Leveraging Simple Regression for Demand Forecasting: A Practical Guide for Supply Chain Professionals

Demand forecasting is a critical function in supply chain management, allowing companies to anticipate customer needs and align resources accordingly. One effective technique for forecasting is simple regression analysis, an extrinsic (causal) forecasting tool that helps identify and quantify the relationship between a key external factor (independent variable) and demand (dependent variable). This article illustrates how simple regression can be applied, using an example where monthly sales of construction equipment are forecasted based on construction permit approvals.

Simple Regression in Demand Forecasting

Simple regression analysis is used to identify and model the relationship between two variables: a dependent variable (e.g., product demand) and an independent variable (e.g., an economic indicator). By establishing this linear relationship, businesses can predict future demand based on expected values of the independent variable.

In this example, the monthly sales of construction equipment (dependent variable) are analyzed in relation to construction permit approvals (independent variable). As more permits are approved, demand for construction equipment is expected to increase.

Data and Model Setup

In our scenario:

• Y-axis (Dependent Variable): Monthly Sales of Construction Equipment (in USD)
• X-axis (Independent Variable): Construction Permit Approvals (units)

The simple linear regression equation is:

y = mx + b

where:

• y: Forecasted monthly sales,
• m: Slope of the line (indicating the change in sales per additional permit approval),
• x: Number of construction permits approved,
• b: Y-intercept (the baseline sales level when there are no permit approvals).

Example Model with Hypothetical Data

Assume we have the following data points for recent months, showing a relationship between construction permit approvals and monthly sales of construction equipment:

Using this data, let’s assume our simple regression analysis gives us the following model parameters:

• Slope (m): $4,000 per permit approval
• Intercept (b): $200,000

Thus, our forecasting model becomes:

Monthly?Sales=4,000×Permit?Approvals+200,000

Forecasting Example

Suppose we want to forecast monthly sales if 120 construction permits are expected to be approved in the coming month. Plugging the numbers into our equation:

Monthly?Sales=4,000×120+200,000=680,000

This calculation suggests that if 120 permits are approved next month, expected monthly sales for construction equipment would be $680,000.

Visualizing the Model

Below is a sample chart illustrating the relationship between construction permit approvals and monthly sales. The data points represent actual sales data, while the line indicates the regression trend based on the calculated model:

• X-axis: Construction Permit Approvals
• Y-axis: Monthly Sales (USD)
• Trend Line: Reflecting the forecast model, showing increasing sales as permits increase.

Benefits and Limitations of Simple Regression in Forecasting

Benefits:

• Ease of Use: Simple regression is straightforward to set up and interpret, making it a valuable tool for supply chain professionals with limited statistical backgrounds.
• Predictive Value: It provides a clear understanding of how external factors impact demand, allowing for strategic inventory and production planning.

Limitations:

• Assumption of Linear Relationship: Simple regression assumes a linear relationship between variables, which may not hold true for all scenarios.
• Single-Factor Focus: This approach considers only one predictor. If demand is influenced by multiple factors, a more complex model like multiple regression would be required.

Conclusion

Simple regression is a fundamental yet powerful tool for causal demand forecasting. By analyzing external indicators such as construction permit approvals, companies in industries like construction can better forecast demand and make proactive adjustments to their supply chain strategies. This technique can help businesses optimize inventory, minimize stockouts, and align production with anticipated market needs—resulting in a more efficient and responsive supply chain.