In today’s fast-paced business world, supply chains are the backbone of seamless operations. The ability to efficiently manage the flow of goods and services from manufacturing to the end consumer is paramount.

In recent years, artificial intelligence (AI) has emerged as a transformative force, revolutionizing the way supply chains operate. The most prevalent use case today is the application of AI and machine learning (ML) models to boost the accuracy of demand forecasting. But, as the adoption of AI and ML becomes more widespread, new applications continue to emerge — revealing significant untapped potential.

At Blue Yonder, we’ve seen firsthand how applying AI and ML to demand planning can increase supply chain resilience, boost planner productivity and bring agility to critical decision-making. AI is a critical enabler of integrated demand and supply planning (IDSP), which allows planners to easily collaborate, model and optimize a 360-planning view in seconds versus days. As lag times are minimized via AI, companies can capitalize on new opportunities and resolve disruptions before cost and service outcomes are affected.  

This blog post will explore the myriad ways AI is reshaping the future of supply chains via IDSP and other next-gen demand planning practices.

Intelligent scenario planning: Manage disruptions, build resilience

In the most recent Blue Yonder Supply Chain Executives Survey, 84% of respondents said their organization has experienced supply chain disruptions over the previous year. The top impacts of these disruptions included customer delays (named by 42% of executives), stalled production (42%), regulatory compliance issues (39%), reputational and monetary damage (38%), and an inability to meet demand (38%) 

Scenario planning is a critical tool for understanding the impact of disruptions — in advance of taking action — to drive more confident, predictable outcomes. However, the scenario-planning tools and processes used by most companies today are suboptimal.

Why? Because they rely on human intuition and manual intervention to create and evaluate multiple complex scenarios. Not only is manual scenario planning a tedious and time-consuming job, but it also results in suboptimal decisions because too many granular scenarios, or too few wide scenarios, were created — missing critical levers and decision points. Given the complexity of modern marketplaces, as well as modern supply chains, it’s hard for human planners and human cognition to create and test meaningful demand planning scenarios.

Enabled by ML, Blue Yonder’s next-gen demand planning solutions rely on advanced algorithms that intelligently and autonomously reduce the problem scope to a logical set of scenarios that are realistic and most applicable. Embedded predictive AI evaluates this feasible set of scenarios and recommends the top scenarios that will achieve the company’s predefined objectives. This allows human planners to map out various levers in a scenario, set boundary values, then fire-and-forget.

AI- and ML-powered scenario planning reduces the average time taken from days or hours to mere minutes. Planners can focus on higher-value strategic decision-making and actions, rather than just collating data.

Complex demand-driving variables: Align decisions with reality

Considering the full range of potential variables that influence demand is essential for businesses to make informed decisions, optimize operations, satisfy customers and maintain a competitive edge.

But not all demand-driving variables are obvious or easy to understand, if only human intelligence is applied to the problem. Variables like weather, new product introductions, holidays, changes in customer preferences and cultural events can all have a significant impact on demand. The ability to identify the right factors, understand their effect on the forecast, and model their impact is critical to ensure forecast accuracy. Yet demand drivers can be hard to predict, measure and quantify.

For example, the November 5, 2024, U.S. Presidential election caused significant changes in consumer behavior, such as product hoarding, even before Donald Trump took office. If affected consumer products companies could have foreseen this demand spike, they could have had extra product quantities in place. But how could they have known?

Enter AI and ML, which can illuminate multiple demand drivers and their complex interplay — positioning businesses to navigate market uncertainties with confidence and agility.

Historically, identifying the right demand-driving variables required hundreds of hours of work from data scientists, industry experts and product specialists. And, due to the complexity of the problem and the element of human error, there was always a significant chance that critical variables were overlooked. The risk level associated with human demand planning methods was high.

But today deep meta learning — a cutting-edge AI innovation used in Blue Yonder’s demand planning solutions — powers a data-driven, algorithmic approach that’s both far faster and far more accurate than manual methods.

With deep meta learning, ML models autonomously and continuously learn to select and configure the best combination of variables to use in a demand model. Not only does deep meta learning remove guesswork and human bias from the process of identifying variables, but it also enables a faster reconfiguration of variables as market and business realities change.

The dynamic nature of deep meta learning allows demand planning teams to stay better aligned with customer preferences, as well as other proven demand drivers. With deep meta learning, demand planners are finally empowered to capture the complete value of unlimited data and unlock the speed of integrated ML.

Generative AI: Unlock productivity and uplevel team performance

There’s a lot of excitement today around the use and impact of generative AI across a range of disciplines. A recent article highlighted how Blue Yonder is using next-gen AI innovations, including generative AI, to transform the world’s supply chains.

In the area of demand planning, Blue Yonder is embedding its solutions with generative AI, using the natural language capabilities of large language models (LLMs). Supported by natural language, Blue Yonder’s generative AI can dramatically improve planner productivity through faster access to data-driven insights, assisted decision-making and process automation.

When generative AI is integrated directly into the user experience, Blue Yonder enables planners to easily ask clarifying questions, request data, visualize influencing factors and assess the effectiveness of past decisions. All these benefits significantly improve the quality of decision-making.

In addition, Blue Yonder’s generative AI models can be trained on enterprise standard operating procedures, business processes, workflows and software documentation, allowing them to respond to planner queries with contextualized, relevant answers. Compared with the current scenario, where planners need to dig through multiple text-based resources to find answers to basic queries, this is a revolutionary capability.

As we look to prepare the next generation of demand planners, a generative AI-based training program can significantly reduce the time and effort required to cross-train planners and onboard new hires.

Embrace the full potential of AI — and outsmart your competitors

The integration of AI into supply chains is not just a technological advancement; it’s a fundamental shift in how businesses operate. Using advanced AI and ML models to improve forecast accuracy is just the starting point.

By harnessing the power of AI, companies can create agile, responsive and sustainable supply chains that can more readily meet the demands of the modern marketplace. Embracing AI is no longer an option, but a necessity for businesses who want to thrive in the ever-evolving landscape of global commerce. Start planning for a big competitive edge by reaching out to Blue Yonder.