Erik Dolk & Filip Magnusson
Business model renewal & the value of a digital solution
A case study of digital transformation in manufacturing companies
Business & Economics C-thesis
Term: ST-20
Supervisor: Sofia Molander
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Abstract
Digitalization has in many ways transformed the value offering for companies in several industries. Now a days, most manufacturing companies face the challenge to understand how to form their business models to ensure that it reflects the real value of a digital solution. A company with a traditional structure will most probably struggle with today’s constant need of business model renewal, where the business models of today should be more flexible and adapted to long term opportunities. In this qualitative extensive case study, the authors aim is to further explore values created by digital solutions and digital transformation of business models in manufacturing companies.
The result includes a framework of the most prominent values created by digital solutions and how they affect the business models in manufacturing companies.
The empirical findings show that developing a business ecosystem with the customer as provider of the data is key for digitally transforming the business model. The value of data facilitates the process of developing comprehensive product-service systems in order to provide values such as availability and proactive abilities.
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Table of contents
1. Introduction ... 6
1.1. Problem background ... 6
1.2. Problem discussion ... 8
1.3. Purpose ... 8
2. Theory ... 9
2.1. Business models ... 9
2.1.1. Digital transformation of business models ... 10
2.2. Creating value with digital solutions... 10
2.2.1. Digital capabilities ... 11
2.2.2. Value co-creation mechanisms ... 12
2.2.3. Value co-creation enabled by digitalization capabilities ... 13
2.3. Servitization & digitalized product-service systems ... 13
2.3.1. Smart service delivery ... 15
2.3.2. Smart product optimization ... 15
2.4. Business model renewal ... 16
3. Method ... 20
3.1. Research approach ... 20
3.2. Selection of case firms ... 20
3.3. Data collection ... 21
3.4. Data analysis ... 22
3.5. Validity and reliability ... 22
3.6. Ethical considerations ... 23
4. Results... 24
4.1. Values from digital solutions ... 24
4.1.1. Flexibility ... 24
4.1.2. Availability ... 25
4.1.3. Traceability, control & accuracy ... 25
4.1.4. Communication ... 26
4.1.5. Scalability ... 26
4.1.6. Data collection ... 27
4.1.7. Proactive abilities ... 28
4.1.8. Ecosystems ... 29
4.2. Business model renewal ... 30
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4.2.1. Drivers for digital transformation ... 30
4.2.2. Revenue models ... 31
4.2.3. Value based pricing ... 32
4.2.4. Data versus revenue ... 33
4.2.5. Sharing economy ... 34
5. Analysis ... 36
5.1. Values created by digital solutions ... 36
5.2. Incorporating digital solutions ... 37
5.3. Business model renewal ... 39
6. Conclusions ... 42
6.1. Contributions and proposals for further research ... 43
References ... 44
Appendix ... 48
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1. Introduction
In this chapter, the authors lay out the context of the study by presenting the background of the research area, which is followed by a problem discussion of why the research is relevant.
1.1. Problem background
Digital transformation is an ongoing phenomenon that is happening all around us, all the time (Schallmo et al. 2017). The force of digital transformation affects many aspects of our society, including economies. The rise of smart connected devices has during the latest century changed the way business and customers communicate and has enabled interactions on an individual basis (Schallmo et al. 2017). Mobile technologies are able to generate customer data on a massive scale which has facilitated the possibility of creating tailored products and solutions for each customer and create customer value that fits specific needs (Schallmo et al. 2017). Digital transformation has also given rise to business model innovation. The collection and processing of data, automatization, and the creation of direct customer access are some of the factors that enables digital transformation of business models (Schallmo et al. 2017).
Digitalization and digital transformation are terms that are used interchangeably in literature (Mergel et al. 2019). In the context of manufacturing companies, digitalization is defined as intelligent and connected machines that are enabled by information and digital technology (Lerch & Gotsch 2015). Digitalization in manufacturing companies gives numerous advantages, including higher reliability, greater efficiency as well as possible optimization capabilities which enhances the value that can be delivered to clients (Porter & Heppelmann 2014).
There are numerous definitions and views of what digital transformation is, and there is no existing definition that is commonly accepted (Schallmo et al. 2017).
Schallmo (2017) describes digital transformation as the use of technology to radically improve the performance or reach of enterprises. The use of new technology is affecting every industry and results in changes in customer relationships, internal processes and value propositions (Schallmo 2017).
Nambisan et al. (2017) characterized digital transformation as a force that initiates and consequently induces change in market offerings and business processes, or as mentioned by Brownlow et al. (2015), a cause for companies to reconsider what role and value that data holds in their current business model.
A characteristic that many authors see as a result of digital transformation is the
7 changing ways how companies create value (Vial 2019). In many cases the company has to acknowledge fundamental change when it comes to its mindset, systems and tools in order to reposition itself because of digital transformation (Gupta 2018). For many companies, digital transformation becomes a decisive factor, whether it can either create competitive advantages through implementing digital services, new revenue streams or on the other hand present a threat for the companies lagging behind (Lerch & Gotsch 2015).
