Machine Learning

Mastering Demand Forecasting: In-Depth Examples

September 8, 2023

In the intricate web of modern commerce, success is not merely determined by the quality of products or services but also by the ability to predict and respond to ever-shifting market demands. This is where the art and science of demand forecasting come into play.

The ability to predict future demand with precision is not just a valuable skill; it's a strategic imperative. Accurate demand forecasting methods empower companies to streamline their operations, optimize inventory control, and align their supply chain management strategies with market dynamics. 

This article takes a deep dive into how Machine Learning (ML) is transforming the complex world of demand forecasting, shedding light on the distinctions between short-term and long-term strategies, the nuances of passive and active methodologies, and the integration of macroeconomic factors and internal business data. 

Plus, we'll introduce you to Akkio, a state-of-the-art predictive Artificial Intelligence (AI) platform that is set to revolutionize how businesses anticipate and cater to customer demand. With its robust features and user-friendly interface, Akkio empowers businesses to make data-driven decisions, ensuring they are always a step ahead in meeting their customers' needs. 

Short-term vs. long-term demand forecasting

Demand forecasting is not a one-size-fits-all approach. The time horizon plays a crucial role in determining the most suitable forecasting method: short-term or long-term.

What is short-term demand forecasting?

Short-term demand forecasting is a critical process that allows businesses to predict customer demand for a product or service over a relatively immediate time period, typically ranging from a few days to a few months into the future. 

The primary goal of short-term demand forecasting is to provide businesses dealing with perishable goods, fast-moving consumer goods, or those operating in dynamic and rapidly changing markets accurate predictions to:

  • Avoid stockouts. 
  • Reduce excess inventory costs.
  • Optimize production schedules.
  • Ensure adequate preparation to meet customer needs.

It focuses on immediate and near-future customer demand and often involves a thorough analysis of historical data, seasonal variations, economic factors, external influences, and real-time data. 

When is short-term demand forecasting the right approach?

  • Operational efficiency: Short-term demand forecasting is appropriate when optimizing day-to-day operations, such as inventory management, production scheduling, and workforce allocation.
  • Seasonal planning: Businesses that experience seasonal demand fluctuations or regularly implement promotions and sales events need short-term forecasts to effectively manage inventory levels and promotional efforts.
  • Promotional planning: Demand forecasting is useful to assess whether a promotional campaign had the desired effect, by predicting the “baseline” demand. In essence, this is a form of A/B testing, where the “B” is simply a prediction.
  • Supply chain agility: Short-term forecasts help businesses respond quickly to sudden changes in customer demand, supply chain disruptions, or unexpected events. Demand forecasts can also help the business understand its reliance on specific factors and implement demand forecasting in the supply chain.
  • Reactive decision-making: Industries with rapidly changing trends or highly volatile markets require short-term forecasts to adapt quickly to dynamic shifts in demand.
  • Immediate resource allocation: Businesses that need to allocate resources in the near term, such as raw materials, labor, or distribution resources, rely on short-term forecasts to make real-time decisions.
  • Inventory control: Short-term forecasting assists in avoiding stockouts and overstock situations by accurately predicting immediate demand. It helps businesses maintain an optimal inventory level, ensuring they can meet customer demand without tying up excessive capital in inventory.

What is long-term demand forecasting?

Long-term demand forecasting is the process of predicting customer demand for a product or service over an extended period, typically spanning several months to several years into the future.

In contrast to short-term forecasting, which is typically more fine-grained, long-term forecasting provides a more coarse-grained perspective. For instance, while short-term forecasting might focus on a range of expected sales for a particular week, long-term forecasting might focus on the trend projection of a specific sector over a longer period. 

Long-term forecasting tends to be less quantitative as it needs to incorporate qualitative assumptions. It's not just about the numbers, but also about understanding market dynamics, consumer behavior, and industry trends. This enables companies to make strategic decisions related to:

  • Capacity planning.
  • Resource allocation.
  • Capital investment.
  • New product development.
  • Market positioning.

Long-term demand forecasting usually considers market trends and growth, technological developments, regulatory and legislative changes, and demographic changes, in order to plan for different scenarios.

When is long-term demand forecasting the right approach?

