Demand Forecasting
Predict future work order volumes and service hours by analyzing historical patterns, using AI-assisted or manual forecast builders.
Demand Forecasting in PTC Orbit helps service organizations predict future workload by analyzing historical work order patterns. The feature generates forecasts based on three key parameters:
• Asset criteria: Determines which assets are included in the forecast. You can filter by Account, Location, and Product.
• Forecast period: Defines the future time window to predict, up to two years from the start date.
• Historical data range: Specifies how far back the system analyzes work orders, up to five years.
You can create a forecast using either of the following methods:
• AI-assisted (LLM): Describe your forecasting goal in plain text. The system parses the request and populates criteria, date ranges, and asset or work order counts automatically.
• Manual: Skip the AI step and fill in each field yourself, or refine what the AI suggested.
Both paths converge at a review screen where you confirm parameters, name the forecast, and generate it. Completed forecasts display in a results overlay with charts, tables, and export options.
Demand forecasting also incorporates preventive maintenance (PM) schedule data from ServiceMax Core. PM Schedules only includes Time Based PM Schedules with an upfront schedule. Non-recurring PM Schedule Definitions that have been synced through the maintenance data pipeline are available as an additional input source. A dedicated maintenance forecasting engine calculates expected service visits from recurring PM Schedule Definitions using plan start and end dates and frequency intervals. You can export completed forecast results as PDF, CSV, or Excel files directly from the forecast output overlay.
Use Cases
The following list represents use cases for the Demand Forecasting feature in PTC Orbit.
• Field workforce staffing: A service operations manager reviews forecasted work hours by month to schedule contractors during peak periods rather than maintaining a fixed head count.
• Budget justification: A finance analyst exports a 2-year demand forecast as a PDF, attaches it to the CapEx request for additional field service vans, and presents concrete data instead of estimates.
• Multi-region comparison: A global asset manager creates separate forecasts for EMEA, APAC, and Americas using location-based criteria, then compares output charts side by side to allocate resources.
Considerations
The following list represents important considerations when working on the Demand Forecasting feature.
• Forecasts require a minimum of 500 closed work orders in the historical date range.
• Forecast period is for a maximum 2 years from the start date. Start date cannot be earlier than next month. For example, you can get a forecast for the next three months or next year.
• Historical data range is up to five years back from the previous month. For example, you can get a historical data for the four prior years or three prior years.
• Maximum 10 conditions are allowed per forecast.
• Supported filter objects are Asset, Product, Location, and Account.
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