The pandemic revealed the vulnerabilities of global supply chains built on the “lean is better” principle. Today, companies are using Prescriptive Simulation Twins to build resistant and robust supply chains of the future.
For many organizations, understanding where their supply chains lacked robustness meant seeing where they were the most vulnerable to disruptions. As they studied their supply chains, most found that disruptions often resulted further down the supply chains to smaller but critical suppliers.
A 2021 Gartner report stated that most organizations lacked a proactive supplier-oriented business continuity planning (BCP) approach centered on aligning their business requirements with supplier capabilities. Sourcing and procurement tended to rely on a reactive and compliance-oriented “check box” approach.
Instead, Gartner’s research found that, for chief procurement officers (CPOs) and sourcing leaders, supplier risk management and the stress testing of suppliers’ BCPs were top priorities going forward. This means going beyond Tier 1 suppliers to include critical Tier 2 and Tier 3 suppliers to ensure supply continuity.
Visibility and stress testing of critical non-tier-one suppliers
As Covid-19 highlighted, Tier 2 and Tier 3 suppliers are increasingly important in ensuring supply continuity. The pandemic also revealed that most companies lacked the visibility to access these suppliers’ BCPs and could not fully understand their risk mitigation and recovery capabilities.
Look no further than medical device suppliers during the height of Covid-19 to understand why this is critical. In Gartner’s 2020 Future of Supply Chain survey, medical device respondents listed “the number of tested/untested BCPs for key suppliers and facilities” as a top-three indicator of resilience.” As a head of supply chain risk stated, “Supplier business continuity is not just a plan; it’s a capability.”
Even though mapping multi-tier supply chains is ardent and time-consuming, doing so is required for managing risk and resilience. Leveraging Prescriptive Simulation Twins’ capacity allows mapping of systems holistically by understanding intricate interconnections and interdependencies, and the impact of decisions is seen throughout other parts of the business. It also enables an organization to analyze and optimize the whole value chain.
Using the technology’s simulation ability, organizations can identify which suppliers have a key impact in the event of a disruption or high-demand variability and with whom to engage in a BCP robustness assessment. Existing optimization tools cannot properly test robustness when faced with uncertainty as they neglect the impact of cascading effects.
Prescriptive Simulation Twins let organizations stress-test plans for robustness to uncertainty, demand variability, lead time, and supplier stock levels. By creating a virtual replica of the entire system, decision-makers can test assumptions against response scenarios to find the optimal course of action. These scenarios are shared with suppliers to ensure alignment.
Real-time-agility and service continuity
As manufacturers relied on outdated forecasting and planning optimization tools pre-pandemic, they were left with unrealistic action plans. And they were often creating plans based on obsolete demand forecasts and events aligning to the forecast model. These outdated tools relying only on machine learning (ML) and deep learning (DL) work in stable environments with historical data. However, they quickly reach their limits when there are major structural changes, as witnessed in 2020 and now.
Manufacturers who had already started—pre-pandemic—to complement these forecasting tools using Prescriptive Simulation Twins were able to run virtual scenarios and, thus, better predict their future. With the proliferation of digital twins across diverse organizations—both small and large—the competitive edge will be how robust their plans are and how resilient their organization is to uncertainty and market volatility.
“Dynamic simulations using a digital twin help organizations make better decisions, not based on guessing or data from the past, but with visibility on the impact of their decisions before they are implemented or even during the unknown,” said Michel Morvan, Executive Chairman and Co-Founder at Cosmo Tech.
Optimized strategic sourcing
Implementing robust optimization using simulation to account for uncertainties during the process will become commonplace. Decision-makers will need to identify optimal stock levels or part sourcing to maximize defined key performance indicators (KPIs) such as future profit.
Michelin, a global tire and mobility products manufacturer, turned to Cosmo Tech Prescriptive Simulation Twins to optimize its complex global sourcing.
“The number of product models we need to manufacture to serve each market is increasingly complex. There is a lot at stake in finding the right balance between locally manufacturing versus exporting,” explained Thibaut d’Hérouville, VP Group Industrial Supply Chain at Michelin.
Using Cosmo Tech’s AI-Simulation Technology, Michelin ran more than 80,000 simulations, each with more than 3,000 different, dynamic decision variables and built-in optimization algorithms to determine the best strategy to adopt. The result was an actionable strategic sourcing plan for the next five years to reduce their logistic costs by €10 million annually.
In the months and years ahead, digital twin technologies are critical in navigating current and future disruptions. As manufacturers build in robustness through supply chain security and duplication, the complexity of supply networks will heighten the requirement to understand cascading effects across the entire value chain. Leveraging Prescriptive Simulation Twins, organizations can ensure that business continuity plans are optimized for potential disruptions.
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