Demand forecasting has recently received a lot of attention in the supply chain and logistics industry. Major retailers have struggled with the problem of accurate forecasts for decades, but it has received renewed attention in the past few weeks. Whether from Walmart's inventory woes, higher safety stock levels at Gap/Old Navy/Banana Republic chains, or similar issues at American Eagle, Urban Outfitters or Target, retailers are facing the crunch of overstocked supply affecting profit margins.
The issue often boils down to demand forecasting or demand planning. The two terms are often used interchangeably. That might be the problem.
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What is demand forecasting?
Demand forecasting is the process of predicting how much of your product will be purchased over a future period. Historically, the demand forecasting function uses past precedents to predict future demand.
This is often how demand planners or buyers define demand forecasting, but it skips a core assumption: do your customers even want those product line?
A better definition for demand forecasting offers a subtle but essential tweak: Demand forecasting is the process of predicting how much consumers are interested in buying your product.
The critical difference between the two definitions involves incorporating a customer-first mentality. Consumers drive your demand, and forecasts need to predict consumer behaviour just as they predict historical sales trends.
Demand forecasting is a scientific, data-centric practice. It is the inception of an organization's underlying hypothesis of the entire supply chain. But it still needs to be adjusted and implemented with careful planning.
What is demand planning?
Demand planning is the overarching management of resources to fulfill forecasted demand. This is where the rubber hits the road, and demand planners enact their forecasts by planning new orders and distribution logistics. It is a complex task that requires skilled planners to pull it off successfully.
It is also a critical junction in the lifecycle of a forecast. After applying the methodology to estimate future demand results in a prediction, demand planners collaborate with marketing and sales teams to adjust the estimates. This can refine unconstrained demand expectations.
The planner also works closely with their fulfillment or supply chain team to understand what can be accomplished in the given time frame. This then boils the forecast into a constrained demand forecast.
What is the difference between demand planning and demand forecasting?
Demand forecasting is the process of predicting consumer interest in buying a given product, while demand planning is the management of resources to fulfill this forecasted demand. Demand forecasting and demand planning can often be mistaken for each other. The confusion usually begins with the role of a demand planner, who takes on both forecasting and planning responsibilities. However, separating the duties and processes creates the space for forecasting and planning to receive dedicated resources, tools, and training.
At Granularity, our mission is to help focus demand planners on expertly forecasting the product demand at their organization. We create tools to reduce the headache of demand forecasting and enable planners to more accurately and quickly size unconstrained consumer demand in a given product. Understanding consumer demand allows for more accurate forecasts to avoid costly overstocking decisions and lower profit margins.
If you're interested in learning more about what we do, follow this link to book a time with our team.
FAQ: Summary of Demand Forecasting vs Demand Planning
- What is demand forecasting?
Demand forecasting predicts consumer interest in buying a product, using historical data and market trends to estimate future demand. - How does demand forecasting differ from demand planning?
While demand forecasting estimates consumer interest, demand planning involves managing resources to meet this forecasted demand. - Why is demand forecasting important in supply chain management?
Demand forecasting guides inventory decisions, preventing overstocking and aligning supply with consumer demand to optimize profit margins. - What challenges do retailers face with demand forecasting?
Retailers struggle with accuracy in forecasts, often leading to either surplus inventory or stock shortages, impacting profits and efficiency. - How can better demand forecasting benefit a retailer?
Improved demand forecasting can lead to more efficient inventory management, reduced costs, better customer satisfaction, and higher profits. - Is demand forecasting only about predicting sales?
It's more than sales predictions; it involves understanding market trends, consumer behaviors, and adapting to changing demands. - How does technology influence demand forecasting?
Advanced analytics and AI enable more accurate, data-driven forecasts, helping businesses anticipate market changes more effectively. - Can demand forecasting guarantee successful demand planning?
Accurate forecasting is crucial but not a guarantee. Successful demand planning also requires strategic resource management and adaptation.
If you're interested in learning more about what we do, follow this link to book a time with our team.
About the Author
Tali Remennik is a Certified Professional Forecaster, certified by the Institute of Business Forecasting and Planning. She is a data scientist working at the intersection of AI, demand forecasting and retail strategy. She is the co-founder of Granularity. Granularity is an AI-powered trends platform AI and data from Tiktok, Instagram, Google and others to predict consumer trends as they emerge.
