender system and populates its database with

historical purchase data. The recommender system is then applied to generate service recommendations for each assignment residing in the test dataset. While the first recommendations might be of a substandard prediction quality, later service recommendations might show significant improvements in the prediction quality, since the records from the test dataset are continuously added to the service recommender database. Moreover, the recommender system could be equipped and tested with diverse clustering algorithms and methods to benchmark the ‘best’ toolset for service recommendations in the battery repurposing domain. Similar approaches are common for evaluation of (itembased) collaborative filtering systems [60]. Another strategy for evaluating our recommender system is to prove the efficiency (decision effort) and effectiveness (decision quality) it provides to (expert) human agents (e.g., battery engineers, service providers), who are the prospective users of the system. It could be shown how the system increases their decision quality and decreases their required timely effort by receiving support of the software compared to the achieved decision quality and required effort without receiving support. For that to happen, we plan to conduct a laboratory experiment [61] with the prospective system users that will be set up as follows. The human agents will be provided with one feasible assignment (battery matched to scenario) from the conjoint analysis dataset (evaluation case). Moreover, the human agents are provided with a comprehensive list of various service categories and service instances. Then, the human agents must create a service offering to meet the service demands raised by second life scenario considered in the evaluation case best without support of the recommender system. Subsequently, the human agents create a service offering with support of the recommender system, which is already populated with all the datasets from the conjoint analysis except for the dataset representing the evaluation case. Finally, both service offerings (created with and without support by the recommender system) are checked against the real demand raised by the evaluation case. The quality of both decision results (service offerings) is assessed and compared in terms of, e.g., their precision, recall, and accuracy [60]. VIII. CONCLUSION Repurposing used EVBs is a significant business problem that will soon gain momentum. When trading used EVBs [26], the information asymmetries that are inherent to selling used goods need to be addressed. We argued that offering ESSs—comprising used EVBs and value-added services—can be an appropriate strategy to mitigate the information asymmetry and to successfully repurpose EVBs. We designed and implemented a configuration software that sales engineers can use to successfully bundle these ESSs. At its core, the software is based on a recommender system using item-based collaborative filtering. The system suggests value-added services that are required or optional to successfully repurpose an EVB, with regards to the properties of a second life scenario. In the spirit of design science research, we designed and implemented a vertical software prototype of the configuration software. Since

vertical prototyping is about generating and evaluating fresh ideas early in a project [16], [17], the prototype provides core functionalities that can be evaluated with potential users. Other researchers can reuse the implemented vertical prototype as a blueprint for designing similar software tools for repurposing used goods as customer solutions. The presented clustering approach (DP3) enables recommendations for highly individual items that can only be sold once. In addition, the prototype can be used as a device to conduct empirical research on the value-added services and ESSs required to successfully repurpose used EVBs. For instance, laboratory experiments could be conducted to evaluate the hit rate of the implemented itembased collaborative filtering recommender system. Since no field data on (historical) service purchases exist yet, we are currently preparing a conjoint analysis with potential customers of used EVBs in order to generate valid service purchase data and to better understand user preferences on value-added services in this domain. Moreover, these data can be used to evaluate the collaborative-filtering component of the service recommender system in terms of, e.g., precision, recall, and accuracy [60]. For that to happen, the prototype will be evaluated in a laboratory experiment involving prospective stakeholders of a business for repurposing EVBs to show improvements in their decision quality and effort with support by the recommender system compared to the decision quality and effort achieved without support. Considering the further development, introducing the configuration software in suitable companies requires strategies to be put in place to deal with the “cold-start problem”. Previous research has shown that data obtained from market research initiative, such as conjoint analyses, can be applied to mitigate the cold-start problem [38]. As soon as appropriate field evidence will be available, the implemented algorithms and parameters can be evaluated and subjected to subsequent design cycles. ACKNOWLEDGMENT This paper was written within the research project “endof-life information systems” (EOL-IS), which is funded by the German Federal Ministry for Education and Research (BMBF) (funding label: 02K12A042). We thank the Projektträger Karlsruhe (PTKA) for their advice. Furthermore, we thank Jonas Gerlach, Ben Matheja, Florian Runschke, and Johannes Voscort, the members of the master’s project seminar “Decision support for used EVBs III”, for implementing the software prototype. We highly appreciate their operative support. REFERENCES [1]

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