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1 Introduction

Since the 1990s, service engineering has established itself as a systematic process for the development of services. As a strategic and creative process that aims at designing and implementing services and individualized solutions in a model-based way, service engineering is to services what product planning and development are to physical products. Among the overarching goals of service engineering are an efficient service development and a high level of service quality. Therefore, service engineering promises a competitive advantage and an increase in quality and customer satisfaction [1, 2]. Various service engineering models and related norms have been published over the course of the past decades. Service engineering models aim at supporting companies in developing successful service offers by providing a course of action that companies can follow in the development process, and many of them have proven their value for the development of services in the past. However, the service industry has changed significantly during the last years. One major factor contributing to this change is the ongoing digitalization, which has created a variety of new challenges. It has drastically changed the way services are created and delivered [3, 4], and lower barriers of entry have paved the way for stronger competition and an overall increased supply [5]. Companies must therefore differentiate themselves from competitors by continuously delivering innovative solutions that speak to the individual customer needs.

As an effective tool to achieve this goal, digital services and smart services in particular have gained significant importance in recent years. Smart services can be defined as individualized combinations of physical and digital services that generate added value for providers and customers and offer demand-oriented value via digital platforms. They are based on smart products, which are connected to the internet, interact with their environment and gather environmental data. The collected data sets are combined with other easily accessible information and processed into so-called smart data, based on which smart services are designed [6]. In this digitalized economic environment, many companies struggle to develop successful digital services. This is partly caused by the lack of service engineering methods that are suited for this task [7], as many old models lack the flexibility that is required to keep up with the dynamic of today’s market. The increasing share of digital components in service engineering reveals deficits in the direct application of classical service engineering methods to smart services. Thus, the development of smart services requires a new service engineering process that can quickly adapt to evolving customer needs, is efficient, requires little resources and is centered on the customer and the value it can create through data insights [8, 9]. The present paper suggests a list of requirements that service engineering models need to fulfill to succeed in today’s economic environment. Before these requirements are presented, however, the following section provides an overview of selected service engineering models and norms that touch upon the topic of service engineering.

2 Selected Service Engineering Models and Related Norms

Numerous service engineering models have been published in the past. They prescribe a course of action that serves as a guideline for the development of new services and usually consists of phases that represent a high-level outline of how the model is structured and describe an overarching goal for each stage of the model. Each phase can comprise various activities that describe the individual tasks the company needs to complete to fulfill the phase’s goal [10]. There are three main types of service engineering models. The first type is the linear model in which each development phase builds on the previous one. While these models benefit from their simplicity and transparency, their one-track direction leads to a lack of flexibility and adaptability. The second model type is the iterative model, in which the individual development phases are meant to be repeated several times. With each iteration, a finer concept of the service is developed. This approach offers quick results and a flexibility in correcting mistakes; however, maintaining an overview of tasks fulfilled requires a high level of coordination. The third type is the prototyping model, which focuses on the early development of prototypes that can be tested with customers and improved based on customer feedback. Prototyping ensures a strong customer orientation although it demands a high level of complex coordination among all parties in order to function properly [7, 11, 12].

Apart from service engineering models, numerous norms touch upon the subject of service engineering directly or indirectly. Table 1 provides a short overview of German and international norms related to the service engineering process and of selected service engineering models. Due to the limited scope of this paper, these cannot be explained in detail. For further information, please refer to the sources listed in Table 1. Many of these norms and models have proven to be valuable aids for the development of services in the past. However, a majority of the available models are not compatible with today’s market situation, as they are too complex and require excessive resources and development time before initial results can be produced and tested for value [8]. They are often inflexible and thus unsuitable for most fields of applications today [32]. Researchers and companies alike agree that existing models must catch up with the new requirements for creating innovative service solutions. The next section explores what requirements a model needs to fulfill in order to be suitable for today’s market.

Table 1. Selected service engineering models and related norms
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3 Requirements for Modern Service Engineering Models

As explained above, many service engineering models are no longer suitable for today’s economic environment as they lack agility and flexibility, which calls for the development of new service engineering methods. In order to identify requirements for service engineering models for the digital age, it seems sensible to have a look at methods that have recently proven their utility and success in practice and to find out what characteristics they share. While no all-encompassing recipe for service engineering has emerged yet, certain methodologies have established their worth in adding value out of a specific focal point. An analysis of recommendations from recent literature and trends in service engineering reveals three main best practices that exhibit proven results in various industries. These will be discussed in the following paragraphs.

The first best practice found in several recent and successful service engineering models is user centricity. While a clear focus on customer needs has always been important in service engineering, it has gained importance in the digital age as customers have become more empowered through a greater selection of increasingly individualized products. In the sense of customer centricity, an offering is created by integrating the user into the entire development process and thus co-creating a positive customer experience. The closer a user is involved in the development process, the more the offering will reflect their needs. Customer ideas can be used to create a first prototype, which is then presented to the user for testing and feedback. This is repeated in as many iterations as needed until the prototype and user expectations are matched. This approach requires an extensive collection and analysis of data concerning customer satisfaction and experience, but it also allows for a high degree of customization [33,34,35].

The second best practice is the utilization of service ecosystems. Services are normally not developed and implemented by a company alone but through a collaboration of a multitude of actors and resources. Service ecosystems can be defined as “relatively self-contained, self-adjusting system[s] of mostly loosely coupled social and economic (resource-integrating) actors connected by shared institutional logics and mutual value creation through service exchange” [36]. They provide a fertile environment for companies to innovate and realize challenging ideas as they enable them to partner with actors that can complement and expand their own resources. Today, digital infrastructures allow for more diverse actors and more resources to be integrated in a service ecosystem, which cultivates value co-creation on an immense scale. In a service ecosystem, all actors should be empowered by gaining access to the various ecosystem assets and infrastructures. That way, companies can tap into a wealth of resources that a service design model can develop into an innovative service offering [4, 36, 37].

The third best practice can be described by the term ‘agile’. An agile mindset involves a quick and flexible development process, is customer centric and collaborative in nature as cross-functional teams are brought together. Moreover, it is output oriented and entails constant reflection on previous work to identify shortfalls. In addition, the agile mindset is efficient in its use of resources [38, 39] and enables the design process to be adapted to changing requirements at any time. Furthermore, it allows for a shorter time to market. One essential method is the development of a minimum viable product (MVP), which means that a new offering is created with the bare minimum of core features that enable sufficient interaction for constructive user feedback. The final product is completed after multiple iterations of the MVP feedback loop. Agile approaches also tend to follow the lean mindset, which includes a reduction of waste and aims to achieve more with fewer resources, including time and information [40, 41].

The requirements resulting from these best practices, their purpose and suggestions for their application can be found in Fig. 1. For further information, please refer to the references listed in the table [2, 9, 36,37,38,39,40,41,42,43,44,45,46,47,48,49,50].

Fig. 1.
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Requirements for modern service engineering models

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4 Outlook

Even though the present paper has argued that many existing service engineering models are no longer suitable for today’s economic environment, it is worth mentioning that some promising models that incorporate the requirements listed in Fig. 1 have already been or are currently being developed. Among these are Smart Service Engineering [51], Multilevel Service Design [52], Design Thinking for Industrial Services [53] and Recombinant Service System Engineering [54]. Whether these models will prove their success in practice in the long run remains to be seen.

5 Acknowledgment

This research and development project Digivation constitutes the superordinate project of the funding line Service Innovation Based on Digitization and is funded by the German Federal Ministry of Education and Research within the research program Innovations for Tomorrow’s Production, Services and Work under the registration number 02K14A221. The author is responsible for the contents of this publication.