Based on the proposed characteristics and definitions above, one can conclude that digital transformation is viewed as a phenomenon with big societal impact.
It has a possible disruptive effect on traditional business models and value chains by undermining existing ways of delivering a service (Mergel et al. 2019).
To implement or to further incorporate digital solutions into business models represent opportunities for manufacturing companies to improve their performance by achieving higher margins, increase their resistance in relation to other companies by differentiating their value proposition, as well as becoming less vulnerable from fluctuations in demand resulting from change in economic cycles (Martín-Peña et al. 2020).
While digital transformation presents itself with mostly positive connotations, it does present challenges to companies that seek to form their business models so that it reflects the true value of the digital services that they offer their customers. To digitally transform the company’s business model is not only a possibility to achieve higher firm performance by accessing new markets and gaining market shares, but also it is emerging as a necessity in some cases to not get replaced by other firms (Mergel et al. 2019).
Demanding customers who seek personalized solutions has made many manufacturing companies to shift their focus from only selling products to integrated solutions consisting of services (Davies 2004). There is an unanimity of continuously shrinking margins for selling goods which is forcing manufacturers to become more service oriented to find new revenue streams that can contribute to the financial result (Gebauer et al. 2005). Moreover, Martín-Peña et al. (2020) consider that digitalization and servitization are highly related. Persona et al. (2007) have described digital technology both as a driver for servitization as well as an enabler of different dimensions of servitization that were not possible before.
In order to fulfil the potential for implementing digital solutions and catch the value from it, the business model needs to address the requirements of the users
8 with an efficient and effective approach (Ratliff 2002; Ballon 2007). According to Chesbrough and Rosenbloom (2002), the business model unlocks inherent values from a technology as it creates a heuristic logic that connects technical potential with the realization of economic value.
1.2. Problem discussion
Many manufacturing companies of today face the challenge of how to digitally transform their business models so that it is adapted to both the opportunities as well as the threat that digital solutions may impose if a firm is lagging behind competitors (Martín-Peña et al. 2018). The opportunities which digital solutions constitutes for companies are many. Servitization and digitalization have positive effect on firm performance, and digitalization positively mediates the relationship between servitization and firm performance (Martín-Peña et al.
2020). Other research points towards an increasing importance for manufacturing companies to integrate services into their business models in order to help differentiation and value creation (Kohtamäki et al. 2013).
Although research has been conducted, the fields of study regarding digital servitization and digital transformation of business models is not exhaustive as they are contemporary and still evolving phenomenons, which is why further research needs to be done (Martín-Peña et al. 2018). Also, Yuan and Zhang (2003) argue that the success achieved by hi-tech companies is not about the technological application itself, but rather the business model behind it.
Based on previous studies, the authors have identified a need for further studies consisting of a more distinct exposition of values created by digital solutions and how manufacturing companies’ business models are affected by them. To facilitate the understanding for the relationship between the values created by digital solutions and business model renewal, this study intends to contribute to a framework of the values created by digital solutions and describe their implications on manufacturing companies’ business models.
1.3. Purpose
The background and problem discussion above results in the following purpose:
Describe how manufacturing companies have realized and incorporated the values of a digital solution to form their digital and renewed business models.
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2. Theory
In this chapter, the theoretical base of the study and the research field which the authors intend to further explore is presented. The chapter first presents definitions of business models and digital transformation of business models as they are important concepts for the understanding of the study. Thereafter, previous theory about the value creating abilities of digital solutions is introduced, followed by theory about product-service systems and business model renewal.
2.1. Business models
The term business model is missing a univocal definition partly because of the varying components that different authors attributes to a business model (Bouwman et al. 2018). This is also because different authors use the term even though they are not referring to the same thing (Osterwalder et al. 2005).
Bouwman et al. (2018) defines a business model as the logic by which an organization or a network of organizations creates and captures value for both the consumer and the business. In accordance with this, Schallmo (2013) means that a business model describes what benefits are provided to customers and partners, as well as how these benefits returns to the company as revenue.
Bouwman et al. (2018) means that a business model consists of business model components, such as a value proposition and revenue model, whereas Schallmo (2013) means that business models are comprised of a number of different dimensions and elements. The ultimate goal is to form the dimensions and elements in such a way that they reinforce one another in order to make the business model harder for competitors to replicate. The dimensions and elements which they contain described by Schallmo (2013) are the following:
• The customer dimension which includes the customer segments, channels and relationships.
• The benefit dimension that consists of products, services and values.
• The value-added dimension that contains resources, skills and processes.
• The partner dimension, which holds the partner, the partners channels as well as partner relations.
• The financial dimension that consists of revenues and expenses.
