Smart Service Canvas – A tool for analyzing and designing smart product-service systems

Smart Service Canvas – A tool for analyzing and designing smart product-service systems

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Procedia CIRP 00 (2017) 000–000 Procedia CIRP 83 (2019) 324–329

11th CIRP Conference on Industrial Product-Service Systems 11th CIRP Conference on Industrial Product-Service Systems

Smart Service Canvas – A tool for analyzing and designing Smart Service Canvas – A tool for 28th CIRP Design Conference, May analyzing 2018, Nantes, and smart product-service systems Francedesigning smart product-service systems A new methodology to analyze the functional and bphysical architecture of a, Jens Poeppelbuss *, Carolin Durst a, Poeppelbuss *, Carolin Durstb family identification existing Ruhr-Universität productsBochum, for Industrial anJens assembly oriented product Sales and Service Engineering, Universitätsstraße 150, 44801 Bochum, Germany a

Ansbach,Sales Hornburgweg 91541 Rothenburg ob der Tauber, Ruhr-Universität Hochschule Bochum, Industrial and Service26, Engineering, Universitätsstraße 150,Germany 44801 Bochum, Germany b Hochschule Ansbach, Hornburgweg 26, 91541 Rothenburg ob der Tauber, Germany * Corresponding author. Tel.: +49-234-32-26401; Fax: +49-234-32-14280. E-mail address: [email protected] a


Paul Stief *, Jean-Yves Dantan, Alain Etienne, Ali Siadat

* Corresponding author. Tel.: +49-234-32-26401; Fax: +49-234-32-14280. E-mail address: [email protected] École Nationale Supérieure d’Arts et Métiers, Arts et Métiers ParisTech, LCFC EA 4495, 4 Rue Augustin Fresnel, Metz 57078, France

*Abstract Corresponding author. Tel.: +33 3 87 37 54 30; E-mail address: [email protected]

Abstract Service that is delivered through so-called smart products is increasingly popular under the term smart service. While customers benefit from smart service thethrough intelligent maintenance of equipment that avoidspopular unwanted andthe expensive downtimes, enables manufacturing Service that isthrough delivered so-called smart products is increasingly under term smart service. itWhile customers benefit firms from Abstract to collect and through analyze the extensive data maintenance on the actualofuse of machinery and equipment, them to downtimes, offer solutions tailoredmanufacturing to customer needs. smart service intelligent equipment that avoids unwantedhelping and expensive it enables firms Methods design of the corresponding smart systems (PSS),helping however, have not yet been adopted in practice; to collect for andthe analyze extensive data on the actual useproduct-service of machinery and equipment, them to offer solutions tailored widely to customer needs. Inemphasizing today’s for business environment, the trendtools towards more product variety and customization is unbroken. Due to this development, the need of to need of forthe lightweight that are ready-to-use. This article introduces the Smart Service Canvas as such an instrument to Methods thethe design corresponding smart product-service systems (PSS), however, have not yet been adopted widely in practice; agile andand reconfigurable systems cope various products and product families. To Canvas design We and optimize production analyze design PSSlightweight concepts. We demonstrated the with canvas in workshop settings with various participants. found the canvas emphasizing to the smart needproduction for toolsemerged that aretoready-to-use. This article introduces the Smart Service as such anthat instrument to systems well as tosmart choose the optimalduring product matches, product analysis methods are needed. Indeed, most of known methods to stimulates discussions service design and that is helpful in providing quick overview ofthe a We smart PSS. analyze as andmulti-perspective design PSS concepts. We demonstrated the canvas init workshop settings withavarious participants. found that the aim canvas analyze a product or one product family onduring the physical families, however, may differ largely termsPSS. of the number and stimulates multi-perspective discussions servicelevel. designDifferent and that product it is helpful in providing a quick overview of ainsmart nature ofThe components. This fact by impedes anB.V. efficient comparison and choice of appropriate product family combinations for the production © 2019 Authors. Published Elsevier © 2019 The Authors. Published by Elsevier B.V. system. new methodology is proposed to analyze existing in CIRP view of their functional andProduct-Service physical architecture. The aim is to cluster Peer-review under responsibility ofthe thescientific scientific committee of 11th Conference on Industrial Product-Service Systems. © 2019AThe Authors. Published by Elsevier B.V. Peer-review under responsibility of committee ofproducts thethe 11th CIRP Conference on Industrial Systems these productsunder in new assembly oriented product families for the optimization existing assembly lines and the creation of future reconfigurable Peer-review responsibility of the scientific committee of the 11th CIRPofConference on Industrial Product-Service Systems. Keywords: Smart service; system; smart canvas; service design assembly systems. Basedproduct-service on Datum Flow Chain, thePSS; physical structure of the products is analyzed. Functional subassemblies are identified, and a Keywords: functionalSmart analysis is performed. Moreover, a hybrid functional anddesign physical architecture graph (HyFPAG) is the output which depicts the service; product-service system; smart PSS; canvas; service similarity between product families by providing design support to both, production system planners and product designers. An illustrative example of a nail-clipper is used to explain the proposed methodology. An industrial case study on two product families of steering columns of 1. Introduction [9,10].ofBased on connected thyssenkrupp Presta France is then carried out to give a first industrial evaluation the proposed approach. products that gather data about ©1.2017 The Authors. Published by Elsevier B.V. their usage, smart PSS are “built on obtaining contextual data Introduction [9,10]. Based on connected products that gather data about Peer-review under responsibility of the scientific committeefaces of thetwo 28th CIRP Design 2018. The industrial sector of developed economies from the Conference field, these on data, automatically making their usage, smartanalyzing PSS are “built obtaining contextual data

significant developments. the one hand, the servitization The industrial sector ofOndeveloped economies faces two

Keywords: Assembly; Design method; Family identification of manufacturing leads to aOn steady increase theservitization importance significant developments. the one hand,inthe

along the industrial valueincrease chain [1,2]. the other of service manufacturing leads to a steady in theOn importance hand, the digital changes traditional of service along transformation the industrial value chain [1,2]. Onproduction the other processes and creates new possibilities for the provision and hand, the digital transformation changes traditional production 1. Introduction marketing and of innovative digital service for offerings [3,4]. Both processes creates new possibilities the provision and developments reinforce each other and lead to changes in marketing of innovative digital service offerings [3,4]. Both Due to the fast development in the domain of industrial business models [5,6]. However, research on the developments reinforce each other and lead to changes in communication and an ongoing trend of digitization and digital transformation in manufacturing has long focused on industrial business models [5,6]. However, research on the digitalization, manufacturing enterprises are facing important technical aspects of in so-called smart products cyberdigital transformation manufacturing has long focused on challenges in today’s market environments: a and continuing physical systems, but neglected an examination of how value technical aspects of so-called smart products and cybertendency towards reduction of product development times and can be co-created and from thesethere technologies physical systems, but captured neglected an examination of increasing how[7]. value shortened product lifecycles. In addition, is an A focus on smart service offers a way to generate superior can be co-created and captured from these technologies [7]. demand of customization, being at the same time in a global value for customers [8]. Smart is atoworld. key component of A focus on smart service offers a way generate competition with competitors allservice over the Thissuperior trend, smart product-service systems (PSS) that integrate tangible value for customers [8]. Smart service is a key component of which is inducing the development from macro to micro products and intangible services through digital architectures smart product-service systems (PSS) that integrate tangible markets, results in diminished lot sizes due to augmenting productsvarieties and intangible services through digital architectures product (high-volume to low-volume production) [1].

decisions and taking action.” p. 2]. Customers making benefit from the field, analyzing these[7,data, automatically through an intelligent monitoring, and decisions and taking action.” [7, p. 2]. readjustment Customers benefit maintenance machinery and equipment, readjustment avoiding unwanted through an of intelligent monitoring, and and expensive downtimes.and Atequipment, the same avoiding time, there is the maintenance of machinery unwanted opportunity forrange manufacturing to manufactured collect and downtimes. At firms the same time, and thereanalyze is the of theexpensive product and characteristics and/or extensive data on the actual use of their installed base and opportunity for manufacturing firms to collect and analyze assembled in this system. In this context, the main challenge to in offer tailored toistheir customers [5]. extensive data on the actual use of their to modelling andsolutions analysis now not only installed to cope base with and single In tailored order tosolutions leverage potential of smart PSS, industrial offer tothe their customers [5].product products, a limited product range or existing families, firms must not only build up internal digital capabilities, but In order to leverage the potential of smart PSS, industrial but also to be able to analyze and to compare products to define also identify the latent and unmet needs of customers and firms must not only build up internal digital capabilities, but new product families. It can be observed that classical existing markets [5]. The design of service-oriented offerings, also identify the latent and unmet needs of customers and product families are regrouped in function of clients or features. however, assembly often poses a challenge traditional markets [5]. The design of to service-oriented offerings, However, oriented product families aremanufacturing hardly to find. firms [11]. Frequently, service innovation is not institutiohowever, often poses a challenge to traditional manufacturing On the product family level, products differ mainly in two nalized like Frequently, the development ofinnovation tangible products [12] firms [11]. service is notand institutiomain characteristics: (i) the number of components (ii)and the occurs unsystematically. Moreover, the design of product and nalized like the development of tangible products [12] type of components (e.g. mechanical, electrical, electronical). service components is likely to not bethe well-integrated. occurs unsystematically. Moreover, design single of product and Classical methodologies considering mainly products service components is likely to not be well-integrated. or solitary, already existing product families analyze the

2212-8271 © 2019 Theaugmenting Authors. Published by Elsevier To cope with this variety as wellB.V. as to be able to product structure on a physical level (components level) which Peer-review the scientific committee the 11th CIRP Conference Product-Service 2212-8271 possible ©under 2019responsibility The optimization Authors. of Published by Elsevier B.V. identify potentials in ofthe existing causeson Industrial difficulties regardingSystems. an efficient definition and doi:10.1016/j.procir.2017.04.009 Peer-review under responsibility of the scientific committee of the 11th CIRP Conference on Industrial Product-Service Systems.families. Addressing this production system, it is important to have a precise knowledge comparison of different product doi:10.1016/j.procir.2017.04.009

2212-8271©©2017 2019The The Authors. Published by Elsevier 2212-8271 Authors. Published by Elsevier B.V. B.V. Peer-reviewunder underresponsibility responsibility scientific committee of the CIRP Conference on 2018. Industrial Product-Service Systems. Peer-review of of thethe scientific committee of the 28th11th CIRP Design Conference 10.1016/j.procir.2019.04.077


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This article presents the Smart Service Canvas as a lightweight tool for the analysis and design of smart PSS concepts. Similar to the Value Proposition Canvas and the popular Business Model Canvas [13], the proposed tool can be used in interactive workshops. This article demonstrates its use and reports on first results from demonstration workshops. 2. Research Background 2.1. Smart Service and Smart PSS Beverungen et al. [7, p. 6] define the term smart service as “the application of specialized competences, through deeds, processes, and performances that are enabled by smart products.” Smart products refer to physical objects with embedded systems and networking capability that enable the intelligent adaptation to customer needs and changes in usage situations. They are the central building block of the (Industrial) Internet of Things and cyber-physical systems [14]. The introduction of smart products allows to transform service systems, which are generally understood as “configurations of people, technologies, and other resources that interact with other service systems to create mutual value” [15, p. 395] into smart service systems, or smart PSS, as a smart product is typically involved. Smart PSS “make use of ICT, such as microchips, software and sensors, which allows them to connect, collect and process information.” [9, p. 13] Specifically focusing on manufacturing, Lerch and Gotsch [5] also use the term digitalized PSS. Such systems are “capable of learning, dynamic adaptation, and decision making based upon data received, transmitted, and/or processed to improve its response to a future situation.” [16, p. 2] When designing a smart PSS, managers and engineers must make decisions about its physical and non-physical components, including, for instance, the central value proposition and the resource configuration. The decisions can be captured in a service concept, which “defines the how and the what of service design” [17, p. 121]. Broadly speaking, the service concept, which is typically also labelled as the offering [18], “refers to the description of the customer’s needs and how they are to be satisfied in the form of the content of the service or the design of the service package.” [19, p. 148] This definition is in line with the Value Proposition Canvas, which provides the basis for our canvas. 2.2. Canvases Inspired by the worldwide success and great acceptance of the Business Model Canvas (BMC) [13], we decided to use a canvas representation for our tool to support the development of smart service concepts. A canvas representation is a concise, easy to understand, and visually appealing overview of the key components required for a specific subject area. In addition, the use of canvases in workshop settings can enforce the discussion of all aspects related to that specific subject area (e.g., the development of smart service concepts). The BMC has become a widely recognized tool to analyze and develop business models [21,22]. The BMC was initially presented as a business model ontology [23]. An ontology


