Open Access

Numerous start-ups and now even some major corporates are currently trying to improve visibility and foresight in the manufacturing industry through connected supply chains, or in other words, through increased data sharing. This study strives to support companies in leveraging the potential of increased data sharing in supply chain collaborations. Despite the great potential of digitizing manufacturing and automated data sharing throughout the supply chain, most companies are not yet able or willing to implement this kind of openness. The main reason for this lack of transparency in the supply chain is the high complexity and high cost of the required interfaces. In practice, instead of automated and extensive data sharing, companies exchange spreadsheets and PDFs with minimum information. This study supports companies in the pre-stage before automated data sharing is technically implemented. We find that building trustful relationships is a necessary step towards extended and automated data sharing. Moreover, we find that social capital provides a means to partially compensate for a lack of automation in terms of shortening lead times and dealing with disruptions. Introducing a supply chain collaboration typology and showcasing descriptive and qualitative results for 36 firms, we show how to navigate the frontend of the Internet of Production.

It is necessary to believe the reality that every process in the industry impact the cus-tomers either directly or indirectly. Hence, with the world class manufacturing Indus-tries moving faster with the evolving Industry 4.0 and integrated product data man-agement methodologies, it is necessary to focus on every data attribute which con-tribute to the wholesome benefit of the business requirements especially the pro-duction processes ensuring even the internal customers to be delighted. The classi-fication of the Bill of materials with respect to the status of usage of the parts are im-portant in the automotive and other manufacturing industries because of their prime importance of making the end users confident to plan their responsible processes error-free thereby enabling a smooth and effective production processes both for new launches and for the series manufacturing of the products. Even though this classification can increase the efficiency and support for a timely build of the prod-ucts, there are also different challenges. The challenges could be the existence of the non-standardized processes, more manual approaches, varied understanding of the problems and difficulty in the integration with the existing and new Information Technology systems which keeps evolving in the automotive industry based on the increasing complexity. This project focuses on the parts classification data attribute in the lead plants of the BMW Group, the methodology of classification of the Parts in the Bills of materials supporting the technological process partners for a smooth new vehicle Launch production which is otherwise referred to as the Preseries production. This Master Thesis aims to provide a conceptual solution proposal for the two major issues. First problem is that currently various lead plants across the BMW Group have a possibil-ity of varied processes for the Bills of materials parts classification in the entire pro-duction network. In order to be more efficient following the common best practices and processes as per the lean concept and to have an integrated and coordinated working style of all the production sites globally, there should be a standardization of the processes across the entire BMW Group. Second problem to be addressed is the methodology of Parts and Bill of Materials classification with the definition of new processes and rules for the parts classification out of the new BMW Bill of Materials Backbone system and the Bill of Materials user-interface system. In other words, al-gorithm for the future process model has to be developed in order to substitute the current conventional Parts classification data attribute in a more logical manner. This solution will have to integrate with the new world of Information Technology Systems. The following sections explain the current processes and rules of this parts classification data attribute, showing the general deficits in the current pro-cesses and how is the variation of the processes in each plants. Further sections will also explain the scientific basis for the subsequent analysis on standardization and optimization of the Parts classification in the business processes. The final sec-tion concludes with the suitable criterium and workflow model which would be pro-posed conceptually to the business processes for the possibility of substituting the current parts classification data attribute for the future systems.

Der Artikel befasst sich mit den Implikationen additiver Fertigungsverfahren auf die Koordination von Wertschöpfungsnetzwerken. Dabei wird die dezentrale Ersatzteilversorgung betrachtet.

Um am Markt weiterhin bestehen zu können, müssen die Unternehmen agiler werden. Ein Mittel dazu ist die digitale Abbildung der Prozesse, wobei die Echtzeitlokalisierung von Teilen in der Produktion ein wichtiger Baustein ist, den die RWTH im Rahmen eines Projekts auf Basis von SAP ERP untersucht hat. Ziel der Aktivitäten in der Demonstrationsfabrik Aachen DFA in Zusammenarbeit mit dem FIR war die Umsetzung von Industrie 4.0 durch ein "Real Time Location System" (RTLS). Diese Echtzeit-Lokalisierung - umgesetzt mit Unterstützung des Technologieanbieters Ubisense und des SAP-Partners Intelligence - kann Rückmeldungen vollautomatisch verarbeiten und in die aktuelle Planung einbinden. Es werden technische Umsetzungsmöglichkeiten sowie die einhergehenden Herausforderungen insbesondere auch der Integrationsfähigkeit in ERP-Systeme erläutert.