Sustainable Supply Chain Management

A quantitative social network analysis approach to mitigate the ripple effect in supply chain networks

Supply chain network disruptions have become an increasingly relevant topic in literature and industry. Just recently the COVID-19 pandemic demonstrated the need for greater resilience in global supply chain networks. Disruptions lead to material shortages adversely impacting demand fulfilment and eventually resulting in operational shutdowns of nodes or edges in the network. Due to the interconnectedness of modern supply chain networks, such impacts may not be contained to one part of the network but cascade through the network, which is known as the ripple effect or risk propagation. The inherent complexity of today’s supply chain networks and the associated dynamics of the ripple effect increase the difficulty to predict and manage disruptions. The unavoidable and unpredictable nature of disruptions requires the ability of supply chain networks to prepare to, respond to, and recover from a disruption. Supply chain networks with such capabilities are called resilient. Current Supply Chain Network Resiliency (SCNR) literature is assuming homogeneous node risk capacities and is mainly focused around network types, proposing methods that are impractical for supply chain management (SCM) practitioners, such as rebuilding an entire network into a certain structure, which is not possible in practice. Further, it is too expensive for practitioners to simply enhance risk capacity of every node in the network.

The Sustainable Supply Chain Management team of the Institute of Sustainable Development fills this research gap by exploring a novel way to identify critical nodes in supply chain networks, which are then to be fortified to increase overall network resiliency. We present a quantitative social network analysis (SNA) based approach to selectively fortify nodes in complex supply chain networks, targeting ripple-effect mitigation and enhancing supply chain network resilience (SCNR). Our model can be used to analyze supply chain resilience and derive strategies to mitigate disruptions, specifically using betweenness centrality for fortification. The model can be applied by supply chain managers to existing supply chain networks.

To find and evaluate an efficient selection logic, we choose a quantitative approach using agent based simulation. First, an instance generator is programmed in Python, by which we generate random graphs and layout them in two-dimensional space. After graph generation and calculation of SNA KPIs (e.g. betweenness centrality) the data is exported to AnyLogic. In AnyLogic we deploy a Susceptible-Infected-Recovered (SIR)-model from epidemiology literature to mimic the disruption propagation of the ripple effect throughout the network. Initially, all nodes in the supply chain network are in susceptible state. By initially infecting one (or multiple) nodes, the propagation process is started and the “infection” spreads naturally throughout the network. Infected nodes recover after a certain time and thereby gain immunity against further infections.

For our simulation experiment we alternate the values of five parameters, thus creating 25 = 32 different scenarios, which are each run 30 times with a random seed. Consequently, we obtain 32 * 30 = 960 observations. This data is exported to Excel where we perform two regression analyses to quantify the impact of our parameter variation on network performance, measured by two different network performance indicators:

1) Healthy Nodes, representing how deep the dip in network performance is.

2) Recovery Time, representing the time duration until the network performance is back on its initial level.

Looking at the regression output tables we can draw the following conclusions:

With Healthy Nodes as dependent variable, we may explain quite a bit of its variation by the independent variables. We find that the interaction term fort_option * fort_metric is statistically significant at the 1% level and has a positive sign, meaning that if we fortify nodes at all, then selection of nodes based on betweenness centrality results in a better network performance compared to randomly fortifying nodes, making betweenness centrality a suitable node selection logic for supply chain managers to mitigate the ripple effect.

When we set Recovery Time as dependent variable, we find only low explanatory power in our regression model. We observe contradictory and implausible results. Obviously, mechanisms impacting Recovery Time are not well understood and most likely not adequately captured by the model.

Future research will focus on overcoming the limitations of our model:

  • Experiments were performed on the same, single graph. We plan to repeat experiments on other, different graphs with the same properties to validate our results.
  • Further, we plan to repeat experiments on graphs varying in size and structure, moving from random graphs towards small world and scale-free networks.
  • Next, we plan to explore the effectiveness of utilizing other SNA KPIs as selection criterion, such as node degree, degree centrality, closeness centrality or page rank.
  • Obviously, a deep dive on mechanisms affecting Recovery Time is required.

First results have already been presented at EurOMA. In a next step, simulation experiments will be extended as has been described above.


Carsharing and the interrelation with electric vehicles

We adopted a stated choice survey with 995 participants from Switzerland to test the car purchase preference of mobility users with and without carsharing experience. Results suggest that – for people living in the countryside – carsharing users have a 3 times higher likelihood of choosing a micro to mid-sized battery electric vehicle (BEV) compared to participants without carsharing experience. We find a similar trend for people living in the agglomerations. We therefore recommend policy makers and mobility planners to take these benefits into account when planning carsharing services and its role in mobility systems.

