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# Knowledge-Based Product-Service Ecosystems

Serdar Bulut and Reiner Anderl

Department of Computer Integrated Design, Technical University of Darmstadt,
Otto-Berndt-Straße 2, 64289 Darmstadt, Germany
bulut@dik.tu-darmstadt.de

**Abstract.** Due to the ongoing digital transformation, businesses are exposed to work with a dynamic business environment. Traditional 1-to-1 relationships between manufacturers and end customers are transformed into a network of relationships between manufacturers, end customers, software developers, suppliers, and third parties. The network of partners evolves into an ecosystem. Using a smart product-service system as the central anchor of value creation for an ecosystem enables hybrid value creation for producers and consumers simultaneously. In this paper we develop a knowledge-based model of smart product-service systems in their corresponding ecosystems, that assists designers and developers in decision making during the product development process.

**Keywords:** Smart product-service systems · Product-service ecosystems · Knowledge-based engineering · Smart product engineering

## 1 Introduction

Product-Service Systems (PSS) offer a value proposition consisting of product and service components [1]. Traditionally, the value proposition is targeted towards end consumers forming a 1-to-1 relationship between manufacturer and end consumer. However, with emerging technology shaping the 4th industrial revolution, the rigid 1-to-1 relationship of manufacturers and consumers gets redefined. The potential of a network model arises, in which PSS take the role of an asset, that unites manufacturers, suppliers, consumers, and further third parties. All of these stakeholders may establish interdependent relations to one another based on the data that the PSS shares [2]. Suppliers may offer new spare parts to the customer based on condition monitoring, third party developers may offer new upgraded algorithms and functionalities as apps for the PSS, and manufacturers may analyze the behavior of the PSS in order to optimize the next generation of PSS. Ultimately, an ecosystem that shares PSS data is created; a product-service ecosystem (PSE) emerges. In order to achieve the data- and PSS-centric ecosystem, the PSS must become a Smart Product-Service System (SPSS). SPSS extend PSS by using smart services and offering a value proposition towards a variety of stakeholders via the Internet of Everything (IoE) [3]. Smart services are data-based services that allow a flexible and individual alignment with customer wishes and expectations [4]. Stakeholders are connected through an SPSS within a PSE. This paper introduces the definition
and the knowledge-based representation of a PSE along with the support it provides to product developers and designers. The knowledge-based representation constitutes the foundation of a graphical assistance system. Section 2 summarizes the current state of the art regarding PSS, SPSS, PSE and modeling techniques for PSEs. Section 3 provides the selection of a modeling technique for PSE representation and introduces the core elements of the proposed PSE model with their corresponding potentials for developers and designers. Section 4 presents the use-case-centric implementation of the PSE model for a multi-purpose drone as the SPSS. Section 5 finishes with a discussion of the results and an outlook for future work.

# 2 State of the Art

## 2.1 PSS and SPSS

PSS have been widely discussed starting with the late 90s. The complementation principle of PSS is the foundation of PSS. The complementation principle states that a tangible product is extended by an intangible service to meet users’ needs and vice versa [5–7]. The offered value proposition may be individualized to some extent, traditionally by adjusting the service component and maintaining the rigid product component. Industrie 4.0 allows for further individuality by using the concept of cyber-physical systems CPS. CPS extend conventional products by integrating embedded systems and an interface for communication and data exchange [8].

Therefore, further smart services such as remote condition monitoring and remote control are enabled transitioning PSS into SPSS [3]. Fundamentally, SPSS are PSS that are extended with data-based services and allow flexible and individual alignment of their linked, tangible product [4]. The communication of SPSS may function decentralized with direct communication, broadcast communication, mesh communication or may function centralized by using platform technology as the center of the infrastructure for information and communication technology.

## 2.2 Socioeconomic and Technical Ecosystems

PSEs are not directly referred in literature yet, as the term will first be introduced in this paper. However, in socioeconomics and especially in the era of Industrie 4.0, there are 3 key ecosystem types that have a significant impact on business models of manufacturers, software developers, suppliers, and commercial & private prosumers. Prosumers are stakeholders that may be producers and consumers simultaneously [9].

### Platform-Based Ecosystems

Multi-sided platforms, on which multiple stakeholders and market participants operate, have recently gained significance, due to their business opportunities [10]. They form a platform-based-ecosystem: a network in which a platform provider offers business opportunities to third parties to develop innovative IT-solutions for the platform [11–14].

