In simple terms, demand forecasting is a technique used by companies within supply chains to prepare or anticipate how customer needs will change in the future. It’s a technique as old as supply chains have existed but has become more accurate with newer technologies and the availability of vast amounts of data.
This guide will explore the ins and outs of demand forecasting in the supply chain. It will cover everything from why demand forecasting is essential for your business, the different types of demand forecasting, its challenges, and how to leverage it to transform your supply chain operations.
The Importance of Demand Forecasting
Accurate demand forecasts serve as the foundation of effective inventory management. By accurately predicting future demands, companies can ensure they have the “right products, in the right quantities, at the right time”— or what White Cap’s Vice President of Supply Chain Lisa Scinta refers to as the “delicate balance” of having enough of the right inventory to help your customers succeed while managing cash and capital. This avoids overstock costs and opens up opportunities for growth and success.
When you get inventory management right, everything else lines up. Think—you get to optimize the whole supply chain, customers get exactly what they want (which translates to enhanced customer satisfaction), your business makes better financial decisions, you’re better positioned to handle potential risks like market fluctuations, and you can maintain a competitive edge.
Consequences of Poor Demand Planning and Forecasting
Not all demand forecasting is accurate. Sometimes, you might get it wrong—and there are consequences.
Poor demand planning and forecasting is the inability to accurately predict and anticipate your customers’ future demands. This means you occasionally experience:
- Excess Stock: Poor planning and demand forecasting can sometimes make you overestimate demand, thus producing or ordering more products than you need. This results in excess inventory that ties up capital, increases storage costs, and leads to inventory glut.
- Stockouts: When you underestimate your customers’ needs, you may experience products running out before you can fully satisfy every customer.
- Unhappy Customers: Stockouts eventually lead to unhappy customers and lost sales, negatively affecting your business’s reputation and customer loyalty.
- Increased Operational Costs: Whatever your case is—excess stock or stockouts—your overall operational costs will increase. For instance, stockouts may lead to rushed production runs or expedited shipping to meet customer demands. At the same time, excess stock contributes to increased holding costs and may force you to offer discounts to clear excess inventory.
- Strained Supplier Relationships: If you depend on external manufacturers for stock, poor demand forecasting frequent changes or cancellations on orders will usually be the order of the day. This unpredictability can cause dissatisfaction with suppliers, leading to delayed shipments and, sometimes, higher prices that affect your overall profitability.
Key Concepts in Demand Forecasting
As already established, demand forecasting is a technique that helps you predict how your customers will behave in the future and then align your business decisions with this. It does so by leveraging various factors, such as historical data on sales, market trends, and economic indicators. The “future” in demand forecasting can be as soon as tomorrow, or a few weeks from now, or as far as next year.
Factors Influencing Demand
Several factors affect demand and, thus, directly influence demand forecasting and planning. These include the following:
Seasonality
Different seasons affect customer behavior and needs. For instance, during winter, products like heavy jackets and boots surge in demand, while holiday seasons like Christmas and Thanksgiving usually experience spikes in gifts like toys.
Competition
Competition can either increase or decrease demand. When too many businesses enter a particular industry, this can influence demand because it impacts several factors, including price, quality, and brand reputation. For instance, if a competitor introduces a product similar to the one you sell at a slightly lower price, this may cause a decline in the demand for your product.
The Types of Goods
Different categories of goods have distinct demand patterns. For instance, while necessities like food and household products tend to have relatively stable demand, luxury items and non-essential goods can experience high volatility rates. Necessities are less sensitive to conditions like price changes and seasonality because consumers need them for survival. As for the non-essentials, changes in consumer sentiment, economic conditions, and discretionary income play a considerable role in demand.
Economic Conditions
Economic conditions like GDP, inflation, and consumer confidence can also affect demand. When these factors, for example, lean on the positive side—high GDP, low inflation, and high customer confidence, your business should anticipate higher demand for products, while the reverse is also true.
Types of Demand Forecasting
While we touched a bit on the significant types of demand forecasting, there exist many more types and methods, including:
- Passive Demand Forecasting: This approach assumes that one year’s sales will be the same for the following year. It uses past data to predict future demand without considering external changes.
- Active Demand Forecasting: Unlike passive forecasting, active demand forecasting considers external factors like market trends, economic indicators, and historical sales data.
- Short-term Demand Forecasting: It focuses on predicting demand for the near future, ranging from a few days to a few months, but less than a year (12 months). You can leverage this planning for day-to-day operations, such as inventory management and production planning.
- Long-term Demand Planning: This involves predicting customer demands for longer periods, usually more than 12 months or longer.
- The Delphi Method: This qualitative forecasting technique leverages expert opinions through multiple surveys to reach a consensus on future demand.
- Trend Projection: It identifies trends from past or historical data and uses them to predict demand.
- Internal Forecasting: Focuses on data from within the company, such as sales records and internal growth projections.
- External Forecasting: Considers external factors, such as market trends and economic conditions, to predict demand.
- Sales Force Composite: This method leverages the expertise of a company’s sales team to forecast the demand.
- Econometric Models: This demand forecasting uses statistical methods and economic theories to forecast demand based on multiple variables.