  • Strategic planning: Long-term demand forecasting is the right approach when a business is focused on long-range strategic planning, such as expansion into new markets, entering new product categories, or making significant capital investments.
  • New product development: When a company is considering the introduction of new products or services that require substantial research, development, and production lead times, long-term forecasting helps align these efforts with anticipated future demand.
  • Capacity planning: Industries with long production cycles, such as manufacturing or infrastructure development, benefit from long-term forecasts to ensure that production capacities meet future demand requirements.
  • Supply chain optimization: Long-term forecasting allows businesses to optimize their supply chains by aligning procurement, manufacturing, and distribution processes with expected demand trends.
  • Market entry or exit: When considering entering a new market or deciding to exit a particular market segment, long-term forecasting helps assess the viability and potential impact on demand.
  • Strategic alliances and partnerships: Businesses planning to form strategic alliances, joint ventures, or partnerships benefit from understanding long-term demand trends to evaluate the potential success of such collaborations.

From the machine learning side, short-term predictions tend to be quite reliant on quantitative methods and other ML models, whereas long-term predictions typically have quite a significant qualitative component (analysts’ beliefs of market growth, etc.). This highlights the importance of a balanced approach in demand forecasting, combining data-driven insights with industry knowledge and market intuition.

Here’s a quick comparative overview:

Responsive Table
Short-term demand forecasting Long-term demand forecasting
Primary goal Immediate resource allocation, inventory control, and operational efficiency. Strategic planning, capacity planning, and market positioning.
Focus Immediate and near-future customer demand. Extended perspective on market trends and growth.
Data utilized Historical data, seasonal variations, and real-time data. Market trends, technological developments, and demographic changes.
Time horizon Typically a few days to a few months into the future. Several months to several years into the future.
Decision-making Reactive decision-making, inventory control. Proactive strategic decision-making, and capacity planning.
Quantitative vs. qualitative More quantitative, relies on data-driven insights. More qualitative, incorporates industry knowledge and market intuition.
Industries Retail, fast-moving consumer goods, highly volatile markets. Manufacturing, infrastructure development, new product development.
Suitable for Inventory management, promotional planning, and supply chain agility. Expansion into new markets, new product development, and capacity planning.

Active vs. passive demand forecasting

The key to sustaining accurate demand forecasts and securing a competitive edge lies in regularly reviewing and fine-tuning demand forecasting techniques, whether active or passive.

What is passive demand forecasting?

Passive demand forecasting refers to a method of forecasting that primarily relies on historical data and trends to predict future demand. This approach focuses on observing, analyzing, and understanding existing patterns to make predictions.

In passive demand forecasting, the process centers around the analysis of past sales data, market trends, economic indicators, and other relevant past information. The goal is to identify recurring patterns and correlations that can be used to project future demand levels. Primarily, this is done without deliberately attempting to manipulate or impact demand through external actions.

When is passive demand forecasting the right approach?

  • Stable markets: Passive forecasting is well-suited for industries or markets with relatively stable demand patterns and minimal short-term fluctuations. If historical data consistently demonstrates consistent trends and changes are gradual, passive forecasting can provide accurate predictions.
  • Limited influence: When the business aims to make predictions without actively trying to alter consumer behavior, or because the market is too large to be influenced by the business, passive forecasting is appropriate. This might be the case when the company wants to focus on understanding the market rather than manipulating it.
  • Resource constraints: If a business lacks the resources, budget, or expertise to launch extensive marketing campaigns or promotions, passive forecasting provides a cost-effective way to make predictions based on historical data. 
  • Long-term trends: For industries where long-term trends play a significant role and customer preferences evolve slowly, passive forecasting can capture these trends effectively. It provides a reliable method for predicting demand in markets where change is slow and steady.

It’s important to note that while this approach can be highly effective in certain situations, it might not be suitable for all businesses or market conditions.

What is active demand forecasting?

Active demand forecasting is a method of predicting future customer demand that involves taking proactive and deliberate actions to influence or shape that demand. Unlike passive forecasting, which primarily relies on historical data, active forecasting involves various strategies and interventions aimed at driving or adjusting demand levels.

In active demand forecasting, businesses actively engage in activities designed to impact customer behavior and preferences. These activities can range from marketing campaigns, product launches, and price adjustments, to seasonal and trend analysis, inventory management, and customer engagement.

Machine learning methods, and quantitative methods in general, are particularly useful in active demand forecasting. They can help identify which marketing campaigns are successful and try to understand what factors drive demand growth. This data-driven approach allows businesses to make more informed decisions and fine-tune their strategies for maximum impact.

When is active demand forecasting the right approach?