10 2.1.1. Digital transformation of business models
In an attempt to develop a clear definition of the digital transformation of business models, Schallmo (2016) used his definition of business models above and combined it with definitions of digital transformation (DT). This resulted in the following definition:
The DT of business models relates to individual business model elements, the entire business model, value-added chains, as well as the networking of different actors in a value-added network. The degree of the DT includes the incremental (marginal) as well as the radical (fundamental) change of a business model. The reference unit with regard to the level of novelty is primarily the customer, but a DT can also affect its own business, partners, industry, and competitors. Within the DT of business models, enabler(s) and technologies (e.g.,big data) are used to generate new applications or services (e.g.,on-demand prediction). These enablers require skills that enable data collection and exchange as well as the ability to analyse, calculate, and evaluate options.
The evaluated options are used to initiate new processes within the business model. The DT of business models is based on an approach with a sequence of tasks and decisions that are related to one another in a logical and temporal context. It affects four target dimensions: time, finance, space, and quality. (Schallmo et al. 2016, s. 46)
As this definition are the product of two conjoined key terms for this study, it is a valuable asset because that it gives a clear description of the characteristics of a digitally transformed business model and its respective elements. It can be used as a framework for assessing the level of digital transformation that a certain business model has underwent.
2.2. Creating value with digital solutions
Some research has described services as a perspective on value creation rather than a market category (Edvardsson et. al. 2005). The origin of value creation is in this service centred view derived from the customers use of the product and services by applying their set of knowledge and skills (Vargo et al. 2008). This implies that the customer is responsible for the creation of value and that the provider acts as a co-creator of value (Grönroos 2008). However, the occurrence of co-creation of value is limited to when both the provider and customer interact in the value creation process (Grönroos & Voima 2013; Vargo et al. 2008). The quality of this interaction is key to value co-creation (Grönroos
& Voima 2013; Vargo et al. 2008). Conducted research points towards the increasingly bigger role of digitalization in supporting the interaction and value co-creation with customers (Lerch & Gotsch 2015).
11 2.2.1. Digital capabilities
By studying four large manufacturing firms established in Europe, all of them prominent in their respective industries in offering advanced services supported by digital platforms, Wincent et al. (2017) examined what digital capabilities that are necessary in supporting the value co-creation with customers and how these digital capabilities enables companies to interact and engage in value co-creation activities. This resulted in three different categories of digital capabilities;
intelligence, connect and analytic.
The intelligence capability refers to the ability to use hardware components to sense and register information with little human intervention. This has two elements to it; first it enhances intelligent functionality to a product or machine, making it able respond to changes in its environment (Wincent et al. 2017). Second, it gives capacity to collect information about the usage of product as well as the condition of certain components (Wincent et al. 2017).
The connect capability is the ability to connect digitalized products through wireless communication networks (Wincent et al. 2017). This enables data to be stored in a virtual platform, thus reducing the need for storage and processing capability onsite which in turn leads to better efficiency and lowered costs.
Another advantage related to connect capability is connectivity between products; either between one product to another or connectedness between many at the same time. This represents potential to achieve new value creation scenarios via simultaneous monitoring, control and optimization activities (Wincent et al. 2017).
Lastly, analytic capability is the capability to, through large amounts of information acquired from intelligent products and networks, transform data into insights and turn these into actionable directives for a company (Wincent et al. 2017).
Wincent et al. (2017) identified two elements related to analytic capability. One of these was predictive insights, derived from analysis of data, carrying operational value for a company. Predictive insights give proactive abilities;
enabling a company to seize opportunities as well as position itself when facing potential risks. As an example in practice, this can be used to avoid stoppage in production through preemptive maintenance (Wincent et al. 2017).
The second element related to analytic capability observed by Wincent et al.
(2017) in the case companies are simulation capabilities made possible by constant inflow of data. Through customer centred simulations, different
12 scenarios can be visualized through input and configuration of different variables, and thereby the case companies studied by Wincent et al. (2017) could accomplish optimization of a customer’s key performance indicators.
2.2.2. Value co-creation mechanisms
The digital capabilities mentioned above increases interaction between the provider and the customer, and thereby enables value co-creation. Wincent et al. (2017) found two mechanisms that contributes to the value co-creation process, which was identified as perceptive and responsive mechanisms. The perceptive mechanisms give the company the ability to identify, assess and take action to address specific customer needs (Wincent et al. 2017). Information that is provided by intelligent products over long periods of time enables longitudinal analysis, which potentially can give insights on best use of a certain asset. Analysis like the one described represents in many cases unexploited opportunities in the form of continuous review of a company's operational elements. Thereby, digital capabilities extend a manufacturing company’s ability to capture customer needs by giving additional space to support the customer and co-create value together through improvement of operational processes and resource use. Furthermore, with intelligent products connected to cloud-based platforms, manufacturing companies can quickly customize digital solutions to specific customer needs (Wincent et al. 2017).
The other value co-creation capability is the responsive mechanism (Wincent et al.
2017). It refers to how quick, if not proactive, that manufacturing companies can address their customers changing and emerging demands and as a result co- create value. The customers operate in fast paced and changing markets which requires quick solutions. An environment in which manufacturing firms, through virtualized analysis and product solutions in cloud platforms, can provide customized product functionalities and limit downtime (Wincent et al.
