Observational Rating
Observational rating is a direct method in work measurement where an analyst observes a worker executing a task over multiple cycles to assess and quantify performance relative to a standard pace. The procedure involves the analyst timing the task elements using a stopwatch while simultaneously evaluating the worker's speed, effort, skill level, and working method against established norms for a qualified operator. Observations are typically conducted for 10 to 20 cycles to capture variability, with abnormal readings (e.g., due to distractions or equipment issues) excluded only if justified by external factors. This real-time assessment allows the analyst to adjust observed times to reflect what a normal performer would achieve under similar conditions.[10]
The rating is expressed on a percentage scale, commonly ranging from 0% to 150%, where 0% denotes complete idleness, 100% represents normal performance by a motivated, skilled worker, and values above 100% indicate exceptional speed or efficiency. To derive the basic or normal time from observations, the formula is applied as follows:
For instance, if an observed time is 1.2 minutes at a 110% rating, the basic time becomes 1.32 minutes. This adjustment ensures the time standard accounts for the observed performance level.[10][11]
Key tools and techniques in observational rating include speed rating, which emphasizes the tempo or pace of movements compared to a benchmark (e.g., walking at 3 miles per hour as normal), and effort rating, which gauges energy expenditure relative to the task's demands. A prominent example is the Westinghouse system, developed in the 1930s, which evaluates four factors—skill, effort, working conditions, and consistency—using a tabular scale with adjustments for each factor, such as skill from -22% to +15%, effort from -17% to +13%, working conditions from -7% to +6%, and consistency from -4% to +4%, resulting in an overall rating often between 70% and 130%. Analysts assign values to each factor during observation (e.g., +0.08 for excellent skill) and sum them to compute the total adjustment factor.[12][10][13]
Training for raters is essential to achieve consistency and minimize observer bias, typically involving exposure to standardized training films depicting operations at known performance levels (e.g., normal tempo for dealing cards in 0.50 minutes). Raters practice rating multiple cycles of these films, compare results with benchmarks, and refine judgments through repeated sessions to align with 100% normal performance. This process emphasizes objective criteria over subjective impressions, with inter-rater reliability checked via group calibrations. Skill level, as a human factor, influences the rating but is assessed relative to the task's requirements during observation.[10]
Synthetic Rating
Synthetic rating is an indirect method in work measurement that estimates operator performance by synthesizing ratings from predefined elemental data, rather than relying on real-time observation. This approach combines performance ratings derived from similar previously observed tasks or utilizes Predetermined Motion Time Systems (PMTS), such as Methods-Time Measurement (MTM), to construct an overall rating for the job. Unlike direct observational rating, which assesses pace during live execution, synthetic rating leverages standardized databases to minimize subjective judgment and enhance consistency.[2][5]
The procedure involves decomposing the task into basic elements, assigning predetermined times or ratings to each from established PMTS databases—such as Time Measurement Units (TMUs) in MTM, where 1 TMU equals 0.00001 hour—and then aggregating these to derive the synthetic rating. For elements with observable similarities, individual ratings are applied based on historical data, and the overall rating is computed as a weighted average. The formula for synthetic rating is:
This calculation normalizes the performance to a standard pace, typically 100% for normal effort, allowing for the determination of normal time as observed time multiplied by the synthetic rating factor.[5][2][14]
One key advantage of synthetic rating is its applicability to complex, hazardous, or repetitive tasks where direct observation is impractical or unsafe, as it avoids the need for prolonged stopwatch timing. For instance, the Maynard Operation Sequence Technique (MOST), a PMTS variant, is widely used in assembly line environments to synthesize times for sequence models like General Move Sequence (GMU), enabling rapid standard setting without live rating. This method reduces variability in ratings and supports method optimization in automated settings.[15][16][17]
Synthetic rating emerged in the post-1950s period, building on the foundational MTM system developed in 1948 by H.B. Maynard, J.L. Schwab, and G.J. Stegemerten to address the limitations of traditional observational techniques amid rising industrial automation. It was formalized by Ralph L. Morrow in the mid-1950s as a way to integrate PMTS with performance evaluation, with MOST further advancing the approach in the 1970s through simplified sequence modeling for broader industrial use. This development responded to the need for more efficient, bias-reduced standards in evolving manufacturing landscapes.[18][2][14]