For manufacturing companies, mitigating risk can seem impossible. The unimaginable can and will happen. And it often isn’t just one but multiple factors that can disrupt mission-critical supply chain operations. Disruptions in a manufacturer’s supply chain—amplified by a growing dependence on global suppliers—means predicting and modeling “what if” scenarios using supply chain digital twin technology is no longer an option but a requirement. As seen in recent months, forward-thinking manufacturing companies that invested in supply chain digitalization and implemented resilient planning using digital twin simulation platforms have mitigated risk better. By modeling the physical supply chain in granular-detail, companies can test the real world’s uncertainty and variability, creating a virtual mirror of reality. This execution-level data is a company’s competitive edge. Even more, a disruption in the supply chain not only impacts manufacturing but other parts of a company, such as research and development, human resources, and sales. This rippling effect across the company and the increased uncertainties in today’s global economies mean reliance on new technologies is instrumental to resilience and future growth.
In 2017, Gartner coined the concept of resilient supply chain planning (SCP) following previous years of perceived hype in the SCP market around artificial intelligence (AI) and machine learning (ML). Since 2017, there has been a paradigm shift among supply chain technology leaders: to make supply chain plans resilient to uncertainty. No longer perceived as hype, the integration of digital twins, AI, ML, and cloud are now critical components of resilient planning and employed as a fully integrated process. “Key to a company’s agility and adaptability is its ability to merge technology advancement with human expertise,” stated Michel Morvan, executive chairman and co-founder of Cosmo Tech. “In today’s volatile environment, digital twins play a powerful role in how fast companies can adapt to real-time changes.”
In traditional supply chain planning, the deterministic data used to model the “what if” scenario relies on stale and static data. This data is often only periodically updated and does not mirror the realities of the actual supply chain. Thus, planning decisions based on a traditional model are, at best, an approximation of the possible outcome and are often inaccurate since they do not take into account uncertainty and volatility. Gartner’s recent report, Innovation Insight for Resilient Planning, argues that the only way to produce higher-quality planning decisions is to consider the stochastic behavior across the end-to-end supply chain. As the report’s author points out, the volatility and uncertainty supply chain professionals must deal with will continue to increase. This means that resiliency is a core requirement in the planning process. In a resilient supply chain, uncertainty in demand, capacity, supplier performance, and quality, for example, are taken into account. By incorporating this stochastic behavior, the resilient supply chain model automatically takes into account uncertainty and variability and thus results in resilient planning decisions.
The real world seldom aligns with the ideal scenario created in traditional supply chain modeling platforms. Even when alternative plans are created with static data (for example, delay in raw material shipments), these alternative scenarios must go through a sequential decision-making process before they eventually become the new baseline plan. This arduous and time-consuming process is continuous and means valuable time is lost. Even more, the scenarios are created from postevent detection, resulting in a substantial lag between the initial trigger and the decision point. By contrast, in resilient planning, scenarios are created postevent for a situation of unknown certainty (for example, fire in a facility) along with pre-event scenarios (hurricane season, for example). As most scenarios are pre-event, the ability to build in defined combinations of uncertainty and variability creates a more robust resiliency level. In addition, resilient planning creates a library of prepared responses to specific potential trigger combinations. And because the initial baseline plan is set to buffer up to a predefined level of uncertainty, only when trigger events cause the baseline plan to go above the maximum point is the pre-approved scenario put in place. Thus, the resilient planning approach ensures that the best plan is quickly initiated. The inherent dynamic capabilities of resilient planning are strategic and competitive opportunities for companies to pre-test and prepare for possible events and scenarios before they happen (if ever).
Getting to the best scenarios is where resilient planning and digital twin modeling give manufacturers a competitive advantage using the technologies. When implemented, resilient planning creates potential scenarios in the thousands, whereas only a few postevent scenarios are created in traditional planning. The vast difference in the number of scenarios means that manufacturers can be more agile and responsive when disruptions occur. A higher number of optimal pre-event scenarios is available and tested for resilience under uncertainty, thus creating a library of possibilities. As stochastic behavior is built into resilient planning, there are more possibilities and combinations to test and evaluate, resulting in a more significant number of scenarios and, hence, resiliency. In today’s volatile global economy, how a manufacturing company manages and designs its supply chain significantly impacts profits and future growth. The emergence of digital twins, the cloud, AI, and ML provides technological tools for greater resiliency in managing the modern supply chain. Forward-thinking manufacturers that make the paradigm shift to resilient planning of their supply chain will reap agility and market responsiveness rewards and outpace competitors who rely on traditional planning.