2017). Digitally connected products enable the manufacturing companies to develop and distribute functionalities at marginally low costs and gives opportunity to offer flexible revenue models to customers linked to variations in their usage. Furthermore, proactive measures can be taken on predictive insights, like scheduling preemptive maintenance for an asset with intervals customized to its breakdown cycles (Wincent et al. 2017).
The perceptive and responsive mechanisms mentioned above are enabled by digital capabilities (Wincent et al. 2017). They give the manufacturing firm insights in the customer’s needs and makes it engaged in the customers
13 processes and resources, thereby promoting the parties’ ability to cooperatively discover and utilize opportunities for value creation. They also give the manufacturing company ability to play a supporting role in their customers strive to be responsive and flexible in changing markets, thereby interacting to co-create value.
2.2.3.Value co-creation enabled by digitalization capabilities Wincent et al. (2017) illustrated that the sphere in which interaction between the customer and service provider occurs is expanded through the value co-creation mechanisms mentioned above. The expansion of the value co-creation sphere is a result of increased breadth and depth of the interaction between customer and provider. Increased breadth refers to how the provider can offer more services, as well as identify new opportunities to co-create value through elevated understanding of the customers processes. Increased depth is a result of close co-operations and partnerships between the customer and the provider.
The model developed by Wincent et al. (2017) shows how digitalization capabilities give rise to opportunities for value co-creation through customer interaction. Thereby, servitization that is enabled or supported by digital solutions unlocks a value-creating exchange between the parties that would not be present without digital capabilities.
2.3. Servitization & digitalized product-service systems
To fulfil highly individual customer needs, physical products can be bundled with intangible services. For more advanced services, product-service systems (PSS) can be made, where digital architectures are used to provide services independently and proactively (Tukker & Tischner 2006; Goedkoop et al. 1999).
Tailoring and delivering individual solutions to the customer is not only bringing more value, but also increases the competitiveness of the provider (Boyt &
Harvey 1997).
Developing servitization by using digitalization, products can be equipped with digital systems that allows the products to communicate with each other and operate independently of human intervention. This has completely changed the industrial landscape since manufacturers are now able to create digital systems to support their services and create new industrial product-service offerings. An opportunity arises for companies to add more value to their customers by creating more customized solutions and also to gain an advantage against their
14 competitors. Ignoring these forces as a manufacturer will probably lead to a difficult struggle of always being a step behind their peers (Munster & Meiren 2011).
Lerch and Gotsch (2015) are describing the progress of incorporating servitization and digitalization as a path consisting of four steps, where each step reflects the individual characteristics and activities of the firm. Manufacturers can move along the transformation and reach new stages through innovation. Information and communications technology (ICT) are presented as a central part of the process. ICT enables the delivery of services and the improvement of service-oriented strategy, allowing industrial firms to adopt new business models based on the ability to use and rapidly process real-time data. This opportunity is particularly important for products with long life cycles (Belvedere et al. 2013).
1. Manufacturer: Obligatory product-related services are provided, including installation, repair and maintenance. ICT solutions are used on a very basic level, but not in a way to differentiate in the market.
2. IT-based services: ICT solutions are used in a more developed way to improve existing service offerings. For instance, machines can be controlled and monitored over distance through teleservices, which creates an opportunity to provide services more efficient with less resources.
3. Pure digital services: ICT systems based on software simulations, virtual or augmented reality applications and digital technical analysis enables companies to offer novel services. As a result, the company's core offering is significantly enhanced and extended into new service offerings.
4. Digitalized PSS: Includes providing complex PSS systems incorporated with ICT solutions. The product-service bundle reaches a level of operating in an intelligent and independent way with high availability.
The operation is optimized and in the same way input resources are reduced.
15 2.3.1. Smart service delivery
Smart service delivery contributes support for maintenance and repair. It allows companies to act proactively to avoid breakdowns by using intelligent systems that communicate their service needs (Lerch & Gotsch 2015).
Lerch and Gotsch (2015) conducted a series of research studies of German manufacturing companies to create a better understanding of how PSS are implemented to create smart service delivery. The research demonstrates an example of how a constructor engaged in developing, designing and assembling stages can use smart service delivery to streamline maintenance and optimize resources. A discovered challenge for this business was the long lifecycle of the stages and not always being able to anticipate the appearance of service needs.
Due to the often far distance to the customers, reactions to breakdowns were very slow since maintenance services were performed manually. By integrating digital service architectures into the technical structures of the stages, data could be collected, processed digitally and for instance predict when components are likely to fail. As a result, time that was earlier spent on error diagnostics could be reduced and optimized for maintenance (Lerch & Gotsch 2015).
2.3.2. Smart product optimization
Lerch and Gotsch (2015) argues that one of the objectives of implementing digital service concepts is to be able to provide availability guarantees for the customer. It requires a digital architecture and a communication network to be compatible with customers’ sites. As a result, the customers are able to avoid expensive production outages and shutdowns. According to Lerch and Gotsch (2015), system availability could increase by several percentage points when using the right digital architecture and service structures.
