Why Would a Company Invest in a Time Study Analysis? The Strategic Lever for Operational Excellence

In the fast-paced industrial landscape of 2026, where AI-driven predictive models and IoT sensors monitor asset health in real-time, the concept of a "time study" might feel like a relic from the early 20th century. You might picture a man in a grey suit holding a stopwatch, looming over a nervous assembly line worker.

However, if you dismiss time study analysis as archaic, you are leaving money on the table.

The modern question isn't "how fast is the worker moving?" The question—and the reason savvy Operations Managers invest heavily in this analysis—is: "Is our operational data telling us the truth?"

When a company asks, "Why would we invest in a time study analysis?", they are usually trying to solve a deeper problem:

• Why is our OEE (Overall Equipment Effectiveness) stagnant despite new equipment?
• Why are our maintenance schedules constantly drifting?
• Why does our CMMS data say a job takes one hour, but it consistently takes three?

This article moves beyond the textbook definitions of work measurement. We will explore time study analysis as a financial and strategic lever that validates your digital infrastructure, exposes hidden capacity, and serves as the foundation for true prescriptive maintenance.

The Core Value Proposition: Validating the "Invisible Factory"

The primary reason a company invests in a time study analysis is to uncover the "Invisible Factory." This is the gap between what you think is happening on the floor and what is actually happening.

In many facilities, there is a massive disconnect between the "Standard Minute Value" (SMV) listed in the ERP or CMMS software and the reality of the workflow.

The Cost of Inaccurate Baselines

If your standard operating procedure (SOP) assumes a preventive maintenance (PM) task on a conveyor motor takes 45 minutes, but it actually takes 90 minutes due to travel time, tool retrieval, and safety lockouts, your entire planning schedule is built on a lie.

This leads to:

1. Schedule Congestion: Work orders stack up because technicians can never finish the scheduled load.
2. Deferred Maintenance: Critical tasks are pushed back, increasing the risk of breakdown.
3. Morale Erosion: Technicians feel defeated because they are held to impossible standards.

A time study analysis provides the "ground truth." It is the audit mechanism for your operational assumptions. By investing in this analysis, you are essentially calibrating your operational instruments. Just as you wouldn't run a boiler with uncalibrated temperature sensors, you shouldn't run a plant with uncalibrated labor standards.

Differentiating "Work" from "Motion"

A core insight from time studies is the distinction between value-added work and non-value-added motion.

• Work: Tightening a bolt, inspecting a belt, lubricating a bearing.
• Motion: Walking to the parts cage, searching for a tool, waiting for a permit, deciphering a confusing work order.

Companies invest in time studies to quantify the "Motion." If a study reveals that highly paid technicians spend 40% of their day walking to get parts, the investment in the study pays for itself immediately by justifying a localized parts locker or a vending machine system near the production line.

Follow-Up: How Does This Translate to ROI? (The Financial Argument)

Once the need for "ground truth" is established, the next logical question is: "What is the return on investment? How does watching work translate to the P&L statement?"

The ROI of a time study analysis is realized through three primary financial levers: Wrench Time Optimization, Labor Cost Reduction, and CapEx Avoidance.

1. Wrench Time Optimization

"Wrench time" refers to the percentage of time a maintenance technician spends physically fixing or maintaining equipment. Industry averages are often shockingly low—hovering between 25% and 35%.

The Calculation: If you have a team of 20 technicians costing $100,000/year each (fully burdened), your labor spend is $2 million.

• At 30% wrench time, you are getting $600,000 of value-added work.
• If a time study identifies bottlenecks (e.g., permit delays) and helps you improve wrench time to 45%, you have effectively unlocked $300,000 in "free" labor capacity without hiring a single new person.

This "hidden capacity" allows you to clear backlogs and move from reactive to preventive maintenance strategies without increasing headcount.

2. CapEx Avoidance

Often, companies believe they need to buy more machines to meet demand. A time study might reveal that the bottleneck isn't the machine speed, but the changeover time (SMED - Single Minute Exchange of Die).

If a time study reduces changeover from 4 hours to 2 hours on a critical asset, you have gained significant production capacity. This analysis can save millions in capital expenditure (CapEx) by proving you don't need a new line; you just need to optimize the existing one.

