Organisational Performance Measurement Training Course

Participants can convert strategic priorities into linked objectives and balanced measures.

• Balanced Scorecard perspectives adapted for commercial, public and nonprofit organisations
• Strategy maps show cause-and-effect assumptions between strategic objectives
• Strategic themes group objectives requiring coordinated cross-functional action
• Objective statements distinguish intended results from activities and projects
• Cause-and-effect hypotheses tested through evidence, review and revision
• Scorecard measures balance financial, customer, process, people and risk outcomes

Participants can map an operating process and locate meaningful measurement points.

• SIPOC identifies suppliers, inputs, process steps, outputs and customers
• Swimlane maps clarify handoffs, roles, delays and control points
• Value stream maps expose waiting, rework, queues and information flows
• Customer journey measures capture demand, response, resolution and experience
• Process boundaries prevent measures being assigned outside managerial control
• Measurement points selected at intake, handoff, completion and follow-up

Participants can select KPIs that are decision-relevant and behaviourally sound.

• Key performance indicators distinguish critical measures from routine operational statistics
• Outcome measures assess results valued by customers, funders and regulators
• Output measures quantify products, transactions, cases and completed services
• Efficiency ratios compare resources consumed with outputs delivered
• Quality measures capture defects, conformance, accuracy and first-time-right performance
• Leading indicators monitor conditions likely to influence future outcomes
• Lagging indicators confirm results after performance has occurred

Participants can document KPIs so that results are reproducible and auditable.

• KPI dictionary fields specify purpose, formula, scope, owner and audience
• Numerator and denominator definitions prevent inconsistent rate calculations
• Inclusion and exclusion rules resolve boundary cases and exceptions
• Data lineage traces source systems, transformations, approvals and report outputs
• Measurement frequency matches decision cadence and process response time
• Data-quality checks test completeness, accuracy, timeliness, validity and uniqueness

Participants can set targets and thresholds using evidence rather than aspiration alone.

• Baseline analysis establishes current performance using consistent historical data
• Target types include compliance, improvement, benchmark, capacity and customer targets
• SMART criteria test whether targets are specific and measurable
• Benchmark comparisons account for scope, case mix and service design
• Thresholds define escalation points, tolerance bands and management responses
• Target cascades align enterprise ambitions with process-level controllable measures

Participants can assess whether measurement data are fit for management use.

• Operational definitions specify exactly what observers record and classify
• Sampling plans balance measurement cost, confidence and decision risk
• Measurement system analysis tests repeatability, reproducibility and bias
• Data validation reconciles operational records with source-system transactions
• Missing-data rules prevent misleading rates, averages and trend conclusions
• Privacy controls protect personal, customer and commercially sensitive data

Participants can summarise performance data clearly and identify meaningful patterns.

• Frequency distributions reveal concentration, spread, skewness and unusual observations
• Mean, median and percentiles answer different operational performance questions
• Pareto charts prioritise defect categories, delay causes and complaint types
• Trend charts display direction, seasonality and recurring performance patterns
• Segmentation compares performance by customer, channel, location and product
• Chart selection matches the data type and intended management decision

Participants can distinguish normal process variation from signals requiring investigation.

• Common-cause variation reflects the stable system of work
• Special-cause variation signals unusual events, changes or process failures
• Run charts apply median lines and non-random pattern rules
• Control charts use centre lines, limits and time-ordered observations
• Individuals charts monitor continuous measures collected one observation at a time
• p-charts track proportions of defective transactions across changing sample sizes
• Process capability compares process spread with customer specification limits

Participants can investigate adverse performance without mistaking symptoms for causes.

• Problem statements define the gap, scope, evidence and affected customers
• Five Whys probes causal chains behind recurring operational failures
• Fishbone diagrams organise potential causes across people, process and technology
• Fault tree analysis tests combinations of conditions producing failure events
• Stratification separates patterns by shift, location, product and customer segment
• Corrective actions address verified causes rather than visible symptoms

Participants can build productivity measures that account for volume, quality and capacity.

• Labour productivity relates verified output to paid or productive hours
• Capacity utilisation compares available capacity with actual demand or workload
• Throughput measures completed units flowing through a constrained process
• Cycle time separates touch time, wait time and total elapsed time
• Work-in-progress measures indicate queues, congestion and flow instability
• Quality-adjusted productivity prevents output targets rewarding avoidable rework

Participants can measure service performance from the customer and quality perspective.

• Service-level agreements define response, resolution, availability and accuracy commitments
• Voice of the customer translates needs into measurable service requirements
• Net Promoter Score limitations assessed alongside operational experience measures
• First-contact resolution measures demand handling without unnecessary repeat contact
• Complaint analysis distinguishes volume, severity, cause and resolution timeliness
• Cost of quality separates prevention, appraisal, internal failure and external failure

Participants can design dashboards that support rapid, focused management decisions.

• Dashboard purpose determines audience, decisions, measures and refresh frequency
• Visual hierarchy directs attention to exceptions, trends and required actions
• Traffic-light status requires explicit thresholds and supporting trend evidence
• Drill-down paths connect executive KPIs with process-level diagnostic measures
• Annotations explain material variation, assumptions, risks and corrective actions
• Report layouts separate performance facts, interpretation, decisions and ownership

Participants can establish review routines that convert measures into accountable action.

• Performance-review cadence matches operational tempo, management level and decision authority
• Tiered meetings escalate unresolved issues through defined organisational levels
• Standard agenda reviews safety, service, quality, delivery, cost and people
• Action logs record decisions, owners, deadlines, dependencies and verification evidence
• RACI matrices clarify metric ownership, data stewardship and approval responsibilities
• Governance charters define scorecard changes, version control and exception handling

Participants can identify and reduce risks created by poorly designed performance measures.

• Goodhart's Law explains behavioural distortion when measures become targets
• Balanced measure sets reduce incentives for speed at quality's expense
• Risk indicators monitor control failures, exposure levels and treatment effectiveness
• Equity analysis tests whether aggregate measures conceal unequal service outcomes
• Data ethics addresses surveillance, consent, access and proportionality
• Assurance reviews test metric integrity before executive or external reporting

Participants can align measurement arrangements with ISO 9001 performance-evaluation requirements.

• ISO 9001:2015 clause 9.1 requirements for monitoring, measurement, analysis and evaluation
• Quality objectives link customer requirements, process controls and improvement priorities
• Internal audit evidence tests whether measurement processes operate as documented
• Management review inputs include trends, nonconformities, risks and improvement opportunities
• Corrective-action effectiveness verified through sustained performance evidence
• Documented information controls metric definitions, reports and retained records

Participants can plan a controlled rollout of a new or revised measurement system.

• Implementation roadmaps sequence design, data preparation, piloting and deployment
• Pilot criteria select processes with clear sponsors, data and improvement need
• Change-impact assessments identify affected roles, reports, systems and routines
• Training plans prepare measure owners, data stewards and review participants
• Adoption measures track usage, decision quality and action completion
• Periodic measure reviews retire obsolete indicators and resolve definition drift

Participants can build, review and present a complete performance measurement system for a selected operating area.

• Strategic objectives linked to process drivers through a documented KPI tree
• KPI dictionary specifies formulas, owners, sources, frequency and thresholds
• Dashboard prototype presents trends, exceptions, segmentation and corrective actions
• Target rationale uses baseline evidence, capability analysis and stakeholder requirements
• Governance model assigns review forums, escalation routes and decision rights
• Implementation plan defines pilot scope, milestones, risks and adoption measures
• Peer review tests measure validity, usability, balance and unintended consequences