PTC Orbit is updating the scoring logic in the OOTB Maintenance Score with a calculation for judging Maintenance Effectiveness. A new weighted formula combines data for all Completed Maintenance (a Percentage with 70% weight in the score) and data for Maintenance Completed On-Time (a Percentage with 30% weight in the score) to produce a ‘Maintenance Effectiveness Score,’ replacing the previous calculation. This weighting reflects a core reliability principle: A late but finished maintenance work order outperforms a missed one entirely. While maintenance that is completed on-time is the ideal, any maintenance that is completed (even if it is late) is a valuable contributor to whether an asset is receiving preventive service suitable to its needs.

Each asset record now displays a single 1-to-100 Maintenance Effectiveness Score. Higher values indicate stronger maintenance performance for that asset. A user can quickly assess maintenance health across the fleet without interpreting multiple independent percentages. The score feeds directly into the Maintenance Performance Dashboard, providing both a per-product and aggregate data point for fleet-wide analytics. The redesigned score activates as an out-of-the-box feature after the upgrade with no configuration needed.

For more information, see Scores.

The Canonical Data Model (CDM) now standardizes preventive maintenance plan data structures ingested from ServiceMax Core. Seven new objects join the CDM: Maintenance Plan, Covered Entity, PM Schedule, PM Schedule Rule, PM Work History, PM Process, and Expressions. A dedicated Input Data Adapter built on a Matillion pipeline reads, extracts, and transforms maintenance plan data from ServiceMax Core into this standardized format within Snowflake. The adapter supports configurable execution cadence and scope-level authentication.

An administrator deploys the Matillion-based pipeline by following the onboarding workflow: establish authentication credentials, configure the extraction scope for maintenance plan objects, and set the data loading cadence. After deployment, maintenance plan data flows automatically into PTC Orbit on the configured schedule. Planners and reliability engineers benefit from a richer dataset; PM schedule data feeds features such as Demand Forecasting and the Maintenance Performance Dashboard without additional manual data preparation.

For more information, see Configuring the Maintenance Data Pipeline.

The work order data model now includes additional fields and calculated metrics that support maintenance analytics. New fields capture completion dates and derived time-based calculations, including time between repairs. Updated Matillion pipelines import the expanded field set from ServiceMax Core into PTC Orbit and populate the calculated values automatically during each data load.

A user accessing the Maintenance Performance Dashboard sees richer work order metrics that reflect time-based maintenance performance indicators. The calculated fields power dashboard KPIs and charts without requiring separate data transformation. Without this update, deriving these metrics requires custom scripts or manual calculation outside the packaged data model. After upgrade, the pipeline handles extraction, transformation, and calculation as part of the standard data load.

For more information, see Work Orders.

Demand Forecasting now delivers a detailed results panel that surfaces forecast outputs directly within PTC Orbit. After a forecast completes, a planner clicks the forecast record to open a panel displaying the forecast name, description, creation date, and the full set of parameters used during generation. Bar charts plot forecasted work orders across the selected time horizon, while a data table breaks down predictions by the planner's specified intervals.

A planner reviewing completed forecasts can export results as PDF, CSV, or Excel files for budget requests, staffing models, or stakeholder presentations. The results panel also displays forecasted work hours per forecast period. Persisted parameters including asset criteria, forecast period, historical data range, asset count, and work order count remain visible, giving the planner full context without recreating the forecast. Available to all users.

For more information, see Exporting Demand Forecast Results.