Introduction

Engineering plants face constant pressure to improve productivity, reduce costs, and maintain quality in an increasingly competitive global market. Automation investments often promise these benefits, but justifying the capital expenditure requires more than intuition or vendor claims. A rigorous time study provides the objective, quantifiable evidence needed to make informed decisions. By systematically measuring how long tasks actually take, plant managers and industrial engineers can identify precisely which processes will yield the highest return from automation. This article explores how to conduct an effective time study, analyze the data, and build a compelling business case for automation investments.

What Is a Time Study?

A time study is a work measurement technique that records the time required to perform a specific task under defined conditions. Originally developed by Frederick Winslow Taylor in the late 19th century, the method remains a cornerstone of industrial engineering. The core idea is to break a job into discrete elements—such as picking a component, positioning it, fastening, and inspecting—and measure each element’s duration with a stopwatch or electronic timing device. The resulting data reveals baseline performance, process variability, and opportunities for improvement.

In the context of automation justification, time study moves beyond simple observation. It becomes a data collection engine that feeds into cost-benefit analysis, return on investment (ROI) calculations, and strategic prioritization. When combined with other inputs like labor rates, defect rates, and maintenance costs, time study data provides the foundation for a defensible investment proposal.

Methods of Time Study

Stopwatch Time Study

The most traditional method involves an observer using a stopwatch (or a digital timer app) to record the time for each element of a task. The observer should also rate the worker’s performance level (e.g., 100% normal pace) to adjust for variations in speed. For automation justification, performance rating is critical because it normalizes the data, showing what a trained operator can achieve under standard conditions. Modern software alternatives like video-based time studies offer higher accuracy and the ability to revisit observations.

Predetermined Motion Time Systems (PMTS)

Systems such as Methods-Time Measurement (MTM) and MOST (Maynard Operation Sequence Technique) assign standard times to basic human motions based on extensive databases. Instead of measuring actual work, an analyst builds the task from predefined motion times. PMTS is valuable when automation decisions are made before a process is fully running, or when benchmarking existing manual tasks against theoretical optimized performance. It eliminates the influence of operator skill variations and provides a consistent baseline.

Work Sampling

Work sampling involves taking instantaneous observations of workers or equipment over a period to estimate the proportion of time spent on different activities. While less precise than continuous timing, it is useful for identifying overall utilization rates and uncovering hidden idle time—a classic indicator that automation may improve throughput.

Steps to Conduct an Effective Time Study for Automation Justification

Define Clear Objectives and Scope

Before any data collection begins, specify exactly which processes are candidates for automation. Common candidates include repetitive assembly tasks, material handling, packaging, inspection, and any operation with high injury risk. Define the boundaries of the study: which shifts, which operators, which product variants. A focused scope prevents data overload and ensures results are actionable.

Select the Measurement Method

Choose stopwatch time study, PMTS, or work sampling based on the available resources, required precision, and stage of the automation project. For a mature process already in production, stopwatch time study with performance rating is straightforward. For a new product line, PMTS may be the only option.

Break Down the Task into Elements

Divide the process into small, measurable elements. Each element should have a clear start and end point. For example, on an assembly line, elements might be: “pick up part A from bin,” “orient part A,” “insert into fixture,” “tighten two screws,” “release assembly.” The level of detail must be sufficient to pinpoint which elements automation can eliminate or reduce.

Collect Sufficient Data

Statistical reliability is essential. A rule of thumb is to collect at least 10–15 cycles for repetitive tasks, or more if the process has high variability. For each cycle, record the time for each element and the performance rating. Use digital tools to reduce transcription errors and enable easy analysis. If possible, collect data across multiple operators and shifts to capture natural variation.

Normalize and Adjust Times

Apply the performance rating to convert actual times into “normal time.” Then add allowances for personal needs, fatigue, and downtime (typically 10–15%). The result is the “standard time” that a qualified operator should achieve under normal conditions. This standard time becomes the baseline against which automated cycle time is measured.

Analyze Variability and Bottlenecks

Calculate the mean, standard deviation, and range for each element. Elements with high standard deviation are prime candidates for automation because machines offer consistent cycle times. Also look for elements that are sequential dependencies—delays in one element cascade to the next. Automation can decouple these dependencies and increase overall throughput. The Institute of Industrial and Systems Engineers (IISE) offers further guidance on variability analysis.

Using Time Study Data to Build the Automation Justification

Quantifying Time Savings

Compare the standard manual time per cycle with the projected automation cycle time. The difference multiplied by the daily production volume gives the total hours saved. Multiply those hours by the fully loaded labor rate (including benefits, overhead, and shift differentials) to estimate annual labor cost savings. For example, if a task takes 5 minutes manually and an automated cell can do it in 2 minutes, on a 500-unit-per-day line the savings are 3 minutes per unit × 500 units = 1,500 minutes (25 hours) per day. At $30/hour, that’s $750/day or $187,500 per year (250 working days).

