Written by Marijn Overvest | Reviewed by Sjoerd Goedhart | Fact Checked by Ruud Emonds | Our editorial policy

4 Elements to Consider in Spend Forecasting

These elements play an important role that can heavily affect your spend forecasting:

1. Spend Patterns

Spending patterns can change over time. Thus, identifying spend increases in specific categories can present opportunities. Adjusting order frequency or quantity based on changing needs or supply uncertainties can be advantageous for your company. 

Unusual or unexpected purchases, especially those without purchase orders (POs), can highlight areas for rationalization or opportunities for new strategic supplier partnerships. Integrating new commodities into your sourcing processes and category management practices enhances visibility and forecast accuracy.

2. Currency Fluctuations

When dealing with imports, monitoring currency choices and exchange rate fluctuations is crucial for determining payment terms, prices, and quality. 

Analyzing currency trends and exchange rate forecasts enables fact-based sourcing decisions. Real-time information allows adjustments during supply chain disruptions.

3. Cost Drivers

Understanding what factors drive the cost of a product is crucial. Typically, it’s raw materials, energy, or labor. 

By monitoring raw material indices and energy prices, you can project potential price fluctuations. To enhance savings forecasting, it’s essential to estimate how price movements in each key cost element may impact future costs.

4. Market Trends

Historical data can provide insights, but market research and indexes are essential for predicting price movements. Analyzing global demand for raw materials and finished products, such as shipping containers, helps identify potential future price increases. 

By looking closely into commodity pricing trends and forecasts, you can create revised cost models at the commodity level. Thus, it will help you decide whether to buy in bulk for economies of scale when prices are favorable.

10 Techniques for Spend Forecasting

Organizations can use different spend forecasting techniques to estimate future costs more accurately, improve procurement planning, and support better financial decision-making.

1. Historical Trend Analysis

Historical trend analysis estimates future spend by reviewing past purchasing patterns across categories, suppliers, and time periods. It is one of the most common starting points because spend forecasting typically relies on historical data, market trends, and business objectives to project future expenses.

This technique works best when spending patterns are relatively stable and the organization has clean, classified data. In procurement, continuously updated historical consumption data helps teams identify recurring patterns and use them as the basis for future forecasts.

How to Use It

You should collect spending data from previous periods and organize it by category, supplier, business unit, or time period. After that, you should look for recurring purchasing patterns, seasonal movements, and cost increases or decreases that may continue in the future.

This technique is best used when the organization has reliable historical records and relatively stable demand behavior. Teams can improve accuracy by updating the analysis regularly and comparing past trends with current business plans and market conditions.

2. Moving Average Forecasting

Moving average forecasting uses the average of prior periods to estimate future spend levels. It is a simple quantitative method that smooths short-term fluctuations and is often used for stable categories with predictable purchasing behavior.

This technique is useful when a company wants a straightforward forecasting model without heavy complexity. However, it tends to lag behind sudden changes because it does not give extra weight to the most recent shifts in demand or price behavior.

How to Use It

You should select several past periods, such as three months or six months, and calculate the average spend across them. That average can then be used as the estimate for the next period, especially in categories with regular and repeat purchasing patterns.

This method works well for routine spend areas where major changes are not expected in the short term. To use it effectively, teams should review the averaging window regularly and adjust it when spending becomes more volatile or seasonal.

3. Exponential Smoothing

Exponential smoothing improves on moving averages by assigning more weight to recent data points. This makes it more responsive when spending levels change due to short-term demand shifts, market movement, or seasonal variation.

It is especially useful for short-term forecasting when procurement teams need a more adaptive model than a basic average. Because it reacts faster to new data, it is often used as a practical method for categories where spending changes more frequently over time.

How to Use It

You should begin with historical spend data and apply a smoothing factor that gives more importance to recent observations. This helps procurement teams create forecasts that reflect current changes faster than simple averaging methods.

The technique is most useful when spending patterns are changing but still show some continuity over time. Teams should test different smoothing factors to find the level that best matches the volatility of the category being forecasted.

4. Regression Analysis

Regression analysis forecasts future spend by measuring how strongly spending is related to one or more explanatory variables, such as demand, price indexes, inflation, or supplier performance. It is a quantitative forecasting method commonly used by organizations to understand not only what may happen but also why it may happen.

This technique is valuable when spending is driven by identifiable business factors rather than by history alone. In practice, it helps procurement and finance teams link spend forecasts to measurable external and internal variables, thereby improving planning accuracy.

How to Use It

You should identify the main variables that influence spend, such as production volume, inflation, commodity prices, or sales growth, and then test how these variables affect purchasing costs. Once those relationships are measured, the model can be used to estimate future spend under expected business conditions.

