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Integrating Causal Foundation Model in Prescriptive Maintenance Framework for Optimizing Production Line OEE

Felix Saretzky, Lucas Andersen, Thomas Engel, Fazel Ansari

TL;DR

Prescriptive analytics in manufacturing often suffers from reliance on predictive models that capture spurious correlations rather than true causal drivers. The authors propose PriMa-Causa, a causal foundation model built on PFN and SCM priors to estimate conditional average treatment effects (CATE) via in-context learning, enabling what-if intervention simulations without requiring explicit causal graphs. A manufacturing-adapted synthetic data generator and the CausalFM architecture are trained on semi-synthetic FMCG data to evaluate causal effect estimation and intervention ranking for improving OEE. Results show competitive CATE performance and robust action ranking, supporting a data-driven, causality-centered prescriptive maintenance framework. The work lays the groundwork for production deployment and benchmarking against existing approaches to reduce downtime and maintenance costs.

Abstract

The transition to prescriptive maintenance in manufacturing is critically constrained by a dependence on predictive models. These models tend to rely on spurious correlations rather than identifying the true causal drivers of failures, often leading to costly misdiagnoses and ineffective interventions. This fundamental limitation results in a key-challenge: while we can predict that a failure may occur, we lack a systematic method to understand why a failure occurs, thereby providing the basis for identifying the most effective intervention. This paper proposes a model based on causal machine learning to bridge this gap. Our objective is to move beyond diagnosis to active prescription by simulating and evaluating potential fixes toward optimizing KPIs such as Overall Equipment Effectiveness (OEE). For this purpose a pre-trained causal foundation model is used as a "what-if" model to estimate the effects of potential fixes. By measuring the causal effect of each intervention on system-level KPIs, it provides a data-driven ranking of actions to recommend at the production line. This process not only identifies root causes but also quantifies their operational impact. The model is evaluated using semi-synthetic manufacturing data and compared with a baseline machine learning model. This paper sets the technical basis for a robust prescriptive maintenance framework, allowing engineers to test potential solutions in a causal environment to make more effective operational decisions and reduce costly downtimes.

Integrating Causal Foundation Model in Prescriptive Maintenance Framework for Optimizing Production Line OEE

TL;DR

Prescriptive analytics in manufacturing often suffers from reliance on predictive models that capture spurious correlations rather than true causal drivers. The authors propose PriMa-Causa, a causal foundation model built on PFN and SCM priors to estimate conditional average treatment effects (CATE) via in-context learning, enabling what-if intervention simulations without requiring explicit causal graphs. A manufacturing-adapted synthetic data generator and the CausalFM architecture are trained on semi-synthetic FMCG data to evaluate causal effect estimation and intervention ranking for improving OEE. Results show competitive CATE performance and robust action ranking, supporting a data-driven, causality-centered prescriptive maintenance framework. The work lays the groundwork for production deployment and benchmarking against existing approaches to reduce downtime and maintenance costs.

Abstract

The transition to prescriptive maintenance in manufacturing is critically constrained by a dependence on predictive models. These models tend to rely on spurious correlations rather than identifying the true causal drivers of failures, often leading to costly misdiagnoses and ineffective interventions. This fundamental limitation results in a key-challenge: while we can predict that a failure may occur, we lack a systematic method to understand why a failure occurs, thereby providing the basis for identifying the most effective intervention. This paper proposes a model based on causal machine learning to bridge this gap. Our objective is to move beyond diagnosis to active prescription by simulating and evaluating potential fixes toward optimizing KPIs such as Overall Equipment Effectiveness (OEE). For this purpose a pre-trained causal foundation model is used as a "what-if" model to estimate the effects of potential fixes. By measuring the causal effect of each intervention on system-level KPIs, it provides a data-driven ranking of actions to recommend at the production line. This process not only identifies root causes but also quantifies their operational impact. The model is evaluated using semi-synthetic manufacturing data and compared with a baseline machine learning model. This paper sets the technical basis for a robust prescriptive maintenance framework, allowing engineers to test potential solutions in a causal environment to make more effective operational decisions and reduce costly downtimes.

Paper Structure

This paper contains 14 sections, 3 equations, 3 figures, 1 table.

Table of Contents

  1. Introduction
  2. Background
  3. Generating Recommendations for Action in Prescriptive Maintenance
  4. Challenges with Standard Machine Learning and XAI in Prescriptive Maintenance
  5. Causal Machine Learning in Technical Processes
  6. Summary of Research Gaps
  7. PriMa-Causa
  8. Technical Basis
  9. Causal Effect Estimation
  10. Synthetic Data Generator
  11. Foundation Model
  12. Industrial Use Case
  13. Results: CATE Evaluation
  14. Conclusion and Outlook

Figures (3)

  • Figure 1: Illustration of the complex dependencies between production data across multiple process stations. Green nodes represent the observed sensor data, while purple nodes represent a latent variable. The arrows represent the physical dependencies.
  • Figure 2: Illustration of the technical basis. Both tasks, on the left, finding root causes of failures, and on the right, recommendations for action, are based on the causal model, which estimates the effects of interventions.
  • Figure 3: Global evaluation of CATE estimation performance. The scatter plot compares the predicted versus true CATE for the proposed PriMa-Causa foundation model (teal) and the S-Learner baseline (purple), aggregated across 10 semi-synthetic datasets. The dashed diagonal line represents perfect prediction ($y=x$)