Themenausschreibungen

This research topic examines how the integration of generative AI into supply chains is changing traditional models of human supervision. While decision-making in the past was based on decentralized, human-controlled supervision, new AI-driven coordination structures point to a more centralized, AI-driven model. The research examines how this shift is altering resilience in supply chain operations, with a particular focus on the interplay between human judgment and autonomous systems. It seeks to critically evaluate the trade-offs between decentralized human input and AI-mediated centralization in order to gain new insights into the balance between efficiency, transparency, and robustness in complex global networks.

This study addresses high-volatility demand environments by developing RNN/ LSTM/ GRU/ Seq2Seq/ Attention models that incorporate exogenous variables (price, promotions, weather, holidays, etc.) and support short- and long-term forecasting as well as state monitoring. Using public datasets from government, enterprise, and open repositories, it benchmarks against standard baselines and evaluates using sMAPE, MASE, WIS, service level, and stockout rate, analyzing links between forecast error and service level. The business impacts on safety-stock and replenishment policies are quantified. Deliverables include a research paper, interactive dashboards (Python/JavaScript/Power BI), original data set and fully reproducible code.

Reviews organizational, technical, economic, and regulatory barriers to blockchain use across supply-chain contexts. Could compare barriers by industry, network design, and implementation stage (pilot, scale etc.). Outputs a taxonomy of barriers and an agenda for future research.

This study focuses on real-time detection of in-warehouse anomalies—damage, misplacement, missing items, and hazardous stacking—using CNN-based detection, segmentation, and recognition. It emphasizes system safety and robustness to illumination changes, occlusion, blur, viewpoint variation, and device/domain transfer. Reported metrics include recall/ false-alarm rates, processing frame rate, and alert latency, alongside an estimate of the economic benefits from error reduction. Outputs include a research paper, original data set and fully reproducible code.

This thesis reviews how supply chains dealing with short shelf-life products are rethinking processes to minimize spoilage and extend product value. It investigates business models that transform by-products into new offerings, turning potential losses into opportunities for efficiency and sustainability.

The study explores how shifting weather patterns, resource scarcity, and environmental volatility influence purchasing strategies for temperature-sensitive goods. It highlights approaches companies adopt to safeguard availability, manage risks, and redesign procurement practices for greater resilience.

The study reviews strategies that extend the life and value of goods with limited durability by reducing losses and utilizing by-products. It systematically analyzes how innovative models create economic and environmental benefits by closing resource loops in perishable supply chains.

This research examines how environmental disruptions such as droughts, temperature shifts, and extreme weather events influence sourcing and purchasing choices. By synthesizing academic and industry literature, it highlights adaptation mechanisms that build resilience in supply networks for perishable goods.

This work targets energy and carbon optimization in warehousing and transport by combining data-driven prediction and decision models with scenario analysis of operational policies (e.g., shift scheduling, vehicle routing, temperature control). It develops energy/load forecasting, routing/ scheduling optimization (optionally reinforcement-learning-based), and dynamic carbon accounting with constraints. Explainable-AI methods (e.g., SHAP) are employed to attribute feature contributions and ensure traceability for compliance and operational decision-making. Using public datasets, the study conducts data selection, preprocessing, modeling, training, and tuning; evaluates against baselines (e.g., linear regression); and reports outcomes in kWh/ cost and CO₂ emission reductions. Deliverables include a research paper, original data set and fully reproducible code.

Examines when and how acquisitions are used to accelerate sustainability outcomes (Scope-3 reduction, traceability, circularity etc.). Focus may include due-diligence criteria, post-merger integration of ESG and procurement data, and governance levers that turn intent into impact. Expert interviews (Corp Dev, ESG, CPO/Procurement, Integration leads) inform a set of mechanisms, pitfalls, and integration archetypes.

This thesis investigates how artificial intelligence can strengthen the coordination and efficiency of transport activities during crises. Through expert interviews, it explores AI’s role in improving routing, resource allocation, and rapid decision-making under unpredictable conditions.

Explores how firms are adapting risk strategies amid protectionism, sanctions, and international conflict. May examine shifts from efficiency to optionality (friend-/nearshoring, multi-sourcing, inventory buffers etc.) and how decision criteria change across sectors. Based on semi-structured expert interviews (procurement, logistics, risk, policy advisors) to surface emerging practices, trade-offs, and a lightweight playbook.

Examines how organizations prepare data-driven tools used in sourcing, risk, planning, and logistics for compliance with emerging AI regulation. Focus may include system classification decisions, human-oversight designs, logging and documentation routines, and vendor–deployer responsibility splits, with optional attention to post-market monitoring. Based on semi-structured interviews with Procurement/SRM, Data/ML, Legal/Compliance, and Risk/Audit stakeholders, the study aims to surface readiness patterns, common pain points, and pragmatic control checklists adaptable across sectors. Outputs can include a lightweight “AI-Act readiness” rubric and example governance templates.

Explores how lead firms and tier-1 suppliers use relationship governance – trust-building, relational contracts, local champions, joint problem-solving, and incentives – to influence compliance in tier-2/3. Through semi-structured interviews with buyers, /lower-tier) suppliers and optionally auditors/NGOs, the study identifies effective mixes of relational and formal controls and distills a concise playbook and maturity rubric for cascading compliance beyond tier-1.

Maps the evidence on leadership styles, team practices, and capability-building in SCM settings. May synthesize links between initiatives (training, autonomy, psychological safety) and outcomes such as service, cost, and quality. Delivers a thematic framework and research gaps.

Synthesizes what is known about cultural competence and cross-border collaboration in supply chains. May cover trust formation, communication routines, governance mechanisms, and the role of training/tools in mitigating friction. Produces an evidence map and concise practitioner takeaways.

This survey focuses on order-picking and transport identification in cost-constrained settings. It systematically reviews the technical principles of camera-based machine-vision systems and UHF-RFID/ NFC, hardware and deployment costs, recognition performance (accuracy, latency, throughput), robustness to occlusion and interference, and operations and maintenance (O&M) usability. By comparing representative applications (e.g., tote/ bin identification, pallet tracking, and inbound verification), public benchmark metrics, published case studies, and vendor white papers, it provides a budget- and scenario-aware decision framework and a cost–performance trade-off matrix.

This survey reviews digital-twin (DT) and discrete-event simulation (DES) approaches for logistics transport chains, covering modeling granularity, data interfaces, reliability and downtime modeling, and optimization strategies aimed at process efficiency and reliability. It proposes a reference workflow and best practices for building an end-to-end transport-chain DT+DES, contrasts major simulation tools in terms of functionality and limitations, and summarizes a “data-to-decision” implementation pathway and KPI framework.

This survey targets cold-chain quality monitoring with physics-informed multimodal Internet of Things (IoT). It consolidates sensor-fusion methods for heterogeneous streams (temperature/ humidity, vibration, location, etc.) and physics-informed modeling approaches. It discusses cloud-based real-time pipelines—data ingestion, forecasting, threshold-/probability-based alerting, visualization, and O&M—and compares edge–cloud co-processing in terms of latency and cost. Mainstream deployment options and typical evaluation metrics are also reviewed.