JMP Statistical Process Control and Quality Improvement Training Course

Course overview

Production, service, and transactional teams often collect defect, cycle-time, yield, and customer-quality data without separating normal process variation from true special causes. The result is over-adjustment, recurring corrective actions, disputed capability claims, and improvement projects that cannot demonstrate a sustained gain. This course equips participants to use JMP to turn operational data into defensible process-control decisions, whether they are monitoring a machining line, laboratory turnaround time, call handling, fulfilment accuracy, or supplier quality.

Participants learn a practical JMP workflow: importing and structuring process data; exploring distributions and variation; selecting and interpreting control charts; applying Western Electric-style run rules; calculating Cp, Cpk, Pp, and Ppk; and assessing measurement-system performance. They use JMP Quality and Process platforms, Graph Builder, Distribution, Control Chart Builder, Process Capability, and Fit Y by X to identify instability, quantify capability, investigate likely drivers, and distinguish a one-off signal from a systemic process problem.

The five-day programme combines instructor-led demonstrations with guided analysis of realistic quality datasets and team-based improvement cases. Participants build a JMP process-monitoring workbook containing control charts, capability studies, annotated findings, and a corrective-action recommendation. They leave with a reusable analysis structure, a documented improvement plan for a selected process, and a certificate of completion that evidences applied statistical process control capability.

The course is designed for professionals who already work with operational or quality data and need to make faster, evidence-based decisions using JMP rather than relying on spreadsheets, manual charting, or anecdotal root-cause discussions.

Course objectives

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

• Configure JMP data tables with process, subgroup, specification-limit, and phase fields for SPC analysis
• Select and build appropriate variable, attribute, and time-series control charts in JMP
• Interpret control limits, zone tests, run rules, and special-cause signals without confusing them with specification limits
• Calculate and explain Cp, Cpk, Pp, Ppk, and non-normal capability results using JMP Process Capability
• Evaluate measurement variation using JMP measurement systems analysis tools and gauge R&R concepts
• Investigate process drivers with stratification, Graph Builder, Fit Y by X, and residual diagnostics
• Prioritise quality-improvement actions using Pareto analysis, defect metrics, and verified process evidence
• Produce a JMP-based process-control report with findings, charts, capability conclusions, and an action plan

Benefits of attending

Target competencies

Who should attend

• Quality Engineers — who must establish process stability and defend capability conclusions
• Continuous Improvement Specialists — who need evidence to target and verify Lean Six Sigma improvement work
• Process Engineers — who monitor manufacturing or service processes and investigate variation
• Operations Managers — who need clear control-chart evidence before changing staffing, equipment, or procedures
• Manufacturing Engineers — who must diagnose yield, scrap, downtime, and specification-conformance issues
• Quality Analysts — who prepare recurring quality reports and need a repeatable JMP workflow

Requirements and prerequisites

Participants should be comfortable working with tabular data and basic descriptive statistics, including mean, median, standard deviation, percentages, and charts. Experience in a quality, operations, engineering, laboratory, or service-delivery role is expected, together with a working understanding of process measures such as defects, cycle time, yield, or customer response time. Familiarity with JMP navigation is helpful but not essential; the course introduces the required menus and platforms. Prior programming, advanced calculus, formal Six Sigma certification, DOE experience, or prior control-chart construction is not required.

Training methodology

Each day combines focused instructor explanation with live JMP demonstrations and supervised analysis in participant workbooks. Short exercises use production, service, and measurement datasets to practise chart selection, subgrouping, capability analysis, and signal investigation. Teams compare interpretations of the same process evidence, making the decision logic visible rather than treating software output as self-explanatory. The final day uses an application-planning workshop in which participants assemble their charts, conclusions, priorities, and control recommendations into a job-relevant JMP process-control report.

Course outline

Day 1: JMP foundations for process variation

• JMP interface, data tables, columns, formulas, and journal workflow
• Importing CSV, Excel, and database-style operational datasets
• Defining continuous, nominal, ordinal, and date-time process variables
• Using Distribution to inspect centre, spread, outliers, and non-normality
• Building exploratory views with Graph Builder and local data filters
• Understanding common-cause versus special-cause variation
• Structuring rational subgroups, process phases, and specification fields

Workshop: Participants prepare a raw process dataset in JMP and produce an exploratory variation profile with documented subgroup and specification assumptions.

Day 2: Control charts and process stability

• Control Chart Builder workflow and control-limit calculations
• Individuals and moving range charts for low-volume or sequential data
• Xbar-R and Xbar-S charts for rationally subgrouped measurements
• p, np, c, and u charts for attribute and defect-count data
• Laney p-prime and u-prime charts for overdispersed attribute data
• Run rules, zone tests, trends, shifts, and special-cause interpretation
• Phase I baseline analysis and Phase II ongoing monitoring

Workshop: Participants build and interpret a control-chart set for a simulated production process, flagging signals and writing an initial containment recommendation.

Day 3: Capability and measurement confidence

• Separating control limits, specification limits, and customer requirements
• Normal capability analysis with Cp, Cpk, Pp, and Ppk indices
• Non-normal capability analysis and distribution fitting decisions
• Within-subgroup and overall variation in capability interpretation
• Capability plots, defect rates, and parts-per-million estimates
• Gauge R&R concepts for repeatability, reproducibility, and discrimination
• Measurement systems analysis outputs and data-collection improvement actions

Workshop: Participants complete a JMP capability study and measurement-system review, then issue a concise accept, improve, or investigate recommendation.

Day 4: Finding drivers and prioritising improvement

• Stratifying process performance by shift, machine, operator, product, and supplier
• Pareto analysis of defects, failure modes, and rework categories
• Fit Y by X for comparing groups and testing suspected factors
• One-way ANOVA and means comparisons for process differences
• Regression modelling for continuous process drivers
• Residual plots, leverage, and model-assumption checks
• Connecting statistical evidence to root-cause and corrective-action logic

Workshop: Teams investigate a yield-loss case in JMP, identify the most credible drivers, and prepare a prioritised improvement hypothesis list.

Day 5: Sustaining control and reporting results

• Designing a practical process-control plan from chart and capability evidence
• Setting response plans for special-cause signals and out-of-control conditions
• Using JMP dashboard and journal features to communicate quality performance
• Automating repeatable analysis through saved scripts and column formulas
• Comparing pre-improvement and post-improvement process performance
• Documenting corrective actions, ownership, verification, and review cadence
• Presenting statistically sound findings to managers, operators, and auditors

Workshop: Participants assemble a JMP process-control report and 90-day application plan for a selected workplace process, including charts, actions, owners, and review measures.

Tools & standards covered

JMP, JMP Pro, AIAG Statistical Process Control Manual, ISO 22514-2

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