Abstract

Abstract. In many industrial sectors, business networks emerged as collaborative partnerships for tackling challenges caused by globalization and changing market needs. These networks are bundling competence and knowledge of different partners for co-operations in development or manufacturing projects. In such networks and collaborations, management of knowledge, competences and capacities at the different network members is crucial. This paper investigates requirements and challenges to knowledge management for business networks and argues that context-orientation is a key feature of modern approaches. The focus on our work is on decision support. Keywords: business network, context, knowledge management, cyber-physical system, organisational knowledge Introduction In many industrial sectors and manufacturing areas, such as automotive industry, aerospace, wood-related industry or construction industries, globalization and the adaption of supply strategies to global markets resulted in network organization forms. The needs for shorter innovation cycles, lead time reduction or mass customization have stimulated the creation of collaborative partnerships, like networks of suppliers and sub-suppliers, value networks [1] or co-operations in product development or construction projects. In such networks and collaborations, management of knowledge, competences and capacities at the different network members is crucial. Relevant knowledge encompasses production capability, services offered, available resources, product variants and configuration options as well as the organizational competences of the members in the network. In this context, concepts and approaches from knowledge management can contribute to decision support and efficient operaCopyright © 2015 by the authors. Copying permitted for private and academic purposes. This volume is published and copyrighted by its editors. 9 tions of the network, if these approaches take the individual context and demands of the network members into account. Furthermore, globalization and digitalization of companies brings to the agenda a number of problems to be solved. Knowledge Management for decision support of efficient configuration of business networks (trends leading to Industry 4.0, Logistics 4.0, and Mobility 4.0) based on customer requirements and preferences, different resources (physical, information, etc.) and their efficient interaction (the Internet of Things and the Internet of Everything concepts), handling cultural differences of making business between representatives (employees and companies) from different countries (organizational behaviour issues related to international dimensions and cross cultural aspects of collaboration and decision making) are among them. Modern business networks are mainly service-oriented and based on integration of number of networks which supported by using following information technologies: • Knowledge management is defined as a complex set of relations between people, processes and technology bound together with the cultural norms, like mentoring and knowledge sharing. Knowledge management consists of the following major processes: knowledge discovery (knowledge entry, capture tacit knowledge, etc.), knowledge engineering (knowledge base (KB) development, knowledge sharing and reuse, knowledge exchange, etc.), and knowledge mapping (identifying knowledge sources (KSs), indexing knowledge, making knowledge accessible, etc.). Today an intensive knowledge integration and knowledge exchange between participants of the global business network are required. Currently major Knowledge Management Problems related to business network are: • Semantic-based Interoperability between network participants; • Taking into account dynamics of the business environment; 10 • Use business network participants as knowledge sources. This paper investigates requirements and challenges to knowledge management for business networks and argues that context-orientation is a key feature of modern approaches. The focus on our work is on decision support. Section 2 shows some examples for business networks in order to illustrate typical constellations and tasks in such networks. Section 3 identifies requirements and challenges to context-oriented knowledge management. Section 4 focuses on theoretical foundations of decision support and section 5 presents selected technological and methodical approaches for knowledge management. Business Networks: Selected Examples In order to illustrate the concept of business networks and the need for knowledge management and decision support, this section will briefly introduce three examples of such networks originating from real-world cases. These cases are taken from collaborative engineering in automotive industries, flexible supply network in manufacturing and production networks in electrical engineering. Collaborative Engineering Collaborative engineering aims at supporting a distributed group of engineers sharing a common collaboration objective in jointly performing an engineering task, like product design, production planning, engineering change management or development of specifications. These engineering tasks are usually knowledge-intensive activities involving different specialists in collaboration processes tailored for the engineering domain under consideration. One example for collaborative engineering in a business network is the case of distributed product development with multi project lifecycles in a networked organization from automotive supplier industry. Main partner is the business area seat comfort of a first tier supplier, which main products are seat comfort components (seat heater, seat ventilation, lumber support and head restraint), gear shifts and commercial vehicle components. The case is focused on development of new products in collaboration of the first tier supplier and its sub-suppliers for heating wires, seat sensors and carrier material. Development of products includes identification of system requirements based on customer requirements, functional specification, development of logical and technical architecture, co-design of electrical and mechanical components, integration testing, and production planning including production logistics, floor planning and product line planning. This process is geographically distributed involving engineers and specialists at several locations and suppliers from the region. A high percentage of seat comfort components are product families, i.e. various versions of the components exist and are maintained and further developed for different product models and different customers. General requirements regarding infrastructure and methodology are: 11 • to support geographical distribution and knowledge sharing between changing partners, • to enable flexible engineering processes reflecting the dynamics of changing customer requirements, • to coordinate a large number of parallel product development activities, • to allow richness of variants while supporting product reuse and generalization. More information about this case is available in [3]. Flexible Supply Networks Automotive production networks are an example for collaborative partnerships aiming at increasing flexibility and lead time reduction (see also Distributed production networks have a number of advantages when compared to vertically controlled companies, but they also pose challenges. Partnering on manufacturing and design has increased the need to integrate and share product information, from initial design to manufacturing and engineering changes, including best practices of processes and their integration over company limits. With the aim of achieving global distributed processes, value chain integration and dynamic collaboration, knowledge management has become of high importance. Together with the above advantages flexible supply networks raise a number of problems. The most important problem is coordination of the large amount of independent members of the large network. When dealing with multiple organizations and multiple processes within a complicated supply network, trying to identify and locate a member that has responsibility and/or competence in a particular part of the network can be a laborious, time-consuming process. Developing and maintaining a competence directory of all the relevant parties associated with troubleshooting and solving potential problems can significantly reduce the time. Further, linking this