  • Dynamic markets: Active forecasting is beneficial in rapidly changing markets with unpredictable shifts in consumer preferences, trends, or external factors. It allows a company to respond quickly to changes and adjust its strategies to maximize demand.
  • New product launches: When introducing new products or services, active forecasting can help generate buzz and excitement through marketing efforts, resulting in higher initial demand.
  • Competitive industries: In highly competitive markets, where businesses are actively vying for consumer attention, active forecasting helps create differentiation and grab a larger market share through strategic promotions.
  • Seasonal or short-term variations: Active forecasting is suitable for businesses that experience significant seasonal fluctuations or short-term demand spikes, enabling them to optimize inventory, production, and marketing efforts accordingly.
  • Maximizing profits: Companies seeking to optimize revenue and profitability by strategically adjusting pricing, promotions, and inventory levels can benefit from active forecasting.
  • Real-time adjustments: Active forecasting allows for real-time adjustments based on emerging trends, customer feedback, and unexpected events, enabling a more agile response to market dynamics.

Active demand forecasting is particularly beneficial in dynamic, competitive markets, and for businesses that want to take a more active role in shaping their future demand. However, it requires a more substantial investment in terms of resources, expertise, and strategic planning.

Here’s a quick comparison between active and passive demand forecasting to recap:

Responsive Table
Passive demand forecasting Active demand forecasting
Primary goal Providing accurate predictions based on historical data. Maximizing demand through strategic actions.
Focus Identifying recurring patterns in historical data. Taking actions to influence or adjust demand levels.
Approach Relies on observing and analyzing existing patterns. Involves proactive actions and interventions to shape demand.
Data utilized Historical data, past sales, trends, and economic indicators. Historical data, past sales, trends, plus real-time data, and customer feedback.
Influence on demand Does not actively try to alter consumer behavior. Actively engages in activities to impact customer preferences.
Resource Requirements Generally requires fewer resources and budget. Requires substantial resources, expertise, and strategic planning.
Use of quantitative methods (e.g., ML) Less reliance on quantitative methods. Greater reliance on quantitative methods, especially for real-time adjustments.
Decision-making More passive and reactive decision-making. Proactive and strategic decision-making.
Suitable for Stable markets, limited influence, resource-constrained situations, and long-term trends. Dynamic markets, new product launches, competitive industries, seasonal variations, profit maximization, and real-time adjustments.

Macro demand forecasting vs. internal demand forecasting

What is macro demand forecasting?

Macro demand forecasting is the process of predicting the overall demand for goods and services within a larger economic context, typically at a national or regional level. It involves analyzing various economic factors and indicators to estimate the aggregate demand from households, businesses, governments, and international trade.

Key components and considerations of macroeconomic demand forecasting

  • Economic indicators such as Gross Domestic Product (GDP), consumer price index (CPI), unemployment rate, industrial production, and trade data provide a snapshot of the overall economic health and can influence consumer purchasing power and business investment.
  • Consumer behavior can be influenced by various macroeconomic factors such as income levels, employment status, and consumer confidence.
  • Business investments affect the demand for capital goods and services.
  • Government policies, including fiscal and monetary policies, can influence the economic environment and thus affect consumer and business spending.
  • Trade and exports impact the demand for domestically produced goods and services.
  • Inflationary pressures influence consumer purchasing power and business costs.

What is internal demand forecasting?

Internal demand forecasting concerns company-specific demand forecasting or organizational demand forecasting. It is the process of predicting the future demand for a company's products or services based on internal data, historical performance, and factors directly related to the organization itself. 

Unlike macroeconomic demand forecasting, which considers broader economic indicators, internal demand forecasting focuses on the specific needs and expectations of the company's customer base.

Key features and considerations of internal demand forecasting

  • Historical sales data, such as customer orders, provides insights into past demand patterns.
  • Product lifecycles influence the demand for a product at various stages of its life.
  • Customer insights help predict future demand based on customer preferences, behaviors, and feedback.
  • Promotional activities stimulate demand and need to be factored into forecasts.
  • Market segmentation involves dividing the market into distinct groups of customers with different needs, characteristics, or behaviors.
  • New product introductions affect the demand for existing products.
  • Feedback loop involves updating forecasts based on actual sales data and adjusting strategies accordingly.

Most businesses, unless they are financial institutions or have a substantial dependence on financial markets, do not really require macroeconomic analysis for their purposes. 

The usefulness of macro analysis is that it can capture global effects on demand in a few parameters that can then be introduced into a model. For example, a model might incorporate interest rates or unemployment rates to predict customer demand.

