Smart Service Engineering

Smart Service Engineering

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Procedia CIRP PROCIR-D-19-00260

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Procedia CIRP 00 (2017) 000–000 Procedia CIRP 83 (2019) 384–388 www.elsevier.com/locate/procedia

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

Smart Service Engineering

Service Engineering 28th CIRPSmart Design Conference, May 2018, Nantes, France

Dr.-Ing. Philipp Jussena, Dipl.-Ing. Jan Kuntza*, Drs. Roman Sendereka, Benedikt Mosera a a Dr.-Ing. Philipp Jussenato , Dipl.-Ing. Kuntz *, Drs. Roman , Benedikt Mosera of new methodology analyzeJanthe functional and Senderek physical architecture

A Institute for industrial management at RWTH Aachen University, Campus-Boulevard 55, 52074 Aachen, Germany Institute for industrial at RWTH Aachenoriented University, Campus-Boulevard 52074 Aachen,identification Germany existing products formanagement an assembly product55,family a a

* Corresponding author. Tel.: +49 241 47705 224; E-mail address: [email protected] * Corresponding author. Tel.: +49 241 47705 224; E-mail address: [email protected]

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

Abstract École Nationale Supérieure d’Arts et Métiers, Arts et Métiers ParisTech, LCFC EA 4495, 4 Rue Augustin Fresnel, Metz 57078, France Abstract *Industry Corresponding author. Tel.: vast +33 3opportunities 87 37 54 30; E-mail address: [email protected] 4.0 has provided for manufacturing companies whilst simultaneously creating multiple challenges. In this new highly digitized globalized marketplace, manufacturing companies find themselves under pressure tocreating becomemultiple more service oriented andnew offer new Industry 4.0 has provided vast opportunities for manufacturing companies whilst simultaneously challenges. In this highly innovative value offerings such as smart services. These are digital data-drivenunder services that, generally, addmore valueservice in conjunction physical digitized globalized marketplace, manufacturing companies find themselves pressure to become oriented with and aoffer new product. However, classicalsuch methods of service engineering adapted sufficiently to the increasing components and requirements innovative value offerings as smart services. These are have digitalnot data-driven services that, generally, adddigital value in conjunction with a physical Abstract of smart However, services. This papermethods presentsofSmart Service Engineering as adapted a novel sufficiently service-engineering approachdigital for industrial smartand services. Smart product. classical service engineering have not to the increasing components requirements Service draws iterative development models and agile and customer-centric decrease the overall of smartEngineering services. This paperfrom presents Smart Service Engineering as aimplements novel service-engineering approach formethods industrialtosmart services. Smart InService today’sEngineering business environment, trend towards more models product variety andon customization iscustomer-centric unbroken. steps Due to this development, the of development time and achieve anthe early market success. The paper focuses theagile service and presents the interaction and draws from iterative development and implements anddevelopment methods to decrease theneed overall agile and reconfigurable production systems emerged tobased cope with various and product To design and optimize production interconnection of different elements of smart services a case studyproducts research. Finally, the families. paper illustrates successful application of development time and achieve an early market success. The on paper focuses on the service development steps and the presents the interaction and systems as Service well as to chooseelements the optimal product matches, analysis methods are needed. Indeed, most of the thesuccessful known methods aim to the Smart approach and itsservices impact on a product German medium-sized company inthe thepaper textileillustrates machine industry. interconnection of Engineering different of smart based on a case study research. Finally, application of analyze a product one productapproach family onand theits physical Different product families, however, differ largelyindustry. in terms of the number and the Smart ServiceorEngineering impactlevel. on a German medium-sized company in themay textile machine 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 © 2019AThe Authors. Published by Elsevier B.V. existing products in view of their functional and physical architecture. The aim is to cluster system. new methodology is proposed to analyze Peer-review under responsibility of the scientific committee 11th CIRP Conference on Industrial Product-Service Systems. © 2019 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of of thethe 11th CIRP Conference on Industrial Product-Service 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. assembly systems. Based service on Datum Flow Chain, the centricity; physical structure of the service products is analyzed. Functional subassemblies are identified, and Keywords: Smart services; engineering; customer digital solutions, innovation a Keywords: functionalSmart analysis is performed. Moreover, a hybrid functional physical architecture services; service engineering; customer centricity; digitaland solutions, service innovation graph (HyFPAG) is the output which depicts the 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 thyssenkrupp Presta France is then carried out to give a first industrial evaluation the proposed 1. Introduction They of generate addedapproach. value for providers and customers and ©1.2017 The Authors. Published by Elsevier B.V. Introduction offer context-related value via digital They generate added and valuedemand-oriented for providers and customers and Peer-review under responsibility of the scientific committee of the 28th CIRP Design Conference platforms [2]. The 2018. reasons manufacturingvalue companies of offer context-related and demand-oriented via digital