When digital and physical systems are combined, Lerch and Gotsch (2015) describe how comprehensive remote systems can be created to collect data. The data can then be analysed and processed and pave the way for lifecycle cost guarantees. Another dimension of using collected data from the customer is the innovation of improving existing product and service offerings. This way of creating service innovation is called the digital brain stage (Lerch & Gotsch 2015).
A study of a global manufacturing company was made by Lerch and Gotsch (2015) to find out how they approached a digital brain and their main goals of increasing digitalization into its products. The main results showed that the
16 company was seeking specifically for two goals: First, the company wanted to make their machine’s systems controllable and transparent to forecast the full lifecycle cost of it. It was made by integrating condition-monitoring systems to identify decisive availability rates for different components and component groups. By collecting data from each component, failure behaviour, failure cause, and spare parts requirements could be evaluated and create a basis for availability and life cycle guarantees. Second, the operational collected data was used to support systematic improvements in new products, which could also shorten product development cycles (Lerch & Gotsch 2015).
The vision for the manufacturing company investigated by Lerch and Gotsch (2015) is to create an availability- and cost-oriented PSS. It paves the way for acting proactively to identify repair and maintenance needs. Forecasting lifecycle costs and create availability guarantees will also reduce the risk associated with offering a comprehensive PSS, since the manufacturer have the machine in control by receiving transparent data from it and already know which costs that will arise during the cycle (Lerch & Gotsch 2015). As a conclusion, Lerch and Gotsch (2015) argues for technological solutions in a way where it creates competitive advantages in the current market field and also feeds the company’s innovation to keep the competitive advantage in the future.
2.4. Business model renewal
Al‐Debei and Avison (2010) describes the business model as one of the company’s most important organizational assets where the definition of it as a vital necessity. As the digital environment is changing rapidly, an explicit business model will enhance digital business manager’s control over the businesses. The business model also provides necessary and appropriate information to extend the knowledge for the managers of how to adapt their strategy to a constantly changing market (Al‐Debei & Avison 2010). Michalik et al. (2018) argues that manufacturing enterprises need to rethink their way of how they conduct business and more focus should be shifted to the digital elements of the business model. This is since data is an ever-increasing status in the value chain and has transformed from being an asset or by-product towards a resource. The utilization of data in a purposeful way is nowadays the critical resource and must be included in the economic evaluation and development for the manufacturing industry. As a result, new digital business models emerge (Michalik et al. 2018).
17 According to Michalik et al. (2018) the transformation process of a traditional, non-digital business model in manufacturing towards a digital business model is a challenge for most companies since it is an ongoing and developing phenomenon changing from one day to another. Implementing PSS and developing digital services to complement the existing product portfolio is a way for manufacturers to utilize the data without having to replace the existing infrastructure (Michalik et al. 2018).
Michalik et al. (2018) present an approach of towards the conceptualization of a framework for a business model resulting from the implementation of a PSS based on the integrations of customer data. The results are gained from findings in a use case based in the German manufacturing industry and scientific concepts of digital business models (Michalik et al. 2018). The study by Michalik et al. (2018) were conducted using different workshops for the manufacturing company. The workshops created a process as they were divided in different focus areas starting with recording the current business model, followed by identification of new hybrid business models and lastly the concretization of individual roadmap development.
The results from recording the current business model showed that the manufacturer provided traditional value. Mostly by offering physical products but also by providing a few services to complement the product portfolio.
Although, Michalik et al. (2018) considered that the data was not utilized in the right way since sensor or process data did not occur as a key resource. By analysing the current business model, a roadmap to development could be made where some bullet points presented a synthesized synopsis of the insights. First, Michalik et al. (2018) point out the importance of continuously harvesting and utilizing the data generated by the customers using physical products to construct digital services. Second, the innovation process needs to be executed incrementally, due to limited availability of resources. The physical infrastructure including sensory attached to the products can only receive a minimal or no change at all. Lastly, the acquisition of data necessary for the digital business model cannot be disturbed by issues of data sovereignty (Michalik et al. 2018).
The initial concept for a digital service complementing the physical products is sketched by Michalik et al. (2018) to create a picture of how PSS entails the implementation of predictive maintenance mechanisms, see figure 1. It creates an opportunity to detect wearing of tools early on and thus initiate maintenance protocols proactively. As a result, the customer’s machine availability would
18 drastically increase. Benefits would also be created from the manufacturer’s perspective such as maintenance personnel and equipment would be deployed more efficiently, creating the fundamentals for higher sustainability of the business model (Michalik et al. 2018).
Figure 1. Cycle of data and values by the example of predictive maintenance.
(Michalik et al. 2018, s. 313).
During the workshops, further ideas were developed based on this initial concept about the business model. The significant finding was that there was a need for a modular service package including for instance higher spare parts availability or improved documentation to address multiple customer needs and not only machine availability as the primary difference in the value proposition (Michalik et al. 2018).
