Generative AI is now finding its place in business applications and is making significant strides in transforming how we plan supply chains. It supports a more conversational interaction with advanced planning solutions. This allows planners to swiftly formulate relevant scenarios to analyze and compare, even in highly intricate situations with lots of conflicting business objectives. Both planners and managers can ask the planning solution to explain a forecast signal, list the most critical issues to review, or perform an optimization.

In 2024, we’ll see more real-life examples of how Gen AI can enhance the user experience in supply chain planning. But even if it doesn’t fully deliver on that promise, the boost Gen AI gives to existing eXplainable AI (XAI) initiatives and planning automation alone makes it a technological evolution worth monitoring.

With the aim of reaching carbon neutrality by 20XX, ESG regulations set a clear deadline for companies to implement comprehensive sustainability programs. This has two major effects. Firstly, it sparks a rush for reporting initiatives, triggering numerous requests for both upstream and downstream Scope 3 data. Secondly, it shifts the focus of boardroom discussions toward sustainable supply chain practices.

2024 is the year to implement Green Planning, which puts environmental, social, and governance (ESG) criteria center-stage in supply chain planning decisions. This planning approach incorporates relevant ‘green’ KPIs and objectives within the supply chain planning digital twin. Companies will need a solid framework to include alternate energy sources, greener materials, sustainable mobility, circularity, and other ESG strategies in the planning process. They will also need to learn how to make green business decisions to optimize for these new KPIs in addition to cost and service level.

Dealing with uncertainty is a crucial skill for supply chain planners. Running different scenarios is their primary instrument to understand how shortages, delays, capacity issues, or market trends might affect their plans. However, as disruptions increase, handling a rapidly increasing number of scenarios becomes challenging. This is where probabilistic planning offers a solution.

Simply put, it means planning for a range of possible values and events instead of focusing on individual scenarios. This approach offers insights into how sensitive the plan is to uncertainties throughout the entire supply chain, covering both demand and supply.

Planners will assess the impact of unfavorable combinations of events more accurately, regardless of their likelihood. They will be able to react faster by using predictive modeling, leveraging outside-in data, and AI and ML techniques.

For example, external data gathered from sources like social media and point-of-sale can provide insight into actual consumer responses to newly launched products or promotional campaigns. Data will increasingly become an asset and, by leveraging AI capabilities, companies will be able to separate signal from noise, relying less on ‘perfect’ data to get started.

Challenges, highlighted by our own Spark Initiative, include finding, ingesting, and incorporating relevant data, and rethinking planning and decision-making processes. Nonetheless, the potential benefits and the ever-increasing complexity of our supply chains will keep pushing companies to adopt this trend.

The pandemic accelerated a shift in consumer-centric market behaviors, with new business models such as direct-to-consumer entering the market, and gradually finding their way into the most hardcore B2B industries. Never before has the customer been more king.

What does this shift mean for supply chains? Supply chain excellence will increasingly be evaluated against the ability to serve a company’s end consumer or B2B customer. Adopting a customer-centric planning approach entails maintaining a high service level and focusing on factors that drive satisfaction and well-being.