1.1. Industry challenges different sizes to develop successful smart services are platforms [2].struggle The reasons manufacturing companies of 1.1. Industry challenges numeroussizes and struggle span from culturalsuccessful to technical difficulties. In different to develop smart services are After many companies having transformed from product to many cases, companies lack service-engineering methods that numerous and span from cultural to technical difficulties. In solution providers in the last 15-20 years, thefrom focusproduct of many After many companies having transformed to are suited forcompanies the specificlack requirements of this taskmethods [3]. many cases, service-engineering that corporate change processes is 15-20 now onyears, digitalthe solutions as providers in the last focus ofsuch many arethe suited for the specific this task [3]. and/or 1.solution Introduction of product range and requirements characteristicsofmanufactured data-driven services. In this is context, is as of corporate change processes now onservice digitaldevelopment solutions such 1.2. Service engineering assembled in this system. In this context, the main challenge in particular for services. develop data-driven services. In industrial this context, service development is of of 1.2. Serviceand engineering Due torelevance the fast development in Companies the domain modelling analysis is now not only to cope with single digital strategies and try to maximize the added value for their particular relevance for industrial services. Companies develop Since the 1990s,product service engineering has established itself as communication and an ongoing trend of digitization and products, a limited range or existing product families, customers, by offering, for example, smart services [1]. digital strategies and try to maximize the added value for their a systematic process for the development of services. Currently Since the 1990s, service engineering has established as digitalization, manufacturing enterprises are facing important but also to be able to analyze and to compare products toitself define Smart services are based on smart products, which are customers, by offering, for example, smart services [1]. existing service engineering processes are based on a systematic process for the development of services. Currently challenges in today’s market environments: a continuing new product families. It can be observed that classical existing connected to the internet, interact withdevelopment their environment and Smarttowards services are based on smart products, which are engineering science and business modelofinnovation toolsets existing service engineering processes are or based on tendency reduction of product times and product families are regrouped in function clients features. gather environmental data. The collected data sets are connected to the internet, interact with their environment and [4]. However, the increasing digital components in service engineering science and business model innovation toolsets shortened product lifecycles. In addition, there is an increasing However, assembly oriented product families are hardly to find. combined with other data. easily accessible and gather environmental collected datain sets are engineering reveal deficits in digital the direct application of two the [4]. thefamily increasing components in service demand of customization, beingThe at the same information time a global OnHowever, the product level, products differ mainly in processed into so-called smart data. Based on this smart data, combined with other easily accessible information and classical methods of service engineering to smart services. engineering reveal deficits in the direct application of competition with competitors all over the world. This trend, main characteristics: (i) the number of components and (ii) the the smart services are designed. They can be defined as processed into so-called smart data. Based on this smart data, Methods such of as(e.g. the mechanical, DIN SPEC electrical, 1082 published in 2008 classical methods service engineering to smart services. which is inducing the development from macro to micro type of components electronical). individualized of lot physical and services. smart services designed. They be defined as were suitable for asa the slower pace change in market and Methods such DIN SPECof1082 published in 2008 markets, resultscombinations inarediminished sizescan due digital to augmenting Classical methodologies considering mainly single products individualized combinations of physical and digital services. were suitable for a slower pace of change in market product varieties (high-volume to low-volume production) [1]. or solitary, already existing product families analyze and 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 Keywords: Assembly; Design method; Family identification

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.2019.04.089

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customer needs. In the decade since its release, however, digitization has led to an exponential rate of change in the way services are created and delivered [5]. Lower barriers of entry due to the democratization of information have paved the way for stronger competition and an overall increased supply [6]. Consequently, companies must differentiate themselves and continuously deliver innovative solutions that speak to the individual customer needs. This requires a faster and less resource intensive service engineering approach, which is centered on the customer and the value it can create through data insights [7]. Since current service engineering models do not meet these criteria, we posit that the successful development and implementation of smart services requires a new agile service engineering process. 2. Methodology For the development of the Smart Service Engineering (SSE) model an action design research approach was applied. Creation was mainly based on practical experience and numerous case studies pertaining to service engineering along with a close validation process with industry partners. The result is a living document which is updated and refined in cohesion with continuous research and findings in the field of smart services. Identifying the main challenges in developing smart services with regard to existing service engineering models as well as company capabilities, the following three issues were focused on. First, development time for digital services needed to be drastically reduced. Studies show that companies that develop services successfully (top-performer) act up to six times faster than those with less success (follower) [8]. Successful services displayed a short development time and benefited from reaching the market quickly. Specifically, companies gained an advantage by being able to establish an early market presence and consistently improve their service offering through direct feedback [9]. This was best achieved by adopting an agile iterative engineering process and focusing development activities on core functions of the service to reduce its development time [10].