Michalik et al. (2018) highlight the importance of utilizing and processing data as a key resource when transforming from a just a manufacturer to a solution provider, which is something the traditional business model is not pointing out.
To exploit the potential of data, Michalik et al. (2018) argues that PSS is the primary way to construct digital business models. Furthermore, it is necessary to identify how data changes the nature of the business model components, as seen in figure 2 (Michalik et al. 2018).
19 Figure 2. Utilizing customer data as a manufacturer. (Michalik et al. 2018, s. 313).
Finally, Michalik et al. (2018) mentions that the ultimate scenario is to implement a platform where all services can be offered and activated modularly.
The focus of the business model switches from being only a supplier to instead provide maintenance solutions as a whole.
When it comes to the money-making scheme behind the digital services the underlying business logic is flexible. Business models such as subscription, on demand, or primary and premium model need to be implemented (Michalik et al. 2018). Furthermore, Michalik et al. (2018) highlight the importance of identifying the business ecosystem and build a digital infrastructure, where the customer takes the role of a data provider.
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3. Method
Based on previous studies, the authors have identified a need for further studies consisting of a more distinct exposition of values created by digital solutions and how manufacturing companies’ business models are affected by them. To facilitate the understanding for the relationship between the values created by digital solutions and business model renewal, this study intends to contribute to a framework of the values created by digital solutions and describe their implications on manufacturing companies’ business models.
Describe how manufacturing companies have realized and incorporated the values of a digital solution to form their digital and renewed business models.
3.1. Research approach
As this study is exploratory in its nature, it has been conducted by following a qualitative extensive case study approach. This is a desirable way of studying a phenomenon that is changing and evolving (Gephart 2004). Other studies that has explored servitization and value creation of digital solutions, like the one conducted by Wincent et al. (2017), has also followed a similar approach.
Extensive case study is fitting when the objective of the research is to extend prior theory, which is the aim of this study (Eriksson & Kovalainen 2008).
Extensive case study is described as research that “focuses on mapping common patterns, mechanisms and properties in a chosen context for the purpose of developing, elaborating or testing theory” (Eriksson & Kovalainen 2008, s. 121).
Moreover, the cases are not described in every detail because of the researchers predefined and distinct research interest. In comparison with intensive case studies, extensive case studies may appear sparsely described (Eriksson &
Kovalainen 2008). The cases in extensive case studies has been described as mini-cases, or even sub-cases, as a result of their well-defined, or sometimes even restricted nature (Stoecker 1991). By describing servitization in manufacturing companies supported and or enabled by digital capabilities, this study intends to develop further understanding for the value of digital solutions and its implications on business models.
3.2. Selection of case firms
The case companies that have been chosen are large international manufacturing firms who operate in different industries and are based in Sweden. They have been selected because they offer physical products bundled with digital solutions and are prominent in their respective industry in digital offerings. Their selection
21 also owes to their size which has enabled them to invest in digital capabilities, since they have the financial means and durability to do so. The sample of case firms has been made because of theoretical reasons. As the study’s aim is to extend existing theories regarding digital solutions and their implications on business models in manufacturing firms, theoretical sampling is preferable because the process of interest (i.e. servitization supported and/or enabled by digital services) must be observable in the chosen case companies to be relevant (Eisenhardt 1989). Random selection would most likely be a waste of resources.
Variations in industry affiliation, whether the companies mainly offer services to business- or consumer customers and how far respective company has come in digitally transforming their business models give possibility for theoretically interesting comparisons, which is one of the advantages of theoretical sampling in case study research (Eriksson & Kovalainen 2008). The selected firms are also estimated to be similar enough to generate new knowledge that can be added to existing theories relating to servitization through digital solutions and digital transformation of business models in manufacturing companies.
Eisenhardt (1989) argues that quantity of cases should be limited to a level where additional cases only contributes marginally to the study. She estimates this level to be between four to ten cases in an extensive case study (Eisenhardt 1989).
Due to this study’s methodological approach; a multiple extensive case study research, five firms were chosen to be included. The number of case companies selected is estimated by the authors of this study to be adequate to contribute to existing theory.
3.3. Data collection
The data was collected by using in-depth interviews with informants who have management positions regarding digital service development in the chosen case companies. Open-ended questions were used because they give opportunity to informants to respond as they see fit and thereby for the study to capture diverse and unexpected information (Walle 2015). Furthermore, this type of interview is appropriate when the informants have specialized knowledge which the researcher wants to explore (Walle 2015).
The interviews were conducted in a manner which Patton (1990) describes as
“interview guide approach”. The topic and issues covered were defined on beforehand, but the order in which the questions was asked and how they were formulated was decided by the interviewer. In this way the data collected was
22 not constrained by standardized questions. Instead it was flexible to certain informants and circumstances which ensured relevance of questions and answers. Also, this method allows for filling gaps in data collected from the informants in a natural way due to the more conversational type of interview, thereby giving room for follow-up questions were the interviewer see fit.