This method is useful when procurement wants a more analytical forecast linked to real business drivers. To improve results, teams should use clean data, review model assumptions, and update the regression as conditions change.

5. Driver-Based Forecasting

Driver-based forecasting builds forecasts from a small number of key operational and financial inputs that strongly influence future spending. These models establish relationships between business drivers and expected financial outcomes instead of projecting future spend only from past transactions.

This technique is useful when procurement wants forecasts that reflect real business activity, such as production volume, headcount, project load, or supplier lead times. When the drivers are measurable and predictive, the forecast becomes easier to align with planning and budgeting decisions.

How to Use It

You should first define the key drivers that shape procurement spend, such as the number of employees, production output, project demand, or customer orders. After that, you should estimate how changes in those drivers will affect spending levels across categories.

This technique works best when procurement and finance teams understand the operational factors behind spend. It is especially useful for planning because forecasts can be adjusted quickly when business assumptions change.

6. Rolling Forecasting

Rolling forecasting is a continuous method that updates the forecast by adding a new future period whenever the current period ends. Unlike a static annual budget, it keeps the planning horizon current and allows teams to revise expected spend on an ongoing basis.

This technique is important in volatile procurement environments because it combines historical patterns with real-time information and changing business conditions. It helps finance and procurement teams respond faster to supply disruptions, staffing changes, macroeconomic pressure, and market fluctuations.

How to Use It

You should update the spend forecast at regular intervals, such as monthly or quarterly, instead of waiting for the next annual planning cycle. Each time one period ends, a new future period is added, so the forecast always covers a full forward-looking horizon.

This method is useful when the organization operates in a dynamic environment with frequent changes in demand, prices, or supply conditions. To use it well, teams need a clear review schedule, updated data, and alignment between procurement, finance, and operations.

7. Delphi Method

The Delphi method is a qualitative forecasting technique that relies on structured input from experts rather than depending only on historical data. It is especially useful when spend is influenced by uncertainty, supplier risk, innovation, or market shifts that are difficult to model numerically.

In this method, experts provide judgments in several rounds, review summarized feedback, and refine their views until a more stable forecast emerges. It works well for long-range procurement planning or for categories where past spend alone is not enough to predict future outcomes.

How to Use It

You should gather a group of experts from procurement, finance, operations, or the supplier market and ask them to provide their forecast assumptions independently. Their responses are then summarized, shared anonymously, and reviewed in several rounds until a more balanced view is reached.

This technique is especially useful when data is limited or when future spending depends on uncertain market developments. It should be used in a structured way, with clear questions and careful comparison of expert feedback across each round.

8. Sales Force Composite

Sales force composite forecasting uses input from sales teams to estimate future demand and the spend required to support it. Because sales teams are close to customers and ongoing projects, they often detect purchasing changes earlier than data alone can show.

This technique is helpful when procurement spending is strongly linked to customer orders, regional demand, or account-level changes. By consolidating sales input into one forecast, organizations can prepare sourcing plans earlier and reduce the risk of urgent, unplanned purchases.

How to Use It

You should collect demand expectations from sales teams across regions, products, or customer accounts and combine them into a single forecast. Procurement can then use that demand signal to estimate future purchasing needs and prepare sourcing plans in advance.

This approach is valuable when customer-facing teams can see market changes earlier than internal reporting systems. To use it effectively, companies should standardize how sales input is collected and compare it with actual outcomes over time.

9. Scenario Planning

Scenario planning forecasts spend by modeling several possible future conditions instead of relying on a single expected outcome. In procurement, this can include best-case, expected-case, and disruption-case scenarios built around economic shifts, supply interruptions, regulatory changes, or price volatility.

This technique is valuable because it helps organizations prepare proactive responses before uncertainty turns into cost pressure. When combined with simulation, scenario planning allows procurement teams to test different sourcing strategies and choose the ones that are most resilient under changing conditions.

How to Use It

You should create several spend forecast scenarios based on different assumptions about demand, price movements, supply risk, inflation, or market conditions. Each scenario should show how procurement costs may change and what actions the organization may need to take in response.

This method is useful when uncertainty is high, and one forecast is not enough for decision-making. Teams should use scenario planning to prepare mitigation actions, compare sourcing options, and improve readiness for unexpected cost changes.

10. AI and Machine Learning Forecasting

AI and machine learning forecasting uses advanced models to analyze large volumes of historical, real-time, and external data in order to predict future spending more accurately. These models can include past purchases, supplier metrics, market trends, economic indicators, and other signals that traditional methods may not capture well.

