Time and motion study
Time and motion study is a systematic analytical technique in industrial engineering that integrates time study—measuring the precise duration of elemental work tasks to establish performance standards—with motion study—examining physical movements to identify and eliminate unnecessary actions, thereby optimizing workflow efficiency and reducing waste.[1][2] Developed primarily by Frederick Winslow Taylor in the late 19th century through his time studies at Midvale Steel, where he demonstrated productivity increases such as raising pig iron loading from 12.5 to 47.5 tons per worker per day via empirically derived methods, the approach was advanced by Frank Bunker Gilbreth and Lillian Moller Gilbreth, who introduced motion analysis using photography and chronocyclegraphs to classify therbligs (basic motion units) and refine task sequences.[1][3] This methodology formed a cornerstone of Taylor's scientific management principles, replacing intuitive work practices with data-driven standards derived from observation and experimentation, which enabled standardized training, incentive pay systems, and scalable production in factories. Empirical applications, such as Gilbreth's bricklaying optimizations that halved laying time per brick through scaffold adjustments and motion reductions, yielded measurable output gains, influencing assembly lines and modern lean manufacturing.[3][4] Despite these achievements in causal efficiency improvements—rooted in first-principles breakdown of tasks into observable components—time and motion study provoked controversies, including union-led strikes against perceived speed-ups and deskilling, as seen in the 1911-1912 U.S. congressional investigations into Taylorism, though subsequent analyses affirm its validity in enhancing productivity without systematic evidence of net harm to workers when implemented with accurate standards. Its legacy persists in contemporary operations research, where similar observational methods continue to drive process refinements across industries, underscoring the enduring value of empirical task dissection over subjective judgments.[5]History
Origins in Scientific Management
Time and motion studies originated as core components of scientific management, a systematic approach to improving industrial efficiency pioneered by Frederick Winslow Taylor in the late 19th century. Taylor developed time study techniques to measure the duration of work elements using a stopwatch, aiming to establish precise standards for task performance and replace inefficient rule-of-thumb practices with data-driven methods. His efforts began around 1881 at the Midvale Steel Company, where he analyzed machine shop operations to identify optimal speeds and feeds, leading to significant productivity gains such as doubling output in some processes without additional labor.[6][7] Frank Bunker Gilbreth extended these principles into motion study, focusing on eliminating unnecessary physical movements to refine work methods before timing them. As a building contractor observing bricklaying inefficiencies in the 1890s and early 1900s, Gilbreth redesigned scaffolds and material placement to reduce motions from as many as 18 per brick to 4.8 or fewer, increasing daily output from 1,000 to over 2,300 bricks per worker in some cases. Collaborating with his wife, Lillian Moller Gilbreth, they employed early cinematography for micromotion analysis, decomposing tasks into fundamental units later termed therbligs (an anagram of Gilbreth), which prioritized path minimization and fatigue reduction.[3][8] The synthesis of Taylor's time-focused quantification and the Gilbreths' motion-oriented optimization formed time and motion studies, integral to scientific management's goal of maximizing output through empirical analysis rather than tradition. Taylor's 1911 monograph The Principles of Scientific Management outlined time study as selecting, training, and cooperating with workers under standardized conditions, while the Gilbreths critiqued stopwatch reliance alone, advocating method improvement first to avoid timing suboptimal processes. This integration, despite tensions—such as the Gilbreths' rejection by Taylor loyalists favoring stopwatches over cameras—drove early 20th-century applications in manufacturing, where combined studies yielded measurable efficiency improvements, like 200-300% productivity boosts in select operations.[9][10][11]Key Contributors and Developments
Frederick Winslow Taylor initiated systematic time studies in the 1880s at Midvale Steel Company, using stopwatches to measure and standardize task durations under optimal conditions, aiming to replace rule-of-thumb methods with data-driven efficiency.[12] His experiments there, starting around 1881, demonstrated productivity gains, such as increasing pig iron loading from 12.5 to 47.5 tons per day per worker by refining shovel loads to 21 pounds.[13] Taylor formalized these principles in The Principles of Scientific Management (1911), advocating for task decomposition, worker selection, and incentive pay tied to measured performance.[14] Frank Bunker Gilbreth complemented Taylor's time-focused approach with motion studies beginning in the early 1900s, drawing from his experience as a bricklaying contractor where he reduced necessary motions from 18 to as few as 4.5 per brick through scaffold positioning and tool placement innovations.