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Second, throughout the engineering process, customer centricity was critical in creating a service that finds wide customer acceptance on the market [11]. Hence, the SSE model would need to adopt the customer’s perspective in ideation activities and follow up with early customer testing to ensure that the service meets the customer’s needs and expectations. Finally, prototyping is an effective mean to ensure a short development time whilst allowing for the customer to remain at the center of the engineering process. That is to say, a flexible prototyping cycle that focuses on building a Minimum Viable Product (MVP), tests its functions with the customer and subsequently reworks the customer feedback into a new prototype guarantees customer needs are met and reduces wasteful activities [12,13]. Accordingly, the development time is reduced significantly. Consequently, prototyping must take center stage in the engineering process and core functionalities for the MVP should be defined as early as possible. These three key issues served as a guideline for how the model should be structured and used. Hence, the result was the following SSE model, see Fig. 1. 3. Smart Service Engineering model The general architecture of the SSE model can be described as three consecutive loops, each with three related tasks. The loops are interconnected and movement within and amongst them is fluid in order to enable an iterative process. This means that tasks of a certain loop can be repeated multiple times before moving forward to the following loop or, alternatively, certain tasks or a complete loop can be foregone altogether as needed. One could number the tasks along the three loops from one to nine; however, this was consciously avoided, as it would suggest an inflexible linear process. Instead, we arranged the tasks and created the loops in a logical yet unrestrictive order that reflects the realities of developing a digital service in practice. This logical order was also chosen to express an agile approach to service engineering. Therefore, we find a focus on the key tasks a company must fulfill to quickly reach the market. The goal of the Develop Strategy loop is to establish a

Fig. 1. The Aachen Smart Service engineering methodology

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plan of action for strategically positioning the company within its surrounding ecosystem. This is done in connection with an initial ideation of potential value offerings for the customer, which are derived from their current pain points. This output serves as an input for the second loop where the company begins to Prototype the smart service. Prototyping at this stage follows the principles of a Minimum Viable Product. Therefore, the main goal is to create a working prototype that contains the core functions of the smart service in order to test it with the user and rework that feedback once more into the prototyping cycle [10]. Moving forward to the third loop, the company begins to Enter Markets for its new smart service. This involves constructing a flexible yet sustainable business model and devising a plan for entering the market. Furthermore, all necessary resources for bringing the service successfully to market are assessed and integrated into the company. As noted earlier, Smart Service Engineering in practice is not a linear process and therefore it is unlikely for the engineering process to move successively from one task to the next. For example, companies often go back to adjust their initial strategy after gaining insights from prototyping or Fig. 1. SSE model

briefly consider their business case early on before finalizing it towards the end of the engineering process. For a better understanding of the model, we delve deeper into the individual tasks and their relation to one another as follows. 3.1. Develop strategy Analyze Ecosystem: Experience has shown that the ideal starting point for Smart Service Engineering is an analysis of the company’s ecosystem. On the one hand, this aims at identifying the largest and most financially powerful customer segments who are representative of a lucrative market and, therefore, most suitable for a deeper analysis. On the other hand, by determining the company’s current position within its ecosystem and establishing a goal for where it would like to be positioned, the company can derive a clear strategy that will allow it to reach said goal and guide its decision-making along the service-engineering process [14]. As part of this analysis, the company should define how it wishes to measure its success. Develop User Stories: Having defined key customer segments which the company should target, the next task should be to develop user stories for the customer’s typical application scenarios. Given the industrial nature of smart services, the point of interest here is, usually, the customer’s interaction with their machines and equipment. Gaining a deep understanding of this behavior and articulating it into user stories allows the company to identify potential for new smart services [15,16]. Furthermore, it guarantees a customer-centric perspective for the service-engineering process as the company begins by focusing on the customer’s immediate pain points and needs and then commences to address them rather than