provides a shared understanding and concepttualization of a domain of interest that can be used as a unifying framework to facilitate knowledge sharing and re-use, as well as interoperability between different organizational entities and systems [24]. While Osterwalder [23] also worked on a formal representation of business models with an own XMLbased language, the BMC as it is known today mainly offers an informal framework, which is to a large extent “expressed loosely in natural language” [24, p. 6]. 3. Smart Service Canvas 3.1. Overview When developing the Smart Service Canvas, we made the design decision to build on the Value Proposition Canvas [25] for two reasons. First, the Value Proposition Canvas focuses on two of the nine fields of the popular BMC (value propositions and customer segments) and thus provides a customer-centered view on the development of a value proposition. We regard this as fundamental for the success of smart PSS since they need to generate customer value and not just make up new ideas of how to process data from smart products. Second, an established approach like the Value Proposition Canvas, which is already known to many people, will increase the likelihood that the Smart Service Canvas will be adopted in practice, too.

Fig. 1: Perspectives and Fields of the Smart Service Canvas

The Smart Service Canvas allows for modeling and assessing as-is and to-be concepts of smart PSS. It comprises four perspectives (Fig. 1). The value map and the customer profile from the Value Proposition Canvas [25], which are each divided into three segments, serve as anchor points for the value perspective and the customer perspective of the Smart Service Canvas. The Smart Service Canvas further adds an ecosystem perspective and the fit between the perspectives. The Smart Service Canvas can be used in the same flexible way as the BMC as the fields can be filled with the help of sticky notes, which can also be re-sorted or removed again. The general direction of filling out is similar to the Value Proposition Canvas [25]. In order to focus on customer demands independent from possible restrictions on the provider side, the customer perspective on the right half comes first. Then, the value perspective on the left half is next, followed by the ecosystem perspective that defines the digital architecture and platform. They connect the customer and the smart PSS provider. Finally, the fit between the


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customer perspective and the value perspective is developed. These four perspectives are described in more detail below. 3.2. Customer Perspective The customer perspective describes a specific customer segment [25]. It comprises the three fields of the customer profile from the Value Proposition Canvas: customer routines and jobs, customer pains, and customer gains. For the application to smart PSS, this perspective is supplemented by the context of customer jobs and routines and contextual things and data. These two additional fields emphasize that the special value proposition of a smart PSS can be based on the synthesis of data from different sources (in addition to the focal smart product) and on a comprehensive understanding of the customer context [7,25]. Customer routines and jobs describe the activities that potential customers in a segment want to accomplish successfully. They can also involve specific results to be achieved, problems to be solved, or needs to be satisfied. They are formulated from the customer’s own point of view, which can differ significantly from that of the provider. To develop convincing smart service concepts, it is necessary to analyze the context of customer routines and jobs [8,26]. The question here is which other activities may precede or follow the customer jobs, e.g., which additional interactions with other persons and systems are likely to happen and how they influence the performance of the activity. It is possible to capture the setting and local environment in which the customer jobs are carried out. Contextual things and data may be useful or even necessary for providing smart service. These are, e.g., temperature and humidity sensors in a plant, which provide data that can be combined with the data of a machine in this plant. Further relevant context data can be provided by web services on weather or traffic conditions or similar. Customer pains stand for all the things that disrupt the potential customers from doing their jobs or prevent them from completing them successfully. They also describe risks like possible negative outcomes from customer jobs. Customer gains are the positive effects and results of service provision that customers need or want. They help them in successfully completing their own jobs [25]. Corresponding advantages can be savings in time, costs and other expenses. They also include superior service quality and positive side effects for the customer, such as an increase in knowledge or reputation, which may even be unexpected. 3.3. Value Perspective The value perspective adopts the value map from the left half of the Value Proposition Canvas [25] in a slightly adapted form. This perspective is also supplemented by two specific fields for the application to smart service concepts, i.e., analytical capabilities and data. They emphasize that the availability of relevant data and the skills required for their processing form a central basis for offering smart PSS [7,8].


