DECISION SUPPORT WITHIN KNOWLEDGE-BASED ENGINEERING – A BUSINESS INTELLIGENCE-BASED CONCEPT

Essential characteristics of products are set during early phases of product development as well as manufacturing. During these processes, decisions are made without awareness of their final impacts on long term key success factors. Industrial businesses lack concepts that enable decision-makers within knowledge based engineering processes, to anticipate possible impacts of their decisions. Due to this, many industrial companies employ so called knowledge engineers to manually gather and analyze information of product lifecycles. In order to improve the decision support within knowledge based engineering, a concept was developed, which contains the extension of business intelligence environments with product-orientated data warehouses. It is thus possible to combine technical information of product features with the traditional dimensions of managerial analysis in order to identify impacts of decisions on the product lifecycle and hence support knowledge engineers in their daily work

A CHANGEABILITY APPROACH FOR PROCESS MANAGEMENT AND DECISION SUPPORT ON THE SHOP FLOOR

This document contains an approach to map the changeability possibilities of machine tools used on the shop floor onto line management understandable business processes. The identified gap is a lack of information transparency on the line management level due to constraints, complexity and speed of a production process on the shop floor. Especially medium-sized enterprises in the supplier sector are forced to operate under strong time restrictions which are predetermined by original equipment manufacturers. Due to competitors and shareholders these enterprises often use a lean management approach which allows them on the one hand to produce under low costs but on the other hand handicaps them to react on disruptive events on the shop floor. We argue that nowadays industrial small and medium sized industrial enterprises have to have a fast reaction on changes and events. It is seen by the authors that changeability of production processes is an essential success factor in this globalized world. Because of the fact that more and more responsibility is handed over to the lower line management, the information support has to be improved in order to make them capable for choosing the best decision. In this paper a concept is shown how the lower management can reallocate production process steps in order to avoid penalty costs if a just in time production is requested by an original equipment manufacturer. To be able to do this, an information support concept for the lower management has to be established within the company to meet the requirements for choosing the best fitting reaction to a disruptive event. The future research concept is described after the analysis of an example production process scenario which is illustrated within this paper

The Symbiosis of Distributed Ledger and Machine Learning as a Relevance for Autonomy in the Internet of Things

The Internet of Things (IoT) describes the fusion of the physical and digital world which enables assets on the edge to send data to a platform where it gets analyzed. Defined actions are then triggered to influence cross-functional edge activities. Furthermore, on the platform tier functionalities and relations need to be identified and implemented to realize assets operating autonomously and ubiquitously. The exploration of this paper results in the identification of autonomous characteristics and shows functional components to implement autonomous assets on the edge. Distributed Ledger Technology (DLT) and its fusion with Machine Learning (ML) as an area of Artificial Intelligence (AI) provides an integral part to realize the described outline. Thus, the recognition of DLT’s and ML’s usage in the IoT and the evaluation of the relevance as well as the synergies build the main focus of this paper

Design Principles for Creating a Pay-per-Part Value Proposition in Data Ecosystems

In practice and research, pay-per-part business models are becoming increasingly popular. Amongst others mechanical engineering companies, banks, insurances, and IT companies are working on these new business models. There is increasing evidence that the enabler for pay-per-part approaches is the cooperative use of data across company boundaries, being discussed in literature under the term data ecosystem. Along two case studies, a total of eleven companies were accompanied from the definition of the cooperative pay-per-part value proposition to the implementation of a proof of concept. Based on these case studies, eleven design principles could be derived. These design principles provide companies a guidance when designing a cooperative value proposition within an ecosystem. The identified design principles were mapped to different stakeholder groups that are involved in the design of a cooperative value proposition. The generated design principles were evaluated and implications for practitioners and research given

Enterprise Information Systems vs. Digital Twins – A Case Study on the Properties, Purpose, and Future Relationship in the Logistics Sector