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independently creating a service and then searching for a customer to market it to [17]. Formulate Value Hypotheses: The goal of this task is to ideate preliminary value propositions based on the developed user stories. Accordingly, the company should construct hypotheses for how the customer might make use of or gain value from the service offering. Furthermore, these first aspects of the value proposition serve as the foundation for the prototyping process. It is worth noting here that the outlined tasks in the first loop represent what we have observed in practice to be essential to start the prototyping process. Prototyping, as opposed to lengthy analysis and development cycles, allows for fast learning effects through the high speed of implementation [10]. Furthermore, relaying business modeling and related questions regarding service delivery toward the end of the engineering process allows the company to focus on creating and refining its value proposition in accordance with the customer. It also reduces activities that are subject to change along the prototyping cycle and possibly become redundant. Thus, the overall development time can be drastically reduced [18]. 3.2. Prototype Define Core Functions: At the heart of the SSE model is the prototyping loop. Here, the company engages in multiple prototyping cycles, which serve to quickly identify the most critical forthcoming challenges in creating the service. By iteratively developing service prototypes and testing them with the user, these challenges can be mitigated early on. In order to ensure an overall short service development time, an agile MVP development scheme for prototyping was adopted [19]. The first step in doing so is defining the core functions and requirements for the smart service, which are essential for user testing. Therefore, it is important for the company at this step to limit the functions it will begin to develop to the bare minimum. Develop Functions: Early iterations of the prototype need not be comprehensive software or any software for that matter. The prototype developed by the company at this stage must simply demonstrate the functions that are to be tested with the user. To this end, paper storyboards or App mockups can easily provide the user with sufficient interaction to determine the service’s strengths and weaknesses [19]. Test Functions with User: The goal of this step is to gain constructive feedback directly from the user on how to enhance the service. Following flexible prototyping techniques, the service prototype can then easily be reworked in correspondence with this feedback. Moreover, user testing allows the company to evaluate its initially established user needs and requirements and adjust its hypotheses and assumptions accordingly [20]. Once more this ensures a strong customer centricity in the engineering process as it is ultimately the customer’s satisfaction with the service determined through testing that serves as a marker for moving forward [21].

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3.3. Enter markets Devise Market Entry Strategy: At this point in the engineering process, the new service should have sufficiently taken shape for the company to devise a strategy for entering the market. Key components of this strategy are selecting suitable sales channels and establishing a communication strategy which directly speaks to the customer and highlight the service’s value offering [22]. Build Up Resources: Building up resources at this stage allows the company to prepare for scaling its smart service. Therefore, all necessary processes and human capital needed to ensure the long-term delivery of the service are adjusted and integrated accordingly [23]. Develop Business Case: Finally, the company should develop its business case and complete remaining aspects of its business model. Most important is establishing the cost structure and revenue streams for the new smart service [17]. Moreover, the value proposition should be concisely defined and finalized. 4. Case study: smart services in the textile machine industry Part of the development and testing of the SSE model involved a project with a German medium-sized producer of textile machinery. This company is active in different international markets and wished to gain a competitive advantage by exploring new business models and service offerings. They identified that their customers had problems controlling the quality of parts attached to their textile products using the company’s machinery. This was mainly due to a low skilled workforce that would use the machinery incorrectly and, for example, cause buttons to be punched with a sharp edge, which represented a safety hazard. Despite the necessity of ensuring the quality and safety of all their products, the cost of worker training or quality management on such a wide scale represented a huge financial burden for their customers. Consequently, the project partner was faced with the challenge of quickly offering a radical solution for their customers’ pain point at a relatively low cost. It quickly became apparent that a solution of this proportion would need to rely on data insights and the potential for innovation which they could offer. Furthermore, the time and cost constraints for addressing the customers’ problem would require a more agile and efficient development process. Given the unique challenges and requirements of this undertaking, the presented SSE model was exceedingly well suited to meet them. Together with the company, the project team started by analyzing its ecosystem to gain an understanding of their market position and the challenges posed by their competitors. Evidently, the market was extremely price- and time-sensitive. More importantly, the global market size was relatively small and competition was very high. Consequently, it could be determined that the company would need to differentiate itself from its competitors through a unique end-to-end approach to

