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| Section | Objectives |
|---|---|
| Topic 1: Working with Targets | - Negative behaviors when setting - Targets in practice - Challenges in working with targets - KPI documentation processes |
| Topic 2: Understanding KPIs | - KPI lifecycle - KPI related terminology - SMART objectives decomposed |
| Topic 3: KPI Selection at Operational Level | - Operational dashboard - Department scorecard |
| Topic 4: KPI Selection | - KPI selection for organizational scorecard - KPI selection techniques - KPI selection sources |
| Topic 5: KPI Project Coordination | - The business case for KPI implementation - Primary and secondary data sources - KPI workshop briefing pack - KPI project plan |
| Topic 6: The World of KPIs | - Challenges in performance measurement - KPIs concept map - Organizational levels - The value added by KPIs - Governance |
| Topic 7: KPIs in Context | - KPIs cascaded to functional area - KPI selection for industry |
| Topic 8: KPI Typology | - Qualitative vs quantitative KPIs - Efficiency vs effectiveness KPIs - Leading vs lagging KPIs |
| Topic 9: Data Visualization | - Scorecard and dashboard examples - Guidelines to design efficient templates - Best practices in scorecard design - Usability in terms of visual design - Best practices in dashboard design |
| Topic 10: KPI Taxonomy | - KPI DNA map - KPI use case scenarios - Interdisciplinary systemic worldview |
| Topic 11: KPI Measurement | - Change log - Data gathering communication - KPI report |
| Topic 12: KPI Documentation | - KPI documentation form design - Weights and Indexes - Organizational KPI libraries development - KPI documentation process - KPI documentation form functions |
| Topic 13: Data Gathering | - Data quality dimensions - Communication with data custodians - KPI activation tools - Community of Practice - KPI reporting data sources - Guidelines to improving - KPI activation techniques |
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NEW QUESTION # 26
In which stage of the Value Flow Analysis should "Customer satisfaction (%)" be monitored?
Answer: D
Explanation:
Customer satisfaction is an Outcome KPI because it measures the end result experienced by the customer, not the internal activity or resources used. Inputs are what you invest (budget, staffing), process KPIs describe how work is executed (cycle time, error rate), and outputs capture deliverables produced (orders delivered, requests resolved). Satisfaction reflects whether those outputs met customer expectations in quality, timeliness, and overall experience. It is also often used at organizational scorecard level, with departmental dashboards showing the operational drivers that influence it (response time, first-contact resolution, defect rate, on-time delivery). Measurement challenges include survey bias, response rate, timing (immediately after interaction vs periodic), and consistency of the rating scale. Proper activation includes setting a clear survey method, minimum sample sizes, segmentation rules, and a reporting cadence aligned with decision cycles. A common pitfall is using satisfaction without driver metrics-teams can see the score but can't identify what to improve. Linking outcome KPIs to leading indicators makes performance management actionable.
NEW QUESTION # 27
Which of the following words is not a KPI lifecycle phase?
Answer: B
Explanation:
A KPI lifecycle typically includes phases such as selection (choosing the right measures aligned to objectives), documentation (defining formula, data source, owner, frequency, target, tolerance), activation (making the KPI operational-instrumentation, data pipelines, roles, reporting cadence), and then ongoing reporting, review, and refinement . "Notification" is not usually recognized as a standard lifecycle phase; notifications can be a feature of reporting tools (alerts, reminders) but they are not a core lifecycle stage.
Treating notifications as the "work" can be a pitfall: KPI success depends more on proper definition, reliable data gathering, governance, and consistent review routines than on automated alerts. In practice, activation often includes assigning a KPI owner and data custodian, confirming the data source, building the collection process, and running a pilot to validate accuracy. A common measurement challenge is poor adoption after selection-teams select KPIs but never operationalize them. Clear lifecycle steps prevent that gap and ensure the KPI becomes a trusted management instrument rather than a one-time exercise.
NEW QUESTION # 28
Which is the calculation formula for "On-time arrivals (%)"?
Answer: C
Explanation:
"On-time arrivals (%)" is a classic ratio KPI : the number of arrivals that met the on-time definition divided by total arrivals, multiplied by 100. Option B matches that structure directly: (on-time arrivals / total arrivals) × 100 . Option A calculates the complement (late arrivals as a percentage), not on-time arrivals.
Option D is an average duration calculation, which is a different type of measure (cycle time) and not an on- time percentage. A key measurement challenge is defining "on-time" precisely-e.g., arrival within 5 minutes of schedule, or within a contractual window. The KPI documentation should specify: time window, inclusion
/exclusion rules (canceled trips, rescheduled arrivals), time source (system timestamp vs manual entry), and how partial data is handled. Without consistent definitions, the KPI becomes easy to dispute and hard to improve. This KPI is also sensitive to data accuracy (clock sync, GPS timestamps), so activation should include data validation checks and ownership for corrections.
NEW QUESTION # 29
What are the most common challenges in data gathering?
Answer: D
Explanation:
The most common data gathering challenges are timeliness (data arrives too late to be useful), completeness (missing records, partial submissions, incomplete fields), and accuracy (incorrect values, wrong time window, calculation errors, or faulty source data). Option A captures this classic trio. "Integrity" and
"consistency" are important concepts but are often encompassed within accuracy/completeness when practical issues arise. "Data visualization" is not a data gathering challenge; it belongs to reporting and communication after data is collected. Addressing these challenges requires activation discipline: clear definitions, documented sources, assigned data custodians, standardized templates or automated extracts, validation checks, and an escalation process for late or missing data. Another frequent root cause is unclear ownership- multiple teams assume someone else provides the number-so RACI and a collection calendar help. KPI reliability depends on trust; if leaders don't believe the numbers, the dashboard becomes ignored. High- quality data gathering is therefore foundational to performance management, not an administrative afterthought.
NEW QUESTION # 30
Which of the following KPIs will influence "Service backlog (#)"?
Answer: D
Explanation:
A service backlog is the volume of pending work not yet completed. The most direct operational KPI among the options that influences backlog is service lead time (how long it takes to complete a service request from initiation to closure). When lead time increases, work stays open longer, and the backlog tends to grow unless intake demand falls. Conversely, reducing lead time (through better scheduling, capacity planning, process improvements, or fewer reworks) helps clear work faster and prevents backlog accumulation. Revenue is typically an outcome financial KPI and does not directly control queue volume. "Service complaints responded to (%)" reflects responsiveness to complaints; it may improve satisfaction but does not necessarily reduce total pending service volume. "Productivity" is too vague as written (it needs a clear formula, e.g., jobs completed per technician per day) and therefore is less defensible than a specific cycle-time measure. In KPI systems, backlog is usually balanced with capacity and flow KPIs (arrival rate, completion rate, lead time).
Measurement challenges include consistent definitions of "open" vs "closed," and ensuring timestamps are accurate to avoid distorted lead-time and backlog analysis.
NEW QUESTION # 31
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