How to manage risks?

Based on the results of the first two SCRM phases, management measures for supply chain risks can be developed. Based on the risk strategy, it is decided which supply chain risks trigger an immediate need for action (Denk et al. 2008, p. 127). Risks that are above or close to the defined tolerance range or for which an additional increase is expected in the foreseeable future require appropriate action.

The aim of risk management is to change the company’s risk landscape in such a way that an optimization of the ratio of earnings opportunity to risk of loss (risk/return optimization) is achieved (Rogler 2002, p. 26; Diederichs 2012, p. 126).

Five categories of SCRM control strategies can be delineated in the literature: Avoidance, Mitigation, Limitation, Pass-Through, and Self-Sustainability. The first two (avoidance and mitigation) target cause-related measures and the remaining three (limitation, pass-through, self-sustainability) focus on impact-related measures (Sodhi et al. 2012, p. 52; Gupta et al. 2014, p. 83; Handfield et al. 2008, p. 41; Brünger 2009, p. 163f.; Rogler 2002, p. 25ff.).

The selection of control strategies must be based on the agreed corporate objectives and the risk strategy. In addition, it depends on legal, socially responsible, and environmental requirements, as well as general cost-benefit considerations during implementation (ISO 31000:2009, p. 19).

How might the use of Blockchain technology affect supply chain risks and their management?

Transparency in the supply chain is essential to maintain efficient supply chain risk management (SCRM). Blockchain technology can be applied to improve visibility in supply chains. But with increased transparency, SCRM is faced with new challenges. In our new article, the application of blockchain technologies’ effects on supply chain risks are analyzed. For this purpose, a fictitious case is created in which blockchain technology is applied to a manufacturer of medical technology products’ supply chain. The findings of the paper include (1) the effects on supply chain risks (2) recommendations for the implementation of future SCRM in blockchain.

Today, I will present our research results at 2020 DSI Annual Conference – Decision Sciences in the Age of Connectivity

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New applications for risk management through the use of Big Data

The increasing digitalization, combined with the generation of extensive amounts of data – also referred to as Big Data. Big Data presents new challenges to the traditional risk management implemented so far on the one hand, but on the other hand it also opens up potential for a broader practical application, especially with regard to risk identification and assessment, which has not been possible to this extent so far.

In processes such as machine learning – a subdomain of artificial intelligence – IT systems develop patterns and regularities for risks on the basis of these existing data sets and with the aid of algorithms, and make corresponding forecasts which are continuously adjusted (Beierle & Kern-Isberne 2003, p. 99f.). This enables more comprehensive early warning systems to be implemented and thus promotes the proactive handling of risks. In addition to accessibility, the validation of data plays a decisive role in this context.

Furthermore, the higher data availability is accompanied by improved transparency, which opens up new potentials for visualization (also called mapping). Thus, it is possible to visualize e.g. movements of persons or objects and to predict future changes in the process due to occurring risks by means of corresponding data sets.

The improvement in risk identification has also resulted in the development of automated analysis and evaluation procedures. The large volumes of data and the large number of sources of data supply require the use of algorithms and complex evaluation procedures in order to draw conclusions about the future development of risks (predictive analytics) and to be able to forecast the company’s optimum response to future risks (prescriptive analytics) (Krause & Pellens 2018, p. 51). As a result, strategies for dealing with risks can be decided at an earlier stage and precautionary measures can be initiated that are tailored to the specific situation. In addition, a better cost-benefit assessment can be made for individual measures, e.g. through better quantification of resources.

In our paper about New Applications for Risk Management through the Use of Big Data – Two Case Studies, we describe two practical examples of the use of Big Data in risk management. Firstly, the containment of epidemics through risk analysis using Big Data. Secondly, the reduction of the risk of pollution from ship hulls through the evaluation of satellite data on global sea conditions, as well as data sets on sea temperatures, cloud cover, biological factors, salinity, acidity and other minerals.

Extract from Schröder et al. 2019, Neue Anwendungsmöglichkeiten für das Risikomanagement durch den Einsatz von Big Data – Zwei Fallbeipspiele, in: Schröder, Meike; Wegner, Kirsten (Hrsg.) Logistik im Wandel der Zeit, Springer: Wiesbaden, pp. 121-136.


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