This technique is especially useful when spend patterns are complex, fast-changing, or influenced by many variables at once. AI-based forecasting can improve responsiveness, reveal subtle patterns, and support more data-driven procurement decisions in dynamic environments.

How to Use It

You should feed the model with large and relevant datasets, including historical spend, supplier performance, market indicators, and operational data. The system can then detect patterns, generate forecasts, and update predictions as new information becomes available.

This technique works best when the organization has strong data quality, digital tools, and analytical capabilities. To use it effectively, teams should monitor model performance, validate results, and combine AI insights with business judgment.

3 Real-Life Examples of Spend Forecasting

1. KeyBank

KeyBank uses spend forecasting as part of a broader source-to-pay process built on a single Workday platform. The company standardized its source-to-pay activities to improve efficiency, reduce risk, and give stakeholders a clearer view of supplier contracts, invoices, and expenses. Workday says this gave KeyBank a more holistic picture of organizational and supplier spend across transactions. 

In practice, KeyBank does this by using automated data feeds and contract information to project contracts over multiple years instead of relying on one annual review cycle. Stakeholders can review contract history, monitor dashboards, and track spend across key transactions, while procurement teams use immediate contract insights to reduce rogue spend and surprise invoices. That means KeyBank’s spend forecasting is closely tied to live contract data, invoice schedules, and ongoing visibility into committed spending.

2. Sonos

Sonos manages a complex global supply network with more than 200 component suppliers, so it needed a more connected way to plan resources, products, and spending. Before changing its approach, the company relied on many spreadsheets, which made planning slow and error-prone and took too much analyst time. Anaplan says Sonos built an end-to-end planning model that connects resources, spending, and product lines in one system.

The company does this by starting with supply-demand balancing, then adding supply planning at factory level, and later integrating demand planning so forecasting becomes part of the same planning environment. Sonos also worked on component-level forecasting and supplier visibility so planners could connect operational forecasts with spending decisions across the supply chain. This approach lets Sonos update demand changes much faster and use less time on spreadsheet maintenance, which supports more responsive and informed spend forecasting.

3. Del Monte

Del Monte connected spend forecasting to finance and supply chain planning after struggling with hundreds of spreadsheets and legacy tools that produced inaccurate forecasts. According to Anaplan, the company improved cost and profitability analysis by linking finance and supply chain plans more closely. In the implementation, Del Monte first focused on logistics, warehouse capacity planning, and bill-of-material-based costing for culinary products.

The company does this by using an integrated planning model where changes in SKU costs flow through the system and can be analyzed quickly across channels, customers, and products. Del Monte says everyone works on the same platform with real-time information, which helps decision-makers see profitability drivers monthly and respond faster to uncertainty such as weather-related harvest effects. In other words, its spend forecasting works by combining costing, logistics, capacity, and commercial planning data into one dynamic forecasting process.

The 5 Differences Between Spend Forecasting, Spend Analysis and Budget Forecasting

5 Benefits of Spend Forecasting

5 Challenges of Spend Forecasting

Why is Spend Forecasting Important?

Spend forecasting is important because it helps organizations plan future purchasing needs, allocate budgets more accurately, and reduce the risk of overspending or underfunding key activities. In procurement, it also improves visibility into expected costs and supports better coordination between finance, operations, and sourcing teams.

It also matters because better spend forecasting supports stronger cost control, more informed supplier decisions, and faster responses to changes in demand, pricing, or business priorities. When companies can anticipate future spending more clearly, they are better positioned to improve efficiency, manage risk, and make procurement more strategic and data-driven.

Conclusion

Spend forecasting helps organizations move from reactive spending control to more proactive and strategic procurement planning. By using historical data, business needs, market signals, and operational inputs, companies can better predict future costs and improve budgeting, sourcing, and supplier decisions. As a result, spend forecasting supports stronger visibility, better resource allocation, and more informed decision-making across the organization.

Its value becomes even greater in complex and changing business environments where costs, demand, and supply conditions can shift quickly. Techniques such as historical analysis, rolling forecasts, scenario planning, and AI-based models help organizations improve forecast accuracy and adapt more effectively over time. When supported by good data, regular updates, and alignment with finance and operations, spend forecasting becomes an important tool for cost control, risk management, and long-term business performance.

Once you are done reading this article, you will have a deeper understanding of spend forecasting that will allow you to make more informed decisions that will enhance your supply chain. I created a free, downloadable spend analysis template. This includes an editable Excel template and a PowerPoint presentation to help you with your spend forecasting. I even created a video where I’ll explain how you can use this template.