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Item type:Publication, Access status: with files , How disruptive start-ups change the World of Warehouse LogisticsPurpose: New technical solutions in logistics change the ways of working within warehouses on different levels, from warehouse layouts and concepts of goods pick-ing to process planning and human resources. Thus, disrupting the previous practice in its core. Methodology: In order to evaluate the impact of the new technologies on the ware-house operations, the multiple case study approach was used. To gain a deeper un-derstanding of the changes within logistics processes, the results of the deep-dive analysis are summarized using morphologic box methodology. Findings: Presented solutions such as AutoStore, Kiva and CarryPick can lead to a substantial increase in the speed of order picking while staying very flexible and de-manding significantly less of expensive warehouse space. Still, the implementation of these technologies requires a systematic approach with clearly stated goals. Originality: In contrary to available papers which are concentrating on a single case study with application of one technology at one particular company, the presented paper analyses several solutions comparing them with each other. Additionally, the research evaluates the impact of the technologies on logistic processes and ware-house layouts. Thus, creating value for practitioners looking for solutions to optimize intralogistics.Publicationtype: Conference Paper324 316 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Breaking through the bottlenecks using artificial intelligencePurpose: Performance of Supply Chain is highly dependent on weak spots, so-called bottlenecks. This research paper presents the findings from the analysis of operation processes of a mid-sized producing company and the digital solution for opening up the bottlenecks in order to achieve effectiveness by cutting down the order lead time. Methodology: The study is employing several rounds of simulation based on processes and data from a manufacturing company. Findings: Simulation results demonstrate that by allowing a system to take autonomous decisions for production planning based on current changes in environment such as new customer order or available capacity, the order lead time can be shortened significantly, while granting additional flexibility and robustness to the whole supply chain. Originality: The findings of this research reveal new insights on potentials of artificial intelligence in solving of existing issues within supply chain IT systems.Publicationtype: Conference Paper355 638 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Use case of self-organizing adaptive supply chainThe main drivers for the development and implementation of self-organizing adaptive operation processes are the increase of flexibility while lowering costs in times of growing consumer demands, shaping the supply chain into ”the source of enterprises core competence”. This paper is based on the ”Guidelines for conducting and reporting case study research in software engineering” and it demonstrates the design of the self-organizing adaptive supply chain through an integration of processes and systems. As a result, the automated near real-time data flow enables shorter order-lead-time with a high accuracy of information. Although the study includes the architecture for the self-organizing adaptive supply chain, it cannot be completely standardized due to individual processes and IT-systems within different companies. Academics and practitioners may find it useful to identify appropriate scenarios while looking for the ways to digitize their supply chains.Publicationtype: Conference Paper447 763