In engineering organizations, the onboarding of new staff is a critical juncture that directly impacts long-term productivity, quality, and retention. Traditional training programs often rely on generic manuals, generalized classroom instruction, or unstructured mentoring, which can leave new engineers underprepared for the specific demands of their roles. A more precise and effective approach is to ground training design in empirical data through time study methodology. By systematically measuring how long tasks actually take, identifying inefficiencies, and mapping skill gaps, time study provides an objective foundation for creating targeted, efficient training programs. This approach not only accelerates time-to-competency but also fosters a culture of continuous improvement that benefits the entire engineering operation.

What Is Time Study?

Time study is a work measurement technique with roots in scientific management, pioneered by Frederick Winslow Taylor in the early 20th century. It involves the direct observation and recording of the time required to perform a specific task or set of tasks under normal working conditions. Modern time study often incorporates video analysis, software-based time tracking, and statistical sampling to ensure accuracy and reduce observer bias. The core objective is to establish standard times for tasks, identify bottlenecks, and uncover opportunities for process improvement. In the context of training, time study provides a granular understanding of what new engineers must learn and how efficiently they should be able to perform.

Unlike subjective assessments of performance, time study yields objective data that can be analyzed to separate inherent process constraints from individual skill deficiencies. For example, if a new engineer consistently takes 40 minutes to complete a diagnostic procedure that experienced staff finish in 18 minutes, time study data can pinpoint exactly where the delays occur—whether in tool preparation, interpretation of schematics, or execution of sequential steps. This level of detail is invaluable for designing training that directly addresses those specific gaps.

Steps to Develop Training Programs Using Time Study

Implementing time study to inform training development requires a structured approach that moves from data collection to curriculum design. Below are the key steps, each with expanded detail.

1. Select Key Tasks for Analysis

Not every task performed by an engineer needs to be studied. Focus on the tasks that are most critical to productivity, safety, and quality—often the same tasks that new hires struggle with most. Prioritize tasks with high frequency, high impact on downstream processes, or a history of errors or rework. Collaborate with team leads, senior engineers, and quality assurance to build a short list of 5 to 10 representative tasks that form the foundation of the new engineer’s role.

2. Conduct the Time Study

Before collecting data, define the boundaries of each task clearly. Break complex tasks into smaller, observable elements (e.g., “retrieve tool,” “measure component,” “tighten fastener”). Then observe and record the time for each element under standard conditions. Use a combination of stopwatch observation, video recording, and electronic data capture to ensure reliability. It is essential to collect data from multiple operators, including both experienced staff and recently onboarded engineers, to establish benchmarks and identify variability. Record at least 10 to 15 cycles per task for statistical confidence.

3. Analyze the Data

Calculate average, minimum, and maximum times for each task element. Identify the “normal time” by applying a performance rating factor—a subjective assessment of the observed operator’s pace relative to a standard work pace. Add allowances for personal time, fatigue, and delays to derive a “standard time.” Beyond basic averages, look for patterns: Which elements have the widest variation? Which consistently take longer than expected? Where do new engineers deviate most from experienced staff? This analysis reveals both process inefficiencies and training priorities.

4. Identify Training Needs

Compare the performance of new hires to the established standard times and to experienced operator times. Significant gaps indicate specific training requirements. For example, if new engineers spend excessive time interpreting drawings, a training module on print reading is warranted. If they waste time searching for tools, training might focus on workplace organization (5S) or tool identification. Also, look for errors—mistakes that require rework often appear as time spikes. Addressing error-prone steps directly in training can prevent costly rework later.

5. Design Targeted Training Modules

Using the insights from time study, create training programs that are modular, focused, and practical. Each module should address a specific skill gap or process step identified in the data. Use a blend of classroom instruction, hands-on practice, and simulation. For example, if the time study reveals that new engineers take twice as long to complete a wiring checkout, develop a dedicated workshop that breaks down the checkout into sub-steps, with timed drills to build speed and accuracy. Incorporate the standard times as performance targets—trainees should understand not only the correct method but also the expected pace.

Include periodic reassessments using time study to track progress. Re-measure trainee performance after training to validate improvement and adjust future training content.

Integrating Time Study with Training Design

The power of time study extends beyond identifying gaps; it can also shape the structure and delivery of training itself. Here are three key integration points.

Task Decomposition and Proceduralization

Time study encourages breaking tasks into discrete elements. This same decomposition can be used to create step-by-step training materials, checklists, and job aids. For complex engineering tasks, such as setting up a CNC machine or conducting a system diagnostic, having a detailed procedural guide derived from actual time data helps new engineers avoid common mistakes and reduces cognitive load.

Skill Progression and Benchmarks

Time study data can be used to define progressive skill levels. For instance, a “Level 1” standard might be twice the experienced standard time, “Level 2” might be 1.5 times, and “Level 3” might be within 10% of the standard. This creates clear, measurable milestones for new hires and gives supervisors objective criteria for evaluating readiness. It also helps in planning rotation schedules and setting expectations with trainees.