To summarize:

Responsive Table
Macro demand forecasting Internal demand forecasting
Primary goal Predicts overall demand in a larger economic context (national or regional level). Predicts demand for a company's products or services.
Focus Broader economic factors and indicators affecting demand. Company-specific factors and customer expectations.
Data utilized Economic indicators (e.g., GDP, CPI, unemployment rate), consumer behavior, business investments, government policies, and trade data. Internal data (e.g., historical sales, customer insights, product lifecycles, promotional activities), market segmentation.
ML advantages Provides quantitative estimates and probability estimates, adapts to different contexts, captures complex patterns, automates feature creation, and supports continuous learning. Enhances forecasting accuracy, automates data analysis, adapts to changing customer behaviors, automates feature creation, and provides real-time insights.
Limitations May not capture company-specific nuances, focuses on external economic factors. May not account for broader economic trends, may need to rely on internal data only.
Human involvement May require economic experts to interpret macroeconomic indicators. Involves business experts to interpret internal data and customer insights.
Suitable for Valuable for businesses with macroeconomic dependencies or global operations. Relevant for most businesses to optimize internal operations.

Utilizing machine learning for all types of demand forecasting

Machine Learning is an innovative concept that has transformed the way businesses approach demand forecasting

ML algorithms are capable of analyzing both historical data for passive forecasting and actively adjusting strategies based on real-time inputs for active forecasting. They can seamlessly tackle short-term and long-term forecasts, accommodating the varying time horizons of demand prediction.

ML algorithms excel in several areas when it comes to demand forecasting, including:

  • Providing probability estimates: ML algorithms can provide quantitative detail, such as estimating how many units will be sold over a period of time. They can also assign probabilities to different outcomes, providing a nuanced understanding of potential demand scenarios.
  • Adapting to different contexts: Whether you're dealing with macroeconomic indicators or internal data, ML algorithms can be trained to recognize patterns and relationships that drive demand. They can effortlessly incorporate both macro and internal factors to create a holistic demand forecasting model.
  • Capturing complex patterns: Machine learning models excel at capturing complex patterns and nonlinear relationships that might be missed by traditional forecasting methods. This is particularly valuable when dealing with multi-faceted demand dynamics influenced by a multitude of variables.
  • Automating feature creation: ML techniques can automatically identify and create meaningful features from raw data, reducing the manual effort required and potentially enhancing forecasting accuracy.
  • Continuous learning: ML models can continuously learn and update their forecasts as new data becomes available. This real-time adaptability makes them well-suited for active forecasting, where immediate adjustments to strategies are necessary to optimize demand response.

Despite all the advantages, ML algorithms are unable to factor in "Black Swan" events such as pandemics and global disasters. Algorithms will inevitably fail and will need to be retrained, after which they can adapt. 

In such scenarios, the importance of utilizing the Human in the Loop methodology cannot be overstated when using ML models for demand forecasting. It’s crucial that human experts interpret predictions to ensure that they make sense in the context of the business and the market.

If you're interested in learning more about how machine learning can revolutionize your demand forecasting, check out our article titled What is ML-Powered Demand Forecasting?’, which dives deeper into the subject and provides valuable insights into the world of ML-powered forecasting.

Discover Akkio for ML demand forecasting

Demand forecasting is a paramount aspect of business planning, and machine learning offers a powerful, adaptable tool for this task. But how can businesses harness the power of ML for demand forecasting without the need for extensive technical expertise? 

The answer is Akkio!

Akkio is the leading no-code AI platform that allows individuals and businesses to create ML models, including demand forecasting algorithms, with ease. It's an ideal solution for businesses that want an efficient and cost-effective solution for demand forecasting that can easily be integrated into their infrastructure.

While Akkio should be used by team members who know how to work with and read data (in this case, should know principles of forecasting), extensive machine learning knowledge is not required. You simply need to know how to connect your datasets and set up a forecasting model, and Akkio will do the rest. It will even choose the right ML algorithm, making the process much more user-friendly.

Akkio’s user-friendly interface makes it easy for you to interact with and visualize data with key features like:

  • Chat Data Prep for data preparation. Akkio makes data preprocessing as simple as chatting with your friend.
A demo of Akkio’s Chat Data Prep feature.
A demo of Akkio’s Chat Explore feature.
  • Live reports of the results of the ML model that can be routed anywhere.
A sample forecast report.

To demonstrate Akkio's capabilities, we created a quick lead scoring model for a hypothetical e-learning platform selling courses around the world. The model scores the likelihood of the person to become a customer and buy a course depending on many varaibles. Test it now:

With Akkio, demand forecasting becomes a streamlined, efficient process, empowering your business to make informed, data-driven decisions with confidence. Discover the future of demand forecasting with Akkio today.

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