The smart service field describes the core of the service offering. It captures the label of the smart service concept and the basic underlying idea. It can be complemented with various partial service elements (modules). The data field describes which data are required as a basis for the provision of the smart PSS. This can be, for example, status information or historical data of objects or persons. Status information is mainly used for real-time diagnosis of products or systems [27]. Consulting and optimization service offerings often use historical data in order to identify developments and trends in performance or usage behavior. Possible sources include single smart products, the entire installed base of products across various customers, as well as external sensors and web services [27]. Furthermore, internal databases and application systems provide additional data sources that should not be neglected [7]. The analytical capabilities describe which competencies must be available for data analysis at the PSS provider. This ranges from simple data visualization and report generation to complex analytical procedures for pattern recognition. In this context, the location of data processing and the necessary computing power is relevant. On the one hand, these can be located in the smart product itself. On the other hand, there may be a central point for processing data from all connected products and sensors [27]. Various aspects influence whether functions for data processing are integrated into the product or outsourced to a central instance, e.g., into the cloud [28]. These include, for example, required response time, degree of automation of the product, availability, reliability and security of the network, location of the product, type of user interface and frequency of maintenance or upgrades [28]. The field solve problems describes how exactly the smart service offering solves certain customer problems. Since not all customer problems can usually be solved at the same time, it is meaningful to prioritize a few of the important problems. It is necessary to determine how the smart service reduces or eliminates challenges and hurdles that stand in the way of customers’ jobs or their use of the smart PSS [25]. The field create value describes how exactly the smart service offering leads to the positive effects and results that customers need or want. At this point, the key mechanisms of action, such as a reduction of uncertainty or user stress are to be mentioned. Here, it also makes sense to prioritize the aspects that can offer a clear competitive advantage [25]. 3.4. Ecosystem Perspective The ecosystem perspective consists of a generic field describing the technical infrastructure and digital platform, i.e., the digital architecture of the smart PSS [7]. It refers, among others, to basic things like power supply, wired and wireless network connection, and mobile network coverage that warrant the connectivity to the smart product and access to the collected data. Digital platforms allow the distribution and marketing of the smart service in digital ecosystems (e.g., platforms like AXOOM or Siemens MindSphere). The market and governance mechanisms and the openness of the selected digital platforms are also to be considered here.


Jens Poeppelbuss et al. / Procedia CIRP 83 (2019) 324–329 Jens Poeppelbuss & Carolin Durst / Procedia CIRP 00 (2019) 000–000 Real-time Access of Machine Health Information

Analytical Capability Adaptive Clustering Method

Prognostics and Health Management

Prognostics According to Different Work Regimes

Smart Service

Data Blade Condition Cutting Parameters Material Type

Speed Adjusted to Blade Condition

Create Value

Revenue Model

Product Quality Reasoning

High Productivity of Manufacturing Line

Service Contract with a Yearly Fee

Fast Production Process

Interaction Level Automated Data Transfer

Solve Problems Evaluation of Health Condition of Critical Components


Dashboard Alerts Smart Product CNC Machine Web-based Interface

Gains Unplanned Downtimes


Bad Quality Products

Context of Customer Routines and Jobs Plant Building and Factory Layout

Production Planning and Scheduling Customer Routines and Jobs Management of Maintenance, Repair and Overhaul (MRO)

Work Shifts Contextual Things and Data Other Machines Customer‘s ERP System

Technical Infrastructure and Digital Platform Cloud Server

Add-on Sensors (Vibration, Acoustic Emissions, Temperature)

Local Area Network

Fig. 2. Exemplary Smart Service Canvas for a CNC Machine Prognostics and Health Management Service

3.5. Fit

4. Illustration with an Example Case

The fit between the previous perspectives is achieved when customers are enthusiastic about the smart service offered and when the offering fits well into their routines, jobs and contexts. Furthermore, it is important that the technical equipment, data flows, and economic incentives of the smart PSS are compatible with the ecosystem that it is embedded in. Osterwalder et al. [25] recommend a simple mapping between the left and right sides for their Value Proposition Canvas. The Smart Service Canvas provides three additional fields at this point, which helps concretize the fit at different levels and capture corresponding smart PSS design decisions. The lowest level shows the smart product, which is a physical object with embedded systems and networking capability. This can be a connected machine at the customer’s plant, but also any other device that offers a user interface to access the smart service (e.g., smartphones or tablets). The interaction level is closely related to the degree of automation of the service provision. Smart products linked to a condition monitoring system generally support proactive maintenance without direct interaction with people. Since status data is automatically transmitted to a central location, these products themselves may not even provide a user interface. A tablet, however, usually requires interaction with people during service provision, for example by using apps, changing settings, and querying or entering data. The level of interaction and the corresponding degree of automation should be geared to the expectations of the customer. The revenue model specifies the revenue flows that the smart PSS generates in order to allow the service provider to appropriate or capture value. Such modes can be, for instance, subscription fees, advertising fees, license fees, transaction fees, or sales commissions [29]. Hence, this field indicates “what methods of payment are used, what is being paid for, and thus in what way income is generated.” [29, p. 59] Closely linked to the design of revenue models are pricing decisions. Potential pricing strategies include cost-based approaches, but also context-based and value-based pricing, as well as strategies of price discrimination, where different prices are charged for equal service offerings [29].

