Postgraduate Diploma in Warranty Analytics and Predictive Maintenance
Online Postgraduate Diploma of 800 hours in Warranty Analytics and Predictive Maintenance. 8 modules with ISO 13374-1:2003, ISO 17359:2018 and Python (pandas, scikit-learn). Assigned tutor and open enrolment.
Take the controls.
Run a real engineering trade-off from this programme. Move the sliders, watch the model react in real time and beat the challenge.
What you will be able to do
Structure warranty and production data for analysis.
Detect emerging quality issues using statistical and text-based methods.
Forecast warranty costs and size provisions.
Design architectures for capturing vehicle telematics data.
Develop predictive models of failure and remaining useful life.
Define condition-based maintenance strategies.
Deploy analytical solutions in compliance with European data protection and artificial intelligence regulations.
Who it's for
- Warranty data analyst who wants to specialise in Warranty Analytics and Predictive Maintenance.
- After-sales data scientist who wants to specialise in Warranty Analytics and Predictive Maintenance.
- Predictive maintenance engineer who wants to specialise in Warranty Analytics and Predictive Maintenance.
- IoT data engineer who wants to specialise in Warranty Analytics and Predictive Maintenance.
- Field quality manager who wants to specialise in Warranty Analytics and Predictive Maintenance.
- Business intelligence analyst who wants to specialise in Warranty Analytics and Predictive Maintenance.
- Graduates in engineering or sciences seeking to enter motorcycle manufacturing.
What you'll work with
- ISO 13374-1:2003
- ISO 17359:2018
- ISO 13381-1:2015
- Reglamento (UE) 2016/679 (RGPD)
- Reglamento (UE) 2024/1689
- Reglamento (UE) 2023/2854
- ISO/IEC 23053:2022
- ISO/IEC 22989:2022
8 modules · 800 h
1.1Claims data model1.2Linking to production and batch1.3Claim rates by months in service1.4MIS maturity matrices1.5Reporting delay and incomplete data1.6Cost per unit and per kilometre1.7Integration with spare parts data1.8Data governance in after-sales1.9Applicable privacy regulation1.10Practice: building an MIS matrix
2.1Statistical control of claim rates2.2Alerts with CUSUM and Poisson models2.3Text mining of technician comments2.4Automatic fault classification with NLP2.5Symptom clustering2.6Anomaly detection by dealer2.7Alert prioritisation2.8Escalation to quality engineering2.9False positives and threshold tuning2.10Laboratory: early warning with Python
3.1Cohort-based forecasting models3.2Rate projection with Weibull and Poisson3.3Warranty accounting provisions3.4Scenarios for review campaigns3.5Warranty spare parts demand3.6Sensitivity to market mix3.7Pricing of extended warranties3.8Communicating results to finance3.9Retrospective validation of forecasts3.10Case study: annual warranty provision for a model
4.1Architecture of the connected motorcycle4.2Useful signals: temperature, vibration and voltage4.3Edge processing and cloud transmission4.4MQTT protocol and IoT platforms4.5Condition monitoring standards4.6Battery health indicators4.7Estimating wear of brake pads and chain4.8Sampling frequency and signal quality4.9Consent and access to vehicle data4.10Workshop: telematics data pipeline for a fleet
5.1Temporal feature engineering5.2Classification with imbalanced classes5.3Random forests and gradient boosting5.4Cox survival models5.5Estimating remaining useful life5.6Recurrent networks for time series5.7Temporal validation and data leakage5.8Explainability with SHAP values5.9Deployment and monitoring of models5.10Practice: predicting voltage regulator failure
6.1Corrective, preventive and predictive maintenance6.2Condition-based maintenance6.3Dynamic scheduling of interventions6.4Notifications to the user and workshop6.5Spare parts stock optimisation6.6Cost-benefit analysis of predictive maintenance6.7Application in delivery and sharing fleets6.8Integration with the workshop management system6.9Change management across the network6.10Case study: predictive plan for electric scooters
7.1Warranty dashboards7.2Indicators for management7.3Communicating results with data7.4Lakehouse-type data architecture7.5Data quality and lineage7.6European Artificial Intelligence Regulation7.7Ethics and bias in models7.8Roles within the data team7.9MLOps lifecycle7.10Workshop: dashboard in Power BI
8.1Defining the business problem8.2Preparing the dataset8.3Exploratory data analysis8.4Early detection model8.5Predictive failure model8.6Economic evaluation of the model8.7Final dashboard8.8Deployment plan8.9Report and presentation8.10Final review with the tutor
How you'll learn
100% online training on the SEIUM campus, with downloadable material, recorded classes and assigned technical tutor. No fixed timetable: students progress at their own pace within the intake, which makes it compatible with work.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is Python (pandas, scikit-learn), Databricks, Apache Spark, Microsoft Power BI, AWS IoT FleetWise, in free, academic or evaluation versions. The case studies work through real procedures: building an early-warning system for warranty claims, predicting voltage regulator failure with telematics data, and forecasting the annual warranty provision for a model. Assessment is continuous by module plus the final integrating project.
Build it end to end
Your final project follows the real engineering workflow, step by step, reviewed by your tutor.
Structure warranty and production data for analysis.
Detect emerging quality issues using statistical and text-based methods.
Forecast warranty costs and size provisions.
Design architectures for capturing vehicle telematics data.
Develop predictive models of failure and remaining useful life.
Where this takes you
Requirements
- Prior technical training or professional experience in the sector. No university degree is required.
- Internet connection and a computer capable of running the programme's software.
- Sufficient Spanish to follow technical standards documentation.
Ready to start?
Send your application today. Our admissions team replies within 72 hours with your study plan and funding options.
Frequently asked questions
The programme totals 800 hours spread over 6 to 8 months. That is between 10 and 15 hours a week on average, compatible with a full-time job.
You work with Python (pandas, scikit-learn), Databricks, Apache Spark, Microsoft Power BI, AWS IoT FleetWise. Free, academic or evaluation versions are used, and the tutor indicates the open-source alternative where one exists.
A SEIUM own diploma upon passing continuous assessment and the integrating project. It is in-house training, not an official qualification from the Ministry.
Yes. Modules passed in a course or postgraduate diploma in the same area are recognised towards the advanced programme. Check the recognition policy when you enrol.
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