Our study about the influence of carsharing on sustainable car purchasing has just been published in Transportation Research Part D: Transport and Environment. The open access article can be found here:


Beitrag „Digitale Intensität und Management der Transformation“ ist veröffentlicht

Digitale Technologien sind zentrale Treiber für das Entwickeln und Umsetzen digitaler Geschäftsmodelle. Die vorliegende Studie zeigt, dass neben der Entwicklung notwendiger Technologien weitere Bausteine wesentlich sind. Das Entwickeln und Umsetzen digitaler Geschäftsmodelle beruht zudem auf der Vernetzung von Prozessen und dem Entwickeln notwendiger Fähigkeiten/Know-how. Weiter sind gezielte Aktivitäten des Managements und des Leaderships notwendig. Die digitale Intensität widerspiegelt den Implementierungsgrad digitaler Technologien und vernetzter Prozesse. Das Management der Transformation bezieht sich auf die Fähigkeiten und das Management/Leadership. Zusammen bilden diese beiden Dimensionen den Digitalisierungsgrad eines Unternehmens. Die Analyse Schweizer Industrieunternehmen zeigt, dass zwischen dem Digitalisierungsgrad eines Unternehmens und der Erhöhung der Prozesseffizienz ein Zusammenhang besteht. Weniger ausgeprägt ist der Zusammenhang zwischen dem Digitalisierungsgrad eines Unternehmens und dem Erzielen von Mehrwerten aus Produkten und Dienstleistungen. Dies könnte daran liegen, dass der Mehrwert von Produkten und Dienstleitungen nicht durch eine reine Weiterentwicklung von Technologien und vernetzten Prozessen generiert werden kann, sondern ein passendes Geschäftsmodell benötigt wird.

Den ganzen Beitrag finden Sie hier –> LINK ZUM BUCH – besonderer Dank an meine* Co-Autoren Patricia Deflorin und Niklas Eberhardt

(*Der Blogpost wurde im Namen und aus Sicht von Prof. Dr. Maike Scherrer verfasst)


Would you be better off switching to a sustainable mobility lifestyle partly based on electric mobility?

Alternative mobility lifestyles are often seen as costly compared to owning a conventional car. We provide a basis for discussion and compare total cost of ownership of Swiss conventional car users and three sustainable mobility lifestyles with participants of the Swiss Household Energy Demand Survey conducted in 2020.

The future of private car mobility might be dominantly electric, powered by batteries. However, their environmental impact increases significantly with larger battery sizes. Relying still on car utilization, vehicles with smaller batteries (i.e. smaller car, shorter range) should generally be preferred. We addressed this trade-off between vehicle size, range, and environmental impact by proposing two mobility lifestyles with a small electric vehicle (EV), the first in combination with public transport (EV + PT) and the second in combination with carsharing (EV + CS). We assume that public transport or carsharing is used for trips when the range of the EV is not sufficient, i.e. for trips that exceed 200km per day. We further proposed a third alternative based on a combination of public transport and carsharing/car-rental without car ownership (PT + CS), as this would result in an even more sustainable mobility lifestyle.

Since total cost of ownership (TCO) for a conventional car is still underestimated and EVs still exhibit higher upfront costs than conventional cars, EVs are often perceived to be costlier. With this research, we wanted to investigate whether it is possible that people might be contrariwise even better off by switching to one of the three alternatives. For this purpose, we calculated the TCO for each proposed mobility lifestyle utilizing the information on stated mobility behavior of 845 participants of the Swiss Household Energy Demand Survey (SHEDS), a representative survey of the Swiss population. For example, we used the average kilometers driven per year, the purchase price of the current car and the number of day trips per year exceeding 200km. Together with a method developed by Touring Club Switzerland (TCS), which addresses costs related to depreciation, maintenance, fuel and tires among others, we were thus able to calculate the TCO of the participants current car and the TCO of the proposed alternatives.

Results suggest that roughly 60% of respondents would be financially better off switching to a combination of a small EV for everyday trips until 200km and use public transport for the cases daytrips exceed 200km. Especially for people who currently own a larger car, this alternative would be cheaper. About 30% of the respondents would be better off with a combination of a small EV and carsharing/car-rental. Again, especially people who currently own a large car or SUV would financially benefit by switching to this alternative. Since carsharing is generally more expensive than public transport in Switzerland, the alternative EV + CS is costlier than EV + PT. Finally, in average, everybody would be better off switching to a combination of public transport and carsharing/car-rental without car ownership. Especially people owning larger cars could benefit the most.

