Today’s blog is brought to you by guest Community member Professor Dr. David Francas of Heilbronn University Graduate School (HUGS), Heilbronn University, Germany. If you are a professor or student looking to learn more about access to tools and course materials, make sure to check out our information on Academic Programs.
The Importance of Resilience:
Novartis states their “success depends on [the] ability to manage risk”. Likewise, Roche, the Swiss healthcare company, “seek to build a resilient supply chain”. Industry reports see a rapidly growing demand for supply chain analytics that create transparency and support developing risk mitigation strategies. A survey by the consultancy McKinsey published in 2020 shows that 90 % of supply chain executives plan to increase their digital supply chain capabilities, and 60 % plan to implement advanced analytics as a response to the global pandemic. Operations research models play a crucial role in implementing advanced analytics in companies. At Heilbronn University, we use FICO© Xpress Insight to demonstrate the benefits of Operations Research models for supply chain risk management. Dedicated apps within Xpress Insight help to explain how analytics can support overcoming the limitations of traditional risk analysis.
Prescriptive Analytics for Supply Chain Risk Management:
Traditional approaches in supply chain risk management require often highly subjective judgment (e.g., type and the probability of risk) by decision-makers, which renders them often impractical to use. A survey by Jüttner (2005) confirms this in an exemplary manner: Brainstorming is named the most frequently used method for identifying risks. Therefore, new supply chain risk management approaches focus on managing and limiting the impact of potential disruptions. Instead of trying to identify each and every possible disruptive event and estimating its impact and probability, they aim to analyze a supply chain’s vulnerability. Simchi-Levi (2015) proposes to shift attention to the impact of potential failures at points along the supply chain (such as the breakdown of a supplier), rather than the cause of the disruption. In line with our experience, Simchi-Levi argues that this type of analysis obviates the need to estimate the probability that any specific risk will occur; this is a valid approach since the mitigation strategies for disruptions are equally effective regardless of their causes.
Operations Research models play a prominent role in implementing this type of risk analysis. Digital twins – computerized models of a supply chain – provide the means to assess supply chain vulnerability and risk exposure. We use FICO Xpress Insight to make such analytics approaches available to our students (Simchi-Levi et al., 2015; Francas 2021a). Operations Research models for measuring risk exposure and optimizing risk mitigation strategies are implemented in the Xpress Mosel. We use Xpress Insight to visualize data and key risk metrics. Furthermore, the platform allows the upload and download of data files. The implemented apps enable students to analyze case studies and export optimization results for further investigation to e.g. Excel and Tableau.
Teaching Supply Chain Resilience Using Analytics:
Xpress Insight plays an integral role in our master course Network Design and Inventory Management at Heilbronn University, Germany. In this course, we discuss supply chain resilience and risk management from different angles. We start with a discussion of common approaches for risk management such as risk matrices. An important insight for students is that the categorization of disruptions by likelihood of occurrence and magnitude of impact works well for known risks such as forecast errors, lead-time fluctuations, and varying yield factors. However, risk matrices tend to struggle for low-probability, high-impact events. We discuss in class that black swans like the 2019 Corona outbreak and the 2011 earthquake and tsunami in Japan or major outages due to unforeseen events such as factory fires and supplier failures are difficult to anticipate.
Subsequently, the course introduces supply chain analytics and digital twins as effective means to assess supply chain vulnerability and risk exposure based on the models discussed in Simchi-Levi et al. (2015) and Francas (2021a). We use Xpress Insight to illustrate why automotive manufactures experienced shortages soon after Chinese suppliers had to shut down operations in mainland China in March 2020. Based on published industry data, we analyze the impact of the shutdowns on automotive manufacturing against the much longer supply chain for drug production (see Francas 2021b). The implemented Operations Research models also allow students to quantify the benefits of single versus dual sourcing and re-allocation of risk inventory. In a case study from the pharmaceutical industry, the students are asked to assess supply chain vulnerability and propose options to increase resilience. We are grateful for this easy-to-use platform allowing us to use innovative prescriptive analytics apps in our courses.
To stay up to date on this type of content, make sure to subscribe to the Optimization blog. If you are interested in becoming a guest contributor on The FICO Community, please reach out to the FICO Community Team.
Additional Research & Reference:
Francas , D. (2021a). Building resilience in pharmaceutical supply chains. Pharm. Ind. 83 (6), 764–772.
Francas , D. (2021b). Global Pharmaceutical Supply Chains and Resilience Strategies: Overview and Implications of the Covid-19 Pandemic.
Jüttner, U. (2005). Supply chain risk management: Understanding the business requirements from a practitioner perspective. The International Journal of Logistics Management 16(1), 120–141.