In order to illustrate how the Smart Service Canvas can be utilized to depict a smart PSS, we present a fictive case of a prognostics and health management service for computerized numerical control (CNC) sawing machines. This case is based on Lee et al. [30], which we slightly extend in order to cover the complete Smart Service Canvas. At the same time, we limit the number of notes per field to a minimum in order to keep the overall canvas comprehensible (Fig. 2). As for the customer profile, we take the perspective of a plant manager at the customer organization. He is responsible for production planning and scheduling, as well as for the management of the machines’ maintenance, repair and overhaul. Pains related to his jobs and routines are unplanned downtimes and bad quality products that have to be discarded. Gains reflect benefits that he would like to achieve, like a speed-up of the production process and an increased productivity. Relevant context is the plant building with its factory layout, e.g., as regards humidity and noise levels inside. In addition, a work shift system influences the production planning and scheduling. Other machines and the customer organization’s ERP system offer additional data that can be considered when offering smart service. The prognostics and health management service offering is provided through the manufacturer of the CNC machine. The service concept intends to solve customer problems through monitoring and evaluating the health condition of critical components and, thus, to avoid unplanned downtimes. It also monitors the quality of the products that are sawed by the CNC machine and adjusts the speed of the machine to warrant that no bad quality products are produced. Additional value is, amongst others, provided to the plant manager as the service adjusts the speed of the machine according to blade conditions and different work regimes. These differ depending on data that the machine gathers and stores, like material type and cutting parameters. Moreover, different work regimes are analyzed using an adaptive clustering method. The smart product is the CNC machine that automatically transfers data from its controller and add-on sensors to the cloud server of the smart service provider. Furthermore, the

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service is accessible through a web-based interface that offers a dashboard with real-time health information. The plant manager can also activate alerts to be informed about problematic health conditions on short notice. Hence, the interaction level can be mainly characterized as a machine-tomachine service, while the dashboard and alerts offer selfservice options for the customer. The revenue model is a service contract with a yearly fee, that is usually settled together with the original sales of the CNC machine. 5. Demonstration Workshops For demonstrating the utility of the Smart Service Canvas, we conducted two workshops with students (A1, A2) and one workshop with practitioners and academics (B), which took about three hours each. In the workshops A1 and A2, we asked groups of master students in business administration and industrial engineering to first analyze existing smart service business models (A1), and then to generate their own ideas for innovative smart service concepts (A2; both B2C and B2B contexts were allowed). Both tasks had to be carried out using the Smart Service Canvas. Workshop B involved both practitioners and academics and took place in a networking meeting about digital service innovation, where the participants were asked to ideate new smart service concepts. In all workshops, we formed groups of three to five people that worked together. At the end of workshops A2 and B, we asked the participants to fill out a structured feedback form in order to capture the experience they made concerning the elements and utility of the Smart Service Canvas. This form was comprised of 13 items (Table 1) with a five-point Likert scale from fully disagree (1) to fully agree (5), which we adapted from Osterwalder [23, p. 140]. It also comprised seven additional open questions (e.g., “What elements do you think are missing in the Smart Service Canvas?” and “How


could the Smart Service Canvas help companies innovate?”). In general, all participants found that the presented Smart Service Canvas is a useful workshop tool to work on smart service concepts. As for the quantitative results, the participants reported higher scores for the description and analysis than for the design of new service concepts. The quantitative results of the two workshop groups show that the customer perspective is seen as central to the analysis and design of smart service concepts, followed by the value perspective, while the ecosystem perspective and the fit are generally considered slightly less relevant. While the participants of workshop groups A1 and A2 found that the fields of the Smart Service Canvas reflect the necessary elements for the analysis and design of smart service concepts very well, the workshop group B consisting of academics and practitioners were more critical regarding these questions. In general, the answers of workshop group B scored lower compared to the workshop group A1/A2. With regard to the open questions, the participants saw the benefit of the Smart Service Canvas in structuring their thoughts on new service concepts. Both groups reported that having an idea on one page greatly stimulates the discussion during service development. Still, there is room for improvement. Respondents from workshop B commented that the canvas does not require the workshop participants to capture details on costs, revenues, and prices and has a very high level of abstraction. Some participants highlighted that for non-business people the customer perspective is difficult to understand. The respondents also found it difficult to understand the difference between the fields data and contextual things and data and asked for additional information and explanations on how to work with the Smart Service Canvas in terms of a methodology handbook or playbook. The same was mentioned for the field technological infrastructure and digital platform.