Curriculum Optimization

By analyzing time study data across a cohort of new engineers, training managers can identify which modules are most effective and which need revision. If a particular topic consistently shows little improvement in time post-training, the module’s content or delivery method may need adjustment. Time study thus becomes a feedback loop for continuous improvement of the training curriculum itself.

Benefits of Using Time Study in Engineering Training

Organizations that adopt time study for training development report tangible improvements beyond simple efficiency gains.

  • Reduced Onboarding Time: Focused training eliminates wasted content and accelerates the path to independent work. Companies have reported shaving weeks off the typical ramp-up period.
  • Higher Quality and Lower Error Rates: By targeting error-prone steps, training directly reduces mistakes, rework, and scrap, saving material costs and protecting customer satisfaction.
  • Data-Driven Resource Allocation: Training budgets and instructor time are directed toward the areas with the greatest performance gaps, maximizing return on investment.
  • Improved Trainee Confidence: When trainees see clear benchmarks and track their own time improvements, they gain a sense of accomplishment and a clearer understanding of expectations.
  • Standardization of Best Practices: Time study often reveals that experienced engineers do not all perform the same task identically. Training can standardize on the most efficient method, raising the performance of the entire team.

Challenges and Solutions When Implementing Time Study

Despite its benefits, time study in training can face resistance and practical hurdles. Recognizing these challenges and addressing them proactively is essential for success.

Resistance from Staff

Engineers may feel that being timed is a form of micromanagement or a prelude to layoffs. To overcome this, communicate the purpose clearly: time study is for improving training, not for evaluating individual performance. Involve operators and engineers in the study design, and share aggregate data rather than singling out individuals. Emphasize that the goal is to help new hires succeed faster.

Observer Bias and Variability

Human observers can introduce inconsistency. Use video recordings and multiple observers to cross-check data. Employ electronic time capture tools that reduce manual entry errors. Applying performance rating factors requires trained observers; consider using a standardized rating system like Westinghouse or objective rating to improve consistency.

Difficulty in Capturing Cognitive Tasks

Engineering work includes significant cognitive elements—problem-solving, design decisions, diagnostic reasoning—that are not easily timed. In such cases, time study may need to be supplemented with other methods like work sampling, think-aloud protocols, or task diaries. Focus time study on the observable, repetitive parts of the role, and use qualitative techniques for the cognitive aspects.

Maintaining Accuracy Over Time

Standard times can become obsolete as processes, tools, or technologies change. Establish a periodic review cycle—every 6 to 12 months—to re-study key tasks. When a major process change occurs, update the relevant time data immediately. Keep the training content aligned with the latest standards.

Measuring Training Effectiveness with Time Study

Time study not only helps design training but also provides a built-in mechanism to evaluate its effectiveness. Post-training, measure the time required for newly trained engineers to complete the same tasks studied earlier. Compare their performance against the standard times and against their own pre-training baseline. Key metrics include:

  • Time to reach standard: How many weeks or cycles after training until the engineer consistently meets the standard time?
  • Improvement in average time: Percentage reduction in task duration between pre- and post-training measurement.
  • Reduction in variability: Are new engineers performing more consistently after training? Lower standard deviation indicates more stable skill acquisition.
  • Error rate change: Count of errors or rework incidents during timed observations before and after training.

This data can be presented in dashboards for training managers and used to justify further investment in time study initiatives. It also helps identify when a trainee may need additional coaching or a different learning approach.

Practical Example: Time Study in a Manufacturing Engineering Setting

Consider a mid-sized aerospace components manufacturer that hired six new manufacturing engineers. One of their key tasks was to program and set up a five-axis CNC machine for a new part. Observational time study revealed that new engineers took an average of 95 minutes to complete the setup, while experienced engineers took 52 minutes. The time study data showed the largest delays occurred during the tool offset entry step and the initial probing sequence. The training team developed a focused 2-hour workshop on efficient tool offset entry using macros, plus a hands-on probing simulation. After the workshop, the new engineers’ average setup time dropped to 60 minutes within three weeks, and error-related rework fell by 40%. The company also used the data to revise the standard operating procedure, making the macro approach mandatory company-wide.

Conclusion

Time study is far more than a legacy industrial engineering tool; it is a modern, data-driven framework for building training programs that actually work. By dissecting tasks into measurable elements, establishing objective benchmarks, and targeting training precisely where it is needed, engineering organizations can dramatically improve the onboarding experience and accelerate the path to full productivity. The approach also creates a virtuous cycle: as training improves, standard times may be revised downward, new benchmarks drive further training refinements, and the organization continuously raises its performance ceiling. For any engineering leader serious about reducing onboarding costs and building a capable workforce, integrating time study into training design is not just an option—it is a strategic imperative.

To learn more about the foundations of time study, refer to the Institute of Industrial and Systems Engineers (IISE) work measurement resources and the American Society for Quality (ASQ) lean and process improvement guidelines. For best practices in engineering training design, the National Society of Professional Engineers (NSPE) offers continuing education frameworks that complement time study methodologies.