The terms ‘demand planning” and “demand forecasting” are often used interchangeably in the supply chain world. You might be one of those people who has used one term when referring to the other and vice versa. And this is understandable when you consider how related these two terms are.
However, demand planning and forecasting terms do not refer to the same thing. Below, you’ll discover:
- The meaning of demand planning and demand forecasting
- How the two terms differ from each other (plus their different roles in supply chain processes)
- The most effective way to carry out demand planning and forecasting
What Is Demand Forecasting?
As the name suggests, demand forecasting involves predicting the future demand of your product or service among your customers.
Traditionally, demand forecasting in supply chain relied on historical data and rigid statistical models to make predictions. Unfortunately, most predictions were far from accurate given the volatility of the market and the dynamic nature of consumer behavior. Today, newer technologies, such as artificial intelligence (AI) and machine learning (ML), allow businesses to analyze large datasets and make more accurate forecasts.
Demand forecasts guide a wide range of supply chain processes, including inventory management, procurement, and product planning.
What Is Demand Planning?
Demand planning goes beyond demand forecasting. It focuses on forecasted product demand while taking into account other critical factors like the available resources, market trends, supply chain disruptions, and the capacities needed to meet that demand. Its aim is to align your company’s resources with future demand.
One of the critical roles of demand planners is inventory management. They will determine the right inventory level to meet customer demand, so you don’t end up with excess inventory or run out of items. As a result, you can maintain customer satisfaction without unnecessary spending.
Also, demand planning requires smooth collaboration among the various functions of your organization, including marketing, sales, finance, and supply chain teams. For example, the demand planners may work closely with the supply chain team to understand what can be achieved in the given time frame. This enables you to fit the forecast within those limits.
Key Differences Between Demand Forecasting vs Planning
If you’re still on the fence about how forecasting demand differs from demand planning, check out the differences below:
Purpose and End Goals
The primary goal of demand forecasting is to predict future demand. During this process, you analyze historical data to anticipate how the demand for your products or services may fluctuate over a given period. Demand forecasts can help you decide where to direct your resources.
For example, let’s say you are managing a restaurant chain that is gearing up for the summer. You can analyze past sales data and seasonal trends to predict the demand for various menu items during the summer. This information will help you adjust your stock levels to ensure you have enough items to meet customer demands.
On the other hand, demand planning focuses on ensuring your business’ resources and capabilities can meet the forecasted demand.
In the example above of a restaurant chain, demand planners may work with the kitchen staff to streamline the production process and allocate additional resources to meet the expected demand. They may also liaise with suppliers to secure timely deliveries of the needed items, hence preventing any disruptions in the supply chain.
Data Utilization and Analysis
Demand forecasting primarily involves quantitative analysis of historical data to predict future demand. It uses statistical methods, such as time series analysis, regression analysis, and machine learning algorithms, to make predictions. How’s how these methods work:
- Time series analysis: Involves examining historical data collected over regular intervals (e.g., daily, weekly, monthly) to identify data patterns. It assumes that future data will follow the behavior of historical data.
- Regression analysis: It helps quantify the impact of independent variables (price, marketing expenditure, etc.) on a dependent variable (e.g., sales volume) and predict future outcomes based on these relationships.
- Machine learning algorithms: Machine learning tools can analyze large volumes of historical data to identify complex behavior and make predictions. These algorithms can learn and improve over time as they are exposed to more data, leading to more accurate forecasts.
Demand planning relies on historical data, market research, input from various stakeholders, and other relevant information, to make strategic decisions. For a manufacturing company, demand planning may involve analyzing not only your product’s forecasted demand, but also other variables like your productivity levels, availability of raw materials, warehousing capacity, and vendor performance. By considering all these factors, you can develop a more realistic and actionable plan for meeting consumer demand.
Time Horizon and Flexibility
When it comes to time horizon, demand forecasting and planning differ in their focus and scope.
Demand forecasting deals with a shorter time frame. In most cases, this may be a few weeks or months. For example, you might use supply chain forecasting to predict inventory levels for the next quarter based on historical sales data.
In contrast, demand planning covers a broader time frame and usually involves ongoing adjustments to meet the changing demand. While forecasting provides insights into short-term demand trends, demand planning takes a more strategic approach by considering long-term business goals and market dynamics. For example, you may develop demand plans for the next year after considering your product’s lifecycle, seasonality, and macroeconomic trends.
Because demand planning supports continuous adjustments, it offers greater flexibility in adapting to changing market conditions and customer preferences over time. This enables you to proactively adjust your strategies and resources to prepare your business for the future.
Let Us Help You Build a Resilient Supply Chain
Demand forecasting and planning are two separate concepts, but both play critical roles in managing the chain of demand. Forecasting offers insights into the future demand for your products, while demand planning uses the forecasts to align resources with demand.
At Surgere, we understand the importance of demand management in driving supply chain efficiency. Instead of analyzing historical data that isn’t guaranteed to produce accurate results, you can utilize our demand planning software, Interius, to predict and meet consumer demands.
Our powerful software offers 99.9 percent data accuracy and real-time visibility into your inventory, sales, and supply chain data, so you can rely on it to make more informed decisions.
Contact us today to see Interius in action.