### Knowledge Ecosystems and Business Ecosystems

Further ecosystem types, that are widely discussed in literature and applied by businesses and academies, are knowledge-ecosystems and traditional business ecosystems
[15]. Knowledge-ecosystems describe the union of businesses and institutions to col- laborate for the sake of research & development: leveraging innovation is the driving intention [16]. A common use-case for knowledge-ecosystems is the collaboration for the standardization of emerging technologies such as OPC-UA. Ordinary business ecosys- tems describe any kind of business-related collaboration of multiple stakeholders to form common value creation & proposition [17].

## 2.3 Representation Techniques for PSS, SPSS, PSE

PSS consist of product and service components. SPSS extend PSS by incorporating shared data. PSE further incorporate a multitude of stakeholders who share the digital artifacts generated by, used by, and provided to the SPSS. Therefore, the representation of the entities PSS, SPSS, and PSE must incorporate interdisciplinarity in the fields of engineering, service design, data aggregation, and business development. UML, SysML, and IDEF Models are partially able to model these entities [18].

However, these technologies share a common trait. Their set of modeling rules, guidelines, statements, defined elements and their characteristics as well as relation- ships forcibly create restrictions, that limit the scope of representation capabilities for interdisciplinary entities [19–22]. Data Flow Diagrams may be used to represent the orga- nization of PSS while IDEF Models are suitable for PSS manufacturing [23], UML 2.0 was used for PSS design and use case modeling [24–26], SysML further extends UML 2.0 by integrating enhanced traceability, rationalization and inter-team communication for co-designing PSS [27].

While these tools and technologies focus on certain aspects of PSS design, they lack a holistic approach on PSS engineering and organization regarding all functional com- ponents, life cycle phases, stakeholders, and value creation flow [28]. It is not possible to freely define types of system elements, types of semantic annotations, types of rela- tionships and the characteristics of elements, annotations, and relationships. Hence, it is required to use a representation technique, that offers a higher degree of freedom. The interrelations between the system elements of a holistically analyzed PSS need further investigation [29]. These interrelations are responsible for the high degree of complexity and interdisciplinarity in PSS.

Ontologies, as conceptualizations of specific domains, offer more freedom due to their limitless possibilities to determine elements and relationships in their taxonomy and structure. Many ontologies have been developed for PSS design including service ontologies, engineering ontologies and product ontologies [30–33]. While these ontolo- gies may represent PSS sufficiently, knowledge deduction and induction still remains insufficiently addressed, especially enabled through data-based communication of SPSS. Knowledge deduction and induction are crucial for interdisciplinary teams of analysts, engineers, designers and developers who aim to offer individual product and service solutions to a multitude of stakeholders within an ecosystem. Intuitive and knowledge- based assistance is required to support the development process of SPSS and ultimately PSE.
<figure><img src="image_2.png" /><figcaption>Fig. 1. Taxonomy of PSEs</figcaption></figure>

## 3 Knowledge-Based Model of a Product-Service Ecosystem

Graphs are best suited to intuitively represent knowledge for humans. Graphical representations are possible with RDF-triples and labelled property graphs LPGs. Both concepts use ontologies to define their scope of representation. LPGs are superior in querying and storage. Especially human-initiated querying is essential as the goal of this paper is to assist human decision makers with a knowledge-based system in designing and developing a PSE. Hence, knowledge retrieval must be designed intuitively. Therefore, we choose LPGs as the foundation of the developed assistance system.

In order to determine the scope of the PSE domain, we first define PSEs. A product-service ecosystem enables joint value proposition to multiple stakeholders based on offerings and needs of a central smart product-service system. The value proposition is embedded in tangible product as well as intangible service components. Value creation within the ecosystem is supported by information and communication technology. A product-service ecosystem contains traits of knowledge ecosystems, platform ecosystems, and traditional business ecosystems. Hence, the PSE domain consists of the main elements: product and smart service, functions, stakeholders, information and communication technology (ICT), data, information, knowledge, generated usage and economic sectors, in which stakeholders operate. The economic sectors are important for the identification of further consumers in different sectors that share similarities to the current
prosumers for ecosystem expansion. Further analysis of the main elements leads to the taxonomy in Fig. 1.

After defining the elements that constitute the representation of PSEs, relationships are added. The relationships between the elements represent dependencies, value creation flows, hierarchical structures, specializations, compatibilities, necessities, and offerings. Therefore, the relationships are: *includes*, *is_a*, *aggregates_to*, *is_compatible*, *uses*, *offers*, *constitutes*, *requires*, *operates_in*, *enables*, *produces*. The elements with the corresponding relationships are seen in Fig. 2 as an LPG. Figure 2 represents the reference model for PSEs in Neo4j.

