Study Validity with Missing Observations
Analyse missing observations, coverage and possible nonresponse bias before interpreting results.
WORKSAMP · LEARNING RESOURCES
Work Sampling guides
Practical guides to design observations, interpret results and apply work sampling with clear assumptions.
What do you need to work on?
Statistical foundations
Sample size, precision, confidence intervals and the limits of inference.
Explore guidesStudy design
Observation plans, categories, routes and reliable data capture.
Explore guidesResults and decisions
Proportions, time, capacity and improvement decisions grounded in data.
Explore guidesIndustry applications
Examples and limits in manufacturing, maintenance and services.
Explore guidesIndustrial management
Tools, organization and context for putting a study into practice.
Explore guidesArticles and practical examples
Showing 37–48 of 92
Work Sampling for Headcount Justification
Use observations to analyse staffing needs alongside demand, quality, safety and service variability.
Opportunity Cost of Down Machinery
Value unused machinery capacity without confusing opportunity cost, accounting cost and actual savings.
Introduction: The hidden pulse of productivity
What buffers and WIP observations reveal, how to apply Little’s law and what data are needed to size them.
The Instant Observation Concept (Snap Reading)
Record a state at a defined instant and distinguish that observation from continuous duration measurement.
Pareto Law in Delays
Build and interpret a delay Pareto chart without treating the 80/20 rule as a law every dataset must follow.
Designing Observation Routes
Design safe routes and an observation plan that documents selection, coverage and dependence between records.
The 'Other' Category: Risks and Management
Define and review an Other category without confusing classification uncertainty with sampling error.
Industrial Productivity Benchmarking
Compare productivity across processes while accounting for products, resources, scope and contextual differences.
Sample Size and the Central Limit Theorem
What the central limit theorem explains and why a large sample does not correct bias or guarantee independence.
Definition and Operational Context: Beyond a Simple Stop
Distinguish planned and unplanned delays and keep consistent denominators when calculating indicators.
Load Balancing Between Operators
Compare workloads using observations, time coverage and complementary measures before reallocating tasks.
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