Don’t Wait to Innovate: 3 Key Benefits of Causal Forecasting

Picture this: You’re a seasoned sailor navigating uncharted waters. You’ve got a trusty map based on old routes, but the sea’s currents constantly change, and new obstacles keep popping up. You quickly realize your old map just isn’t enough anymore. Sounds familiar? That’s precisely what demand forecasting feels like for many businesses today. Enter causal forecasting.

Think of it as your upgraded, super-detailed map. It’s not just about what happened in the past but also includes current factors like market trends, promotions, and even the weather. All these elements can help you predict where demand is heading.

Unfortunately, many companies hesitate to use causal forecasting, thinking it’s too complicated or resource-hungry. And the result? They’re stuck with inaccurate forecasts, ending up with too much or too little inventory, missed orders, and unsatisfied customers. That’s why we’re here to break it down for you and show why it’s high time you included causal forecasting in your demand planning toolkit.

What is Causal Forecasting?

Causal forecasting is an artificial intelligence (ai) enhanced predictive forecasting technique that goes beyond traditional time-series forecasting. Rather than relying solely on historical data, causal forecasting models also incorporate various factors or causal variables that can influence the forecasted quantity.

Let’s consider a simple example—daily sales of ice cream. It’s a given that these sales won’t be consistent throughout the year. A traditional forecasting model might look at last year’s sales metrics to predict this year’s. But what about other significant influences, like the season, the average temperature, or even the day of the week? That’s where causal forecasting comes into play.

Causal models would consider these factors, allowing a more nuanced and accurate forecast. So, instead of making blanket predictions based on past sales, a causal forecast for ice cream might predict higher sales on hot weekends in summer and lower sales on cold weekdays in winter. This type of forecasting provides a more detailed, accurate view of future demand, helping businesses to plan more effectively and avoid costly mistakes.

What are Causal Models?

Causal models, also known as explanatory models, are a type of forecasting method that incorporates variables that might influence the quantity being forecasted. They’re based on the idea that the variable to be forecasted (the dependent variable) is affected, or caused by, one or more other variables (the independent variables).

For example, the sales of ice cream (dependent variable) could be influenced by the average temperature (independent variable), the day of the week, the season of the year, promotional activities, etc. These models utilize artificial intelligence to determine relationships and correlations between these variables to create more accurate and dynamic forecasts.

Causal models often use techniques such as regression analysis or clustering to estimate the relationship between the dependent variable and the independent variables, which can be a linear or nonlinear relationship.

These models are particularly useful when forecasting in conditions where historical data may not be a reliable predictor of future outcomes, or when external factors are known to influence the forecasted variable.

The Power of a Comprehensive Approach

Over fifty years ago, Harvard Business Review was already emphasizing the critical importance of causal forecasting in the world of business. While the core principles they discussed remain valid, the evolution and sophistication of technology since then, particularly AI and machine learning, have significantly enhanced our ability to incorporate causal factors, making forecasting more accurate and transformative than ever before.

Causal forecasting shines in its ability to integrate these external factors affecting demand, delivering a comprehensive, reliable forecast. This method captures current conditions and probable future trends, aligning forecasts with market realities and informing strategic decision-making.

Especially in industries where external factors like seasonality, economic indicators, and promotional activities have a significant impact, causal forecasting enhances forecast accuracy. Coupled with demand sensing—providing real-time data and market signals—causal forecasting becomes more robust and adaptable, increasing the forecast’s precision.

The ability to quickly adjust forecasts and respond to changing market conditions is crucial in today’s rapidly evolving business environment. Causal forecasting can help businesses adapt to changes faster, can proactively manage risks, seize new opportunities to stay ahead of the competition.

Powerful Reasons to Embrace Causal Forecasting Now

Foresight is more valuable than ever in today’s rapidly changing business environment. When employed effectively, can give you that crucial edge. Here are the top three reasons you shouldn’t wait to incorporate causal forecasting into your operations.

1.        Supercharge Your Forecast Accuracy

Traditional forecasting methods often fall short as they rely predominantly on historical data. Causal forecasting, on the other hand, brings external factors like market trends, seasonality, promotional activities, and more into the mix. This holistic approach leads to precise predictions that align more closely with reality. By leveraging Logility’s advanced machine-learning capabilities, you can seamlessly incorporate these causal factors into your forecasting process, achieving unprecedented accuracy.

2.      Minimize Overstock and Understock Situations

Causal forecasting allows you to manage your inventory  more efficiently, leading to substantial cost savings. By accurately predicting demand, you can maintain optimal stock levels, reducing the need for last-minute expediting costs due to stock-outs. Additionally, you can reduce overstocks and lower inventory levels mean reduced holding costs, freeing up resources for other strategic initiatives. With Logility’s demand planning software, you can integrate causal forecasting into your supply chain strategy, driving down costs while ensuring you’re always ready to meet customer demand.

Overstocks and understocks are costly scenarios that can harm customer relationships and affect profitability. Causal forecasting helps you sidestep these pitfalls by generating accurate demand forecasts, enabling you to effectively align your supply with customer demand. This means fewer wasted products, fewer missed sales opportunities, and more satisfied customers. Logility’s powerful forecasting technology supports this by incorporating real-time data and causal factors, giving you the tools to stay one step ahead and maintain the perfect inventory balance.

With Logility’s demand forecasting software, you’re armed with the insights to make proactive, data-driven decisions that drive profitability and customer satisfaction.

Stepping into the Future

The power of causal forecasting is undeniable. It enhances forecast accuracy, lowers expediting and inventory costs, and reduces over/understock situations. Manufacturers can supercharge their predictive analytics capabilities by embracing innovative technology that supports it, like Logility. Don’t wait to reap these benefits. Incorporate causal forecasting into your demand planning today and better prepare for tomorrow’s market realities.

Learn how Logility can streamline your efforts and get you started on your journey for a more optimized supply management system.