Process Capability and Productivity for Industrial Engineers Training Course

Course overview

Industrial engineers are frequently asked to explain why a line that appears to meet its output target still produces excessive scrap, rework, overtime, queue time, or customer complaints. The answer requires more than calculating a Cp or OEE figure: engineers must establish whether the process is stable, define the right specification and performance measures, separate common-cause from special-cause variation, and quantify the financial consequence of lost capability. This course equips participants to turn production and quality data into defensible improvement priorities for manufacturing, assembly, packaging, and process operations.

Participants learn to build a capability study from a valid measurement system through to a management-ready recommendation. They apply control charts, histograms, normality testing, Cp, Cpk, Pp, Ppk, Z-bench, yield, defect-per-unit, takt time, cycle time, OEE, bottleneck analysis, and value-stream measures. The programme also covers rational subgrouping, capability analysis for non-normal data, short-run processes, and the connection between capability loss, throughput, capacity, labour productivity, and cost of poor quality. Participants practise selecting measures that reveal the real constraint rather than reporting isolated quality metrics.

Instruction combines worked industrial datasets, Minitab and Excel analysis, production-line case studies, facilitated root-cause discussions, and calculation workshops. Each participant develops a process capability and productivity improvement dossier for a realistic operating scenario, including a data collection plan, stability assessment, capability report, bottleneck diagnosis, improvement options, benefit estimate, and control plan. The finished dossier is structured for use in a plant review, operational excellence meeting, or improvement-project charter.

The course is designed for industrial engineers who already work with production data and need stronger analytical judgement when prioritising quality and productivity interventions. It is equally valuable where engineering, quality, and operations teams need a common method for deciding whether to improve, control, redesign, or add capacity to a process.

Course objectives

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

• Construct a process capability study using valid specifications, rational subgrouping, and an appropriate sampling plan
• Evaluate process stability with X-bar and R, I-MR, p, np, c, and u control charts
• Calculate and interpret Cp, Cpk, Pp, Ppk, Z-bench, yield, and predicted defect rates
• Test distributional assumptions and select capability methods for non-normal process data
• Diagnose throughput constraints using takt time, cycle time, work content, WIP, and bottleneck analysis
• Quantify productivity loss through OEE, first-pass yield, labour productivity, scrap, rework, and capacity calculations
• Prioritise improvement actions using Pareto analysis, cause-and-effect analysis, and benefit-versus-effort evaluation
• Produce a capability and productivity improvement dossier with a control plan and quantified business case

Benefits of attending

Target competencies

Who should attend

• Industrial Engineers — who must diagnose variation, constraints, and productivity losses across operating processes
• Manufacturing Engineers — who need to establish whether process changes improve capability and output
• Process Engineers — who analyse process performance and define technically credible improvement actions
• Continuous Improvement Engineers — who lead Lean Six Sigma projects requiring validated baseline metrics
• Quality Engineers — who must translate capability evidence into control plans and corrective actions
• Production Managers — who need to make capacity, staffing, and improvement decisions from production data

Requirements and prerequisites

Participants should be comfortable reading production reports and working with basic descriptive statistics such as mean, range, standard deviation, percentage, and rate. Prior exposure to manufacturing or service operations is expected, including familiarity with cycle time, defects, specifications, or production targets. Participants should be able to use Excel for data entry, formulas, sorting, and charts; Minitab experience is helpful but not required because guided exercises cover the required functions. A prior Lean Six Sigma Green Belt is not required. This is not a beginner statistics course: attendees should be prepared to interpret real process data and discuss operational trade-offs.

Training methodology

The five-day programme uses short instructor-led technical sessions followed by applied analysis of production-line datasets. Participants build control charts, capability studies, Pareto charts, OEE loss trees, and bottleneck calculations in Minitab and Excel, then interpret the results in small engineering review teams. Case work includes an unstable machining process, a packaging-line throughput problem, and a non-normal dimensional characteristic. Daily exercises build toward an end-of-course improvement dossier, with instructor feedback on assumptions, calculations, recommended actions, and control measures.

Course outline

Day 1: Establishing a credible performance baseline

• Linking customer specifications, process targets, and operational requirements
• Voice of the customer versus voice of the process
• Defining defect, unit, opportunity, yield, and first-pass yield measures
• Measurement system suitability and the role of Gage R&R
• Data collection plans, sampling frequency, and rational subgrouping
• Descriptive statistics, histograms, run charts, and stratification
• Baseline cost of poor quality and productivity-loss calculations

Workshop: Participants create a measurement and baseline plan for a machining-cell case, producing an operational definition sheet and data collection template.

Day 2: Demonstrating process stability with SPC

• Common-cause and special-cause variation
• Control-chart selection by data type and subgroup structure
• X-bar and R chart construction and interpretation
• Individuals and moving range chart analysis
• Attribute charts including p, np, c, and u charts
• Western Electric rules and out-of-control signal investigation
• Control limits, specification limits, and reaction-plan design

Workshop: Participants analyse a time-ordered production dataset in Minitab and produce a control-chart interpretation with an escalation reaction plan.

Day 3: Measuring capability and predicting quality performance

• Within-subgroup versus overall variation
• Cp and Cpk calculation and interpretation
• Pp and Ppk calculation and interpretation
• Capability confidence intervals and sample-size implications
• Normality assessment using probability plots and Anderson-Darling testing
• Non-normal capability analysis and transformation options
• Z-bench, predicted ppm, yield, and sigma-level conversion

Workshop: Participants complete a capability report for a dimensional characteristic, including distribution assessment, index selection, and a management recommendation.

Day 4: Connecting quality variation to productivity and flow

• Takt time, cycle time, lead time, and work-content analysis
• OEE calculation across availability, performance, and quality losses
• Bottleneck identification using capacity and utilisation evidence
• Little's Law, WIP, queue time, and throughput relationships
• Value-stream analysis for rework loops and inspection delays
• Pareto analysis of downtime, defects, and speed losses
• Capacity recovery and productivity benefit calculations

Workshop: Participants diagnose a packaging-line constraint and produce an OEE loss tree, bottleneck statement, and recovered-capacity estimate.

Day 5: Prioritising and sustaining process improvement

• Root-cause analysis using fishbone diagrams and 5 Whys
• Cause prioritisation with Pareto evidence and validation tests
• Improvement option screening using benefit, effort, risk, and controllability
• Linking capability targets to process settings and standard work
• Control-plan design with monitoring frequency and reaction triggers
• Visual management and KPI review cadence
• Building an engineering business case and improvement charter

Workshop: Participants assemble and present a capability and productivity improvement dossier containing their baseline, analysis, prioritized actions, benefit estimate, and control plan.

Tools & standards covered

Minitab Statistical Software, Microsoft Excel, ISO 22514 Process Capability and Performance, AIAG Statistical Process Control Manual

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