Demand forecasting is truly the unsung hero of supply chain management, particularly in CPG and retail sectors. Why we say this is because studies like McKinsey digital report shows that companies that excel in demand forecasting can achieve up to 15% lower inventory costs and 10% higher service levels. And why these aren’t just statistics is because they reflect real-world impacts on profitability and customer satisfaction. Because the final forecasting data acts as the input data of the further S&OP process which aligns with production schedules, optimize inventory levels, and enhance customer satisfaction.
Hence, Supply chain managers recognize the immense potential that demand forecasting holds. It can be developed at various levels of granularity—monthly, weekly, daily, or even hourly. Given the numerous touchpoints it enables, it’s clear that getting demand forecasting right can truly make or break a business . And as the business strive for accuracy in their demand forecasts, AI in demand forecasting emerges as a transformative force.
Why AI in Demand Forecasting is a must for every business
It’s not long time back that demand forecasting relied heavily on basic statistical methods and intuition. While these approaches have their merits and make a strong foundation for upcoming techniques, they often fall short in accuracy especially when faced with complex variables like seasonality, promotions and economic shifts (which CPG/Retail industry can’t do without).
This is because over the past few years, these industries have been a battleground of uncertainty and hence industry leaders soon understood that predicting what customer wants/needs is THE enigma of deciphering for meeting ever-shifting customer demands.
And considering traditional forecasting methods often struggle to keep pace with the complexities of modern markets and relying heavily on historical data and manual analysis, AI in demand forecasting is here to change that.
To put it in simple words – AI in demand forecasting leverages advanced algorithms to analyze vast amounts of data quickly and efficiently. How? Let’s find out:
1. Data Pool Utilization: Demand forecasting in the Consumer-Packaged Goods (CPG) industry involves a treasure trove of data. We’re talking about historical sales figures, market trends, customer preferences, and even external factors like economic indicators and weather conditions. By using advanced data mining techniques, AI systems can dig into both structured and unstructured data—think social media sentiment and customer reviews. This kind of comprehensive data integration gives businesses a much clearer picture of demand signals.
2. Demand Sensing: Now once we have all the data sources in place, the next step is capturing immediate signals from it and demand sensing does just that. The fact is when it comes to demand forecasting, traditional methods often struggle with complex data relationships, while AI algorithms excel in this area.
Now for a CPG supply chain manager this represents a significant opportunity to be more agile and responsive short- to mid-term forecasting. By leveraging real-time analytics to understand and predict customer demand through accurate, they can proactively respond to market fluctuations and consumer behaviour changes.
For example, companies like Procter & Gamble use AI-driven demand sensing to analyze point-of-sale data and social media trends, allowing them to adjust inventory levels swiftly and reduce stockouts.
3. Scenario Planning: While we have talked about short-to-mid-term planning, organizations obviously can’t/shouldn’t do without understanding it’s long-term implications. This is exactly where scenario planning helps. As a technique that involves creating and analysing multiple plausible scenarios of the future. These scenarios are based on different assumptions, drivers, and events.
Now what makes is this power is its adaptive learning capability. Machine learning models improve over time as they are exposed to new data inputs. But you need to be mindful of the fact that these scenarios are not predictions; rather, they are narratives that describe how the external environment and the internal capabilities of the business might evolve in various ways.
By analysing past performance and keeping an eye on shifting consumer preferences, companies can tweak their strategies accordingly.
This dynamic adjustment means they can respond more effectively to market changes, ensuring they meet customer needs while minimizing waste.
4. Risk Mitigation: Whether due to economic fluctuations or shifting consumer preferences—effective demand forecasting is essential for risk management. AI really helps CPG companies get ahead by anticipating changes in demand and allowing them to create solid contingency plans.
For example, using predictive modeling techniques like Monte Carlo simulations can help businesses account for uncertainty. This way, they can better navigate potential disruptions in their supply chains. It’s all about being proactive and ready to adapt to whatever challenges come your way.
The Road Ahead
As we look ahead, it’s clear that effective forecasting is vital for success, and integrating AI into business operations is a is a major step in that direction. To kick off this journey, consider taking an incremental approach. Instead of going for a full-scale rollout right away, start with small pilot projects. This way, you can test feasibility and gather valuable insights without overwhelming your team.
Plus, it allows you to adjust based on real-world feedback, building momentum for larger implementations down the line.
Collaboration is essential in this process. Working closely with AI experts can really enhance your integration efforts. Having partners like Polestar solutions can guide your organization through the nuances of adopting AI. From identifying high-impact opportunities to facilitating collaboration among your teams having an external partner ensures that your AI initiatives are both effective and sustainable.
Engaging with these experts not only deepens your understanding of AI. This makes sure that your organization is well-prepared to navigate the complexities of AI implementation.
This helps you turn challenges into opportunities and set the stage for long-term success in a constantly changing market.