Table 1. Quantitative feedback from the workshops



Items Using the Smart Service Canvas, I was able to accurately describe the analyzed smart service. The elements of the Smart Service Canvas provide a perfect reflection of the smart service I analyzed. The Value Perspective of the Smart Service Canvas is important to me to analyze the smart service. The Fit section of the Smart Service Canvas is important to me to analyze the smart service. The Customer Perspective of the Smart Service Canvas is important to me to analyze the smart service. The Ecosystem Perspective of the Smart Service Canvas is important to me to analyze the smart service. Using the Smart Service Canvas, I was able to design a new smart service. The elements of the Smart Service Canvas provide a perfect reflection of the Smart Service I designed. The Value Perspective of the Smart Service Canvas is important to me to design a new smart service. The Fit section of the Smart Service Canvas is important to me to design a new smart service. The Customer Perspective of the Smart Service Canvas is important to me to design a new smart service. The Ecosystem Perspective of the Smart Service Canvas is important to me to design a new smart service. The Smart Service Canvas is a useful tool.

Workshops A1/A2 (n = 19) Mean SD

Workshop B (n = 8) Mean SD






















































Jens Poeppelbuss et al. / Procedia CIRP 83 (2019) 324–329 Jens Poeppelbuss & Carolin Durst / Procedia CIRP 00 (2019) 000–000

6. Conclusions This article presented the Smart Service Canvas as a tool for describing, analyzing and developing smart service concepts, which we developed based on the Value Proposition Canvas. Our enhancements enable the targeted development of innovative and customer-oriented smart PSS. Due to the explicit customer focus, manufacturing companies can expand their product-oriented way of thinking with a demandoriented view on value and customer experience. In first workshops (A1/A2, B), the developed Smart Service Canvas was successfully applied. The tool was found particularly suitable for describing and analyzing existing smart service concepts. In addition, the workshop participants were also able to develop new concepts using the canvas. Difficulties were particularly encountered in distinguishing some fields. In the future, we will try to make the distinctions clearer through detailed examples in a playbook that we plan to develop. However, we also consider a consolidation of fields of the canvas, as some have been perceived as being redundant. This article is intended to encourage interested parties to use the Smart Service Canvas and to share their experiences with us, and, thus, to also contribute to its further development. Acknowledgements This contribution is part of the projects “Design Thinking for Industrial Services” (DETHIS) and “Smart Service Retrofits for the Highest Availability of Machinery and Equipment” (retrosmart) funded by the German Federal Ministry of Education and Research (promotional codes: 02K14A148 and 02K16C000).

[7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21]


References [1]



[4] [5] [6]

Baines TS, Lightfoot HW, Benedettini O, Kay JM. The servitization of manufacturing: A review of literature and reflection on future challenges. J Manuf Technol Manag 2009;20:547–67. doi:10.1108/17410380910960984. Kastalli IV, Van Looy B. Servitization: Disentangling the impact of service business model innovation on manufacturing firm performance. J Oper Manag 2013;31:169–80. doi:10.1016/j.jom.2013.02.001. Ardolino M, Rapaccini M, Saccani N, Gaiardelli P, Crespi G, Ruggeri C. The role of digital technologies for the service transformation of industrial companies. Int J Prod Res 2017;0:1–17. doi:10.1080/00207543.2017.1324224. Lerch C, Gotsch M. How digitalization can accelerate the transformation from manufacturer to service provider. Proc. Spring Servitization Conf. Aston Univ., 2015, p. 76–82. Lerch C, Gotsch M. Digitalized Product-Service Systems in Manufacturing Firms: A Case Study Analysis. Res-Technol Manag 2015;58:45–52. Martín‐Peña ML, Díaz‐Garrido E, Sánchez‐López JM. The digitalization and servitization of manufacturing: A review on digital business models. Strateg Change 2018;27:91–9.

