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Towards a model for holistic mapping of supply chains by means of tracking and tracing technologies
(2022)
The usage of tracking and tracing technologies not only enables transparency and visibility of supply chains but also offers far-reaching advantages for companies, such as ensuring product quality or reducing supplier risks. Increasing the amount of shared information supports both internal and external planning processes as well as the stability and resilience of globally operating value chains. This paper aims to differentiate and define the functionalities of tracking and tracing technologies that are frequently used interchangeably in literature. Furthermore, this paper incorporates influencing factors impacting a sequencing of the connected world in Industry4.0 supply chain networks. This includes legal influences, the embedment of supply chain-related standards, and new possibilities of emerging technologies. Finally, the results are summarized in a model for the holistic mapping of supply chains by means of tracking and tracing technologies. The resulting technological solutions that can be derived from the model enable companies to address missing elements in order to enable the holistic mapping of supply chain events as well as the transparent representation of a digital shadow throughout the entire supply chain.
The paper describes a new stimulus using learning factories and an academic research programme - an M.Sc. in Digital Industrial Management and Engineering (DIME) comprising a double degree - to enhance international collaboration between four partner universities. The programme will be structured in such a way as to maintain or improve the level of innovation at the learning factories of each partner. The partners agreed to use Learning Factory focus areas along with DIME learning modules to stimulate international collaboration. Furthermore, they identified several research areas within the framework of the DIME program to encourage horizontal and vertical collaboration. Vertical collaboration connects faculty expertise across the Learning Factory network to advance knowledge in one of the focus areas, while Horizontal collaboration connects knowledge and expertise across multiple focus areas. Together they offer a platform for students to develop disciplinary and cross-disciplinary applied research skills necessary for addressing the complex challenges faced by industry. Hence, the university partners have the opportunity to develop the learning factory capabilities in alignment with the smart manufacturing concept. The learning factory is thus an important pillar in this venture. While postgraduate students/researchers in the DIME program are the enablers to ensure the success of entire projects, the learning factory provides a learning environment which is entirely conducive to fostering these successful collaborations. Ultimately, the partners are focussed on utilising smart technologies in line with the digitalization of the production process.
Der Anteil mittelständischer Unternehmen, die Standorte im Ausland unterhalten, nimmt seit einigen Jahren zu. Oft finden Auslandsaktivitäten dieser Art in Niedriglohnländern statt. Dort ergeben sich u.a durch die infrastrukturellen Gegebenheiten und durch die verfügbaren Personalressourcen diverse Herausforderungen, insbesondere für die Produktivitätsermittlung und -bewertung innerhalb der Produktion. Dieser Beitrag soll für diese Herausforderungen geeignete Technologien und eine mögliche Vorgehensweise für deren Auswahl vor dem Hintergrund der ländertypischen Herausforderungen aufzeigen.
Globalisation, shorter product life cycles, and increasing product varieties have led to complex supply chains. At the same time, there is a growing interest of customers and governments in having a greater transparency of brands, manufacturers, and producers throughout the supply chain. Due to the complex structure of collaborative manufacturing networks, the increase of supply chain transparency is a challenge for manufacturing companies. The blockchain technology offers an innovative solution to increase the transparency, security, authenticity, and auditability of products. However, there are still uncertainties when applying the blockchain technology to manufacturing scenarios and thus enable all stakeholders to trace back each component of an assembled product. This paper proposes a framework design to increase the transparency and auditability of products in collaborative manufacturing networks by adopting the blockchain technology. In this context, each component of a product is marked with a unique identification number generated by blockchain-based smart contracts. In this way, a transparent auditability of assembled products and their components can be achieved for all stakeholders, including the custome.
