Conducting a time study for pilot production runs is a cornerstone activity in manufacturing engineering and operational excellence. It provides the quantitative foundation for understanding how work is performed, where inefficiencies lurk, and how processes can be refined before full-scale production begins. A pilot run simulates the final production environment at a reduced volume, making it the ideal moment to capture reliable timing data, test assumptions, and validate workflows. Without a rigorous time study, organizations risk scaling up flawed processes, incurring waste, and missing delivery targets.

Time studies belong to the broader toolkit of work measurement techniques, alongside predetermined motion time systems (PMTS) such as Methods-Time Measurement (MTM) and standard data. In a pilot production setting, direct observation with timing devices remains one of the most flexible and accurate methods, especially when the process is new or substantially changed. This guide expands on the essential steps and provides detailed techniques, analytical methods, and practical considerations for conducting a meaningful time study during pilot production runs.

Preparing for the Time Study

Thorough preparation determines the validity of the entire study. Rushing into data collection without a clear plan yields unreliable results and wasted effort. The preparation phase covers objective setting, environmental conditioning, tool selection, and human factors.

Define Clear Objectives and Scope

Begin by articulating what you aim to achieve. Common objectives include establishing a baseline cycle time, identifying bottlenecks, setting labor standards, validating process design, or comparing alternative methods. The scope should specify which tasks, operators, shifts, and product variants are included. For pilot runs, the scope often covers only the core manufacturing steps, excluding maintenance and changeover, unless those are part of the study’s purpose. Write the objectives in measurable terms, such as “determine the average time to assemble subassembly A within a 95% confidence interval of ±5%.”

Select the Right Operators

Choose operators who are fully trained on the pilot process and have reached a steady performance level. Avoid selecting the fastest or slowest worker unless you are studying extremes. Ideally, include operators of average skill representing the typical workforce that will handle production. If the pilot run uses temporary workers or engineers, note that their performance may differ from future production staff. Operators should be informed about the study’s purpose and given assurance that the data will be used for process improvement, not performance evaluation. Gaining trust reduces anxiety and yields more natural behavior.

Prepare the Environment and Equipment

Ensure that the pilot production environment mirrors normal conditions as closely as possible. This means consistent material supply, proper lighting, ergonomic workstations, and functioning tools. Any deviations should be documented. For timing, the classic stopwatch remains effective for short cycles (under 5 minutes), but for longer processes or those with multiple operators, consider using digital timers, video recording, or software tools like Simpler or Proplanner. Video recording allows post-hoc analysis and reduces observer interference. Data collection sheets should have clear fields: operation description, element name, time reading, operator name, date, and notes on anomalies.

Break Down the Process into Work Elements

Before any timing, decompose the process into discrete, measurable work elements. An element is a logical sub-task with a definite start and end point. Typical elements might be “pick up part from bin,” “align part to fixture,” “fasten four screws,” or “inspect assembly.” Each element should be short enough to observe accurately (preferably under 2 minutes) but long enough to be meaningful. Use the following rules for element definition:

  • Clear boundaries: The start and stop points must be easily identifiable, such as a hand reaching for a tool or a audible click from a fixture.
  • Mutually exclusive: Elements should not overlap.
  • Collectively exhaustive: The sum of all elements equals the total cycle time.
  • Consistent sequence: Follow the same order each cycle.
  • Separate manual and machine elements: Distinguish operator-controlled time from automatic machine cycles.

For pilot runs, creating an initial element list based on process documentation and observation helps ensure completeness. You can refine it after the first few cycles of observation.

Determine Sample Size

Statistical validity requires enough observations to achieve a desired confidence level and precision. The minimum number of cycles to observe can be estimated using the formula:

n = (z × s / e)2

Where z is the z-value for the desired confidence (typically 1.96 for 95% confidence), s is the standard deviation of preliminary sample times, and e is the acceptable error (often 5% or 10% of the mean). For a pilot run with limited cycles, take at least 10 preliminary readings, calculate the mean and standard deviation, then compute the required sample. If the recommended n exceeds the available production cycles in the pilot, consider increasing the pilot duration or accepting wider confidence intervals. A minimum of 10 to 20 cycles per element is common in practice.

Steps to Conduct the Time Study

With preparation complete, the actual timing process can begin. Follow a disciplined, systematic approach to capture high-integrity data.

Observe Without Interference

Position yourself where you can clearly see all operations without obstructing the operator. Do not engage in conversation during the cycle. Explain that you are studying the process, not the person. Use a clipboard or tablet so your recording does not distract. If possible, use a remote stopwatch or video to reduce the observer effect. For the first few cycles, simply observe without recording to let the operator settle into a natural rhythm.

Record Time for Each Element

Using the continuous timing method (stopwatch runs continuously, and the cumulative time is noted at each element’s endpoint) is preferred for pilot studies because it provides a complete record and avoids resetting errors. Alternatively, snapback timing (resetting watch after each element) can be used for very short cycles, but risks losing time between elements. Record times in seconds or decimals of minutes. Note any interruptions or abnormal delays, with a code such as “D” for delay, “I” for inspector, or “M” for machine malfunction. These abnormal entries must be excluded from normal cycle time calculations but analyzed separately.

