In today’s economic landscape, uncertainty across the supply chain is inevitable, whether due to the effects of climate change, demand and supply variability, or global unrest. To keep up, supply chain leaders need to continuously make good decisions quickly that consider the ever-changing circumstances, emerging opportunities and disruptions to improve how responsive and adaptable they can be. For most companies, the obvious response to these challenges is to mitigate risk, the idea being that if they can predict changes to their operating environment either from inside or outside the four walls of the company such as supplier constraints, shocks to the economy, regulatory shifts, and more far enough in advance, they can avoid it altogether, or at least limit the fallout. However, leading companies that thrive across business climates take a different approach, one that focuses on higher quality decision making that helps them master uncertainty and harness complexity.

In order to execute decision-making strategies, companies have long been using supply chain planning technology to gain predictive insights and prescriptive scenarios to mitigate risk. As technology has evolved, new approaches to create value and drive competitive advantage have emerged — one of them is decision intelligence powered by AI and machine learning, to help inform and shape the steps of decision-making amidst increasing levels of complexity and variability. This represents a significant departure from traditional planning approaches, which are focused on avoiding uncertainty. In this new era of planning, advanced capabilities to make dynamic decisions, stress test the supply chain and utilize probability-based planning are among the most significant drivers to transforming uncertainty into new forms of value.  

In today’s unpredictable world, a faster, more agile method that accounts for uncertainty is often a more effective way for supply chains to navigate the tumult of our current business landscape.

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