Supply chains have become increasingly complex, making it difficult to ensure transparency throughout the whole supply chain. In this context, first approaches came up, adopting the immutable, decentralised, and secure characteristics of the blockchain technology to increase the transparency, security, authenticity, and auditability of assets in supply chains. This paper investigates recent publications combining the blockchain technology and supply chain management and classifies them regarding the complexity to be mapped on the blockchain. As a result, the increase of supply chain transparency is identified as the main objective of recent blockchain projects in supply chain management. Thereby, most of the recent publications deal with simple supply chains and products. The few approaches dealing with complex parts only map sub-areas of supply chains. Currently no example exists which has the aim of increasing the transparency of complex manufacturing supply chains, and which enables the mapping of complex assembly processes, an efficient auditability of all assets, and an implementation of dynamic adjustments.
The proper selection of a demand forecasting method is directly linked to the success of supply chain management (SCM). However, today’s manufacturing companies are confronted with uncertain and dynamic markets. Consequently, classical statistical methods are not always appropriate for accurate and reliable forecasting. Algorithms of Artificial intelligence (AI) are currently used to improve statistical methods. Existing literature only gives a very general overview of the AI methods used in combination with demand forecasting. This paper provides an analysis of the AI methods published in the last five years (2017-2021). Furthermore, a classification is presented by clustering the AI methods in order to define the trend of the methods applied. Finally, a classification of the different AI methods according to the dimensionality of data, volume of data, and time horizon of the forecast is presented. The goal is to support the selection of the appropriate AI method to optimize demand forecasting.
Der Zusammenschluss von Unternehmen in Lieferantennetzwerken auf Basis digitaler Plattformen bietet eine Möglichkeit, der Forderung nach Flexibilität in der Industrie 4.0 nachzukommen. Anhand der Charakterisierung eines realen Lieferantennetzwerkes werden use cases für die Lieferantenanbindung hergeleitet. Diese dienen als Diskussionsgrundlage von Potenzialen und Herausforderungen der Anbindung, wobei sich die Frage nach der optimalen Integrationstiefe stellt. Hierzu wurde ein anwenderorientiertes Entscheidungsmodell abgeleitet.
Angesichts des breiten Angebotsspektrums neuer Technologien und der Vielzahl verschieden verwendeter Begriffe rund um Industrie 4.0, stehen Unternehmen nicht selten orientierungslos vor der Herausforderung, individuelle Umsetzungsstrategien abzuleiten. Das vorliegende Reifegradmodell ermöglicht die Erfassung bereits im Produktionssystem implementierter Lean Management-Prinzipien und gibt praktikable Antworten auf die evolutionären Visionen, indem es realisierbare und individuelle Migrationspfade in Richtung Industrie 4.0 für Unternehmen aufzeigt.
5G-Campusnetze sind vielversprechende Umgebungen für industrielle Anwendungen in Produktion und Intralogistik. Diese erreichen jedoch bisher nicht die versprochenen Leistungen, um intralogistischen Anwendungen das volle Potenzial von 5G bieten zu können. Die im Rahmen des Projekts 5G4KMU erhobenen und in diesem Beitrag vorgestellten Leistungsmessungen dienen zur Evaluierung der derzeitigen Praxistauglichkeit von 5G-Campusnetzen.
Um sich in einem schnelllebigen und globalen Markt nachhaltig wettbewerbsfähig aufzustellen, bedarf es innovativer Ansätze, Produkte sichtbar zu machen. Vorreiter wie Apple oder Microsoft stehen mit ihren Marketingstrategien und der Präsentation ihrer Produkte für eine neue Denkweise. Doch wie kann ein klein- oder mittelständiges Unternehmen (KMU) mit solchen Strategien konkurrieren und sich und die eigenen Produkte am Markt erfolgreich platzieren? Der vorliegende Beitrag zeigt auf, wie ein Markteinführungskonzept mithilfe des Design-Thinking-Ansatzes auf Basis der Kundenbedürfnisse modular und skalierbar ausgestaltet werden kann, um auf die jeweiligen Anforderungen des einzuführenden Produktes adaptierbar zu sein.