Research Group on Food Supply Chain Management

Research Group on Food Supply Chain Management

Motivated by environmental, social, and health challenges – such as the pressure of climate change and concern for animal welfare – the food industry has seen significant changes over the past decades, with an increasing demand for transparency, focus on food safety, and sustainably-produced products. This was further exacerbated by the COVID-19 pandemic, which revealed the fragility of food supply chains. Local foods, organic and/or fair trade, and less resource-intensive food is becoming more popular among consumers within the mainstream market, evidenced by a significant growth in sales. In terms of total sales, the plant-based food sector increased 49% in Europe between 2019-2021, while the organic food sector increased by a historic 22% in Germany in 2020. The industrialized food system is expected to continue to dynamically develop in production, processing, and distribution in the coming years to meet the food security challenges of a growing global population.
KLU’s research group on Food Supply Chain Management seeks to combine rigorous academic research with practical insights along the entire food supply chain – i.e. from farm to fork – to investigate initiatives and improve efficiency and sustainability along the supply chain. We strive to create a knowledge platform for state-of-the-art research and innovative concepts for managing food logistics and food supply chains of the future.

Research Projects

Publications

DOI: 10.1007/s12599-020-00653-0 

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.

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DOI: 10.3390/ijerph17020444 

Abstract: Computational traceback methodologies are important tools for investigations of widespread foodborne disease outbreaks as they assist investigators to determine the causative outbreak location and food item. In modeling the entire food supply chain from farm to fork, however, these methodologies have paid little attention to consumer behavior and mobility, instead making the simplifying assumption that consumers shop in the area adjacent to their home location. This paper aims to fill this gap by introducing a gravity-based approach to model food-flows from supermarkets to consumers and demonstrating how models of consumer shopping behavior can be used to improve computational methodologies to infer the source of an outbreak of foodborne disease. To demonstrate our approach, we develop and calibrate a gravity model of German retail shopping behavior at the postal-code level. Modeling results show that on average about 70 percent of all groceries are sourced from non-home zip codes. The value of considering shopping behavior in computational approaches for inferring the source of an outbreak is illustrated through an application example to identify a retail brand source of an outbreak. We demonstrate a significant increase in the accuracy of a network-theoretic source estimator for the outbreak source when the gravity model is included in the food supply network compared with the baseline case when contaminated individuals are assumed to shop only in their home location. Our approach illustrates how gravity models can enrich computational inference models for identifying the source (retail brand, food item, location) of an outbreak of foodborne disease. More broadly, results show how gravity models can contribute to computational approaches to model consumer shopping interactions relating to retail food environments, nutrition, and public health.

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DOI: 10.1016/j.ecolecon.2019.05.022 

Abstract: We compute degrees of food self-sufficiency for regions in North Germany with the city state of Hamburg at the centre, given different diets (the German average diet versus increasing substitution of legumes for meat) and production methods (conventional versus organic). Triangulating data of statistical databases, literature, and our own collection, we compute land footprints per capita and multiply by regional populations. Our findings indicate that there is great potential to feed the regional community surrounding Hamburg solely with regionally, organically grown foods, but this result depends on (1) composition of diets — specifically, the per capita meat consumption – and (2) agricultural area available in the defined region. On the basis of simplifying assumptions, the computation indicates an approximation of what is possible.

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DOI: 10.1098/rsif.2018.0624 

Abstract: In today’s globally interconnected food system, outbreaks of foodborne disease can spread widely and cause considerable impact on public health. We study the problem of identifying the source of emerging large-scale outbreaks of foodborne disease; a crucial step in mitigating their proliferation. To solve the source identification problem, we formulate a probabilistic model of the contamination diffusion process as a random walk on a network and derive the maximum-likelihood estimator for the source location. By modelling the transmission process as a random walk, we are able to develop a novel, computationally tractable solution that accounts for all possible paths of travel through the network. This is in contrast to existing approaches to network source identification, which assume that the contamination travels along either the shortest or highest probability paths. We demonstrate the benefits of the multiple-paths approach through application to different network topologies, including stylized models of food supply network structure and real data from the 2011 Shiga toxin-producing Escherichia coli outbreak in Germany. We show significant improvements in accuracy and reliability compared with the relevant state-of-the-art approach to source identification. Beyond foodborne disease, these methods should find application in identifying the source of spread in network-based diffusion processes more generally, including in networks not well approximated by tree-like structure.

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DOI: 10.1080/00207543.2019.1657248 

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).

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DOI: 10.1016/j.tre.2018.03.002 

Abstract: This paper presents a calibrated dynamic multi-scale multi-regional input–output (MSMRIO) model of the German food supply system based on real data. The model comprises 51 commodity groups from farm to fork differentiating three different temperature ranges as well as living animals. Spatially, it works on an aggregate level of 402 regions within Germany as well as its 50 most important trading nations. It determines the commodity flows and the additionally needed transport capacity in case of disruptions. Showing how changes in production, inventories, sourcing, and consumption affect commodity flows, the model uncovers vulnerabilities and makes risk evaluation possible.

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DOI: 10.1016/j.tre.2017.08.009 

Abstract: The paper introduces a model to determine possible impacts of changes in supply chain structures on freight transport demand. Examples are centralisation or vertical (des)integration within supply chains. The model first generates a population of establishments and commodity flows in space which is then manipulated according to different scenarios. It uses methods from transport planning and optimisation as well as scenario technique. To demonstrate its applicability a centralisation in food supply chain structures in Germany is analysed. The results show that a more educated discussion is needed for such changes since the range of possible impacts is large.

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The Food Research Group Team

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Prof. Dr. Sandra Transchel

Professor for Supply Chain and Operations Management

Kühne Logistics University - KLU

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Prof. Dr. Hanno Friedrich

Associate Professor of Freight Transportation - Modelling and Policy

Kühne Logistics University - KLU

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Sarah Joseph

PhD Candidate

Kühne Logistics University GmbH

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Nina Mayer

PhD Candidate

Kühne Logistics University - KLU

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Navid Mohamadi

PhD Candidate

Kühne Logistics University - KLU

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Sandra Rudeloff

PhD Candidate

Kühne Logistics University - KLU

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Tim Schlaich

PhD Candidate

Kühne Logistics University - KLU

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Ute Menski

Research Associate

Kühne Logistics University - KLU

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Sissi Adeli Bazan Santos

External PhD student

Technische Universität München

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