[15] Collaborating with his wife, Lillian Moller Gilbreth, a psychologist, they pioneered micromotion analysis using high-speed cinematography and chronocycle graphs to capture and diagram movements frame-by-frame, as detailed in their 1911 book Motion Study.[16] This allowed identification of inefficiencies undetectable by stopwatches alone, emphasizing fatigue reduction and ergonomic principles over mere speed. Key developments included the Gilbreths' introduction of therbligs—17 basic motion elements like grasp, transport, and release—formalized in presentations and publications by the early 1920s, providing a granular taxonomy for process redesign.[17] While Taylor prioritized overall cycle time via averaging multiple observations, the Gilbreths critiqued this for ignoring suboptimal motions, advocating integrated time-motion synthesis; their methods diverged publicly in debates like the 1911 ASME conference but converged in practice to form comprehensive efficiency tools applied in manufacturing and surgery by the 1910s.[18] This evolution shifted focus from isolated timing to holistic workflow optimization, influencing subsequent industrial engineering.[19]Time Studies
Principles and Direct Measurement Procedures
The principles of time study emphasize empirical determination of task duration through systematic observation of skilled workers under standardized conditions to establish productivity benchmarks. Originating with Frederick Winslow Taylor's application of stopwatch timing in the 1880s at Midvale Steel, these principles prioritize breaking operations into discrete elements for precise measurement, ensuring methods are optimized prior to timing to eliminate inefficiencies. Taylor advocated for exact subdivision of jobs into elementary operations, with each unit timed meticulously to replace rule-of-thumb estimates with data-driven standards. This approach assumes that time variations stem from measurable factors like worker pace and method, enabling causal identification of bottlenecks via repeated trials. Direct measurement procedures center on stopwatch-based observation, conducted by a trained analyst who selects a representative cycle of the task performed by a qualified operator using proper tools and layout. The process begins with defining work elements—short, homogeneous segments lasting 0.1 to 0.5 minutes—to facilitate accurate timing and analysis of variances. Multiple cycles, typically 10 to 20 or more depending on cycle length variability, are observed continuously or intermittently to compute average observed time, accounting for random fluctuations through statistical averaging.[20] Stopwatch techniques include the continuous timing method, where the device runs unbroken across the full cycle and element durations are calculated by subtracting cumulative readings, suitable for fluid operations; and the snapback method, resetting to zero after each element for immediate recording, preferred for discrete tasks to minimize mental computation errors. Observations incorporate performance rating, a subjective yet calibrated assessment of the worker's speed relative to a "normal" 100% pace—often 80-120% range—multiplied by observed time to derive normal time, reflecting true capability without undue haste or slack.[21][22] Standard time is then obtained by adding allowances—typically 4-20% for personal needs, fatigue, and unavoidable delays—to normal time, ensuring realistic allowances based on empirical data rather than arbitrary padding. These procedures demand analyst proficiency to avoid rating biases, with validation through multiple raters or predetermination checks for reliability. Empirical validation in manufacturing contexts has shown such methods reducing cycle times by 20-50% when combined with method improvements, though accuracy hinges on representative sampling to counter outliers.[23][24]Conducting and Analyzing Time Studies
Conducting a time study begins with preparation, including selecting a representative job or process, breaking it into elemental tasks with clear start and end points, and choosing a proficient operator who follows standard operating procedures to ensure data reliability.[25] The observer must familiarize themselves with the task through preliminary observations to identify variations and establish consistent element definitions, minimizing subjective errors in measurement.[26] Equipment typically includes a decimal-minute stopwatch for precise readings, often paired with a time study board or digital tool for recording observations without disrupting workflow.[27] During observation, the analyst times multiple cycles—ideally 10 to 20 or more, depending on cycle variability—to achieve statistical confidence, using either continuous timing (cumulative across cycles) or snapback (flyback) timing (resetting to zero per element).[28] Each element's time is recorded immediately, noting any irregularities such as delays or foreign elements, which are excluded from core calculations but analyzed separately for process improvements.[27] Operator performance is rated concurrently on a scale relative to a "normal" pace (typically 100% for average skilled effort), adjusting for factors like speed, effort, and conditions to normalize data across workers.[29] Analysis starts with computing the average observed time for each element from the recorded cycles, excluding outliers via statistical tests for validity.[27] Normal time is then derived by multiplying this average by the performance rating factor (e.g., 110% for above-normal speed yields a multiplier of 1.10).