<figure><img src="image_3.png" /><figcaption>Fig. 2. Reference model of PSE with main elements in purple and sub-elements in gray</figcaption></figure>
The proposed knowledge-based model of PSE consists of 2 sub-models: the reference model and the applied knowledge model. The reference model is seen in Fig. 2. Figure 2 contains the relationships and nodes of the LPG without depicting the attributes of the relationships and the attributes of the nodes. The attributes are out of scope of this paper. The applied knowledge model is presented in Sect. 4.

While the reference model is supposed to support decision makers in communication and on how to represent PSEs, the applied knowledge model contributes to the deduction and induction of knowledge. Both models combined unlock potentials to support the uses-cases: production planning, compatibility check of modular products, service development, product development, similarity check of stakeholders and economic sectors for ecosystem expansion.

# 4 Use Case for Applied Knowledge Model

Section 3 introduced the reference model for PSE and presented its potentials. This section presents how to apply the reference model to generate the applied knowledge model and how the applied knowledge model supports in compatibility check and ecosystem expansion. In order to use the applied knowledge model, we determine an SPSS and focus on a specific use-case. The SPSS is a multi-purpose drone, that is used for smart fertilizing of crop, that is difficult to access for terrestrial machines. Wine grape harvesting on cliffsides represents a sufficient use-case for multi-purpose drones. Figure 3 displays a segment of the applied knowledge model. The full model is out of scope for this paper. Starting with the Knowledge-Based Service Smart Farming the model tracks down the intelligence necessary to constitute the service. Knowledge for condition-based fertilizing is required which is constituted by various information segments such as pesticide effects, crop condition, fertilizer effects, geo localization, and also a data segment rain detection. The service is mostly used by farmers for resource optimization, reduction of emissions, data analytics, and provision of data. The service requires a supply function: the provision of fertilizer. The supply function is constituted by the tank.

A full tank significantly increases the mass of the drone, which has a direct effect on the drive. The drive consists of the rotors, motors, and the battery. Due to the structure of the physical components, there are compatibilities between selected components. The compatibilities narrow down the choices to realize a drive unit for the drone. Further requirements of the drive such as power and range are considered by querying the node attributes of the applied knowledge-model. Ultimately, the model leads to the only possible drive combination (red circles in Fig. 3): Hacker LiPo 6s, T-Motor Antigravity MN5208, and T-Motor NS17x5.8.

The insight deducted from the applied knowledge model directly supports decision makers whether or not a new supplier should be integrated into the ecosystem. Furthermore, additional use-cases for multi-purpose drones can be modeled to determine the economic impact of the tank & drive and the services they enable across sectors.
<figure><img src="image_4.png" /><figcaption>Fig. 3. Fraction of the applied knowledge model of PSE with a drone as SPSS depicting a smart farming service. Elements encircled in red are sufficient to constitute the functional group 'drive'.</figcaption></figure>

# 5 Discussion of Results

In this paper we defined PSEs and used LPGs to model them. We developed a reference model for PSEs along with its instantiation as the applied knowledge model. The models are used for various purposes: representation of PSEs for common understanding, communication in interdisciplinary teams, inductive and deductive reasoning. Ultimately, the models assist in decision making in an interdisciplinary and dynamic environment.
The models support in production planning, compatibility check of modular prod-
ucts, service development, product development, similarity check for stakeholders &
economic sectors, and ecosystem expansion. Section 4 showed the applied knowledge
model and presented how compatibility check and ecosystem expansion are supported
through the model. The models incorporate abstraction, instantiation, and generalization.

The parameters for tank size and tank weight are determined within the attributes of the tank. The attributes however are beyond the scope of this paper. This paper mainly focuses on the eligibility of LPGs to represent PSE with a knowledge base. The elements and relationships for the models are chosen to allow for sufficient, human-assisted reasoning through Cypher queries while minimizing the complexity of the model. The model represents human-readable interdisciplinary knowledge of the elements and relationships forming a PSE. With the proposed reference model, decision makers can model their own PSE and gain insight through querying the instantiated applied knowledge model. The complexity of the model increases with more distinct relationships and distinct nodes: balancing accuracy and granularity is key. Further research will focus on the diversity of potentials derived from querying the nodes and relationships. Furthermore, a guideline to determine the attributes of the nodes and relationships will be developed to enable sufficient querying and reasoning.

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