Today’s supply chains are fraught with uncertainties across demand and supply yet are tasked with adding incremental value to their organizations while also meeting commercial, working capital and sustainability goals. This balancing act is a challenge even in the best of times. However, far too many companies are stuck because traditional planning methods that rely on static, one-number plans fall short in this demanding and dynamic landscape.
The challenge for supply chain teams lies in increasing knowledge to create value amid this complexity. This requires a new mindset that focuses on more than just surviving disruptions and minimizing losses — to one that leverages uncertainty for gain. Within uncertainty lies the potential to drive profit and innovation, to transform disruptions into opportunities for growth and competitive advantage. To succeed, it’s time to challenge conventional thinking and adopt more elegant approaches.
Beyond Conventional What-Ifs
The reality of uncertainty and unanticipated risks emerging almost daily calls for continuous monitoring and testing of your supply chain health. Conventional what-ifs only evaluate the outcomes (e.g. service, profit, capacity) of a small number of demand scenarios (e.g. base, opportunities, and risks). This approach is too narrow in scope. Similarly, scenario planning that considers only a few supply scenarios (e.g. limited, late, or constrained) against a one-number forecast limits an organization’s ability to adapt to real-world uncertainty and unexpected changes. Traditional what-ifs assess the outcomes of a snapshot of supply chain possibilities but fail to provide a comprehensive understanding of the supply chain’s behavior under varying conditions. The reality is supply chain planning decisions must position you for success across a broad range of overlapping factors, not just a few.
Understanding through Experimentation
Stress testing, in the context of experiment-driven planning, involves running countless scenarios to simulate and understand how a supply chain responds across the likely ranges of demand and supply outcomes, along with a wide array of potential disruptions and operational conditions. This multi-faceted approach allows companies to learn by observing behavior patterns and identifying weak links that could collapse under stress. Supply chain policies and configurations can be evaluated and then optimized across the likely ranges of demand, supply, disruptions, and financial drivers – providing the best plans across strategic and tactical horizons. Despite its immense value, stress testing remains underutilized in today’s supply chains.
Moving from a small set of scenarios to many, a supply chain can be properly evaluated by simultaneously varying the demand and supply ranges, considering more likely demand and supply outcomes, as well as edge cases. Contrast this with conventional what-if which lacks the nuance of whether upside demand is more likely than downside. Additionally, traditional what-if fails to pair upside possibilities with their unintended consequences such as lower yields or fill rates.
Companies can also test-drive their supply chains by introducing the uncertainty of events that are difficult, if not impossible, to predict with accuracy. These events can range from minor supply disruption or canceled shipments to significant black swan events. By modeling these potential disruptions, it becomes possible to understand the impact and develop the right alternative response plans to mitigate the impact.
AI-Driven Simulation
Supply chains can benefit tremendously from advanced analytics like AI simulation and a digital supply chain twin. The digital twin, for example, can be subjected to numerous stress tests that mimic real-world conditions and observe how different variables interact and impact the entire network. Supply chain policies and configurations are tested and leverage reinforcement learning to yield the best possible strategies.
Manufacturers, for instance, can vary production yields, quality, uptime, and material supplier reliability (fill rates and lead times) for a comprehensive analysis that allows them to identify weak links and potential failure points to identify proactive measures to mitigate risks and the agility to seize new opportunities. Stress testing empowers manufacturers to anticipate and respond to disruptions, and to harness complexity as a competitive advantage. By understanding the behavior of the supply chain under diverse conditions, companies can develop strategies that enhance flexibility and responsiveness.
For example, the analysis from stress testing can reveal a particular supplier or production resource is a frequent point of failure under high-demand scenarios. Armed with this knowledge, a company may choose to diversify its supplier base or invest in redundant production capacity to ensure continuity. Similarly, if a certain supplier’s material fill rate proves to be unreliable, alternative sourcing strategies, more flexible recipes, or increased safety stock levels might be implemented.
Beyond Loss Mitigation
The benefits from stress testing are rooted in the ability to examine a broad range of factors and to see the ripples across the entire supply chain. An example is insight beyond your four walls to understand how tier one, two, three, etc. suppliers impact your ability to deliver.
The goal goes beyond minimizing losses to optimize operations in a way that drives profitability even during turbulent times. Dynamically adjusting plans based on real-time data and evolving conditions is key to achieving both efficiency and agility. It’s a proactive approach that enables better decision-making and quicker responsiveness.
Faster, Better Decision Alignment
To thrive from complexity, it is crucial for supply chain leaders to get more robust analytics, align on decisions and enable those decisions faster. Decision intelligence is flipping the script on how organizations both make and implement supply chain decisions. Traditionally, decisions have been influenced by human biases and personal experience, leading to suboptimal choices. For example, a manager might consistently choose a familiar supplier or route without fully evaluating alternatives, missing better options, and failing to account for all risks. Decision intelligence changes this paradigm by leveraging AI to simulate thousands of potential decisions and their outcomes.
Supply chain teams must be able to evaluate a vast array of scenarios, and fully comprehend how different strategies might play out in real life. Keep in mind, these scenarios are not static; they continuously adapt and improve over time to enrich decision capabilities and become an integral part of the end-to-end planning process rather than isolated exercises.
The role of AI in decision intelligence extends to both optimizing decision-making and enacting those decisions. AI helps identify the best trade-offs by analyzing vast amounts of data and considering numerous variables to support decision-makers. This process involves various stakeholders at different levels, from on the plant floor to executives, to ensure decisions are well-informed and aligned with the overall strategy.
AI helps companies define and update their decision thresholds and playbooks to increase the speed and efficiency of executing decisions and empower local decision-makers within the guidelines and thresholds of the overall strategy. Digital playbooks enable these decision-makers with dynamic, continuously evolving documents that reflect the latest insights and scenarios. As new information becomes available, the playbooks are updated, providing a living, breathing guide for decision-making. This adaptability is essential in today’s fast-paced, ever-changing business environment.
Time to Gain from Complexity
Stress testing is essential for creating real value in an increasingly complex environment. The era of static, one-size-fits-all plans is over as today’s demanding supply chains require dynamic, threshold-based playbooks that leverage decision intelligence to navigate continuous change and uncertainty. Stress testing allows companies to simulate countless scenarios to uncover vulnerabilities, evaluate policies and identify strategies that can turn potential challenges into opportunities for growth. By integrating stress testing with decision-making supported by AI, businesses can move from reactive to proactive, building flexibility and agility with policies and thresholds to quickly adapt as uncertainties change and disruptions occur. This forward-thinking approach helps companies go beyond survival mode to thrive amid uncertainty, transforming complexity into a competitive advantage.
Matt Hoffman is the Vice President of Product and Industry Solutions at John Galt Solutions. Matt specializes in delivering transformational from analysis through execution across a diverse range of clients in manufacturing, distribution, and retail. Matt is committed to ensuring that processes drive solution adoption, resulting in measurable outcomes. Throughout his career, Matt has successfully led software implementations utilizing best-in-class supply chain planning systems, execution systems, and merchandising planning systems.
