What is demand forecasting?

The demand forecasting process enhances forecasting accuracy in real-time, helps organizations manage their inventory levels and guides data-driven business decisions. Forward-thinking organizations are turning to artificial intelligence (AI) tools, machine learning (ML), predictive analytics and automation in their demand forecasting approach.

Using these emerging technologies drives organizations to think proactively about supply chain management and fuels more accurate predictions of customer needs. The approach to forecasting is changing and evolving as other areas of organizations get influenced by AI, including AI-driven analytics, sales intelligence and AI-powered inventory management.

A recent IBM Institute for Business Value report highlights the crucial role AI is going to play in supply chain operations in the coming years. In fact, 64% of the Chief Supply Chain Officers (CSCOs) surveyed say that generative AI is completely transforming their supply chain workflows. The report also predicts that digital assistants will increase the volume of decision-making by 21% by 2026.

“It’s not just about explaining how materials will get from point A to point B. It’s also measuring the supply chain cost of every business decision—and making sure those costs are considered from the start,” said the report.

With demand forecasting, organizations have the tools and datasets to predict future demand and drive smarter decision-making that can save an organization both time and money.

Through detailed data analysis and pattern detection, organizations can make accurate predictions about sales forecasting and cash flow, facilitating informed decisions about the future. The demand forecasting approach gives enterprises and their stakeholders more control and oversight into daily operations.

Accurate forecasting ensured proper stock keeping units (SKU) and enough product stock by pulling from multiple data sources, such as databases, past sales and spreadsheets. Without this approach, organizations risk overstocking or understocking inventory, which can lead to backorders or stockouts.

Accurate demand forecasting can lead to greater customer satisfaction and nurture more strategic business strategies.

Demand forecasting can be approached in many different ways. The demand forecasting method that a business chooses will depend on the scope and goals for the future. Most methods fall into two categories: qualitative and quantitative approaches.

The purpose of qualitative demand forecasting is to measure what’s happening on the ground and gain expert opinion. It polls and gathers data from employees, stakeholders and customers to help forecast future decisions within an organization.

• Delphi method: A panel of experts works to answer a question independently and comes together to share their findings. After they share, the panel creates an answer that is agreed upon by all experts. This qualitative method can require subsequent rounds of feedback until reaching a consensus.
• Sales force composite: Salespeople are a source to understand customer satisfaction and what potential customers are looking for. In this method, sales teams are asked for the rate at which they expect to sell over a set time. The method is popular and is even built into some inventory management platforms.
• Expert or insider knowledge: While historical data and quantitative metrics accurately forecast demand, they cannot replicate the instincts and insider knowledge that field experts provide. The purpose of this method is to find the exceptions to the patterns and incorporate them into the forecasts.
• Surveys: A survey is a great way to learn about a customer base and their activity. If a business is curious about whether to renew a particular product, the easiest approach is to ask the customers directly. A stand-alone survey might not give the most effective insight, but over time, organizations can compare surveys and start to find patterns or correlations.
• Focus groups: Organizations seeking more detailed feedback can turn to interviews or focus groups. This method is an opportunity to gain focused feedback and details about specific product or services in a more private approach.

A quantitative approach to demand forecasting is at the core of the entire process. The methods typically include basic projections from historical sales data and sophisticated models use AI-driven predictive analytics.

• Moving averages: This method calculates the mean of a number over a trailing period. For example, a seven-day moving average of sales would be the average over the last seven days. While it seems like a backward-looking approach, it helps form readable patterns and accessible trend lines.
• Statistical: A way to employ statistical methods is through trend prediction and regression analysis. Trend projection looks at the past (historical datasets) to predict future demands. Regression analysis analyzes the relationship between certain variables, such as email campaigns or conversion rates.
  
• Seasonal averages: Many factors play into demand, including day of the week, time of the month (holidays) and weather conditions. Something like bathing suit sales rise in the summer months, especially in July. They then plummet in October or November. It’s important to combine these known factors with other trend analyses for a holistic view.
• Econometric modeling: This model considers economic factors and data. The model uses statistical and mathematical models to create theoretical representations of economic theories. Some of the common techniques are regression analysis, time-series analysis and structural equation modeling.
• Barometrics: This forecasting method uses three different types of indicators: leading, lagging and coincidental. The leading indicators attempt to predict what the future holds. Lagging analyzes the past and looks at either decreases in sales or spikes that need to be tracked more closely. Coincidental indicators are looking at real-time data points to measure the current state of an organization.

There are varying demand forecasting strategies available to an organization. They each cover many different approaches, models and formulas, depending on the size and scope of the demand forecasting strategy.

The definition for a short-term approach differs depending on what the organization qualifies as “short.” However, the usual length of time ranges from the upcoming quarter to the full year. There might even be a particular series of dates the organization plans on targeting.

Long-term demand forecasting is measured in years and is less accurate due to the nature of the time frame in which predictions are being made.

Organizations struggle to make assumptions about the future 10 or even 5 years out, regardless of how extensively they work with forecasters. However, the data from the forecast is still useful and can provide guidance for organizations trying to think through different “what-if” scenarios.