Traditional enterprise information systems have been around for more than 40 years. They are designed to support business processes and deliver information to the people within a company who require it for their work. However, there are blind spots that these systems are unable to address. In this article, we investigate how digital twins, which are based on the technology and architecture of the Industrial Internet of Things, as well as the principles of cyber-physical systems, can be used to fill such gaps and elucidate how their application will affect the prospective relationship between internal information systems and digital twins. The insights are based on a single case study within the logistics department of an industrial company and its service provider. From the case study, properties of both system types were identified that provided a basis for comparison and stimulated discussion about their future dependencies

Crafting an IoT-Ecosystem – A Three-Phased Approach

The Internet of Things acts as a seed for enterprises to collaborate and create new value. This value creation is often concentrated in big enterprises that command large amounts of resources. The craft sector’s small and medium sized enterprises struggle to adopt such new technologies. Lacking resources and in-house capabilities, they increasingly rely on services provided by large enterprises. Collaboration among equals can offer an alternative path for these small and medium sized enterprises. Combining their strengths in an IoT-ecosystem is one way to overcome these limitations. We conducted a case study in the electrical engineering craft to build such an IoT-ecosystem. Participating organizations planned how to develop the existing ecosystem into an IoT-ecosystem. This process was observed to be structured into a status quo and three sequential phases. Our research shows, that sharing data can act as the initial phase to unlock new value in an existing ecosystem. Every enterprise can then work on connecting its clients’ systems to enable an eventual opening to join the IoT-ecosystem. This three-phased approach offers enterprises a tool to work towards an IoT-ecosystem. Researchers can apply the three-phased approach as an analytic tool to reason about progress towards an IoT-ecosystem

Identifying Business Potentials of Additive Manufacturing as Part of Digital Value Creation in SMEs – An Explorative Case Study

Additive Manufacturing allows the production of parts based on layer by layer, three-dimensional printing. With a unique set of characteristics, Additive Manufacturing is an important technology regarding the digital transformation and digital value creation of the manufacturing domain. Particularly small and medium sized enterprises are challenged by digital transformation processes and decisions on where to invest their limited resources. This paper identifies business potentials of Additive Manufacturing based on a recent case study conducted during collaborative workshops with five small and medium sized enterprises. Considering the special capabilities of Additive Manufacturing technology, business potentials are examined alongside the entire product lifecycle. It was found that these potentials appear primarily on a digital level and are therefore not limited to the physical domain. The potentials may channel enterprise transformation and enable the purposive generation of digital value

SEMANTISCHE KUNDEN-FEATURE-OBJEKTE IN ERWEITERTEN DIGITALEN PRODUKTMODELLEN

Zur Entwicklung und Verbesserung von Industriegütern wird Kundenwissen permanent in den Unternehmensbereichen F&E und Produktion benötigt. Wie mehrere Untersuchungen gezeigt haben, besteht in Industriebetrieben häufig eine systemtechnisch bedingte Informationslücke zwischen den kundenorientierten und den produktorientierten Unternehmensbereichen, so dass eine strukturierte Erfassung von relevantem Kundenwissen an der Kundenschnittstelle und eine Weitergabe in die produktorientierten Unternehmensbereiche nicht erfolgt. Als Beitrag zur Lösung dieses Problembereiches wurde ein Konzept zur Schließung von bestehenden Informationslücken entwickelt, prototypisch umgesetzt und erprobt. Im Mittelpunkt steht hierbei die strukturierte Erfassung des Kundenwissens an der Kundenschnittstelle sowie die medienbruchfreie Integration in Form sog. Kundenfeatures in IT-Systeme der produktorientierten IT-Landschaft

Manufacturing Execution Systems and Business Intelligence for Production Environments

In the domain of production, Manufacturing Execution Systems (MES) are becoming increasingly popular. State of the art MES not only bring interfaces to a large variety of shop floor systems, they also come with functionality for data integration, data analysis, and dashboard generation – features commonly associated with Business Intelligence (BI) systems. At the same time, Data Warehouse (DHW) based BI infrastructures are increasingly extended to the support of operational managerial levels (Operational BI). This contribution sheds light on whether or not BI systems and MES are at odds and in how far they are complementary. To achieve this, two subsequent studies have been conducted: a case study based exploration and a quantitative online survey. The study results motivate an integration framework for MES and BI systems