Work Study: Method Study & Time Study

Finding the best way to do a job and how long it should take. Learn method study (select, record, examine, develop, install, maintain), process charts and symbols, flow diagrams, motion economy and therbligs, time study with a stopwatch (cycles, rating, normal time, allowances, standard time), required sample size, predetermined motion time systems (MTM, MOST), work sampling, and standards for planning and incentives — with Python for a full time study.

Work study has two parts. Method study asks 'what is the best way to do this?' — removing unnecessary steps, motions and transport. Work measurement asks 'how long should it take a qualified worker at a normal pace?' — producing time standards for planning, costing, line balancing and fair incentives.

Good standards are the foundation of almost every other IE calculation: capacity, staffing, scheduling and cost all depend on them.

An analogy

Method study is like planning the fastest route before a road trip; time study is timing that route with a stopwatch, adjusting for how fast the driver actually drove and adding sensible stops for fuel and rest.

Method study

Steps (SREDIM): Select the job, Record the current method, Examine critically (what, where, when, who, how — and why?), Develop a better method, Install it, Maintain it. ASME process-chart symbols: operation (○), inspection (□), transport (→), delay (D), storage (▽). Flow diagrams and spaghetti charts reveal wasted walking and transport.

Principles of motion economy

From the Gilbreths' work:

Time study

Break the job into elements, time several cycles with a stopwatch (or video), rate the operator's pace against a defined normal, compute normal time, and add allowances (personal needs ~5%, basic fatigue ~4%, variable fatigue and delays) to obtain the standard time. Discard abnormal readings and foreign elements; use statistics to decide how many cycles are needed.

Predetermined times, work sampling and use of standards

Predetermined motion time systems (MTM-1, MTM-UAS, MOST) build times from tables of basic motions (reach, grasp, move, position) measured in TMUs (1 TMU = 0.036 s) — useful before a job exists. Work sampling uses random observations to estimate the fraction of time in each activity (e.g. machine idle %). Standards feed capacity planning, line balancing, costing and incentive schemes; they must be updated when methods change and applied ethically with worker involvement.

Formulas

Worked example: Standard time

Given: OT 0.514 min, rating 110%, allowance 15%

Find: ST.

Answer: 0.650 min

Worked example: Allowance on workday

Given: NT 2.0 min, allowance 12% of the workday

Find: ST.

Answer: 2.27 min

Worked example: Work sampling

Given: Estimate machine idle % ≈ 20%, ±3% absolute, 95% confidence

Find: Observations.

Answer: 683 random observations

Stopwatch time study

import math, statistics
# Stopwatch time study: normal time, standard time and required number of cycles
obs = [0.52, 0.49, 0.55, 0.50, 0.53, 0.48, 0.51, 0.54, 0.50, 0.52]   # minutes
rating = 1.10                  # operator works 10% faster than normal
allowance = 0.15               # personal, fatigue & delay allowance
mean = statistics.mean(obs)
normal = mean * rating
standard = normal * (1 + allowance)
print(f"observed {mean:.3f} min, normal {normal:.3f} min, standard {standard:.3f} min")
print(f"standard output: {60/standard:.1f} units/hour, {480/standard:.0f} per 8-h shift")
# Cycles needed for +/-5% accuracy at 95% confidence
s = statistics.stdev(obs)
n = (1.96 * s / (0.05 * mean)) ** 2
print(f"s = {s:.4f} min -> required observations n = {math.ceil(n)}")
# observed 0.514 min, normal 0.565 min, standard 0.650 min
# standard output: 92.3 units/hour, 738 per 8-h shift
# s = 0.0222 min -> required observations n = 3

Because this operator was faster than normal, the rating raises the time; allowances then add personal, fatigue and delay time. This task is very consistent (small s), so few cycles are needed; variable tasks need many more. Always observe enough cycles and multiple operators.

Common mistakes

Applications

Key takeaways

Going further