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Postgraduate Diploma in Operational BI and Plant KPIs

Online 800-hour Postgraduate Diploma in Operational BI and Plant KPIs. 8 modules covering ISO 22400-2, EN 15341 and Microsoft Power BI. Assigned tutor and open enrolment.

Duration800 h
ModeOnline
AwardSEIUM Institute diploma
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Learning outcomes

What you will be able to do

01

Define indicator trees aligned with plant objectives.

02

Calculate OEE and ISO 22400 indicators with rigour.

03

Capture and contextualise production data.

04

Build dimensional data models for BI.

05

Design clear, actionable operational dashboards.

06

Apply statistical analysis to plant performance.

07

Implement KPI-based management geared towards improvement.

Profile

Who it's for

  • Industrial BI analyst wishing to specialise in Operational BI and Plant KPIs.
  • Process engineer wishing to specialise in Operational BI and Plant KPIs.
  • Continuous improvement manager wishing to specialise in Operational BI and Plant KPIs.
  • Production manager wishing to specialise in Operational BI and Plant KPIs.
  • Plant data engineer wishing to specialise in Operational BI and Plant KPIs.
  • Lean consultant wishing to specialise in Operational BI and Plant KPIs.
  • Graduates in engineering or sciences seeking to enter manufacturing.
Standards & tools

What you'll work with

  • ISO 22400-2
  • EN 15341
  • ISO 50006
  • IEC 62264
  • ISO 9001
  • ISO 50001
Syllabus

8 modules · 800 h

  1. 1.1Objectives and KPI hierarchy
  2. 1.2ISO 22400-2 indicators
  3. 1.3OEE: availability, performance and quality
  4. 1.4The six big losses of TPM
  5. 1.5Productivity and cycle times
  6. 1.6Quality indicators: FPY and PPM
  7. 1.7Maintenance indicators according to EN 15341
  8. 1.8Energy indicators under ISO 50006
  9. 1.9Leading and lagging indicators
  10. 1.10Case study: KPI tree for a line
  1. 2.1Sources: PLCs, SCADA, MES and historian
  2. 2.2Stoppage logging and causes
  3. 2.3Reason codes and taxonomies
  4. 2.4Order, product and shift context
  5. 2.5Manual versus automatic data
  6. 2.6Clock and event synchronisation
  7. 2.7Plant data models
  8. 2.8Production data quality
  9. 2.9Integration via OPC UA and MQTT
  10. 2.10Practice: designing a stoppages table
  1. 3.1Dimensional models and star schema
  2. 3.2Production fact tables
  3. 3.3Time, asset and product dimensions
  4. 3.4Slowly changing dimensions
  5. 3.5Calculated measures with DAX
  6. 3.6Granularity and aggregations
  7. 3.7Shared semantic models
  8. 3.8Performance of large models
  9. 3.9Model documentation
  10. 3.10Lab: OEE model in Power BI
  1. 4.1Principles of effective visualisation
  2. 4.2Strategic, tactical and operational dashboards
  3. 4.3Line displays and andon boards
  4. 4.4Control charts and trends
  5. 4.5Pareto of stoppages and defects
  6. 4.6Designing for mobile devices
  7. 4.7Use of colour and visual alerts
  8. 4.8Data storytelling
  9. 4.9Dashboard accessibility
  10. 4.10Workshop: shift dashboard for line supervisors
  1. 5.1Root cause analysis with data
  2. 5.2Statistical process control
  3. 5.3Process capability Cp and Cpk
  4. 5.4Correlation between process variables
  5. 5.5Bottleneck analysis
  6. 5.6Benchmarking across lines and plants
  7. 5.7Simulation of improvement scenarios
  8. 5.8Production forecasting
  9. 5.9Basic prescriptive analytics
  10. 5.10Practice: capability study in Minitab
  1. 6.1Data latency and freshness
  2. 6.2Real-time dashboards with Grafana
  3. 6.3Alerts and notifications to operators
  4. 6.4Integration with Ignition and SCADA
  5. 6.5Streaming to BI tools
  6. 6.6Incremental KPI calculation
  7. 6.7Shop-floor displays and large screens
  8. 6.8Multi-plant scalability
  9. 6.9Security and role-based access
  10. 6.10Lab: real-time OEE dashboard
  1. 7.1Daily meetings and visual management
  2. 7.2PDCA cycle and action tracking
  3. 7.3Hoshin Kanri policy deployment
  4. 7.4KPI governance and definitions
  5. 7.5Perverse indicators and biases
  6. 7.6Data-driven lean continuous improvement
  7. 7.7Relationship with ISO 9001 and ISO 50001
  8. 7.8User adoption and cultural change
  9. 7.9Assessment of economic impact
  10. 7.10Case study: reducing micro-stoppages
  1. 8.1Diagnosis of the current situation
  2. 8.2Definition of the KPI tree
  3. 8.3Data source map
  4. 8.4Dimensional data model
  5. 8.5Dashboard design
  6. 8.6Prototype implementation
  7. 8.7User validation
  8. 8.8Deployment and governance plan
  9. 8.9Report and presentation
  10. 8.10Final review with the tutor
Methodology, software & labs

How you'll learn

100% online training on the SEIUM Institute campus, with downloadable material, recorded classes and an assigned technical tutor. No fixed timetable: students progress at their own pace within the intake period, which makes it compatible with work.

Each module combines theory, guided reading of the original standard and applied exercises. The working software is Microsoft Power BI, Grafana, Tableau, Inductive Automation Ignition, Minitab, in free, academic or evaluation versions. Trials are studied using real OEE calculation procedures on a live line, dashboard validation with users, and stoppage Pareto analysis. Assessment is continuous per module plus the final integrative project.

Laboratories capable of accreditation(EN ISO/IEC 17025) with a rotor test rig, EMC/Lightning pre-compliance, HIL/SIL, vibration/acoustics.
Evidence-oriented final project:safety case, test plan, compliance dossier and operational limits.
Industry mentoring:lecturers with careers in training, , eVTOL/UAM and flight test.
Flexible format(hybrid/online), international cohorts and support from SEIUM Career Services.
Ethics and safety:a safety-by-design approach, cyber-OT, DIH and compliance as core pillars.
Capstone project

Build it end to end

Your final project follows the real engineering workflow, step by step, reviewed by your tutor.

Diagnosis of the current situation

Define indicator trees aligned with plant objectives.

Definition of the KPI tree

Calculate OEE and ISO 22400 indicators with rigour.

Data source map

Capture and contextualise production data.

Dimensional data model

Build dimensional data models for BI.

Dashboard design

Design clear, actionable operational dashboards.

Career opportunities

Where this takes you

Industrial BI analystProcess engineerContinuous improvement managerProduction managerPlant data engineerLean consultant
Admissions & scholarships

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 proficiency in Spanish to follow technical standards reading.

Ready to start?

Send your application today. Our admissions team replies within 72 hours with your study plan and funding options.

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FAQ

Frequently asked questions

The programme totals 800 hours spread over 6 to 8 months. That is an average of 10 to 15 hours per week, compatible with a full-time job.

The programme uses Microsoft Power BI, Grafana, Tableau, Inductive Automation Ignition, Minitab. Free, academic or evaluation versions are used, and the tutor indicates the free alternative where one exists.

An own diploma from SEIUM Institute upon passing the continuous assessment and the integrative project. It is in-house training, not an official Ministry qualification.

Yes. Modules passed in a course or diploma in the same area are recognised in the advanced programme. Check the recognition policy when you enrol.

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