3. Accurate Costing and Pricing

For contract manufacturers, knowing the exact labor content of a product is vital for margins. If you underestimate the time it takes to produce a unit because you haven't done a time study, you might be selling products at a loss. Time studies ensure that the Standard Cost in your accounting system matches reality, protecting your gross margin.

Follow-Up: "My CMMS Says We Are Efficient. Why Don't I Just Trust That?"

This is the "Data Integrity" angle. In 2026, we rely heavily on digital dashboards. But a dashboard is only a visualization of data inputs.

The "Pencil-Whipping" Phenomenon

Without time study validation, CMMS data is prone to human error and manipulation.

• Scenario: A technician finishes a job in 20 minutes but logs 60 minutes because that’s what the system expects, or to cover for a break.
• Result: Your historical data shows a consistent 60-minute repair time. When you use AI predictive maintenance tools to forecast labor needs, the AI learns from this bad data.

Calibrating the Digital Twin

As companies move toward Digital Twins and prescriptive maintenance, the algorithms need accurate inputs.

• If your predictive maintenance system for pumps flags a potential bearing failure, it will also estimate the "Time to Repair" to help schedulers fit it into a shift.
• If that estimate is based on unverified historical data, the schedule will break.

A time study acts as a "calibration event." It resets the baseline. You invest in the study to tell your software: "Ignore the last 5 years of bad logs; here is exactly how long this task takes under normal conditions." This ensures that when you utilize features like work order software, the scheduling logic holds up under pressure.

Follow-Up: How Does This Apply to Maintenance vs. Production?

A common objection is: "Time studies work for repetitive assembly lines, but maintenance is variable. You can't time study a breakdown."

This is a misconception. While you cannot predict the nature of every breakdown, you can absolutely standardize and measure the elements of maintenance work. This is often where the highest ROI lies.

Studying the "Standardizable" Elements

Even in reactive maintenance, certain elements are constant:

1. Trip Time: Travel from shop to asset.
2. Setup: Lockout/Tagout (LOTO), permitting, area prep.
3. Tear Down: Removing guards, cleaning up.

A time study analysis breaks these components down. You might find that LOTO procedures are taking 40 minutes due to poorly labeled breaker panels. That is an actionable insight that applies to every job, regardless of whether it's a pump repair or a conveyor fix.

Optimizing PM Routes

Preventive Maintenance (PM) is highly repetitive and perfect for time studies.

• Route Analysis: A time study can track a technician performing a lube route. It might reveal that the route sequence forces the technician to backtrack across the facility three times.
• Optimization: Re-sequencing the route based on the study data can cut travel time by 50%.

For specific assets, such as predictive maintenance for overhead conveyors, where access is difficult, a time study can determine the cost-benefit of installing permanent remote sensors versus manual inspection. If the study shows manual inspection takes 2 hours of high-risk labor, the ROI for sensor installation becomes obvious.

Follow-Up: How Do We Execute This Without "Big Brother" Culture Shock?

The biggest risk in a time study is not technical; it is cultural. If workers feel they are being spied on to justify layoffs, they will alter their behavior (the Hawthorne Effect) or sabotage the data.

Framing the Narrative

Successful companies frame time studies as "Process Studies," not "People Studies."

• Bad Messaging: "We are timing you to see if you are working fast enough."
• Good Messaging: "We are timing the process to see what barriers are stopping you from doing your job. We want to identify the frustrations—like waiting for parts or struggling with old tools—and remove them."

The "Day in the Life" Approach

Instead of just standing over a worker with a stopwatch, use a "Day in the Life of a Work Order" approach.

1. Follow the work order, not just the person.
2. Track the time the ticket sits in "Pending Approval."
3. Track the time spent waiting at the inventory window.
4. Track the time spent deciphering the manual.

When you present the data, show the team: "Look, you guys are spending 2 hours a day just dealing with bureaucracy. We are going to fix that." This turns the workforce into allies rather than adversaries.

According to iSixSigma, involving operators in the data collection process significantly increases the accuracy of the results and the acceptance of the subsequent changes.

