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Postgraduate DiplomaData, AI, Simulation & Analytics Applications open

Diploma in Operational Metrics and Low Latency KPIs

Online postgraduate diploma of 800 hours in operational metrics and low-latency KPIs. 8 modules covering KPI design, real-time analytical databases, streaming computation, operational visualisation and metrics governance, with an assigned technical tutor.

Duration800 hours · 6–8 months
ModeOnline
LanguageEnglish
Start datesSet by the faculty
AwardSEIUM Institute diploma
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Learning outcomes

What you will be able to do

01

Define precise, decision-oriented operational KPIs.

02

Design incremental calculations for low-latency indicators.

03

Select and optimise real-time analytical databases.

04

Compute continuous KPIs over event streams.

05

Design effective operational dashboards.

06

Use KPIs in the day-to-day management of operations.

07

Govern metric definitions with a semantic layer.

Profile

Who it's for

  • Real-time analytics engineers building live operational indicators.
  • Operations analysts who need trustworthy, up-to-the-minute figures.
  • Data engineers working on streaming pipelines and OLAP stores.
  • Operational excellence managers running tiered performance meetings.
  • Dashboard developers designing shop-floor and management displays.
  • MES systems engineers integrating plant data with business KPIs.
  • Engineering or science graduates looking to move into manufacturing.
Standards & tools

What you'll work with

  • ISO 22400-2:2014 (KPIs for manufacturing operations management)
  • ISO 50006:2023 (energy performance indicators)
  • IEC 62264 (ISA-95: enterprise-control integration)
  • ISO 9241-112 (presentation of information)
  • ISA-101 (high-performance human-machine interfaces)
  • ISO 22400-10:2018 (operational sequence for data acquisition)
  • ClickHouse
  • Apache Flink
  • Grafana
  • Apache Pinot
  • dbt Semantic Layer
Syllabus

8 modules

  1. 1.1Difference between metrics, KPIs and indicators
  2. 1.2Manufacturing KPIs according to ISO 22400
  3. 1.3Process, machine and business metrics
  4. 1.4Leading and lagging indicators
  5. 1.5KPI hierarchies and trees
  6. 1.6Decision frequency and required latency
  7. 1.7Precise definitions and ambiguities
  8. 1.8Common interpretation errors
  9. 1.9Goodhart's law and perverse effects
  10. 1.10Workshop: KPI tree for a plant
  1. 2.1Incremental calculation of indicators
  2. 2.2Aggregations over time windows
  3. 2.3Real-time OEE
  4. 2.4In-line quality indicators
  5. 2.5Energy indicators per unit produced
  6. 2.6Machine states and state machines
  7. 2.7Handling late-arriving data
  8. 2.8Recalculation and corrections
  9. 2.9Definition contract for each KPI
  10. 2.10Practical exercise: incremental OEE per shift
  1. 3.1Real-time columnar OLAP databases
  2. 3.2ClickHouse: MergeTree engines
  3. 3.3Apache Druid and Apache Pinot
  4. 3.4Incremental materialised views
  5. 3.5Rollups and pre-computation
  6. 3.6Indexes and time-based partitioning
  7. 3.7Queries with millisecond latency
  8. 3.8Performance comparison
  9. 3.9Cost and sizing
  10. 3.10Lab: materialised views in ClickHouse
  1. 4.1Real-time ingestion from Kafka
  2. 4.2Flink SQL for continuous KPIs
  3. 4.3Materialize and RisingWave
  4. 4.4Joins with context data
  5. 4.5KPI state management
  6. 4.6Accuracy versus latency
  7. 4.7Reconciliation with batch data
  8. 4.8Correctness testing of calculations
  9. 4.9Measuring end-to-end latency
  10. 4.10Lab: continuous KPIs with Flink SQL
  1. 5.1Dashboard design principles
  2. 5.2Shop-floor displays and digital andon
  3. 5.3Grafana for operational metrics
  4. 5.4Real-time updates with WebSockets
  5. 5.5Thresholds, colours and visual semantics
  6. 5.6Design for different decision levels
  7. 5.7Visualisation on mobile devices
  8. 5.8Accessibility and legibility
  9. 5.9Avoiding information overload
  10. 5.10Practical exercise: line dashboard in Grafana
  1. 6.1Tier one to three meetings
  2. 6.2Objectives and targets per shift
  3. 6.3Deviation analysis
  4. 6.4Actions triggered by alerts
  5. 6.5Cause and effect indicators
  6. 6.6Internal benchmarking between lines
  7. 6.7Biases in reading data
  8. 6.8Communication with senior management
  9. 6.9Periodic review of KPIs
  10. 6.10Case study: daily management with real-time KPIs
  1. 7.1Semantic metrics layer
  2. 7.2dbt Semantic Layer and Cube
  3. 7.3Single source of definitions
  4. 7.4Versioning of KPI definitions
  5. 7.5Auditing of calculations
  6. 7.6Metrics lineage
  7. 7.7Input data quality
  8. 7.8Security and access to KPIs
  9. 7.9User documentation
  10. 7.10Workshop: metrics catalogue with a semantic layer
  1. 8.1Decision needs
  2. 8.2KPI definition
  3. 8.3Low-latency architecture
  4. 8.4Ingestion and computation
  5. 8.5Analytical storage of the KPIs
  6. 8.6Dashboard
  7. 8.7Validation of accuracy and latency
  8. 8.8KPI adoption plan
  9. 8.9Report and presentation
  10. 8.10Final project: final review with the tutor
Methodology, software & labs

