Use this Method to Measure and Quantify Supply Chain Risk
Recently, a series of crises including the COVID-19 pandemic and the obstruction of the Suez Canal had a major impact on the global supply chain, resulting in disruptions such as temporary shortages, delayed deliveries, increased uncertainty, and higher costs for goods and services.
Natural disasters also wreaked havoc on the supply chain. In 2011, floods in Thailand reduced global industrial production by 2.5%, and companies such as Toyota, Honda, and Nissan, not directly impacted by the floods, were forced to shut down operations due to a shortage of parts from suppliers (Haraguchi & Lall, 2015).
How can supply chain design help you better prepare for the next supply chain crisis?
Optilogic's risk rating engine, built into the Cosmic Frog supply chain network design platform, provides a comprehensive view into potential risks throughout the supply chain. Scenario modeling with optimization and simulation can then be used to stress test potential disruptions, such as the shortage of a critical component or reduced manufacturing capacity. This way, you can evaluate the performance impact of each scenario and prioritize your response.
Let’s look at a real-world example of how to use supply chain design to evaluate risk and measure the performance impact of complex interactions not only on revenue but also on profit.
How to Evaluate Risk and Measure Supply Chain Impact
The ability to effectively identify and assess potential risks in the supply chain is crucial for ensuring its continuity. This includes the identification of risks for suppliers, facilities, customers, means of production, or transport. These risks can be geographical, climatic, economic, or political in nature.
Businesses must also quantify the potential supply chain effects of these risks. This information can then be used to prioritize risk mitigation efforts and ensure that the right steps are taken to minimize the impact of disruptions.
By combining these two aspects of risk management (identification and impact quantification), organizations improve decision-making, efficiency, and risk management through earlier identification of potential risks that could transform in major problems and better prioritization of resources and efforts.
Legacy Methods of Measuring and Quantifying Risk are Flawed
Legacy methods for measuring and quantifying supply chain risk are flawed for several reasons.
1. They often lack the granularity to accurately capture the complexities and interdependencies of modern supply chains, leading to an incomplete picture of potential risks.
Some examples of factors that could contribute to the granularity needed include the consideration of the end-to-end supply chain network from suppliers to customers, the types of products or materials being transported, and the potential risks that are specific to each stage of the supply chain.
Additionally, the level of detail and data needed to accurately capture the interdependencies and complexities of the supply chain may also vary. For example, data on factors such as delivery times, transport costs, production capacities, and demand forecasts may be necessary to accurately quantify risk.
As a result, the information provided by these methods may be incomplete or inaccurate, leading to an incomplete picture of potential risks and their impact on profit.
2. Legacy methods are often reactive in nature, only addressing risks after they have become problems, rather than proactively mitigating them.
By using what-if analysis, on the other hand, organizations can gain a better understanding of the potential impact of different risks and can identify the most effective strategies for mitigating these risks. This approach uses simulation and optimization to test different scenarios and assess the potential impacts and trade-offs of different risks on the supply chain.
3. Legacy methods often focus solely on financial and operational risks, ignoring other important risk factors such as political and environmental risks.
Legacy methods for measuring and quantifying supply chain risk are flawed because they lack granularity, are reactive in nature, and have a limited scope.
A Different Approach to Risk Management Is Needed for Better Results
David Simchi-Levi, a renowned supply chain expert and professor at MIT, highlighted the importance of considering both the likelihood and impact of supply chain disruptions in risk management. By combining this information, organizations can get a more complete picture of their supply chain risks and take a more proactive approach to risk management.
Step 1: Create a comprehensive supply chain model that considers all the key components, suppliers, and production processes involved as well as their associated policies and costs.
Step 2: Using the Opti-Risk score computed by Cosmic Frog for each facility and supplier, we can identify potential disruptions that could impact a company’s supply chain, such as natural disasters or labor strikes.
Step 3: Using what-if analysis, we can measure the impact of each potential disruption, considering the time it would take to recover from the disruption, the availability of alternative suppliers, and the impact on the end customer.
Using such a supply chain model together with what-if analysis and simulation provides the granularity and level of detail required to capture the complexities and interdependencies of modern supply chains accurately. We can then assess the profit impact of each disruption by considering the costs associated with the disruption, such as lost sales, increased costs, and lost productivity.
Step 4: Finally, we can prioritize risk mitigation efforts based on the estimated profit impact of each potential disruption, allowing the organization to focus on the most critical risks first.
This information can then be used to inform risk management decisions, improve supply chain design, and increase supply chain resilience.
A Real-World Example Built Out in Cosmic Frog
ABC Supply Chain
Consider the example of a sportswear apparel company, named ABC, which specializes in the production and packaging of three product lines: Apparel, Shoes, and Accessories. These products are packaged in three assembly plants before being sold to customers.
The products are assembled and created in eight production plants from different components sourced from tier 1 suppliers located across Europe and Asia. These tier 1 suppliers are in turn supplied by a variety of tier 2 suppliers, adding further complexity to the company's supply chain.