Follow-Up: What Methodologies Should We Use? (Tactical Options)

Not all time studies are created equal. Depending on your goals, you might choose different methods.

1. Direct Time Study (Stopwatch)

• Best For: Highly repetitive, short-cycle tasks (e.g., assembly, packaging, specific PM tasks).
• Pros: Extremely accurate, granular detail.
• Cons: High labor effort to collect data, highest risk of "Hawthorne Effect."

2. Work Sampling (Statistical)

• Best For: Maintenance, warehouse operations, indirect labor.
• Method: An observer takes random "snapshots" of what workers are doing throughout the day (e.g., working, walking, waiting, talking).
• Pros: Less intrusive, provides a great macro view of "Wrench Time" vs. "Delay."
• Cons: Doesn't give detailed task times, only category percentages.

3. MOST / MTM (Predetermined Motion Time Systems)

• Best For: Setting standards for new processes that don't exist yet.
• Method: Uses synthetic data tables (e.g., "reach 12 inches," "grasp small object") to calculate how long a task should take based on human physiology.
• Pros: No stopwatch needed, highly objective.
• Cons: Requires certified analysts, can feel disconnected from the messy reality of the plant floor.

For most maintenance organizations, Work Sampling is the best starting point to identify systemic waste, followed by Direct Time Study on the most critical/frequent PM procedures.

Follow-Up: Time Studies in the Age of AI and IoT

In 2026, manual data collection seems counterintuitive. Why not just use sensors?

The "Ground Truth" for AI

AI models are hungry for labeled data. Sensors can tell you when a machine stopped and started, but they cannot tell you why the repair took 4 hours.

• Did the technician lack the right training?
• Was the spare part defective?
• Did the bolt seize?

A time study provides the qualitative context that sensors miss. By integrating time study observations with machine data, you create a robust dataset.

Wearables and Digital Tracking

Modern time studies often utilize digital badges or smart PPE to track movement (spaghetti diagrams) automatically. This removes the observer from the floor, reducing the Hawthorne Effect. However, ethical considerations regarding privacy must be managed carefully.

This data feeds directly into asset management systems, allowing for dynamic scheduling. If the system knows that "Task A" usually takes 20% longer on "Line 3" due to physical constraints identified in a study, it will automatically adjust the schedule for technicians assigned to Line 3.

Follow-Up: Implementation - How to Get Started

If you are convinced of the value, here is a roadmap for execution.

Phase 1: Define the Scope

Don't try to study the whole plant. Pick a bottleneck.

• Is it the packaging line changeover?
• Is it the quarterly PM on the compressors?
• Is it the response time for emergency calls?

Phase 2: Communicate

Hold a town hall. Explain the "Why." Emphasize that this is about finding process inefficiencies, not punishing people.

Phase 3: Select the Tool

• For maintenance utilization: Use Work Sampling.
• For machine changeover: Use Video Analysis (record the changeover and analyze it frame-by-frame).

Phase 4: Analyze and Act

Data without action is waste.

• If the study shows 15% of time is spent looking for tools -> Implement 5S shadow boards.
• If the study shows 20% of time is travel -> Buy industrial tricycles or golf carts.
• If the study shows PMs are finished in half the allotted time -> Update the PM procedures and rebalance the workload.

Phase 5: Sustain

Update the SOPs and the CMMS. Make the new time standard the official baseline. Monitor variance against this new baseline to ensure the improvements stick.

According to the National Institute of Standards and Technology (NIST), standardization is the baseline for continuous improvement. You cannot improve what you have not measured and standardized.

Conclusion

Why would a company invest in a time study analysis? Because in a competitive industrial environment, time is the only non-renewable resource.

Investing in a time study is not about micromanagement. It is a strategic investment in data integrity, capacity planning, and financial accuracy. It bridges the gap between the digital promise of your CMMS and the physical reality of your shop floor.

By uncovering the hidden capacity within your existing workforce and assets, a time study analysis often yields a higher ROI than purchasing new equipment. It transforms your operation from one based on "gut feeling" and historical guesses to one driven by precision, engineering standards, and verifiable truth.