Economic Forecasting Helps Manufacturers Navigate Market Uncertainty

Manufacturers are facing ongoing decisions about production capacity, capital investments, workforce requirements and market expansion amid changing economic conditions. At MTForecast 2026 in October, General Motors Chief Economist Joelle Copeland outlined strategies manufacturers can use to incorporate economic forecasts into business planning and make decisions under uncertain market conditions.

Speaking to an audience of machine tool builders, Copeland emphasized that economic forecasts should serve as decision-making tools rather than definitive predictions. She recommended establishing a reasonable outlook based on transparent assumptions, monitoring relevant economic indicators and adjusting business plans as conditions change.

Her framework outlines three levels of forecasting, ranging from basic scenario planning to advanced statistical modeling, allowing manufacturers to select an approach based on their resources, business needs and analytical capabilities.

Economic Forecasts Support Manufacturing Business Decisions

Economic forecasts provide estimates of future conditions, including economic growth, industrial production, consumer demand and interest rates. While these projections cannot eliminate uncertainty, they can help manufacturers evaluate potential risks and opportunities.

For example, a machine tool manufacturer anticipating stronger demand may consider increasing production, expanding facilities, hiring additional employees or building inventory. A weaker economic outlook could lead to more conservative production schedules and delayed capital expenditures.

Copeland emphasized the importance of understanding the assumptions behind a forecast rather than relying solely on its projected outcome.

Evaluating Economic Forecasts and Data Sources

Manufacturers using third-party economic forecasts should evaluate the information behind the projections before incorporating them into business plans.

Even companies with internal economics teams, such as General Motors, use outside forecasts to benchmark their own projections and provide additional inputs for internal models.

Available forecasting resources include subscription-based providers such as S&P Global, Moody's, Oxford Economics, Bloomberg and Consensus Economics. Publicly available sources include the International Monetary Fund, World Bank, Organisation for Economic Co-operation and Development and Federal Reserve.

Copeland recommended evaluating forecasts based on several considerations:

• Forecast subject: Identify the specific industry, market segment, measurement and units being projected.

• Time horizon: Determine whether the forecast addresses short-term operational needs or longer-term investment decisions.

• Underlying assumptions: Review factors such as economic growth, interest rates and inflation that influence the projections.

• Supporting indicators: Identify measurable economic signals that could confirm or challenge the forecast.

• Alternative scenarios: Consider reasonable upside and downside outcomes rather than relying on a single projection.

Understanding these factors can help manufacturers determine whether a forecast is appropriate for a particular business decision and when changing market conditions may require adjustments.

Level 1: Begin With Basic Economic Scenario Planning

Manufacturers without dedicated forecasting departments can begin with a relatively simple approach.

Copeland recommended using an external economic forecast to establish a baseline outlook, then identifying the indicators most likely to affect the company's customers and markets. Businesses can develop alternative scenarios to evaluate how changes in those indicators could influence demand and operations.

For example, vehicle sales are influenced by broader economic conditions, including gross domestic product (GDP) growth, automotive loan interest rates, inflation, vehicle prices and consumer credit availability.

A baseline scenario might assume economic growth follows consensus expectations and inflation gradually moderates. An upside scenario could include stronger economic growth, lower borrowing costs and improved access to credit. A downside scenario might account for persistent inflation, higher interest rates and slower consumer spending.

For machine tool builders serving automotive manufacturers, these scenarios could influence equipment demand, inventory requirements, production schedules and capacity investments.

This approach does not require sophisticated statistical models. Its primary value is establishing clear assumptions that businesses can monitor and update as economic conditions evolve.

AMT – The Association For Manufacturing Technology also offers Research Services to help members access manufacturing industry information and economic research.

Level 2: Use Statistical Models to Evaluate Economic Relationships

Manufacturers seeking a more quantitative approach can incorporate historical data and statistical regression into spreadsheet-based forecasting models.

Regression analysis allows businesses to examine relationships between economic indicators and market demand. Manufacturers can use these relationships to evaluate how changes in economic conditions might affect future equipment purchases or production activity.

Copeland illustrated this approach using U.S. vehicle sales and automotive loan interest rates from 2013 through 2025.

A basic regression analysis showed that a one-percentage-point increase in automotive loan interest rates corresponded to approximately 200,000 fewer vehicles in the seasonally adjusted annual sales rate.

However, GM's research model indicated that interest rates alone explained only about 6% of the observed variation in vehicle sales.

The example demonstrated that a measurable relationship between two variables does not necessarily provide a reliable standalone forecast. Additional factors, including GDP, income, vehicle prices and incentives, can influence demand.

Machine tool manufacturers can apply similar methods by examining relationships between equipment demand and industrial production, manufacturing investment or activity within major customer industries.

Spreadsheet regression tools and commercial economic platforms can provide manufacturers with options for developing quantitative forecasts without establishing extensive internal analytical departments. Artificial intelligence tools may also assist with data analysis and modeling.

Level 3: Advanced Forecasting Models for Complex Business Decisions

More sophisticated forecasting methods may be appropriate when manufacturers are evaluating complex business conditions, significant financial commitments or decisions requiring greater predictive accuracy.

Advanced forecasting techniques can incorporate larger datasets, multiple economic indicators and relationships that simpler models may not capture.

Methods discussed by Copeland included:

• Dynamic factor models: Identify common trends across multiple economic indicators.

• Vector autoregression: Evaluates how different variables influence one another over time.

• Hierarchical models: Account for differences among groups or segments within a larger dataset.

• Gradient boosting: Uses machine learning techniques to identify complex and nonlinear relationships.

Although these methods can provide more detailed analysis, they also require additional investment in technology, specialized expertise, model validation and ongoing management.

Copeland recommended evaluating whether the additional analytical capabilities provide enough value to justify the investment.

Manufacturers must also consider whether to develop forecasting capabilities internally, purchase services from outside providers or combine the two approaches. The appropriate strategy depends on business complexity, available resources and the potential financial impact of forecasting errors.

Economic and Political Factors Influence Long-Term Planning

Economic indicators are not the only factors manufacturers need to consider when making business decisions. Political developments and other market conditions can also influence demand, supply chains, investment decisions and business expansion plans.

These considerations become particularly important when evaluating major capital investments, including new facilities, expanded production capacity or entry into new markets.

Because these projects often require substantial financial commitments and long implementation timelines, manufacturers must distinguish temporary economic fluctuations from longer-term structural changes.

Responding too aggressively to a short-term downturn could limit a company's ability to meet demand when market conditions improve. Conversely, expanding production capacity based on overly optimistic assumptions could create additional financial and operational challenges.

Regularly reviewing economic assumptions and evaluating alternative scenarios can help manufacturers account for these risks when developing long-term business plans.

Turning Economic Forecasts Into Business Strategies

Copeland's framework emphasizes that manufacturers do not need to become economic forecasting specialists to incorporate economic data into strategic planning.

Executives should understand the baseline outlook, the assumptions supporting it and the economic conditions that could require adjustments to business plans.

Sharing this information across leadership, finance, sales and operations teams can establish a consistent approach to evaluating market conditions and investment decisions.

For machine tool builders and other manufacturers, economic forecasting can support decisions involving production planning, equipment investment, workforce development, inventory management and market expansion.

Rather than attempting to predict every economic development, Copeland's approach focuses on using available data, monitoring changing conditions and adjusting business strategies as new information becomes available.

The objective is to make informed decisions that account for uncertainty while supporting both near-term operations and long-term manufacturing goals.