The increased costs, lack of reliable transportation, and scarcity of supplies all clearly tell the story that’s reigned on news feeds since 2020; The global manufacturing sector is still adjusting to the disruption of the COVID-19 pandemic and the war in Ukraine and there remains high supply chain risk. A September 2022 analysis of the supply chain crisis by the recruiting firm Randstad reported that almost three years after the beginning of COVID’s first wave, materials were still slow to ship, energy costs were still high, and labor shortages remained a persistent issue. In fact, a 2021 study by Deloitte and the National Association of Manufacturers projects that firms in the United States could have as many as 2.1 million unfilled vacancies by 2030.
Given this uncertainty, it’s no surprise that 93% of supply chain executives surveyed by McKinsey in 2021 said they were planning to develop more agile and resilient supply chains. A few steps that companies have already taken include regionalizing supply lines, diversifying their suppliers, and relocating production closer to customer bases. It also means more attention to operations risk management. In the same McKinsey survey, 59% of supply chain executives said they had adopted new risk analysis and management practices over the previous 12 months.
"With climate change and geopolitical tensions expected to impact food and cause disruptions, now is the time for supply chain leaders to take initiative and be creative as to how they can invest and improve their operations,” said Dr. Madhav Durbha, Vice President of Supply Chain Innovation at Coupa in a PR Newswire article.
Predictive analytics tools can play an important role in helping manufacturers understand risks to supply chains, discover creative solutions, and take steps to optimize their operations. Lumivero’s @RISK and DecisionTools Suite use Monte Carlo simulation, a computational technique that calculates the probability of all possible outcomes based in uncertainties in the input values. In this article, we’ll look at how two organizations have improved their supply chain risk management practices and overall operations using the Monte Carlo simulation models generated by @RISK and DecisionTools Suite.
Hitachi Solutions East Japan – Driving Better Supply Chain Risk Analysis with @RISK Developer Kit
Hitachi Solutions East Japan, Ltd. is a software and systems engineering firm that also oversees the installation of computer hardware systems for manufacturing companies. One significant challenge Hitachi faces is in providing their customers with a tool for guiding informed decision-making about manufacturing operations based on fluctuations in material costs, foreign currency exchange rates, and consumer demand. With @RISK Developer Kit , they found the solution they needed to help avoid pitfalls and make decisions with confidence
@RISK’s Monte Carlo simulation modeling allowed Hitachi’s planning team to generate simulations that showed all possible outcomes of and the likelihood of adverse events that could impact operations. However, @RISK’s tools run in Microsoft Excel, and Hitachi wanted a more visual graphic user interface (GUI) that could incorporate @RISK analyses.
With the @RISK Developer Kit and C++ programming tools, Hitachi’s team developed a new user dashboard that pulls in data from a wide range of their customers’ systems including production data, sales data, inventory, and more. This interface allows customers to understand how producing more or less of a product can potentially impact profitability and helps them decide how to prepare facilities or adjust operations to mitigate the impact of risk.
Novelis – Visualizing Manufacturing Risk Analysis to Optimize Operations
Novelis is the largest aluminum rolling and recycling company in the world. Its aluminum is found in everything from beverage cans and buildings to auto components and aircraft. With a global footprint that covers 33 facilities in nine countries, Novelis is constantly developing new products to serve its customers—and new production processes to improve its existing products.
Until recently, this multinational corporation lacked a qualitative risk analysis process. Its Research and Technology team instead relied heavily on information from the commercial side of the business when launching new products or production methods. With @RISK, the Novelis team was able to draw on data from the scientific branch as well.
Armed with testing data from the R&D team, Novelis’s Senior Manager of Innovation Strategy, Dave MacAdam, was able to analyze the risk involved with changing to a new technique intended to improve the efficiency of aluminum recycling. With Monte Carlo simulation in @RISK, MacAdam calculated the probability of certain failure modes based on changing variables in the process. Sensitivity analysis was then used to generate tornado charts ranking the effect of the input variables on the outcome. These graphs allowed MacAdam to show executives that most of the risk involved with the new recycling process appeared to coalesce around a few specific technical factors. The team was then able to commission more research into those factors and devise mitigation strategies.
MacAdam’s team also used PrecisionTree to develop models that showed decision-makers how risk compounds at each stage of the product launch process. By analyzing how one link in the production chain influences the next, decision-makers could take practical steps to adjust operations or request further testing from their researchers to reduce or retire risk from prototyping all the way to production.
Optimize Your Manufacturing Operations with Monte Carlo Simulation
Improve your understanding of supply chain risk to inform better operational decision-making with tools like @RISK, @RISK Developer Kit, and PrecisionTree. Explore our free models based on real-world manufacturing scenarios today: