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Supply chain forecasting is a little like trying to understand Marvel’s Multiverse. You have to consider the likelihood of any possible scenario and gauge each scenario’s impact on your supply chain’s ability to ensure products and materials are in the right place at the right time, meet sustainability goals, and match customer demand.

What is supply chain forecasting?

Supply chain forecasting refers to the process of predicting future demand, volume, and pricing for the products a company will produce to meet customer demand. It involves analyzing historical data, current market trends, and other external factors to anticipate changes and make informed decisions.

Supply chain forecasting:

This is the process of predicting future demand, volume, and pricing for the products a company will produce to meet customer demand. It involves analyzing historical data, current market trends, and other external factors to anticipate changes and make informed decisions.

Unless you’re the only company producing a unique and high-demand product (like Apple with the iPhone in the early 2000s), being able to forecast accurately is a critical part of your business’s overall competitive strategy.

Supply chain forecasting informs key business operations, including:

  • Understanding customer demand: Predicting future customer demand — including changes due to seasonality or customer preferences — to avoid over- or under-stocking
  • Inventory management: Ensuring optimal stock levels and efficient warehouse operations to meet customer demand while minimizing storage costs
  • Pricing: Determining pricing strategies that maximize margins based on demand and supply forecasts
  • Transportation and logistics: Planning for transportation needs, including drivers, fuel, and route planning based on predicted demand
  • Labor force demand: Ensuring the business has enough skilled labor to meet production schedules and quality standards — while also being able to weather demand spikes
  • Production needs and planning: Planning the procurement of raw materials or components to meet production requirements
  • Supplier management: Ensuring you’ve partnered with the right suppliers to fulfill material, inventory, and logistical demands
  • Disruption planning: Having contingency plans in place in the event of a supply chain disruption due to any number of causes — from local road closures to global pandemics

Two sides of the coin: Where demand forecasting meets demand modeling

Forecasting is only one side of the coin. It typically relies on historical data and current trends to make statistical predictions about the future. But circumstances can change quickly. We all learned that during the COVID-19 pandemic — whether because we were trying to buy a house or budget for groceries, or because we were forecasting our company’s supply chain needs, dealing with spikes for in-demand PPE materials, or navigating swings in demand. When circumstances change dramatically, historical data and traditional outlooks can become irrelevant.

Modern supply chain forecasting also includes demand modeling, which looks forward in order to predict and model real-world scenarios using near real-time data.

A graphic showing two sides of a coin. One is demand forecasting. One is demand modeling.

Advanced supply chain forecasting and demand modeling incorporate AI technologies like machine learning (ML) to enhance accuracy, efficiency, and responsiveness. AI-powered platforms can process large volumes of data from suppliers, customers, and other sources, integrating factors such as weather or global events to refine predictions.

Predicting the future is impossible. But with modern tools and intelligent demand modeling, businesses can prepare for the multitude of scenarios they may face.

The importance of supply chain forecasting and demand modeling for businesses

Supply chain forecasting is all about striking the perfect balance of meeting demand with appropriate supply. Forecast too high of a demand, and you may end up with excess inventory. Set the forecast too low, and you miss out on revenue. Both scenarios cut into your company’s margins and growth.

Supply chain forecasting is crucial for businesses for several reasons. It can help businesses:

  1. Match customer demand: Over- or under-stocking products is an obvious example of mismatched supply and demand, but so is stocking the wrong product. Products that don’t sell well are the result of poor forecasting. Strong demand forecasting balances the “three V’s”: volume, variety, and volatility.
  2. Lower costs: By predicting demand, businesses can more effectively plan production schedules, inventory, transportation, and labor needs. This can reduce operational costs and minimize waste.
  3. Improve brand reputation and inspire higher customer satisfaction: Accurate forecasting ensures that products are available when customers expect them, reducing wait times and backorders.
  4. Enhance supply chain resilience: Forecasting allows businesses to anticipate and respond to changes in demand, supply chain disruptions, or market conditions. This agility helps companies stay competitive and adapt quickly to unforeseen events, such as natural disasters or economic shifts.
  5. Manage and mitigate risk: By anticipating potential demand fluctuations and supply chain disruptions, businesses can develop contingency plans and mitigate risks. This proactive approach minimizes the impact of unforeseen events on operations and profitability.
  6. Make more strategic decisions: Supply chain forecasting provides valuable insights into market trends, customer behavior, and competitive dynamics. These insights inform strategic decisions related to product development, market expansion, and partnership opportunities.
  7. Achieve sustainability goals: Efficient forecasting contributes to more sustainable business practices by reducing waste, optimizing resource use, and minimizing the environmental impact of overproduction and excessive transportation.

Overall, supply chain forecasting is essential for businesses to operate and maintain a competitive edge in the modern economy.

Types of supply chain forecasting methods

There are many different approaches to supply chain forecasting. Generally speaking, supply chain forecasting methods can be categorized into two types: quantitative and qualitative.

Quantitative forecasting methods prioritize numerical data and mathematical models to generate forecasts, relying on the use of historical data and statistical techniques to predict future demand.

You might choose to rely on quantitative methods because they are objective, data-driven, and precise — especially if you have large amounts of data to leverage. However, quantitative methods are only as good at the data that drives them. They require accurate and extensive historical data to provide consistent forecasts. They’re also limited to what historical data tells them and don’t adapt well to sudden market changes or supply disruptions.

Qualitative forecasting methods rely on expert human judgment, market research, and intuition to predict future demand. These methods are useful when historical data is limited or when predicting new or unique situations.

They incorporate insights on new products or emerging markets, can adapt to fluctuating conditions, and are still useful when historical data is missing or unreliable. However, they are susceptible to personal biases, may lack precision or consistency, and are difficult to validate.

A graphic showing two columns, comparing quantitative forecasting methods to qualitative forecasting methods.

Quantitative supply chain forecasting methods

Quantitative methods rely on data and statistical models, which makes them easier to automate and scale. They also provide consistent, precise forecasts, making them popular with industries and businesses with deep data pockets and established markets.

Regression analysis

Regression analysis is a statistical method that identifies the relationship between a dependent variable (such as demand) and one or more independent variables (such as price, seasonality, or economic indicators).

In supply chain forecasting, regression analysis can predict future demand by modeling how these variables interact. For instance, it can be used to forecast sales based on marketing expenditures or the impact of economic conditions on product demand. By understanding these relationships, businesses can make data-driven decisions to optimize inventory levels and production schedules.

Exponential smoothing

Exponential smoothing is a time series forecasting method that applies decreasing weight to past observations, giving more importance to recent data. This technique “smooths” out short-term fluctuations to reveal longer-term trends or cycles.

Exponential smoothing accounts for recent changes more heavily than older data. This method is particularly useful for data with no clear trend or seasonal pattern, allowing businesses to respond swiftly to recent demand changes.

Adaptive smoothing

Adaptive smoothing is a more advanced form of exponential smoothing. It works by adjusting the smoothing parameters dynamically based on the accuracy of previous forecasts — allowing the model to adapt to changes in data patterns. In other words, it uses machine learning to self-correct and generate more consistent forecasts over time.

Adaptive smoothing is better at handling volatile demand patterns and adjusting to shifts in trends and seasonality.

Moving average

The moving average method uses the average of one period to forecast for the next. This technique rides the wave of short-term fluctuations while also highlighting longer-term trends.

Moving averages can predict future demand by averaging out the effects of irregular or random variations in historical data. This method is simple to implement and useful for short-term forecasting, helping businesses quickly stabilize inventory management and production planning.

Life cycle modeling

Life cycle modeling predicts the demand for a product based on its stage in the product life cycle: introduction, growth, maturity, and decline. Each stage has distinct demand patterns, and forecasting models are tailored to these patterns.

Life cycle modeling helps businesses anticipate changes in demand as a product moves through its life cycle stages. This method allows companies to plan for product launches, manage inventory during growth phases, and efficiently phase out declining products, ensuring that supply chain operations are aligned with market dynamics.

Qualitative supply chain forecasting methods

Qualitative methods offer swifter adaptability, especially for new products or emerging markets where historical data may be limited. These approaches are popular among industries where innovation is paramount, such as technology and pharmaceuticals, as well as startups and new ventures. However, established, data-driven businesses can also benefit from incorporating the expertise and flexibility of these qualitative approaches into their supply chain forecasting.

1. Market research

Market research involves collecting and analyzing data to learn about consumer preferences, behavior, and perceptions. Market research can be conducted using methods such as surveys, focus groups, or interviews.

In supply chain forecasting, market research helps businesses understand customer demand trends and preferences, providing broader insights that historical data alone cannot offer. Market research can be especially valuable when forecasting demand for new products or in new or rapidly changing markets.

2. Historical analysis

Historical analysis examines past sales data and other historical information to identify trends, patterns, and cycles in demand. Businesses can analyze this data to inform predictions about future demand based on past consumer behavior.

Historical analysis helps companies anticipate seasonal fluctuations, recognize long-term trends, and prepare for recurring events. This method provides a solid foundation for demand planning, inventory management, and resource allocation.

3. Delphi method

The Delphi method is a structured communication technique in which a panel of experts anonymously provides forecasts over multiple rounds. After each round, a facilitator provides a summary of the forecasts and rationales, encouraging experts to revise their earlier answers based on the collective group’s feedback.

The Delphi method is used to reach an informed consensus on future demand, particularly in situations where historical data is limited or where new products and market conditions are being considered. This method leverages the collective wisdom and experience of experts to improve forecasting accuracy.

4. Panel consensus

Panel consensus is similar to the Delphi method in that it gathers a group of experts or stakeholders to discuss and agree on future demand predictions. Unlike the Delphi method, however, panel consensus typically involves open discussions and collaborative decision-making in lieu of anonymous rounds.

Forecasting through panel consensus leverages diverse perspectives and expertise for a more comprehensive and balanced forecast. This method is especially useful for strategic planning and scenarios where multiple viewpoints are critical to understanding market dynamics.

Modern challenges in supply chain forecasting

Supply chain forecasting has always been critical for effective supply chain management, but supply chain leaders today face novel and ever-shifting challenges due to changing economic, global, and climate-related factors.

Here are some of the top challenges faced by supply chain forecasters today:

1. Seasonality

Seasonal demand fluctuations have always been part of forecasting and can be difficult to predict accurately. Traditional models, however, may not capture the complexities of modern seasonality, which can be influenced by changing consumer behaviors and new market trends. The rise of e-commerce and global markets has led to more pronounced and varied seasonal patterns. For example, online shopping events like Black Friday and Amazon Prime Day have created new peaks in demand.

2. Siloed data

Data silos within organizations can block the flow of information, leading to incomplete or inconsistent data for forecasting. As businesses increasingly adopt digital technologies, the volume and sources of data have expanded. However, integrating data on a unified, AI Total Spend Management platform like Coupa can bring visibility and greater forecasting accuracy.

3. Volatility

Both demand and supply can be highly volatile, making it difficult to create accurate forecasts. Social media and a globally connected world have led to consumer preferences that may change on a dime, while inflation peaks limit consumer spending. Geopolitical events and natural disasters due to climate change can cause sudden and unpredictable supply chain disruptions. The COVID-19 pandemic, for example, highlighted the fragility and interconnectedness of global supply chains, leading to unprecedented volatility.

4. Inflation and interest rates

Rising record inflation and fluctuating interest rates can impact both consumer purchasing power and business costs, often at the same time, and make it difficult to forecast accurately — especially if your forecasting relies on historical data.

5. Complex economic indicators

Modern economies — and modern supply chains — are becoming more global and more complex, spanning multiple countries, laws, and regulations. Changes in one region can have ripple effects worldwide. Forecasters have to account for this complexity with more sophisticated models and tools. Scenario planning with the use of a digital twin gives businesses the option to model any number of scenarios in a digital space and experiment with different responses.

6. Technological disruptions

New technologies can quickly change market conditions and consumer behaviors, making it difficult to rely on historical data for forecasts. For example, technologies such as AI, the Internet of Things (IoT), and blockchain are transforming supply chains, providing new data sources and capabilities, but also requiring new skills and approaches to forecasting.

7. Environmental and sustainability concerns

Strengthening environmental regulations can affect the availability of raw materials, while increased attention on sustainability can have impacts on consumer demand for certain products. Companies are also adopting aggressive corporate ESG goals. All these can change cost structures, supplier relationships, and overall supply chain dynamics. Supply chain forecasts must also change to account for it.

Emerging technologies in supply chain forecasting and demand modeling

New technologies are emerging to help supply chain managers navigate these challenges and accurately forecast demand. Here are a few of the most prominent.

Internet of Things (IoT)

Most people are familiar with the IoT through wearables like smartwatches or smart home fixtures. IoT devices are also proving valuable for improving supply chain efficiency and automation. Sensors can provide real-time data on inventory levels or predict equipment failures before they happen. These devices — and the data they provide — can improve the accuracy of forecasts while also ensuring smoother, more predictable operations.

Supply chain digital twins

A supply chain digital twin is a digital replica of a physical supply chain. It helps organizations recreate their real supply chain in a virtual world so they can test scenarios, model different nodes, modes, flows, and policies, and understand how decisions and disruptions will impact network operations. These models can be updated in real time with live data for more accurate and reliable forecasts.

Artificial intelligence (AI) and machine learning (ML)

Algorithms powered by AI and ML (a type of AI) can analyze vast amounts of data to uncover nuanced patterns and predict future demand more accurately. Because it can analyze so much data at once, AI has the potential to detect patterns humans may miss. For example, AI-powered Network Optimization can identify what revenue may be at risk if a business’s demand forecast changes.

To illustrate this, imagine a grocer notices a sudden spike in demand for avocados as a result of a trending diet. AI quickly identifies that the company’s current supply chain won’t meet this rapid demand increase, and the business could lose out on $250,000 in potential revenue. AI-powered prescriptions recommend securing an alternative supplier and expediting shipments, allowing the company to meet demand and take advantage of a major revenue opportunity.

How Coupa is leading innovation in supply chain forecasting technology

Coupa’s AI-driven tools, like Demand Modeler and Forecast Collaboration, take your supply chain planning to the next level. With Coupa’s AI platform, you can:

  • Make better decisions by automatically identifying complex trends and factors across the entire supply chain
  • Forecast demand more accurately with real-time, data-driven insights in several key areas, like customer behavior and channel shifts
  • Collaborate with suppliers quickly and easily to prevent product delays or stop critical material bottlenecks

Optimize forecasting and get ahead of disruptions with the tools made for today’s modern, complex economy.

Plan more precisely and accurately with Coupa.