Challenges in Demand Forecasting
Like most processes and techniques, demand forecasting has its fair share of limitations. One of the most common challenges most businesses face is data-related, with data availability and inaccuracy being what your business will likely encounter. Demand forecast accuracy depends on the data you use, so when you have inconsistent, missing, or inaccurate data, you may not create an optimized demand plan.
Another common challenge in demand forecasting is changes in business processes, such as how you manufacture your products. Say, for instance, you modify an existing product. Demand forecasting becomes a problem after this because you can’t rely on past data to make accurate demand forecasts. This is because modifications introduce new complexities that historical data cannot account for.
How to Forecast Demand: Expert Tips and Steps
Approach demand forecasting strategically to get accurate insights that you can use to prepare. That said, there are five main steps you can follow for accurate forecasts:
- Establish Forecast Goals: The first and most crucial step is to determine the objective of your forecasting, which may involve asking several questions. What do you want to achieve? Are you forecasting to optimize inventory levels, improve production schedules, or enhance customer service?
- Collect Data: As already established, demand forecasting depends on data, so you must ensure you have sufficient accurate data. This may require integrating data from multiple sources to give you a more holistic view of demand.
- Choose and Apply Forecasting Techniques: With your data in hand, the next step is to select the appropriate forecasting techniques. The choice of method depends on various factors, including the type of goods, the time horizon, and the complexity of the market.
- Interpret and Apply the Forecast: After you generate a forecast, it’s time to understand the results and see how best to react. For instance, if the forecast indicates a surge in demand for a particular product, you may need to adjust your inventory levels, ramp up production, or enhance your supply chain capabilities.
- Track Accuracy and Improve: Lastly, demand forecasting isn’t a one-time process. You must constantly keep track of the adjustments you make to be sure they’re indeed working. If not, it’s best to return to the original data to check for inaccuracies.
How do you calculate a demand forecast?
You can use several demand forecasting models to calculate demand forecasting manually.
A common and simple method you can leverage is the Simple Moving Average (SMA), whose mathematical formula is as follows:
Sum of past demand over n periods / n
For example, if you’re forecasting demand based on the past three months, you’ll add the sales figures from those three months and divide by three.
Another method is Exponential Smoothing:
(α×Last Period’s Actual Demand) + ((1−α) × Last Period’s Forecast), where α is the smoothing constant, typically between 0 and 1.
Latest Trends in Demand Forecasting
Trends in demand forecasting worth exploring include AI and ML technologies. These technologies’ capabilities, including scalability, speed, and high accuracy levels, make them attractive tools across industries, and the supply chain is no exception. They can handle large volumes of data in less time compared to manual demand forecasters, which is essential for process optimization.
Another trend in demand forecasting gaining popularity is demand sensing, where forecasters depend on real-time data. This technique collects data from sources, such as point of sale (POS) systems, social media, and e-commerce websites, to help combat overstocking and stockouts.
Tools and Technologies for Demand Forecasting
Amber Salley, a seasoned expert with over a decade of experience in supply chain planning technologies, emphasizes the critical role of AI, advanced analytics and demand forecasting in today’s volatile market. During her recent MDM Webcast sponsored by GAINS, Salley pointed out that “the goal for every distributor is to strike the optimal level of product inventory to support future sales demand.” This balance helps maintain financial health and promotes customer satisfaction and loyalty.
Real-World Examples of Demand Forecasting Applications
A clothes retailing business may want to prepare for the upcoming winter season. It can use demand forecasting by relying on past sales data in other winter seasons, combined with economic conditions like inflation. This data can help the business determine or estimate the number of coats, sweaters, and boots needed to meet customer demand.
Simplify Demand Planning and Forecasting With GAINS
Demand forecasting is an essential component of successfully running a business. However, it isn’t always straightforward, and you may require some help by relying on software. With GAINS, you can easily and quickly leverage demand planning and forecasting software to stay ahead of the constant competition and changing customer needs.
Demand Forecast FAQs
What’s the Difference Between Qualitative and Quantitative Demand Forecasting?
Qualitative forecasting relies on human expertise and judgment to predict customers’ demands in the future, while quantitative demand forecasting uses data to predict future demand. Because of this difference, qualitative methods are ideal for predicting the demand for new products entering a market without historical data, while quantitative methods excel with established products where past data is in abundance. Additionally, since qualitative demand forecasting relies on subjective opinions, this method can be more flexible and adaptable to changes in the market that are not yet reflected in data, whereas quantitative forecasting might miss these nuances if the data doesn’t capture emerging trends.
What are the 5 Methods of Demand Forecasting?
The five primary demand forecasting methods include market research, the Delphi method, historical analogy, time series analysis, and econometric models.
What are the 3 Levels of Demand Forecasting?
The three levels of demand forecasting are macro, micro, and industry level. Macro-level demand forecasting is for an entire market or industry, micro-level is for a specific company or product, and industry-level is for a particular economic sector.
Which Method Makes Demand Forecast More Accurate?
All methods can be equally accurate as long you use enough data and incorporate forecasting best practices, such as using accurate and consistent data for your forecasting models, adjusting for external factors, and regularly conducting demand forecasting. Additionally, combining different forecasting methods can enhance accuracy because one method may compensate for the weakness of the other.