directory to key decision points and frequent problems can further enhance its effectiveness In flexible supply networks it is important to derive and process knowledge from various sources including best practices, technology forecasting, products in the marketplace (who is buying them and why?), what competitors are selling now and what they are planning to sell in the future. The knowledge supply as a part of knowledge management in a flexible supply network requires interoperability at both technical and semantic levels. The approach described in [6] relies on the ontological knowledge representation for its sharing. The ontology describes common entities of the enterprise systems and relationships between them. The dynamic nature of the flexible supply networks requires considering the current situation in order to provide for actual knowledge or information. For this purpose, the idea of contexts is used. Context represents additional information that helps to identify specifics of the current transaction. It defines 12 a narrow domain that the user of the knowledge management platform works with. One more important aspect covered by the approach is the competence profiling. Profiles contain such information as the network member's capabilities and capacities, terminological specifics, preferred ways of interaction, etc. Production network A supply network aggregates independent companies based on the principle of cooperation within a defined application domain and capable of coordinating their activities for production and delivery of the desired product. Organizations of this form use information and communication technologies to extend their value creation possibilities In order to illustrate the concept of supply networks, we consider a case from distributed product engineering in a networked organisation from automotive supplier industry, which originates from the MAPPER project Within the first tier supplier, this process is geographically distributed involving engineers and specialists at several locations and SMEs from the region. A high percentage of seat comfort components are product families, i.e. various versions of the components exist and have to be maintained and further engineered for different product models and different customers. In this context, fast and flexible product engineering and integrated management of concurrently performed forwardengineering processes is of crucial importance. Smooth collaboration and information sharing is a key success factor to meet these basic needs. Requirements to Knowledge Management in Business Networks Due to the rapidly changing business environment, increasing global competition and wide acceptance of information technologies, knowledge-based systems are currently highly demanded in the area of business network management including the cases presented in section 2. However, there still exists lack of systems that work with knowledge at the level of semantics. This is especially important for knowledge sharing when it is necessary to process knowledge stored in distributed heterogeneous sources in different terminology, languages, etc. For knowledge sharing the systems operating in these areas have to provide efficient knowledge integration and sharing between multiple participating parties. This knowledge must be pertinent, clear, and correct, and it must be timely processed and delivered to appropriate locations. Thereby, such systems have to meet a number of requirements including (i) support of knowledge sharing, (ii) distributed architecture for collaborative work, (iii) interoperability with other information systems at both technological and semantic levels, (iv) dynamic (on-the-fly) problem solving, (v) ability to work with uncertain information, (vi) constraint satisfaction notation for real-world problem description, and other. The knowledge sharing problem in the presented approach (detailed description of the approach can be found in Furthermore, modern business networks are based on Industry 4.0 concept using the Internet of Thing and the Internet of Everything paradigms. The European Research Cluster on the Internet of Think defines it as "a dynamic global network infrastructure with self-configuring capabilities based on standard and interoperable com-14 munication protocols where physical and virtual things have identities, physical attributes, and virtual personalities, use intelligent interfaces, and are seamlessly integrated into the information network" [11]. The Internet of Everything which defines as "a complex, self-configuring, and adaptive system of networks of sensors and smart objects whose purpose is to connect all things, including commonplace and industrial objects" As a conclusion, several requirements to modern knowledge management systems for business networks can be stated: • Flexibility. The system must be ready for sudden changes in the target problem requirements. It maintains its flexibility by keeping minimal information volume in the sources. • Learning from the user. If the user declines a suggested solution or believes that it is not optimal, it is necessary to provide for an ability to include required changes into the system behavior rules. • Integrity. During development of the system it is necessary to perform monitoring of information environment KSs for their availability and changes. If KS becomes unavailable it is necessary to remove all the references to it and check knowledge, synthesized while using this source. When the source content changes it is also necessary to perform checks for knowledge consistency. • Velocity. The system permanently seeks for the ways to reduce and/or compensate the variability in customer/user demand and suppliers/sources. • Open Connectivity. Ontologies and KBs built during the process of system utilizing must be available for shared access by external users. Besides, database and KB developers can represent the sources in the required form to expand the set of available KSs. • Reasoning. The system must have clear plan of actions to achieve its goals and reasoning for proposed solutions. • Customizability. The system must be ready to build any possible configuration of knowledge domain model that a customer (user) requests. Besides, it must be able to motivate the suggested solution. • Hard' real-time. The system must have features that guarantee a response within a fixed amount of real-time. Context-Aware Decision Support: Theoretical Foundations Decision support in the business environments has to take into account constant environmental changes. In the present research, resources of the environment provide information of any changes to the DSS. These resources are referred to as information resources. The information resources perform the needed computations and solve problems, as well. The collection of information resources comprises various kinds of sensors, electronic devices, databases, services, etc. Besides information resources, the research distinguishes one more type of resources that is acting resources. These 15 resources include physical resources, people and /or organizations that can be involved in the joint actions. The research follows the knowledge-based methodology to building decision support systems (DSSs). The idea behind the research is to represent the application knowledge by means of constraints. This knowledge is described using two independent sorts of reusable components: domain ontology and task ontology. The domain ontology represents conceptual knowledge about the application domain. The task ontology describes problems occurring in the application domain and methods for achieving solutions to these problems (problem-solving methods). The both components make up the application ontology, which is represented as a set of constraints. This ontology specifies non-instantiated knowledge. The resources' representations are supposed to be compatible with the ontology representation. The application ontology and the resources' representations are aligned. The alignment indicates what information resource(s) instantiates the given property of the given object specified in the ontology. In the research, context model serves to represent the knowledge about a decision situation (the settings in which decisions occur and the problems requiring solutions). Context is suggested being modeled at two levels: abstract and operational. These levels are represented by abstract and operational contexts, respectively