Simchi-Levi, D. (2015). Find the weak link in your supply chain. Harvard Business Review.
Simchi-Levi D., Schmidt W., Wei Y., Zhang P.Y., Combs K., Ge Y., Gusikhin O., Sander M., Zhang D. (2015) Identifying Risks and Mitigating Disruptions in the Automotive Supply Chain. Interfaces, 45(5), 375-390.
The Importance of Resilience:
Novartis states their “success depends on [the] ability to manage risk”. Likewise, Roche, the Swiss healthcare company, “seek
Prescriptive Analytics for Supply Chain Risk Management:
Traditional approaches in supply chain risk management require often highly subjective judgment (e.g., type and the probability of risk) by decision-makers, which renders them often impractical to use. A survey by Jüttner (2005) confirms this in an exemplary manner: Brainstorming is named the most frequently used method for identifying risks. Therefore, new supply chain risk management approaches focus on managing and limiting the impact of potential disruptions. Instead of trying to identify each and every possible disruptive event and estimating its impact and probability, they aim to analyze a supply chain’s vulnerability. Simchi-Levi (2015) proposes to shift attention to the impact of potential failures at points along the supply chain (such as the breakdown of a supplier), rather than the cause of the disruption. In line with our experience, Simchi-Levi argues that this type of analysis obviates the need to estimate the probability that any specific risk will occur; this is a valid approach since the mitigation strategies for disruptions are equally effective regardless of their causes.
Operations Research models play a prominent role in implementing this type of risk analysis. Digital twins – computerized models of a supply chain – provide the means to assess supply chain vulnerability and risk exposure. We use FICO Xpress Insight to make such analytics approaches available to our students (Simchi-Levi et al., 2015; Francas 2021a). Operations Research models for measuring risk exposure and optimizing risk mitigation strategies are implemented in the Xpress Mosel. We use Xpress Insight to visualize data and key risk metrics. Furthermore, the platform allows the upload and download of data files. The implemented apps enable students to analyze case studies and export optimization results for further investigation to e.g. Excel and Tableau.
Teaching Supply Chain Resilience Using Analytics:
Xpress Insight plays an integral role in our master course Network Design and Inventory Management at Heilbronn University, Germany. In this course, we discuss supply chain resilience and risk management from different angles. We start with a discussion of common approaches for risk management such as risk matrices. An important insight for students is that the categorization of disruptions by likelihood of occurrence and magnitude of impact works well for known risks such as forecast errors, lead-time fluctuations, and varying yield factors. However, risk matrices tend to struggle for low-probability, high-impact events. We discuss in class that black swans like the 2019 Corona outbreak and the 2011 earthquake and tsunami in Japan or major outages due to unforeseen events such as factory fires and supplier failures are difficult to anticipate.
Subsequently, the course introduces supply chain analytics and digital twins as effective means to assess supply chain vulnerability and risk exposure based on the models discussed in Simchi-Levi et al. (2015) and Francas (2021a). We use Xpress Insight to illustrate why automotive manufactures experienced shortages soon after Chinese suppliers had to shut down operations in mainland China in March 2020. Based on published industry data, we analyze the impact of the shutdowns on automotive manufacturing against the much longer supply chain for drug production (see Francas 2021b). The implemented Operations Research models also allow students to quantify the benefits of single versus dual sourcing and re-allocation of risk inventory. In a case study from the pharmaceutical industry, the students are asked to assess supply chain vulnerability and propose options to increase resilience. We are grateful for this easy-to-use platform allowing us to use innovative prescriptive analytics apps in our courses.
To stay up to date on this type of content, make sure to subscribe to the Optimization blog. If you are interested in becoming a guest contributor on The FICO Community, please reach out to the FICO Community Team.
Additional Research & Reference:
Francas , D. (2021a). Building resilience in pharmaceutical supply chains. Pharm. Ind. 83 (6), 764–772.
Francas , D. (2021b). Global Pharmaceutical Supply Chains and Resilience Strategies: Overview and Implications of the Covid-19 Pandemic.
Jüttner, U. (2005). Supply chain risk management: Understanding the business requirements from a practitioner perspective. The International Journal of Logistics Management 16(1), 120–141.
Simchi-Levi, D. (2015). Find the weak link in your supply chain. Harvard Business Review.
Simchi-Levi D., Schmidt W., Wei Y., Zhang P.Y., Combs K., Ge Y., Gusikhin O., Sander M., Zhang D. (2015) Identifying Risks and Mitigating Disruptions in the Automotive Supply Chain. Interfaces, 45(5), 375-390.
Updated 5 years ago
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