What is demand forecasting?

Demand forecasting predicts what customers will buy, when, and where – turning data and signals into confident plans for supply, production, and growth.

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What is demand forecasting?

Importance of demand forecasting
Demand forecasting process
Long vs. short-term demand forecasting
What is demand sensing?
Quantitative vs qualitative methods
Statistical techniques and models
AI-based demand forecasting
Common pitfalls
FAQs

With the power to process vast, diverse data sets in real time, modern AI-powered demand forecasting solutions can uncover trends and patterns that traditional models miss. And beyond just flagging these issues, today’s advanced systems can automatically refine forecasts and recommend actions – right in the moment. These intelligent systems help balance availability with efficiency – reducing overstocks, preventing shortages, and keeping operations synchronised with actual demand. The result is more reliable insight, faster adaptation, and stronger performance across every link of the supply chain.

What is the importance of demand forecasting today?

According to the US Census, the past 10 years have seen e-commerce sales rise from about 5% of total retail sales, to over 15%. That may not sound like a huge leap, but it represents hundreds of billions of dollars per year and millions upon millions more transactions. And that’s not to mention the impact of omnichannel proliferation, social media phenomena, and the many other global trends and shifts that are moving so quickly. Inventory and supply chain management have never been more challenging or more competitive. But the good news is that today’s cloud-based forecasting tools have evolved to the point where they can leverage AI, machine learning, and powerful end-to-end supply chain integrations – to ensure that modern businesses are able to stay a step ahead of their customers’ needs and their competitors’ moves.

How does the demand forecasting process work?

To accurately forecast demand, it’s important to establish a smooth integration of data science, domain expertise, and structured collaboration. The process typically follows a loop that moves from raw data to actionable plans.

Data collection

The first step is to gather essential data such as historical sales, market indicators, and other inputs. It is essential to have integrated supply chain systems and good data handling processes in place. Clean, consistent data is critical to avoiding gaps or distortions down the road.

Feature and driver identification

Deep, ongoing analysis looks for insights and patterns in seasonality, price elasticity, promotions, lead times, and economic conditions. As your systems learn, your understanding of these drivers builds over time – so you can establish not just what happened, but why.

Model selection and generation

Using statistical and AI-based methods, the system tests different forecasting models such as regression, moving averages, or machine-learning ensembles. It then chooses the one that best fits each product or category. This helps to ensure accuracy at scale.

Exception review and collaboration

From produced forecasts, you can more clearly see anomalies such as sudden spikes, dips, or unexpected outliers. Planning systems can then flag and prioritise these exceptions so human experts can best determine the next set of actions – and when to take them.

Consensus and execution

Forecasts can be shared across teams such as sales, operations, procurement, and finance. This lets each team plan from the same expectations. This coordination keeps production, purchasing, logistics, and budgets aligned – preventing misunderstandings.

Continuous refinement

Forecasts aren’t static. As new orders and external data arrive, AI models are retrained and updated – refreshing in near real time to incrementally incorporate new signals rather than rebuilding forecasts from scratch. This allows you to stay synchronised.

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Long-term vs. short-term demand forecasting

When everything is marked “urgent,” it can be tempting to postpone long-term planning in favour of firefighting. In truth, both short- and long-term scenarios are equally important, but they must be managed with strategy – ensuring the bigger picture is always kept in sight, and the important long-range tasks are never at the mercy of momentary panic.

Short-term forecasting

Focuses on near-term execution. This means ensuring that inventory, labour, and transportation align with upcoming orders and seasonal fluctuations. Depending upon your sector, you may need daily, weekly, or even hourly cross-business reporting to stay on top of replenishment cycles, promotions, and demand-driven production planning.

Long-term forecasting

Looks beyond immediate operations to shape strategy, investment, and capacity over months or years. It helps you prepare for market shifts, product lifecycles, and sourcing challenges before they happen. By modelling scenarios across economic and seasonal horizons, long-term forecasting supports successful expansion, capital allocation, and supplier relationships.

What is demand sensing?

Demand sensing is a component of short-term forecasting, most often used for fast-moving inventories. It uses real-time signals to detect change faster and adjust plans immediately. Traditional short-term forecasts have relied upon historical trends and scheduled updates, whereas sensing systems continuously monitor live data such as customer orders, shipments, and external market inputs. This lets you spot shifts as they happen. Powered by AI and machine learning, these tools evaluate multiple algorithms and integrate internal and external data to refine short-term forecasts dynamically. The result is the ability to turn forecasting into a continuous, responsive process rather than a periodic one.

Demand forecasting methods: quantitative vs qualitative

While smart, integrated technologies are essential to smooth supply chain management, there’s no substitute for human experience and know-how. As with so many of today’s processes, a hybrid mix of smart people and smart technologies often brings the best results.

