Ole Hansen is a PhD Candidate in the field of Logistics at Kühne Logistics University, supervised by Prof. Dr. Hanno Friedrich. He holds a diploma in Economics with a focus on International Economics and Supply Chain Management from the University of Kiel. Following his studies, he worked as a researcher and consultant for the logistics services company 4flow for over 4 years. He investigated the German food supply systems’ vulnerability as part of a research project funded by the Federal Ministry of Education and Research (BMBF) and consulted international companies on how to optimize and implement new logistics processes.
In his research, he focusses on the assessment and usage of enterprise food inventories in disaster management and investigates ways to use this information in vulnerability analyses.
|2013 - 2017||Researcher and Consultant at 4flow AG|
|2011 - 2012||Internship and working student at CITTI GV-Partner Großhandel GmbH & Co|
|Since 2017||PhD Candidate in Logistics at Kühne Logistics University|
|2012||Diploma in Economics at the University of Kiel, with majors in International Economics and Supply Chain Management|
Journal Articles (Peer-Reviewed)
Balster, Andreas, Ole Hansen, Hanno Friedrich and André Ludwig (In press): An ETA Prediction Model for Intermodal Transport Networks Based on Machine Learning, Business & Information Systems Engineering.
Abstract: Transparency in transport processes is becoming increasingly important for transport companies to improve internal processes and to be able to compete for customers. One important element to increase transparency is reliable, up-to-date and accurate arrival time prediction, commonly referred to as estimated time of arrival (ETA). ETAs are not easy to determine, especially for intermodal freight transports, in which freight is transported in an intermodal container, using multiple modes of transportation. This computational study describes the structure of an ETA prediction model for intermodal freight transport networks (IFTN), in which schedule-based and non-schedule-based transports are combined, based on machine learning (ML). For each leg of the intermodal freight transport, an individual ML prediction model is developed and trained using the corresponding historical transport data and external data. The research presented in this study shows that the ML approach produces reliable ETA predictions for intermodal freight transport. These predictions comprise processing times at logistics nodes such as inland terminals and transport times on road and rail. Consequently, the outcome of this research allows decision makers to proactively communicate disruption effects to actors along the intermodal transportation chain. These actors can then initiate measures to counteract potential critical delays at subsequent stages of transport. This approach leads to increased process efficiency for all actors in the realization of complex transport operations and thus has a positive effect on the resilience and profitability of IFTNs.
Schätter, Frank, Ole Hansen, Markus Wiens and Frank Schultmann (2019): A decision support methodology for a disaster-caused business continuity management, Decision Support Systems, 118: 10-20.
Abstract: Supply chain risk management typically deals with the systematic identification, analysis and mitigation of risks which affect the whole supply chain network of a company. Business continuity management (BCM) forms part of supply chain risk management and is an important competitive factor for companies by ensuring the smooth functioning of critical business processes in the case of failures. If business operations are severely disrupted, the companies’ decision maker is confronted with a situation which is characterized by a high degree of uncertainty, complexity and time pressure. In such a context, decision support can be of significant value. This article presents a novel decision support methodology which leads to an improved and more robust BCM for severe disruptions caused by disasters. The methodology is part of the Reactive Disaster and supply chain Risk decision Support System (ReDRiSS) to deal with different levels of information availability and to provide decision makers with a robust decision recommendation regarding resource allocation problems. It combines scenario techniques, optimization models and approaches from decision theory to operate in an environment characterized by sparse or lacking information and dynamic changes over time. A simulation case study is presented where the methodology is applied within the BCM of a food retail company in Berlin that is affected by a pandemic disaster.
Hansen, Ole, Hanno Friedrich and Sandra Transchel (2019): An inventory management approximation for estimating aggregated regional food stock levels, International Journal of Production Research, 175 (1): 1-17.
Abstract: Food is an important resource in disaster management, and food stock levels hold significance for disaster mitigation research and practice. The presence or absence of food stocks is a vulnerability indicator of a region. A large part of overall food stock, before a disaster strikes, is held by private companies (retailers, wholesalers and food producers). However, there is little-to-no information on the food stock levels of commercial companies, and no approach exists to derive such information. We develop an approximation model based on essential inventory management principles and available data sources to estimate aggregated food stock levels in supply networks. The model is applied in a case example that features dairy product stock levels in the German state of Saxonia. The resulting overall stock levels are normalised, and their usability is showcased in a simple vulnerability analysis. Disaster managers are provided with a model that can be used estimate otherwise unavailable data and facilitates investigations into the regional resilience of an area. The limitations of our study are based on the aggregated nature of the supply network structure and data usage (i.e. in the model, we do not consider any seasonality or trend effects).
Schätter, Frank, Ole Hansen, Maja Herrmannsdörfer, Markus Wiens and Frank Schultmann (2015): Conception of a Simulation Model for Business Continuity Management Against Food Supply Chain Disruptions, Procedia Engineering, 107: 146-153.
Abstract: This paper focusses on robust decision-making in disaster response where pre-existing logistical structures have not been destructed yet but where a great risk of delayed consequences exists if the functioning of these structures is not strengthened. Responsible decision-makers are companies as operators of the logistical structures themselves, particularly those whose businesses refer to the critical infrastructure sectors food, water, health care, and energy. This paper outlines a conception of a simulation model which combines approaches of scenario-based optimization, stress testing, and robustness measurement.The conception is developed for a decision problem of a food retail company where a society must be prevented from threatening food shortages due to a flu epidemic in Berlin, Germany.
Hansen, Ole and Hanno Friedrich (2015): An Inventory-Focused Analysis of German Food Supply Chains: The Case of Dairy Products, in: Clausen, Uwe, Hanno Friedrich, Carina Thaller and Christiane Geiger (ed.): Commercial transport - Proceedings of the 2nd Interdiciplinary Conference on Production, Logistics and Traffic 2015, Springer: Cham, Switzerland, 337-347.
Abstract: This work was created as part of the research project SEAK, which looks into possible causes and consequences of food shortfalls in Germany and is moreover also aimed at developing and evaluating possible mitigation strategies for these shortfalls. For the management of shortfalls in food supply it would be, as a first step, crucial to have information on existing inventories. Making for example decisions on the reallocation of food products into regions affected by disasters is only possible if knowledge about the (regional) availability of food quantities is present in the first place. This could be considered as a necessary transparency. However, in the German food sector, it is hard to get data about the inventories kept by companies like producers, logistic service providers (LSP’s), wholesalers or retailers. This is due to the fact that usually companies are not obliged to publish this information. Moreover, this information is also considered confidential in most companies, since it would give competitors insight into their business model and processes, which are oftentimes the basis for their success. Since information concerning food inventories is not publicly available, it has to be derived in another manner. This work is aimed at providing a scientific basis for the modelling of inventories along food supply chains. More specifically, it does so for the food commodity group of dairy products. We gathered information on all available food products, but limit this particular analysis to dairy products as a showcase of our approach. First, we introduce the data set used for the analysis and the methodology applied to it. In a next step, characteristics of typical German dairy supply chains are described using practical evidence as well as literature findings. The description follows the supply chain’s structure from start to finish, downstream. In the end, concluding remarks are made and possible further research ventures are suggested.