[29] Standard time incorporates allowances for fatigue, personal needs, and unavoidable delays—often 10-15% of normal time—using the formula: standard time = normal time × (1 + allowance percentage).[30] For example, if normal time is 5.0 minutes and allowances total 12%, standard time equals 5.6 minutes, providing a benchmark for scheduling and incentive systems.[30] To ensure accuracy, analysts apply confidence intervals (e.g., 95%) based on cycle variability, requiring sufficient observations to limit error margins to 5% or less, as derived from t-distribution statistics on the data set.[27] Validation involves cross-checking against historical data or multiple observers to mitigate bias, with results synthesized into process maps for identifying inefficiencies like excessive motion or bottlenecks.[31] This empirical approach yields quantifiable productivity standards, though it assumes stable conditions and skilled execution, necessitating periodic re-studies for evolving operations.[25]Empirical Applications and Productivity Outcomes
Time studies have been applied empirically in industrial settings to establish task standards and optimize workflows, with productivity outcomes often quantified through before-and-after comparisons of output rates. At Bethlehem Steel between 1898 and 1901, Frederick Taylor conducted stopwatch-based observations of pig iron loading, identifying optimal load sizes, movement sequences, and rest intervals that replaced inefficient habitual practices; this resulted in daily output per worker rising from an average of 12.5 long tons to 47.5 long tons for selected high-capacity individuals incentivized with higher wages. Similar applications in machining and shoveling at Midvale Steel and other sites under Taylor's methods yielded comparable efficiency gains by standardizing elemental times for repetitive operations, enabling managers to forecast labor needs and reduce idle time. These historical cases established a causal mechanism wherein precise timing data allowed for the elimination of unnecessary delays and the scaling of worker effort to physiological limits, often doubling or quadrupling unit output without additional machinery. In a 20th-century extension, Henry Ford's adoption of time study principles for Model T assembly reduced per-vehicle labor time from approximately 12.5 man-hours to 1.5 man-hours by 1913, facilitating mass production volumes exceeding 250,000 units annually by 1914.[32] Contemporary empirical applications in manufacturing continue to validate these outcomes, as seen in a Pakistani apparel factory where time studies of sewing cycles led to layout adjustments and operator training, boosting average machine productivity by 36% through balanced workloads and minimized non-value-adding activities.[33] Such results underscore that productivity enhancements from time studies derive from data-verified reductions in cycle variability, though real-world gains require accounting for worker skill levels and allowance factors for fatigue, with reported increases typically ranging from 20% to 50% in controlled implementations.[34]Motion Studies
Fundamental Concepts and Motion Economy
Motion studies examine the elemental components of manual tasks to identify and eliminate wasteful movements, thereby optimizing worker productivity and reducing physical strain. A core concept is the decomposition of work into discrete motion elements, allowing for the standardization of efficient methods through observation and experimentation. This approach, distinct from time studies that measure duration, emphasizes qualitative analysis of motion patterns to achieve rhythmic, habitual performance. Motion economy constitutes a codified set of heuristics derived from biomechanical and observational data to conserve energy and minimize fatigue in repetitive tasks. Originating from Frank B. Gilbreth's 1911 analysis of bricklaying, where he reduced motions from 18 to 5 per brick through scaffold adjustments and tool modifications, these principles were systematized by Ralph M. Barnes in the 1930s into guidelines applicable across manufacturing and assembly.[2][35] Empirical validation, such as Gilbreth's field tests yielding up to 200% productivity gains in construction, underscores their causal link to efficiency via reduced unnecessary exertion.[36] The principles are categorized into three domains:- Use of the human body: Motions should leverage natural physiology, such as initiating and terminating hand movements simultaneously to balance workload, avoiding idle hands except during scheduled rests, and employing momentum in straight-line or slightly curved paths for ballistic (eye-hand coordinated) actions. Symmetrical, habitual motions at the lowest skill classification feasible minimize learning curves and errors.[36]
- Arrangement of the workplace: Tools and materials must be prepositioned within normal (frequent) or maximum (occasional) reach zones, typically defined by hemispherical arcs from the worker's torso, with fixed locations to prevent search time; eye focus should alternate minimally between tasks, ideally not exceeding two per cycle.[36][35]
- Design of tools and equipment: Handles should accommodate finger, palm, or wrist grasps as appropriate, with controls grouped by frequency of use and momentum-assisted for repetitive actions; drop deliveries and fixtures enable gravity-assisted positioning, reducing muscular effort by up to 30% in observed assembly lines.[36]