Themenausschreibungen

Sustainability is a rising topic in all research areas. To take action towards a sustainable development from the ecological, economic and social dimension, collaborations play a key role. Supply chain collaboration refers to the practice of different entities within a supply chain working together to achieve common goals and improve overall efficiency. It involves sharing information, resources, and responsibilities among suppliers, manufacturers, distributors, and retailers. By collaborating, these entities can streamline processes, reduce costs, enhance product quality, and respond more effectively to market demands. It is an essential aspect of modern supply chain management, as it promotes transparency, trust, and cooperation among all stakeholders. Strategic alliances, which exist in various industries (e.g. Drive Sustainability in the automotive industry), are a particular type of collaboration in supply chains. The aim of the bachelor and master thesis is to investigate the phenomenon of alliances and the underlying behavior of the participants leading to collaborative partnerships.

Digital servitization refers to the transformation of a traditional product-based business model into a digital service-based business model. It involves leveraging digital technologies and data to offer value-added services alongside physical products. In a digital servitization model, companies go beyond simply selling products and instead focus on providing a complete solution to their customers‘ needs. This can include services such as maintenance, repair, upgrades, customization, and remote monitoring. These services are enabled by the integration of digital technologies, such as Internet of Things (IoT) devices, cloud computing, artificial intelligence, and data analytics. Digital servitization allows companies to create new revenue streams, increase customer loyalty, and differentiate themselves from competitors. By offering services that enhance the value and performance of their products, companies can establish long-term relationships with customers and generate recurring revenue through service contracts or subscriptions. Overall, digital servitization represents a shift from a product-centric approach to a more customer-centric approach, where companies focus on delivering outcomes and experiences rather than just selling physical goods. The aim of this master thesis is to investigate collaboration in terms of digital servitization focusing on potential dependencies.

Supply ecosystems, also known as supply chain ecosystems or value chains, refer to the interconnected network of organizations, resources, activities, and technologies involved in the production, distribution, and delivery of goods and services to customers. A supply ecosystem encompasses all the entities and processes involved in the flow of products, information, and finances across the entire supply chain, from raw material suppliers to end customers. It includes manufacturers, suppliers, distributors, retailers, logistics providers, service providers, and other intermediaries. The goal of a supply ecosystem is to ensure the smooth and efficient movement of goods and services from the point of origin to the point of consumption. It involves coordinating and optimizing various activities such as sourcing, procurement, production, inventory management, transportation, warehousing, and customer service. In a supply ecosystem, collaboration and coordination among different stakeholders are crucial to achieving operational efficiency, cost reduction, quality improvement, and customer satisfaction. Supply ecosystems are becoming increasingly complex and globalized, with companies relying on extensive networks of suppliers and partners across different countries and regions. As a result, the management and optimization of supply ecosystems have become strategic priorities for organizations seeking competitive advantage and resilience in today’s dynamic business environment. The aim of this master thesis is to investigate the evolving complexity in supply ecosystems based on game theory.

In the face of climate change and growing resource scarcity, global supply chains must fundamentally change their behaviour in the sourcing, production, delivery, use and recovery of materials to maintain current consumption habits (Hazen et al. 2017). In this context, circular supply chain models offer enormous sustainability potential compared to classical linear forms (Farooque et al. 2019). Industry 4.0 technologies are expected to have a positive impact on circular economy capabilities in companies (Da Hennemann Hilario Silva und Sehnem 2022) and enable circular supply chains (Hettiarachchi et al. 2021). While technologies such as big data analytics or artificial intelligence enable recycling, waste reduction and process efficiency (Bag et al. 2021), the internet of things supports energy management and smart transportation (Esmaeilian et al. 2020). Accordingly, research interest in the synergies and potentials of circular supply chains and industry 4.0 technologies is growing (Nascimento et al. 2019; Dev et al. 2020; Lopes de Sousa Jabbour et al. 2018), establishing the concept of smart circular supply chains (Kayikci et al. 2022). Since industry 4.0 technologies are highly reliant on information and data, the questions arises as to what information processing requirements emerge on the way to smart circular supply chains and what capacities companies will need to develop to meet them (Galbraith 1974; Tushman und Nadler 1978). The aim of the bachelor and master thesis is to develop future projections by conducting a literature review, expert interviews and an expert workshop.

Artificial intelligence has become a cornerstone of numerous business models and has been integrated into the strategies of many companies in industries such as healthcare, retail, and manufacturing. The technology is also already being used by large companies in supply chain management, for example for forecasting and fulfilment tasks. (Dwivedi et al. 2021) Artificial intelligence can further be used in various supply chain tasks including supply chain disruption prevention (Akhtar et al. 2022), supplier selection, inventory management or vehicle routing (Vishwakarma und Singh 2022). Apart from optimization, automation and performance improvements, Artificial intelligence can also play an important role in sustainable supply chain management (Dhamija und Bag 2020). Fields of application reach from carbon emission assessments and data analytics to sustainable transportation and reverse logistics  models (Naz et al. 2022). Against the backdrop of increasing pressure from stakeholders such as customers and governments (Seuring und Müller 2008), achieving sustainability in the supply chain is gaining strategic importance. Thus, it is of interest to analyse why and how sustainable companies are adopting and implementing artificial intelligence technologies for sustainable supply chain management. The aim of this master thesis is to conduct a multiple case study to investigate the constraints and opportunities of artificial intelligence for sustainable supply chain management and its context.