To the best of the authors’ knowledge, this is the first study to investigate the TCO of multimodal mobility lifestyles compared to a lifestyle based only on conventional private car use. Our results could be relevant for public policy, mobility planners as well as mobility service providers who could use our results for promoting the cost advantages of alternative mobility lifestyles.

We will present the results in the upcoming European Transport Conference on Tuesday 14th of September 2021.

In the next step, we will investigate 3 interventions to increase the likelihood to opt for one of the alternatives instead of keeping a combustion engine car.


Gestaltung von energieautonomen Logistik-Kühlketten durch die Entwicklung eines Simulationsmodells

Die Nachfrage nach temperaturgeführten Produkten, insbesondere für Nahrungsmittel und pharmazeutische Produkte, steigt kontinuierlich an. Bis 2025 wird in diesem Markt mit einem Wachstum von bis zu 18% gerechnet. Dies ist jedoch mit einem steigenden Energiebedarf und daraus resultierenden Treibhausgasemissionen verbunden. Die zur Kühlung von Lebensmitteln benötigte Energie macht 8 % des weltweiten Stromverbrauchs und 2,5 % der Treibhausgasemissionen aus. Bisherige Versuche, die Effizienz von Kühlketten zu steigern, konzentrierten sich auf Insellösungen wie Routenoptimierung oder effiziente Gebäudekühlung. Was fehlt, ist eine holistische Dekarbonisierungsstrategie für Logistik-Kühlketten, die auf Systemebene ansetzt und alle Schritte, vom Ausgangsprodukt bis zum finalen Distributionspunkt, berücksichtigt.

Um diese Forschungslücke zu schliessen, entwickelt das Team für Nachhaltiges Supply Chain Management des Instituts für Nachhaltige Entwicklung ein Simulationsmodell als Entscheidungshilfe für die Ausgestaltung einer energieautonomen Logistik-Kühlket­te unter Berücksichtigung von Transport inkl. Kühlung, Lagerung und Distribution der Produkte. Das Simulationsmodell wird eine konsistente Analyse des Energiebedarfs und der Treibhausgasemissionen von Kühlketten ermöglichen.

Das Modell kombiniert Life Cycle Assessment (LCA) mit System Dynamics. LCA wird angewendet, um die Umweltauswirkungen zu quantifizieren. System Dynamics wird als Tool zur Entscheidungsfindung eingesetzt. Es dient dazu, die Konfiguration des untersuchten Kühlkettennetzwerks darzustellen und das Zusammenspiel verschiedener Energiesparmaßnahmen zu simulieren. Darüber hinaus soll System Dynamics zur Modellierung und Optimierung verschiedener Kombinationen von Technologien eingesetzt werden. Das Modell soll generisch und für verschiedene Kontexte und Regionen skalierbar sein. Außerdem soll es aus modularen Elementen bestehen, die flexibel an den jeweiligen Untersuchungsgegenstand angepasst werden können. Zusätzlich soll eine dynamische Simulation über einen bestimmten Zeitraum ermöglicht werden. Dies ist beispielsweise bei der Einbindung von PV-Anlagen wichtig, die tages- und jahreszeitlichen Schwankungen unterliegen.

Mit der Umsetzung dieses Forschungsvorhaben kann aus wissenschaftlicher Perspektive ein wesentlicher Beitrag zur Schliessung der Forschungslücke einer ganzheitlichen und systemischen Betrachtung der logistischen Kühlkette und der damit verbundenen Potenziale zur Realisierung von Energie- und Emissionseinsparungen geleistet werden. Zudem wird Wissen im Bereich der Entwicklung eines Logistik-Simulationstools generiert, was die Analyse verschiedener Szenarien erlaubt.

Aus Sicht der Praxis leistet dieses Projekt einen Beitrag zur Erreichung von Energie- und CO2-Emissionsreduktionszielen und schafft die Rahmenbedingungen für die Entscheidungsfindung für die zwischen- und innerbetriebliche Ausgestaltung von Kühlketten. Darüber hinaus wird die Grundlage für Energiezertifikate für verderbliche Produkte geschaffen.

Erste Ergebnisse wurden bereits an der Sustainability EUROMA präsentiert. Im nächsten Schritt kann mit der Erhebung einer Kühlkette eines Projektpartners und mit der Umsetzung des Simulationsmodells begonnen werden.