This approach to demand forecasting looks at external factors through both a macro and micro lens. These external factors could be economic conditions, competitors or changing consumer trends.

Organizations must consider external forces that might disrupt commerce, identify which offerings to expand and anticipate potential shortages.

Internal factors are as important as external. Internal demand forecasting is necessary so the organization’s internal capacity can meet the forecasted business growth. This type of demand forecasting uses the organization’s own data to forecast demand. The internal data can be sales history, inventory levels, capacity and other data points that focus on internal operations.

If the business is expected to double in customer demand in the next two years, the business operation must meet that demand. Therefore, internal demand forecasting would look at inventory, staffing and budgeting to gain greater insight into whether the business can meet the demand. To keep the operation running smoothly, organizations must consider their people and ensure they have the internal capacity to meet future expectations.

Organizations seeking a minimally-invasive approach should consider passive demand forecasting. It’s a forecasting process that is automated by using historical data from within the organization.

This approach is best suited for organizations with stable sales and growth. The passive orientation of this demand forecasting model makes projections based on the assumption that the organization is not going to change much over time. This characteristic makes it a less ideal approach for businesses in disruptive markets or growing rapidly.

This approach is for fast-growing businesses that expect rapid expansion (for example, startups). Active demand forecasting takes a proactive approach to measure and predict future product demand. It incorporates internal business activities, such as marketing campaigns and market research.

The approach also considers external factors like the economic outlook and trends in the current market.

Demand forecasting is evolving with the help of AI and machine learning (ML) methods. Specifically, AI demand forecasting is the use of artificial intelligence to estimate future demand for products or services.

These advanced analytics can analyze historical data and provide actionable insights for forecasters, leading them to more informed decision-making. This new method is revolutionizing forecasting by being able to handle vast datasets and adapt to market conditions in real-time.

Although AI demand forecasting is considered passive, the argument can be made that it’s a hybrid, featuring aspects of both passive and active forecasting methods.

There is no singular way to go about demand forecasting. It all depends on the situation that the organization is in and what it is trying to achieve. While there are many methods to consider, there are some consistent features that can apply to most demand forecasting teams.

1. Establish the goals of the forecast: Define what it is the organization is looking to forecast and why. Get specific about how the forecast will be used and what the output will look like.
2. Determine the information needed: Identify the data elements necessary to run a demand forecasting process and try to narrow down which data is going to be the most useful to reach the forecasting goal.
3. Run a data collection plan: Gather data from approved systems and stakeholders. Validate completeness, resolve anomalies and document all transformations to guarantee consistent, trusted inputs.
4. Apply forecasting methods: Run selected forecasting methods that use clean, high-quality data. Test multiple techniques, compare performance and choose the method that best meets the organization’s needs.
   
5. Analyze and interpret the results: Review forecast outputs, highlight trends and assess variance against historical patterns. After analysis, convert insights into clear, actionable findings.
6. Monitor results and modify as needed: Track forecast accuracy through metrics and evaluate changing conditions. Adjust the planning process as needed and consider profit margin changes. Update assumptions and refine the parameters set by the organization to help ensure continuous improvement.

Demand forecasting offers several important benefits to an organization. The approach can help increase long-term business value and optimize supply chain operations through strategic initiatives.

Demand forecasting can provide clear visibility into future resource needs, allowing organizations to scale operations proactively instead of reacting to bottlenecks or market shifts. With demand forecasting, supply chain teams can adjust production capacity, workforce levels and technology requirements confidently. By using advanced analytics and other demand forecasting techniques, organizations can reduce waste from over-expansion and avoid delays caused by under-sourcing.

Through a disciplined approach, organizations can have consistent performance during growth cycles and be prepared when there’s spike in demand. With the right forecasting tools, teams can accelerate their time to market and strategically offer new products and services at the right time.

Demand forecasting can strengthen financial planning by grounding budgets in data rather than assumptions. Teams can also estimate revenue, costs and cash flow with greater precision.

Within finance, demand forecasting plays a significant role in building funding strategies that match operational needs. Accurate demand and sales forecasting reduce the risk of overspending during slow periods or underinvesting before growth.

The process also supports stronger discussions with investors, lenders and stakeholders because projections can be justified with evidence. With better budget accuracy, organizations can do a better job of capacity planning, inventory planning and being prepared for when disruptions occur.

Demand forecasting helps organizations maintain inventory management at the right time and mitigate fluctuations, stockouts and carrying costs. Through demand planning, operations teams can align activities in areas like procurement, production and distribution and consider seasonality and lead time series.

The process of demand forecasting improves supply chain resilience throughout the entire lifecycle and helps ensure products reach customers without delay. The approach also provides better visibility into new market potential and demand variability, which enables smarter safety stock planning and tighter supply coordination.

Strategic inventory management and supply chain management also boost customer satisfaction by securing consistent product availability.

Demand forecasting equips leaders with actionable insights that go beyond the spreadsheet and gives them definitive information that clarifies choices and reduces uncertainty. Organizations can compare scenarios, evaluate risk, pricing and select options that align with business goals.

They can also respond faster to market trends because they are using real-time and historical data rather than guesswork. Intentional and detailed market research drives clear forecasts and helps teams allocate resources with precision.