The dense and complex flow of materials, coupled with a wide geographical distribution, presents a challenging task for ABC company in terms of risk estimation.
For each supplier, facility and customer, Cosmic Frog provides a set of risk scores (e.g., geographical, economic resiliency, political, or natural disaster to name a few) that identify risk and the potential for disruption that may impact the supply chain.
Using Cosmic Frog, ABC company aims to gain greater visibility and insight into potential supply chain risks.
What-if analysis and hyper-scaling
Because cities are critical nodes in the global supply chain network, we start by evaluating how city-level disruption would impact the overall ABC network. Disruptions at that level have a significant impact not only on the given city but on all stakeholders interacting directly or indirectly with this one. These complex interactions are difficult to estimate without the right level of detail and the appropriate solution.
As supply chain modelers, our approach to evaluating the impact of disruptions involves the creation of multiple scenarios. Specifically, we aim to create over 80 scenarios in which we exclude a given city due to events such as COVID-19 or natural disasters. Hyper-scaling capabilities allow us to run these scenarios in parallel, ensuring quick and efficient results.
To create these scenarios, we utilize Cosmic Frog’s scenario management studio, where we can easily exclude facilities and suppliers based on their city. Additionally, we utilize the scenario builder app to generate the scenarios automatically.
While running these scenarios in sequence could take a significant amount of time, Cosmic Frog allows us to run them in parallel, greatly reducing the time needed to obtain results. For example, if each scenario takes one minute to run, we will typically expect to wait over an hour for the results. With Cosmic Frog, the same scenarios set would only take two minutes to complete.
It's important to note that as the complexity of the supply chain grows, with a larger number of suppliers, for example, the time required to run these scenarios increases accordingly. With a single scenario taking ten minutes or more to run, the time savings provided by Cosmic Frog becomes even more important.
Supply chain modelers utilizing Cosmic Frog Analytic can compute the performance impact of each city in the event of a disruption. The performance impact of a given city is calculated as the difference between profit when all sites and products are operating normally and profit when the given city is disrupted.
To standardize the performance impact, we scale the KPI to have 100% impact for the city with the highest impact and 0% for the one with the smallest impact. Our analysis shows that Bacolod, Rome, and Changsha have the highest impact on the supply chain, which aligns with the locations of our plants and assembly sites.
By combining the performance impact KPI with total spending, we can develop a risk strategy as proposed by David Simchi-Levi (Simchi-Levi, 2015).
As supply chain designers, we utilize performance impact on profit and total spend data to inform the development of risk mitigation strategies. This allows us to take a data-driven approach to identifying areas of potential risk and determining the most appropriate strategies for minimizing their impact.
In situations where both spend and performance impact are high, the implementation of risk-sharing partnerships, performance tracking mechanisms, and the utilization of multiple sites can help to reduce risk and minimize the impact of supply chain disruptions. By utilizing these strategies and taking a data-driven approach to risk mitigation, supply chain design technology can help ABC company to build more resilient and sustainable supply chains.
For low-spend, high-performance impact scenarios, strategies such as increasing inventory levels, implementing dual sourcing, or considering new product designs may be appropriate.
In cases where there is a high spend and low-performance impact, tracking inventory levels and negotiating long-term contracts can help to minimize risk.
Technology Points to Remember
The Cosmic Frog supply chain design platform can help organizations improve supply chain resilience and avoid supply chain disruptions with these important features:
- Comprehensive Modeling: Cosmic Frog provides a comprehensive view of the supply chain network by modeling various scenarios and considering all aspects of the supply chain, such as suppliers, production plants, and assembly lines.
- Automated Scenario Generation: Cosmic Frog's scenario builder app automates the creation of multiple scenarios, saving time and effort for the user.
- Parallel Scenario Execution: Cosmic Frog allows users to run multiple scenarios in parallel, speeding up the results generation process.
- Performance Impact KPI: Cosmic Frog’s Analytics helps computing the performance impact KPIs for each city, enabling a comprehensive understanding of supply chain risk.
- Optimization Engine: Cosmic Frog’s optimization engine provides decision-making support, allowing organizations to select the best strategies for reducing risk and improving their supply chain performance.
- Simulation Engine: Cosmic Frog is built on a powerful simulation engine specifically designed for supply chain modeling. Simulation is an ideal way to stress-test your system, refine strategies and policies, and test the feasibility of a design and the true impact of proposed changes.
About the Author
Maxime is part of the Applied Research team. He has experience in developing, modeling and demonstrating the value of innovative machine learning and optimization solutions.
Haraguchi, M., & Lall, U. (2015). Flood risks and impacts: A case study of Thailand’s floods in 2011 and research questions for supply chain decision making. International Journal of Disaster Risk Reduction.
Simchi-Levi, D. (2015). Identifying Risks and Mitigating Disruptions in the Automotive Supply Chain. MIT Open Access Articles.