Quantitative forecasting

Uses measurable data such as sales history, production figures, and external market indicators. Techniques include time-series analysis, regression, and econometric modelling. It’s ideal for stable, data-rich categories where patterns like seasonality or price sensitivity are well established. Modern systems apply AI and machine learning to automate model selection and refine accuracy in an ongoing manner as new data arrives.

Qualitative forecasting

Relies on human insight and experience when assessing historical data and changing market conditions. Inputs can include expert panels, market research, customer feedback, or competitive intelligence. Scenario workshops and consensus meetings can also help planners weigh external influences such as emerging trends or regulatory shifts that numbers alone can’t reveal. When integrated into a common planning cycle, qualitative insights help guide model adjustments and improve confidence in results.

Core statistical demand forecasting techniques and models

The techniques and tools involved in demand forecasting are essentially mathematical frameworks that use past data (from a minute ago or a year ago) to predict future outcomes. They translate patterns into measurable insights about what’s likely to happen next. Without needing a maths degree, here is a very simple rundown of how it all comes together.

Moving averages

Smooths short-term fluctuations to reveal overall demand trends over time. Ideal for stable, predictable product lines where consistent movement matters more than daily variation.

Exponential smoothing

Gives more weight to recent data than older data so forecasts are able to adjust quickly to change. Useful for categories that experience frequent but moderate swings in demand.

Trend and seasonality analysis

Breaks demand into its underlying components like growth, cycles, and peaks. This better shows predictable patterns and helps planners anticipate recurring highs and lows throughout the year.

Regression and econometric models

Analyses relationships between demand and influencing factors such as pricing, sales, or economic conditions. It helps to clarify how cause-and-effect relationships drive real-world performance.

Causal or multivariate models

Extends regression analysis to include simultaneous (and often external) events, such as weather or regional and cultural factors. Allows planners to simulate "what-if" scenarios across diverse conditions.

AI and machine-learning ensembles

Uses AI-powered forecasting to test many algorithms, selecting the most accurate for each product, region, or time frame – as examined in a bit more depth below.

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AI-based demand forecasting: The future of supply chains

Instead of choosing a single model, advanced systems use machine learning so they can simultaneously evaluate multiple algorithms to choose the best option (or combination).

Automated model selection at scale

AI systems compare hundreds of potential algorithms across multiple data series, selecting the most accurate based on real-world outcomes. This lets you forecast large assortments or complex product hierarchies more efficiently – without the need for manual micromanagement.

Continuous, near real-time updates

Rather than rebuilding entire datasets every time, forecasting engines can process incremental updates as they happen. This allows you to respond to new orders, cancellations, or supply delays within hours instead of weeks – keeping plans synchronised with dynamic demand.

Demand sensing integration

Instead of using standalone demand sensing tools, integrated solutions use AI to combine historical data with live signals like POS feeds, promotions, or events. It detects deviations and corrects forecasts automatically, creating a system that learns continuously and acts fast.

Probabilistic and contextual modelling

AI looks at forecasts as a range of possible outcomes instead of one fixed number. This helps you see where results could vary, and prepare for both higher and lower demand. By factoring in real-world market influences, forecasts become more adaptive and useful for decision-making.

Human-in-the-loop collaboration

AI does the heavy lifting, but people remain central. Planners can override assumptions, annotate changes, and document rationales. And because the system is always learning, these human insights feed back into the system, improving both trust and accuracy over time.

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Managing by exception with demand forecasting solutions

Managing by exception refers to focusing human attention on the few forecasts or items that truly affect performance. This means that automated systems can handle routine tasks, leaving experienced team members to concentrate on the high-priority outliers, disruptions, and new opportunities that matter most:

Prioritised exceptions and alerts

Exception dashboards flag only the most significant deviations such as an unexpected sales spike or supplier problem. Each is scored for its potential business impact so planners can act where attention is most critical.

Focus on anomalies, not maintenance

With automated recalculations and baseline stability, you can spend time investigating causes rather than crunching numbers. This approach encourages proactive problem-solving instead of knee-jerk firefighting.

Traceable decision history

Every adjustment can be logged with notes or context to support continuous learning. Over time, this helps identify recurring root causes such as poor data quality or misaligned promotions, and helps you build greater confidence.

Faster time-to-action

Because exceptions update in near real time, planners can respond before minor discrepancies turn into inventory crises. Cross-functional visibility ensures that downstream teams can quickly adjust production, logistics, or budgets.

Farewell to silos

The ultimate goal is a synchronised ecosystem where forecast changes cascade automatically through supply, finance, and operations. Managing by exception makes forecasting both leaner and smarter – keeping teams collaborative.