Accounting for Quality and Rework

Time studies also capture indirect time spent on rework, inspection, and handling defects. If manual operations have a 5% defect rate that each requires 10 minutes of rework, automation that reduces defects to 0.5% saves additional labor and material costs. Include these figures in the justification. Quality Digest provides a framework for calculating the cost of poor quality.

Calculating Return on Investment (ROI)

Combine labor savings, quality savings, and any other benefits (reduced training costs, lower injury claims) into a total annual benefit. Subtract the annual operating cost of the automation (electricity, maintenance, spare parts). The net annual benefit divided by the initial investment gives the simple payback period. For a more rigorous analysis, use discounted cash flow (net present value or internal rate of return) over the expected equipment life. Time study data provides the evidence that these future savings are realistic, not just optimistic projections.

Identifying Intangible Benefits

Time studies also reveal intangibles that strengthen the case: improved ergonomics (reducing repetitive strain injuries), faster changeovers, ability to run unattended during breaks, and data capture for continuous improvement. While these are harder to quantify, they can be listed as supporting justifications.

Common Pitfalls in Time Study for Automation Justification

Insufficient Sample Size

Drawing conclusions from a few cycles can lead to misleading averages. Always verify statistical confidence. For high-variability tasks, consider collecting more samples or using statistical process control charts to identify outliers.

Ignoring Setup and Handling Time

Automation often reduces cycle time but may increase setup time or require frequent changeovers. A time study that only measures the core task and ignores material handling, adjustments, and maintenance will overstate savings. Include all elements of the process in the study.

Failing to Account for Machine Reliability

Automated equipment has its own downtime. Factor in the expected overall equipment effectiveness (OEE) of the proposed automation. If the manual process runs at 95% uptime but the automated cell is projected at 85%, the net gain shrinks. Use reliability data from similar installations to adjust projections.

Not Considering Human Factors

Even after automation, operators may still need to load parts, monitor the machine, or handle exceptions. A time study on the remaining manual elements is essential to avoid overestimating the reduction in headcount. A detailed task analysis ensures the new process is not simply shifting labor from direct to indirect roles.

Integrating Time Study with Other Justification Tools

Time study is most powerful when combined with process mapping, value stream mapping, and cost modeling. For example, a value stream map identifies all non-value-added steps; time study quantifies how long each step takes. Pairing time study data with simulation software (e.g., Arena, FlexSim, or Simio) allows you to model the impact of automation before capital is committed. Simio’s discrete event simulation platform offers a case study library for manufacturing.

Furthermore, time study supports standardized work documentation as defined by Lean Enterprise Institute. Standard times from the study become the baseline for continuous improvement and for setting realistic production targets post-automation.

Case Example: Justifying a Robotic Welding Cell with Time Study

Consider a plant that fabricates steel frames. Manual welding of a joint takes an average of 12 minutes per weld (including part positioning, tack welding, and finishing). A time study of 20 cycles shows a standard deviation of 3 minutes due to operator skill variation and rework. Welding consumables cost $2 per joint, and the labor rate is $35/hour. Annual volume is 10,000 joints.

Current annual labor cost: 10,000 × (12/60) hours × $35 = $70,000. Rework adds 8% of joints at 5 minutes each: 800 × (5/60) × $35 = $2,333. Consumables: $20,000. Total = $92,333.

Proposed robotic cell: cycle time 7 minutes per joint, rework <1%, consumables $2.10 per joint (slightly higher wire usage). Annual labor cost: 10,000 × (7/60) × $35 = $40,833 (some operator monitoring still needed). Rework: 100 × (5/60) × $35 = $292. Consumables: $21,000. Total = $62,125. Annual savings = $30,208.

Cell investment (robot, workcell, installation): $150,000. Simple payback = 5 years. But including reduced quality inspection (savings of $5,000/year) and lower injury risk (estimated $3,000/year savings in workers’ comp), payback drops to 4 years. Time study data made the case credible to the finance team.

Conclusion

Time study remains one of the most powerful, low-cost tools for justifying automation in engineering plants. By providing hard data on current cycle times, variability, and inefficiencies, it moves automation decisions from guesswork to evidence-based engineering. Plant managers who invest in proper time studies will not only build stronger ROI cases but also uncover additional opportunities for process improvement. As automation technology advances, the ability to objectively measure and compare manual versus automated performance becomes even more critical. Start with a well-defined scope, collect robust data, and let the numbers guide your investment strategy.