3.4. Data analysis
Yin (2014) differentiates between two approaches to data analysis in case studies. One where the analysis is conducted with theoretical propositions and a coding system formulated beforehand, and another where analysis is based on development of a case description in which themes, categories and codings are extracted from the empirical data. This study practiced the second as it catches natural variations in data, and it is favoured by several business researchers (Eisenhardt 1989; Fox-Wolfgramm 1997). The analysis was initiated through within-case analysis, which was followed by cross-case analysis in order to distinguish similarities and differences between the cases as well as how the cases related to existing theory (Stake 1995). The analysis was made to find patterns in the empirical data in order to make comparisons with existing theory and find similarities, differences and also to add new knowledge to the field of research.
3.5. Validity and reliability
Validity refers to what extent an instrument measures and depicts what it is supposed to measure (David & Sutton 2016). To ensure the validity of the empirical findings produced by this study, the data has been collected through a careful approach towards the selection of case the firms as well as the questions presented to the informants. The questions have been directed towards two key areas; what values the case firms experience with digital solutions and the effects of digital solutions on the firm’s business model in various aspects. The phenomenon of interest; servitization enabled and/or supported by digital solutions, have been observable in the selected companies which is crucial to capture relevant data. This is in accordance with the purpose of the study and upholds the relevance of the empirical findings.
The informants who have participated in the study have key roles in the case firms digital service development programs, which also speaks for the validity of the findings produced by this study. The informants’ identities and their company affiliation has not been declared. This is to ensure that their answers
23 are not affected by exposure of their identities, which Bryman and Bell (2013) states as a potential risk when declaring the identities of the informants. The data collected from the different case firms has been processed consistently in the same way through codifying of transcribed interviews to minimize the risk of administrative flaws that could affect the data or other misrepresentations of the findings. This speaks for that the empirical findings properly depicts the reality.
In qualitative research like this study were the data is collected through interviews, the researcher is an integrated part of the data collection process (Denscombe 2018). Therefore, the question of reliability of a qualitative study is directed towards whether if other researchers would generate the same results and conclusions (Denscombe 2018). When facing the question whether the empirical findings of this study should be considered reliable in terms of what results and conclusions other researchers would establish, one could not with certainty argue for either yes or no. A qualitative study were data is collected through interviews like this cannot be replicated in the exact same way by others.
The reasons for this is partly because the passing of time inevitably results in changes in the phenomenons that are the subject of a study, and partly because the social context in which the data has been collected is almost impossible to replicate (Denscombe 2018). A qualitative case study that includes a relatively small sample of case firms requires an approach where the reader of the study assesses to which extent the findings could be applicable to another similar situation (Denscombe 2018). Therefore, the assessment of whether the results and conclusions made from this study is applicable to other manufacturing companies or not should be made by the reader. However, the findings are corresponding with previous research, which speaks for the validity and reliability of the findings produced by this study.
3.6. Ethical considerations
With the informant privacy in mind we have decided not to declare their names or their employers name. This is also because it does not seem to add any value for the readers of this study to know the identities of the informants or their company affiliation. The informants have been informed of how we have processed their personal information and their consent with regard to their participation as well as how their personal information has been handled has been obtained.
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4. Results
In this chapter the empirical findings obtained from the interviews with the case firms is presented. The headlines are based on themes and categories generated by the codifying of the collected data.
4.1. Values from digital solutions
The following are values which are either created by or supported by digital solutions. They were identified through analysis of the gathered empirical findings. While some of the values identified were exclusive to some firms, others were shared by several of the case firms. Through categorization of the different values, the authors seek to create a clear overview and to create better understanding for the characteristics of the values created by digital solutions.
4.1.1. Flexibility
The term flexibility refers to how digital solutions enables companies to update and give new functionalities to their physical products depending on customer needs. By giving the option to scale up and down in functionality with minimal delay, one of the case companies could provide their customers with the solution that they required in their production when they needed it. Thus, giving the customer increased capability to manage a wider product portfolio and introduce new products into their production by changing functionality in tools or machines. Also, the manufacturing firm offered their digital solutions through a subscription payment model. This makes customers able to avoid big capital investments that often comes with buying software on license. By paying for the level of functionality provided to them by the case firm, customers were also in a sense given financial flexibility.
Making physical products connected to the internet enabled for two of the case companies to update their products and distribute new functions and improvements throughout the product’s life cycle, without necessarily having to physically improve it. One of the manufacturing firms explained that they try to avoid implementing technology like screens as they quickly become outdated and instead makes their products controllable by the customer's own phone through an application. They also focus on bringing new functions through software updates, thereby giving the product a more modern look and experience. In this way, the value of the physical product experienced by the customer is enhanced as digital solutions gives the flexibility to add features to the product and gives it a more modern feel throughout its life cycle.