[23] [24] [25] [26] [27]

[28] [29] [30]


doi:10.1002/jsc.2184. Beverungen D, Müller O, Matzner M, Mendling J, Brocke J vom. Conceptualizing smart service systems. Electron Mark 2017:1–12. doi:10.1007/s12525-017-0270-5. Allmendinger G, Lombreglia R. Four strategies for the age of smart services. Harv Bus Rev 2005;83:131. Valencia Cardona AM, Mugge R, Schoormans JP, Schifferstein HN. The design of smart product-service systems (PSSs): An exploration of design characteristics. Int J Des 9 1 2015 2015. Abramovici M, Göbel JC, Neges M. Smart engineering as enabler for the 4th industrial revolution. Integr. Syst. Innov. Appl., Springer; 2015, p. 163–170. Hou J, Neely A. Barriers of servitization: Results of a systematic literature review. Framew Anal 2013;189. Nijssen EJ, Hillebrand B, Vermeulen PAM, Kemp RGM. Exploring product and service innovation similarities and differences. Int J Res Mark 2006;23:241–51. doi:10.1016/j.ijresmar.2006.02.001. Osterwalder A, Pigneur Y. Business Model Generation: A Handbook for Visionaries, Game Changers, and Challengers. 1. Auflage. Hoboken, NJ: John Wiley & Sons; 2010. Serpanos D, Wolf M. Industrial Internet of Things. Internet--Things IoT Syst., Springer, Cham; 2018, p. 37–54. doi:10.1007/978-3-31969715-4_5. Maglio PP, Vargo SL, Caswell N, Spohrer J. The service system is the basic abstraction of service science. Inf Syst E-Bus Manag 2009;7:395–406. doi:10.1007/s10257-008-0105-1. National Science Foundation. Partnerships for Innovation: Building Innovation Capacity (PFI:BIC) 2014. Goldstein SM, Johnston R, Duffy J, Rao J. The service concept: the missing link in service design research? J Oper Manag 2002;20:121– 34. doi:10.1016/S0272-6963(01)00090-0. Frei FX. The four things a service business must get right. Harv Bus Rev 2008;86:70–80. Edvardsson B, Olsson J. Key Concepts for New Service Development. Serv Ind J 1996;16:140–64. doi:10.1080/02642069600000019. Kim WC, Mauborgne R. Charting your company’s future. Harv Bus Rev 2002;80:76–85. Wallin J, Chirumalla K, Thompson A. Developing PSS Concepts from Traditional Product Sales Situation: The Use of Business Model Canvas. In: Meier H, editor. Prod.-Serv. Integr. Sustain. Solut., Springer Berlin Heidelberg; 2013, p. 263–74. doi:10.1007/978-3-64230820-8_23. Zolnowski A, Weiß C, Böhmann T. Representing Service Business Models with the Service Business Model Canvas – The Case of a Mobile Payment Service in the Retail Industry. 2014 47th Hawaii Int. Conf. Syst. Sci., 2014, p. 718–27. doi:10.1109/HICSS.2014.96. Osterwalder A. The business model ontology: A proposition in a design science approach 2004. Uschold M, Gruninger M. Ontologies: Principles, methods and applications. Knowl Eng Rev 1996;11:93–136. Osterwalder A, Pigneur Y, Bernarda G, Smith A. Value Proposition Design: How to Create Products and Services Customers Want. John Wiley & Sons; 2015. Harbor Research. Designing Smart Systems: Field Manual 2016. Herterich MM, Buehnen T, Uebernickel F, Brenner W. A Taxonomy of Industrial Service Systems Enabled by Digital Product Innovation. 2016 49th Hawaii Int. Conf. Syst. Sci. HICSS, 2016, p. 1236–45. doi:10.1109/HICSS.2016.157. Porter ME, Heppelmann JE. How smart, connected products are transforming competition. Harv Bus Rev 2014;92:64–88. Bouwman H, Faber E, Haaker T, Kijl B, De Reuver M. Conceptualizing the STOF model. Mob. Serv. Innov. Bus. Models, Springer; 2008, p. 31–70. Lee J, Ardakani HD, Yang S, Bagheri B. Industrial Big Data Analytics and Cyber-physical Systems for Future Maintenance & Service Innovation. Proc 4th Int Conf -Life Eng Serv 2015;38:3–7. doi:10.1016/j.procir.2015.08.026.