How you'll learn

Training is 100% online on the SEIUM campus, with downloadable material, recorded classes and an assigned technical tutor. There are no fixed timetables: you progress at your own pace within the intake, so the programme fits around your job.

Each module combines theory, guided reading of the original standard and applied exercises. The working software is ClickHouse, Apache Flink, Grafana, Apache Pinot and dbt Semantic Layer, in free, academic or evaluation versions. Exercises are based on real procedures: incremental OEE calculation with streaming data, latency testing of analytical queries and shop-floor dashboard design. Assessment is continuous by module, plus the final capstone project.

Standards covered: ISO 22400-2:2014, ISO 50006:2023, IEC 62264, ISO 9241-112, ISA-101, ISO 22400-10:2018.

Assigned technical tutor:every module ends with a deliverable that your tutor marks individually.
Standards-based learning:guided reading of ISO 22400-2, ISO 50006 and IEC 62264 applied to real cases, not as a theoretical annex.
Industry software:ClickHouse, Apache Flink, Grafana, Apache Pinot and dbt Semantic Layer, in free, academic or evaluation versions.
Final project:a real-time KPI system covering definition, low-latency architecture, dashboard and validation of accuracy and latency, reviewed by your tutor.
Flexible format:100% online, no fixed timetables, compatible with full-time work.
Capstone project

Build it end to end

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

Define decision-driven KPIs
Identify the decision needs and write a precise definition contract for each KPI.
Design low-latency architecture
Plan the ingestion, streaming computation and analytical storage of the indicators.
Compute continuous KPIs
Implement incremental OEE and other KPIs with Flink SQL and ClickHouse materialised views.
Build the line dashboard
Create a Grafana dashboard tailored to each decision level.
Final project
Validate accuracy and latency, prepare the adoption plan and present the report to your tutor.
Career opportunities

Where this takes you

Real-time analytics engineerOperations analystData engineerOperational excellence managerDashboard developerMES systems engineer
Admissions & scholarships

Requirements

  • Prior technical training or professional experience in the sector. No university degree is required.
  • An internet connection and a computer capable of running the programme software.
  • English sufficient to read technical standards.

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 over 6–8 months. You set your own pace within each intake, and your tutor helps you plan the workload around your job.

You work with ClickHouse, Apache Flink, Grafana, Apache Pinot and dbt Semantic Layer, using free, academic or evaluation versions. Your tutor indicates the free alternative wherever one exists.

A SEIUM Institute diploma, awarded on passing the continuous assessment and the final project. It is SEIUM's own qualification, not an official state degree; its recognition depends on the programme completed and the regulations of each country.

Yes. Modules passed are recognised in a higher programme in the same area (postgraduate diploma or master's). Ask about the recognition policy when you enrol.

Prior technical training or professional experience in the sector; no university degree is required.

Start dates are set by SEIUM's faculty for each intake. Our admissions team will confirm the next available date when you apply.

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