25 4.1.2. Availability
Availability is an overall value that were mentioned by all interviewed companies as a key value that comes from digital solutions. In this case, it is referred to the availability to the product and being able to use the product as efficient as possible. With digital solutions integrated into the products, it can be connected and controlled from distance. A few of the interviewed companies have developed products that before were in need of human interaction but are nowadays innovated to work independently. The concerned companies mention that the customer still wants to be in control of the device as it is working while a human is not around. The digital solutions create the opportunity to monitor the device even from a distance and also being able to see that device is doing what it is supposed to do.
Availability to the product is not only a value for the customer but also for the manufacturing company. For instance, when the product is in need of service, the repairer can in some cases connect to the product and get indicators of what is wrong, before visiting the customer. As a result, the repairer can be prepared and solve the problem in less time and thereby be more efficient. In some cases, the repairer does not necessarily have to visit the customer since the problem can be solved from a distance.
Another dimension of availability mentioned by a few companies is the consumerization of IT, meaning that consumers living at different places nowadays are able to buy things and use services they could not before. One of the interviewed companies describe how their products only could be bought from physical retail stores before the digitalization, and only people who lived nearby were able to buy them. Nowadays the product can be designed by the customer online and delivered globally to almost anyone. It creates value for the company in terms of the opportunity of increased sales and the customer experience the value of accessing more products.
4.1.3. Traceability, control & accuracy
The following values are described by a company that operates as a provider of industrial tools. Digital solutions enabled the company to sell a complete system instead of just a product to make it easier to monitor and use the tool. In this case, the customer got enhanced value from this control; better ergonomics, less energy consumption and better quality of the tightening. If the company got a complaint from the field, it had traceability in the systems so the company easily
26 could find the source of the problem. It also helped the company to investigate if there was something wrong in the production of a certain product or if the customer were using a tool in the right way. The same firm also mentions that the traceability creates evidence in case of a lawsuit and reduces a lot of the risk for the company in that aspect.
4.1.4. Communication
Digital solutions create an opportunity to have a direct contact with the customer which enhances the communication both ways. Several of the interviewed companies are working with retail dealers, which in a natural way has most of the contact with the customer. By offering the customer a digital solution, for instance an app, the communication may instead arise directly between the customer and the manufacturer. The customer can easier get support and give feedback while the company can work closer to the customer to understand their behaviour and expectations. There is a consensus between the interviewed participants that the direct contact leads to a deeper relationship with the customer, which is mentioned as a key value for their business.
4.1.5. Scalability
The term scalability can be used to describe how digital solutions can be offered to customers at low marginal costs due to little or no extra cost of producing and distributing another unit. One case firm describes that they strive to keep their digital solutions relatively standardized and make as few customer-specific adaptations as possible. However, the company mentioned experienced challenges regarding the development of digital solutions that are applicable to many customers. As the firm operates and sells its products in a business-to- business environment with specialized solutions, the number of potential customers are naturally fewer than in a business-to-consumer context. In combination with this, the case firm experiences that when selling their software to a large and strong counterpart, the customer demands solutions integrated and adapted to their production systems which reduces the scalability of the digital solutions. Although the challenges mentioned, when achieving a digital solution that is fairly standardized the case firm can add more customers to their platform at low marginal costs which has positive effect on business profitability.
Another dimension of scalability is represented in one of the studied companies who can share their competencies and experience that they hold within their
27 organization, without having to be physically present. Through the ability to assist customers at a distance, the case firm has the capacity to scale their knowledge by sharing it with many more of their customers than they would be able to do without digital solutions. By looking at the customer data and applying their expertise in a certain niche of industry production, it allows for the company concerned to give specialized insights and support regarding the use of their products in different processes to their customers. As the case firm collects, saves and thereby accumulates large quantities of knowledge about customer production and their own tools in use, they can help address operational problems and save the customer time.
4.1.6. Data collection
The overall viewpoint among the interviewed participants is that data can bring enormous value to the business, where digital solutions pave the way for collecting data on a massive scale. All of the case firms are collecting data to understand about their customers’ behaviour, how the products are used and then fed back the data into the organization to use it for development in different ways. Thus, the approaches of how to utilize the data differs between the case firms. One of the participants working for an industrial tool provider said:
So, the biggest value is; first to collect the right amount of data, the second value is to make decisions based on that data, and that decision can be maintenance, it can be replacement or whatever.
Those are only actions, but the value is in the data. And to do any services you need to be fully in control of the data in the ecosystem. If you jump on to services before you people are in control of that, then it can be a disaster. First you cannot price it correctly, second you can’t give the right services also. You don’t know what services people are looking for.
In this case the company’s process of utilizing data starts with being in full control of the data before making decisions. A different approach was described by another case firm which is more consumer-oriented firm, where the approach was more explorative. The firm started a few years ago to build an app to their products because the firm needed to keep up with their competitors, but really did not have a strategy for where they wanted to go. The firm describes their development as in different steps where it started with trying to make the most of the existing resources and trying to get an understanding of what values data could bring, before making large investments. Henceforth, the firm started
