In our everyday lives, we often navigate uncertainty using a probabilistic approach without even realizing it. Take weather-dependent activities, for instance. When planning your weekend, you might check a 5-day weather forecast, which provides probabilities of rain, sunshine, or cloudy skies. Armed with this information, you make decisions like whether to have a picnic, garden or stay indoors.
Another example is commuting. Recently, Steve Banker wrote an article about taking a flight and planning the drive to the airport. You estimate the time it takes to get there based on past experiences, traffic patterns, time of day, and other variables or potential occurrences such as construction or an accident. You might consider that there’s a 70% chance of arriving at the airport on time taking the shorter route, but there’s a 30% chance it might take longer due to traffic. This prompts you to plan and choose a route based on these different probabilities and your willingness to accept the risks associated with each option.
In supply chain planning, a probabilistic approach that considers a wide range of possibilities is increasingly important amid complexity and changing conditions, to help planners decode uncertainty and make more informed decisions. Amid shifting market dynamics and unexpected turns, companies are turning towards probabilistic planning to enhance their agility to quickly pivot and make risk-adjusted decisions based on scenarios and the likelihood of successful outcomes.
Picture this: you need to plan your inventory levels for the upcoming holiday season. By leveraging AI and machine learning, along with probabilistic planning techniques, now you can analyze multiple variables such as historical demand, supplier lead times, transportation delays, and market trends at the same time and understand the impact of each of these inputs on each other and the end-to-end plan.
Today, most supply chains rely solely on a deterministic approach, which assumes a single outcome where the plan is based on that sole outcome, probabilistic planning considers a range of possible outcomes and their probabilities. This allows you to better anticipate potential scenarios, such as real-world variation in sales and lead times including spikes in demand or disruptions across the end-to-end supply chain. A probabilistic approach delivers a new level of actionable insights based on key factors and the impact of each factor driven by more relevant data to support better decision-making.
AI-Driven Probabilistic Planning to Embrace the Unexpected
We’ve all witnessed the remarkable evolution of artificial intelligence (AI) and how its innovations have made waves across industries. The supply chain is no exception. Over the years, AI capabilities for prediction, clustering, segmentation, and more have accelerated supply chain strategies, automating daily operations and augmenting data-driven intelligent decision support. These advancements have revolutionized supply chain planning. Companies have gradually moved away from the constraints of ERP and rigid systems to adopt advanced supply chain planning technology that enables them to better forecast demand, optimize inventory levels, streamline logistics, and improve overall efficiency to deliver meaningful, positive impact to the bottom line.
A basic form of probabilistic modeling has long been used in demand planning. However, in more recent years, as the use of ML and AI techniques like deep learning have become more prevalent and environments have grown increasingly complex and unpredictable, the relevance of probabilistic planning has increased. Driven by advanced AI techniques, probabilistic planning leverages new math and machine learning algorithms to tackle uncertainty head-on, representing a significant leap forward in our ability to handle the complexities and fluctuations inherent in modern supply chains.
Why Probabilistic, Why Now?
Supply chains have historically relied on deterministic models fixated on one-number plans, failing to account for variability and unpredictability. Instead, probabilistic planning equips companies with a powerful tool at their disposal to mitigate risks and uncover hidden opportunities. Utilizing AI-powered techniques and advanced machine learning, a probabilistic approach analyzes multiple variables across key areas of the supply chain – including demand, supply, inventory, financial impacts, and beyond – to identify the probabilities of a range of possible outcomes, while factoring in changes in conditions for reoptimizing the view of risks.
The ability to employ probabilistic planning beyond a single function within the supply chain (e.g. just demand planning or inventory) and consider the end-to-end supply chain further sets leaders apart in forging their competitive advantage. This critical distinction allows planners to adjust risk tolerance and align their strategies with a company’s goals, running thousands of simulations and estimating value at each step in the decision-making process. At the core of this method, AI algorithms utilize self-learning automation, meaning that the system continuously gathers data, refines its models, and adapts its strategies based on real-world feedback to monitor and sense significant changes in volumes or certainty. As a result, companies can make more informed decisions as essential factors change and optimize their operations for a wide range of potential scenarios.
Your Supply Chain Isn’t Static
The imperative to effectively account for variability is now greater than ever. In the end, your supply chain isn’t static, so why should your plan be? The business landscape is dynamic, rife with variations and disruption, and companies require a more adaptable system – a probabilistic one – for addressing this unpredictability and estimating value throughout the end-to-end supply chain.
Uncertainty needs to become an integral part of the planning process. As AI continues to evolve, so too will the capabilities of probabilistic planning, empowering companies to navigate supply chain complexities with confidence and resilience, and to stay ahead of the curve in an increasingly unpredictable world.
Alex Pradhan is the Global Product Strategy Leader and Member of the Executive Leadership team at John Galt Solutions. In this position, Alex is responsible for leading the strategic development and product vision of John Galt Solutions’ end-to-end supply chain planning software solution. Alex has extensive expertise at the intersection of digital, supply chain and technology and is passionate about the role that technology plays in creating resilient, high performing supply chains.
In her prior role as a Research Analyst, she advised over 1000 global companies on a range of supply chain strategic and operational topics at the intersection of digital and technology. Before this experience, Alex spent several years at Subway where she was responsible for managing demand planning for promotional, limited time offers, and R&D test products. Alex received her MBA from the University of Miami and her postgraduate degree in Data Science from the University of California, Irvine. She lives in the Miami-Ft Lauderdale area with her family.
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