Manufacturing Data Analytics for Operational Performance Training Course

Course overview

Manufacturing leaders need more than monthly production reports. They need reliable evidence on why throughput falls, where scrap originates, which changeovers create lost capacity, and whether maintenance or process changes improve OEE. Yet plant data is commonly split between MES, ERP, SCADA historians, quality systems and spreadsheets. Analysts and operational staff must reconcile inconsistent timestamps, product codes, shift calendars and downtime reasons before they can make a defensible recommendation. This course addresses that practical gap: turning production, quality and maintenance data into decisions that improve operational performance.

Participants learn a structured manufacturing analytics workflow, from defining an operational question and mapping data sources through cleaning, joining, analysing and communicating results. They work with production counts, cycle times, downtime events, reject records, maintenance history and shift data using Excel Power Query, SQL, Power BI and Minitab. Core methods include OEE decomposition, Pareto analysis, process capability analysis, control charts, trend and variation analysis, correlation checks, and root-cause investigation. The course also covers KPI definitions, data-quality controls and dashboard design for operators, supervisors and plant managers.

Teaching combines instructor-led demonstrations with guided analysis of realistic plant datasets. Teams investigate a simulated performance problem involving declining line output, rising defects and recurring unplanned stops, then build an evidence trail from raw records to an action proposal. Each participant leaves with a reusable manufacturing analytics workbook, a Power BI operational dashboard design, an OEE loss tree, and a 90-day application plan for a priority production area.

The course is designed for manufacturing professionals who already work with operational data and need to convert it into measurable process-improvement decisions. It is equally useful for managers commissioning analytics work and needing to assess whether reported findings are credible, actionable and aligned to plant KPIs.

Course objectives

By the end of this course, participants will be able to:

• Define a manufacturing analytics problem statement linked to throughput, quality, cost, delivery or OEE targets
• Map MES, ERP, quality, maintenance and historian data fields into an operational data model
• Clean and transform production-event data using Excel Power Query validation and standardisation steps
• Write SQL queries that join production, downtime, quality and shift tables for line-level analysis
• Calculate availability, performance, quality and OEE losses using consistent manufacturing KPI definitions
• Apply Pareto charts, control charts and capability analysis to identify significant sources of variation
• Build a Power BI dashboard with shift, line, product and loss-category drill-downs
• Produce an evidence-based improvement brief that prioritises actions, owners, measures and expected impact

Benefits of attending

Target competencies

Who should attend

• Manufacturing Engineers — who must diagnose losses and justify process-improvement priorities with data
• Continuous Improvement Managers — who need repeatable analytics methods for Lean, Six Sigma and OEE programmes
• Production Managers — who need to convert shift and line data into focused operational interventions
• Quality Engineers — who investigate defects, process variation and capability across products and machines
• Maintenance and Reliability Engineers — who need to connect failure history and downtime patterns to production impact
• Manufacturing Data Analysts — who need plant-context methods beyond general business intelligence reporting

Requirements and prerequisites

Participants should be comfortable reading production reports and working with tabular data in Excel, including filters, basic formulas and pivot tables. Familiarity with common manufacturing measures such as output, scrap, downtime, cycle time, changeover and OEE is expected, although the course standardises definitions before analysis begins. Some prior exposure to SQL, Power BI or Minitab is useful but not required; guided exercises provide the required syntax and workflows. Participants do not need programming experience, advanced statistics, data-science credentials, or access to their organisation’s live plant systems.

Training methodology

The course uses short instructor-led briefings followed by hands-on analysis in a realistic discrete-manufacturing dataset. Participants clean event data, query relational tables, calculate OEE, test variation and construct visualisations while the instructor demonstrates each workflow in Excel Power Query, SQL, Power BI and Minitab. Small groups compare competing explanations for line losses and challenge the evidence behind each recommendation. The final day is an application workshop in which participants convert analysis findings into a prioritised plant improvement brief and 90-day implementation plan.

Course outline

Day 1: Manufacturing data foundations and problem framing

• Operational questions for throughput, quality, delivery, cost and OEE
• Manufacturing data landscape across MES, ERP, SCADA, QMS and CMMS systems
• Production event grain, timestamps, shift calendars and product genealogy
• KPI dictionaries for output, scrap, rework, cycle time and downtime
• OEE calculation rules and the six big losses framework
• Data-quality dimensions: completeness, validity, consistency, timeliness and uniqueness
• Analytics problem statements, hypotheses and decision criteria

Workshop: Participants create a data-source map, KPI definition sheet and problem statement for a declining line-performance case.

Day 2: Preparing production data for analysis

• Excel Power Query import, profiling and transformation workflows
• Standardising machine identifiers, product codes, reason codes and units of measure
• Parsing timestamps and assigning production records to shifts and planned time
• Handling missing records, duplicates, outliers and inconsistent downtime classifications
• Joining production, quality, maintenance and operator datasets
• SQL SELECT, WHERE, GROUP BY and CASE expressions for manufacturing data
• Creating an analysis-ready line, shift and product performance table

Workshop: Participants use Power Query and SQL to clean and join raw production, downtime and reject records into an auditable analysis dataset.

Day 3: Diagnosing losses and process variation

• Availability, performance and quality loss decomposition
• Pareto analysis of downtime, defects and changeover causes
• Trend analysis by shift, line, product family and machine
• Run charts and control charts for detecting special-cause variation
• Process capability indices Cp, Cpk, Pp and Ppk
• Correlation versus causation in operational performance investigations
• Root-cause drill-down using stratification and the loss tree

Workshop: Participants diagnose the primary drivers of falling OEE and produce a ranked loss analysis with supporting control-chart evidence.

Day 4: Visualising operational performance for action

• Power BI data model design for production and downtime facts
• Relationships, date tables and shift-aware calendar logic
• DAX measures for output, scrap rate, downtime and OEE components
• Dashboard layouts for operators, supervisors and plant managers
• Drill-through analysis by line, asset, product, shift and reason code
• Targets, alert thresholds and exception-based operational reporting
• Visual integrity, annotation and avoidance of misleading performance charts

Workshop: Participants build a Power BI line-performance dashboard with OEE, loss Pareto, trend and drill-down views.

Day 5: From analysis to sustained operational improvement

• Interpreting findings for production, quality and maintenance stakeholders
• Estimating improvement potential from recoverable loss categories
• Prioritisation using impact, effort, confidence and controllability criteria
• Linking analytics findings to A3 problem solving and corrective actions
• Designing leading and lagging measures for improvement tracking
• Data-governance controls for KPI ownership, refreshes and reason-code discipline
• Ninety-day implementation roadmap and benefits-review cadence

Workshop: Participants present an evidence-based improvement brief, dashboard specification and 90-day action plan for the case-study plant.

Tools & standards covered

Microsoft Excel Power Query, Microsoft SQL Server, Microsoft Power BI, Minitab

A typical training day

Live online deliveries follow the same structure in the East Africa Time zone, with shorter screen blocks and longer breaks.

What the fee includes

How you can take this course

Certification

Participants who complete the full five days receive the Skillset Development Certificate of Completion, stating the course title, course code, dates and delivery format — suitable for professional-development records and employer reimbursement.

Frequently asked questions