Apply Performance Rating

To convert observed time into “normal time,” a performance rating factor is applied. The rating accounts for the operator’s pace relative to the expected standard. A rating of 100% indicates normal pace under standard conditions. Common rating systems include Westinghouse (skill, effort, conditions, consistency) or Objective Rating (pace and difficulty). In pilot runs, operators may work slower due to learning or faster due to motivation. The analyst must assign a rating for each element. For example, if a step took 0.50 minutes but the operator was working at a pace 10% faster than normal, the normal time would be 0.50 × 1.10 = 0.55 minutes. When multiple operators are studied, average rating across all is often used.

Include Allowances

Standard time adds allowances for rest, personal needs, delays, and fatigue. Typical allowances range from 10% to 20% of normal time, depending on job demands and regulatory requirements. For pilot runs, you can use standard company allowances or estimate based on industrial engineering guidelines from sources like the International Labour Organization or ASQ. Record the allowance percentage used and the rationale.

Document Everything

Good documentation makes the study reproducible and defendable. For each observation, record:

  • Operator name and experience level
  • Date, time, shift
  • Product variant or part number
  • Machine settings, tooling, material lot
  • Environmental conditions (temperature, noise, lighting)
  • Any deviations from standard procedure
  • Performance rating for each element
  • Allowances used

Analyzing the Data

Data analysis transforms raw time readings into actionable insights. The goal is to determine reliable normal times, standard times, and identify patterns of variation.

Calculate Average and Standard Deviation

For each element, compute the arithmetic mean of the observed times (excluding abnormal cycles). Also compute the standard deviation to understand variability. High variability may indicate a process that is not well standardized or that operators are using different methods. Examine any outlier readings; if they are due to one-time events (e.g., dropped part, tool change), exclude them from the mean but note their occurrence.

Determine Normal Time and Standard Time

Normal Time (NT) for each element = (Average Observed Time) × (Performance Rating Factor). For example, if average observed time is 0.45 minutes and rating factor is 1.05 (5% faster than normal), NT = 0.4725 minutes. If multiple operators are observed, average their ratings or use a weighted approach.

Standard Time (ST) for each element = NT × (1 + Allowance %). If allowance is 15%, then ST = 0.4725 × 1.15 = 0.5434 minutes. Sum the standard times of all elements to get the total standard cycle time for the process.

Create Operation Standard Sheets

Document the standard times for each element in a format that can be used for line balancing, costing, and scheduling. Include the element description, normal time, standard time, and a note on the basis of the rating. This sheet becomes a baseline for future comparisons and continuous improvement.

Analyze Process Flow and Bottlenecks

Plot the standard times per workstation on a bar chart or value stream map. The workstation with the longest standard time is the bottleneck. In pilot runs, imbalance between stations indicates where to focus improvement: reallocate tasks, add parallel stations, or redesign the process. Use the cycle time to calculate takt time (available production time divided by customer demand). If takt time is less than the bottleneck cycle time, the process cannot meet demand; if significantly greater, the process may be overstaffed or have excess capacity.

Identify Variation Patterns

Use simple control charts (e.g., X-bar and R chart) for element times to detect special cause variation. Special causes include operator fatigue, material inconsistencies, tool wear, or environmental changes. Common cause variation (natural fluctuation) is expected; reducing it improves predictability. For pilot runs with limited data, use a run chart or scatter plot to visualize trends over consecutive cycles.

Implementing Improvements

The ultimate purpose of a time study is to drive action. Once data is analyzed, prioritize improvements based on impact and feasibility.

Target the Bottleneck

Focus first on the workstation with the highest standard time. Apply lean techniques such as:

  • Methods improvement: Eliminate unnecessary motions, combine steps, or change sequence.
  • Workplace organization (5S): Reduce search time and movement.
  • Ergonomics: Redesign workstations to reduce fatigue and improve speed.
  • Automation or tooling: Introduce simple fixturing or power tools to reduce manual time.

Establish Standard Work

Based on the time study, write standard work instructions that specify the exact sequence, time allowed, and quality checks. Training all operators on the standard ensures consistency and sets a baseline for improvement. The standard work sheet should include cycle time, work sequence, and in-process stock (WIP) limits.

Perform a Follow-up Time Study

After implementing changes, conduct a second time study to verify improvements. Use the same methodology and compare before-and-after standard times. Calculate the percentage reduction in cycle time and any changes in variation. If the improvement meets or exceeds targets, document the new standard. If not, repeat the analysis cycle.

Integrate with Production Ramp-Up

The pilot run is a dress rehearsal for full production. Use time study results to set realistic production rates, determine labor requirements, and plan shift schedules. Share the standard times with supply chain and planning teams to align material flow and capacity. The standard times also become inputs for cost estimation and quoting new orders.

Conclusion

Conducting a time study for pilot production runs is more than a data collection exercise; it is a disciplined practice that builds the analytical foundation for operational excellence. By preparing meticulously, executing systematic observation, applying performance rating and allowances, and analyzing results with statistical tools, you gain deep insight into how your process truly performs. The outputs—normal times, standard times, bottleneck identification, and variation patterns—enable targeted improvements that directly impact throughput, cost, and quality.

Pilot runs provide a unique opportunity to refine processes before committing to full-scale production. Time studies conducted during this phase yield high leverage because changes are easier and cheaper to implement. The discipline of time study also fosters a culture of continuous improvement, empowering teams to challenge assumptions and drive waste reduction. For further reading, explore resources from the Lean Enterprise Institute on standard work and waste elimination, or consult the Institution of Mechanical Engineers’ guide to work measurement. Integrating time study practices into your pilot production run toolkit ensures that you enter full production with confidence, ready to meet demand with efficiency and consistency.