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Optimizing Manufacturing Supply Chains with Monte Carlo Simulation

Jul. 5, 2023

Lumivero

Published: Jul. 5, 2023

How to Keep Supply Chains Flowing Amid Global Threats with Risk Analysis for Optimizing Manufacturing

For manufacturers, COVID-19 revealed many vulnerabilities in today’s complex global supply chains and highlighted a need for optimizing manufacturing supply chains. Lockdowns shut factories, border closures disrupted shipping routes, and sudden shifts in consumer demand resulted in swollen inventories for some companies and shortages for others.

60% of supply chain executives who responded to a survey by EY said the pandemic has increased their supply chain’s strategic importance within their organizations. As the pandemic fades, manufacturers are now facing challenges from a host of other factors including extreme weather, labor shortages and strikes, shifting consumer behavior due to inflation, and the war in Ukraine.

63% of respondents to the EY survey said they planned to invest in technology to improve the efficiency and resiliency of their supply chains. These planned investments include sensors to monitor the location and condition of goods in the supply chain and cloud-based platforms to enable more efficient supplier collaboration. However, there’s another type of technology that can help manufacturers strengthen supply chains and their operations: risk-management and decision-making solutions that use Monte Carlo simulation.

Monte Carlo Simulation: Making Sense of Uncertainty for Optimizing Manufacturing

Monte Carlo simulation is a computational technique developed by scientists at the Los Alamos National Laboratory during the 1940s. It was named after the famous gambling destination because it accounts for random chance. Today, the method is applied to assess risk in a wide range of industries, from meteorology and astronomy to insurance and finance.

For manufacturers, Monte Carlo simulations can help identify the most likely risks to supply chains and operations. Monte Carlo simulations can be configured to account for a wide range of variables, including:

Seasonality of raw materials
Changes in consumer demand
Trade policy changes
Foreign exchange rate fluctuations
Commodities market volatility
Labor shortages/workforce capacity
Equipment failures and other safety issues
Product defects
Extreme weather events

Monte Carlo simulation modeling generates the probability of a range of different outcomes. Using these simulations, manufacturers can identify which risks are most likely to occur, which risks pose the greatest threat to business goals, and which links in a given supply chain may be most susceptible to threats.

Lumivero offers a range of Monte Carlo simulation-based risk analysis tools such as @RISK, TopRank, PrecisionTree, RISKOptimizer, Evolver, and NeuralTools. Manufacturers around the world have utilized these tools for strengthening and optimization of supply chains and operations. Here are a few examples of how companies have put Monte Carlo simulation to work for them.

Tata Steel: Understanding the Implications of a Furnace Shutdown

Tata Steel operates Europe’s largest blast furnace at the Teesside Works in Redcar, England. When Tata Steel needed to shut the furnace down for a regularly scheduled relining, they enlisted HVR Consulting, a risk management firm, to help them understand how the shutdown would impact their business, and whether the 70-day timeline they had set for completing the repairs would be feasible.

HVR Consulting used @RISK to assess the probability of a range of potential threats to the success of the relining operation, identify potential opportunities for saving time on the project, and determine supply chain management needs for its other steel mills. Using these models, Corus Steel was able to effectively divert slab steel stock to other mills while the Teesside furnace was offline. Additionally, the reline was completed within the 70-day timeline, minimizing lost production time.

Amway: Better Workforce Capacity Planning with @RISK

Amway produces over 450 home care, beauty, and nutrition products, manufactured in 18 different plants in four countries. In 2014, their Industrial Engineering (IE) team had become frustrated with the slow process of determining work capacity whenever products were added or removed from the company’s offerings. The IE department would need to contact multiple departments for information and data, then manually model different scenarios to determine optimization strategies to mitigate disruption to operations while lines were retooled or decommissioned.

This process could take weeks, and data used in the models would often be out of date once the forecasts were produced. With the company set to bring more plants online in 2015, the IE team began to look for a better solution.

Amway’s IE partnered with Lumivero to develop a custom capacity-planning tool powered by @RISK’s Monte Carlo simulations. This new tool allows Amway to quickly analyze the potential work capacity impact of a wide range of variables, including adding new equipment, discontinuing a product line, and so on. It can produce relevant probabilistic analyses for executives in as little as 30-60 minutes, saving considerable time and effort on supply chain management decision making.

Unilever: Building a Structure for Informed Innovation

Unilever’s global portfolio of consumer goods includes brands from Ben and Jerry’s ice cream to Suave shampoo. Its leadership teams need to be able to understand how strategic decisions about managing this portfolio can impact operations and outcomes all along the supply chain—a tall order given the sheer size and complexity of Unilever’s organization, and the wide network of suppliers it works with.

Unilever needed a structured, informed approach to decision making that would allow its project teams to introduce innovations with a full understanding of the impact on operations. It developed the Decision Making Under Uncertainty (DMUU) model, which is based on complex probabilistic analysis powered by Lumivero’s DecisionTools Suite. DecisionTools Suite integrates seven different tools for risk analysis, decision making, and data analysis, all of which is run through Microsoft Excel.

DecisionTools Suite makes it possible for Unilever’s project teams to develop probabilistic outcomes of different scenarios through Monte Carlo simulations created by @RISK and DecisionTree Suite. They can use these simulations to make informed strategic decisions — say, about a new product launch — with an understanding of the resources necessary to carry each decision out, the impacts each decision would have on supply chain management, and the likely consequences for each possible course of action.

Learn More About Making Monte Carlo Simulation Work for You

“The era of predictable unpredictability is not going away,” declared The Economist in late 2021. Fortunately, risk-management tools based on Monte Carlo simulations offer manufacturers a solution for mitigating the impacts of our new era of unpredictability. Using insights developed from probabilistic analysis, manufacturers can manage inventory and workforces more efficiently, develop contingency plans for materials shortages, and take steps to improve the quality and safety of processes and production all along the supply chain — effectively optimizing manufacturing supply chains.