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solving their customer’s problems whilst maintaining a short and cost-effective development cycle. Afterwards, user stories were developed encapsulating all interactions customers and their workers have with the button-punching machine in question. The broad net we cast at this stage to capture all customer and user needs would later prove critical to driving a customer-centric service-engineering process forward. Next, value propositions have been conceiving that would draw from new data insights to address our customer needs. The notion was that specific usage data would enable us to learn and refine the company’s machines as well as open the door to a variety of new digital solutions. The main solution in this case was to digitally identify quality issues arising from misuse of the machine and communicate them directly to the customer in the form of a smart service. Thus, the customer should get enabled to cut costs of manual quality control and closely bind them through this premium service. Accordingly, a button-punching machine with sensors and continuously reviewed the data gathered during different usage scenarios was retrofitted. Following the MVP prototyping process, the process starts by defining and developing the essential core functions and iteratively expanding them after user testing. The third loop of finalizing business activities regarding market entry was not fully undergone with the project partner. However, an initial draft of the new business model that would need to be adopted to support the new service offering was addressed in conjunction with the strategy development in the beginning of the engineering process. As a result, the project partner was efficiently able to develop a customer-centric smart service. Moreover, the groundwork for a business model that would rely on innovative data-driven services and allow for differentiation and market expansion was laid successfully. In addition to the successful development of a smart service, it was observed that the use of our SSE model also had a positive effect on the company’s work methods. Specifically, the agile approach followed by the SSE model in the form of iterative sprints as well as the creation of interdisciplinary project teams were adopted throughout the company for other projects with a notable improvement in overall productivity. 5. Contributions and future research This paper adds to the body of knowledge around industry 4.0 and service digitization by proposing Smart Service Engineering as a new model for developing industrial smart services. Combining agile working methods and focus on customer centricity, the model delivers high speed and quality in project implementation. We identify key deficits in current service-engineering approaches within the context of smart services and amend them through concrete recommendations and plans of action. Furthermore, the SSE model demonstrates high usability and practical relevance through its extensive testing and validation with cooperation partners from the industry.

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Moving forward, we see that there is still much room for research and optimization of the SSE model. For instance, we find that the third loop of the model requires closer testing to substantiate the proposed order and tasks further. Additionally, we believe that further identification and prioritization of tasks unique to smart services is needed to address the specific requirements of this industrial challenge. References [1] Barrett, Michael; Davidson, Elizabeth; Prabhu, Jaideep; L. Vargo, Stephen (2015): Service Innovaton In The Digital Age. Key Contributions And Future Directions. In: MIS Quarterly 39 (Special Issue: Service Innovation In The Digital Age). [2] Arbeitskreis Smart Service Welt (2014): Smart Service Welt. Umsetzungsempfehlungen für das Zukunftsprojekt Internetbasierte Dienste für die Wirtschaft. In: Smart Service Welt. [3] Bullinger et al., Springer, Service Engineering 2003 [4] Schuh, Günther; Gudergan, Gerhard; Senderek, Roman; Frombach, Ralf (2016): Service Engineering. In: Günther Schuh, Gerhard Gudergan und Achim Kampker (Hg.): Management industrieller Dienstleistungen. Handbuch Produktion und Management 8. 2. Aufl.: Springer, S. 169–199. [5] acatech – Deutsche Akademie der Technikwissenschaften (2016): Smart Service Welt. Digitale Serviceplattformen - Praxiserfahrungen aus der Industrie. Best Practices. In: Smart Service Welt. [6] Gotsch, Matthias; Fiechtner, Simon; Krämer, Hagen (2017): Open Innovation Ansätze für den Dienstleistungsinnovationsprozess. Die Entwicklung eines Service Open Innovation Frameworks. In: Oliver Thomas, Markus Nüttgens und Michael Fellmann (Hg.): Smart Service Engineering. Wiesbaden: Springer Fachmedien Wiesbaden, S. 29–54. [7] Leimeister, J (2012): Dienstleistungsengineering und -management. Springer, Berlin. [8] FIR e.V. an der RWTH Aachen, (2017): Konsortial-Benchmarking „Datenbasierte Dienstleistungen“. Aachen. [9] Husmann, M., Harland, T. and Jussen, P. (2017) ‘Service-Innovation: 6 Prinzipien für erfolgreiche, datenbasierte Service-Innovation in Industrieunternehmen’. [10] Ries, Eric (2011): The Lean Startup. How today's entrepreneurs use continuous innovation to create radically successful businesses. 1. ed. New York, NY: Crown Business. [11] Gudergan, Gerhard (2010): Service Engineering. Multiperspective and Interdisciplinary Framework for New Solution Design. In: Paul P. Maglio, Cheryl A. Kieliszewski und James C. Spohrer (Hg.): Handbook of Service

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