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Good data foundations for demand forecasting in supply chains

Top-performing athletes are fuelled by a diet of balanced, high-quality food. And top-performing AI-powered solutions also need the best “food,” which for them, is data. Even the most advanced modern solutions are only as good as the data that feeds them. Below are some examples of how a strong data foundation allows forecasts to evolve from descriptive to predictive:

Unified and connected data sources

Forecasting depends on visibility across a range of supply chain activities. A unified data layer prevents logjams by connecting internal systems and external partners so that every function has access to the same insights and is on the same page about priorities.

Data governance and quality assurance

Standardised taxonomies, version control, and audit trails keep datasets reliable and transparent. It’s essential to establish clear ownership of data elements early on to avoid preventable pitfalls such as duplication, drift, and other common forecasting errors.

Detail and focus

Detailed data by product, location, or channel supports more targeted forecasting. For example, it lets teams more accurately apply different models to fast-moving versus long-tail items. Granularity improves both accuracy and agility when demand shifts expectedly or unexpectedly.

Adding context to the numbers

Forecast accuracy improves when data isn’t viewed in isolation. By tracing how events, timing, and behaviour interact, planners can see the reasons behind each shift in demand. This perspective turns raw figures into insight about customer intent and market movement.

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Demand forecasting and inventory management: Tying it together

Demand forecasting and inventory management are two sides of the same process coin: predicting what customers want to buy and ensuring it’s available when and where they need it. Effective integration between the two transforms forecasting from an abstract projection into an actionable plan that drives real-world decisions.

Forecasts as the trigger for supply plans

Once demand forecasts are approved, they flow directly into purchasing, production, and distribution schedules. Linking in this way ensures that replenishment aligns with actual demand patterns rather than fixed reorder rules.

Dynamic safety stock and reorder levels

Accurate forecasts let you set inventory buffers scientifically, not arbitrarily. AI-enhanced models can adjust safety stock in real time based on forecast uncertainty, lead times, or service targets. This reduces both shortages and surpluses.

Cross-functional alignment

Sharing forecast data with sales, operations, logistics, and finance teams means they all plan from the same information. This boosts end-to-end coordination and minimises the “bullwhip effect,” where miscommunication causes inflated orders upstream.

Financial and operational visibility

Tying inventory decisions to forecast accuracy links physical supply with financial outcomes. It quantifies the value of precision and helps teams to more accurately measure the cost of misalignment – like carrying surplus stock or missing sales opportunities.

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Common pitfalls (and how to avoid them)

Advanced forecasting processes are giving today’s companies an enormous competitive edge. But set-it-and-forget solutions are never the answer. The most common problems stem not from the technology itself, but from a lack of data discipline, weak communication, or human error and bias. Below are some common risks and how to avoid them.

Poor data hygiene

Inconsistent data makes forecasts lose credibility. Gaps in history or unvalidated inputs distort results. Enforce strong governance, clear ownership, version control, and automated validation. This keeps information consistent and ensures forecasts are built on trusted data.

Overreliance on a single model

No single model captures every market behaviour. Relying on one formula limits flexibility when conditions shift. Employ your AI-powered systems to test multiple models and confirm they are selecting the best fit for each situation – and are retrained as patterns evolve.

Ignoring short-term volatility

Relying on infrequent forecasting reports is risky. Integrate near real-time signals and demand sensing tools so planners can adjust quickly, minimise overcorrections, and stay aligned with changing market conditions.

Human bias

Personal judgement can skew even strong data-driven systems. And overconfidence or internal politics may override facts. Establish clear collaboration structures and change-tracking to always keep decisions evidence-based and lessons visible.

Disconnected systems

When teams work from separate databases or disconnected systems, assumptions drift. Make it your goal to achieve a unified data environment where automated synchronisation ensures sales, finance, and operations run on a unified platform.

Lack of AI transparency

As models grow more complex, it becomes harder to see how results are produced. This “black box” effect can hide data bias or weighting errors. Always maintain visibility into model logic. Ensure that AI insights are subject to human review to keep forecasts explainable.

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Conclusion

These days, it seems like the moment your teams wrap their heads around one enormous shift in consumer behaviours or shopping technologies, the next one is following instantly behind it. It’s exhausting and even a little scary to try to imagine what might be coming next. But fortunately, you’re not alone in your struggle. When you arm your business with advanced, connected solutions that leverage the scalable power of AI, you empower your people and your solutions to grow stronger and more accurate with every bit of data and all the learning they gain from ongoing events – both inside and outside your business. Instead of getting overrun by upsets and fast-paced change, your systems use that intel as fuel to keep pace and keep your forecasting one step ahead of demand.

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See how Infor’s AI-powered demand forecasting solutions are transforming today’s complex supply chains.

Explore Infor Demand Forecasting

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What is demand forecasting?

What is demand forecasting?

What is the importance of demand forecasting today?