by Konstantin Tskhay​

January 26

What manufacturing can teach us about HR efficiency?

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Hi Everyone,

Welcome back.

It’s been a minute—I’ve been heads-down designing a potential People Analytics Cohort-Based Bootcamp.

Is this something you would be interested in? Reply and let me know. If there is enough demand, I’ll open up a waitlist.

Next, I am currently fighting a refund battle with Mentimeter. Despite not using the tool, I was charged via a silent background renewal. It’s a frustrating reminder to diligently check your subscriptions. If you don’t, you might find yourself trapped by these types of opaque consumer practices.

Now onto business, let’s talk about the theme of 2026: Efficiency.

If you look at the broader economic outlook, the mandate is clear. Organizations are obsessively focused on productivity via:

• Headcount reductions to remove redundancy
• Cost-squeezing across software and non-human resources
• Leveraging AI to streamline workflows

We often view employees as a massive cost center to be optimized.

But here is the uncomfortable question: Are we actually quantifying human efficiency correctly?

The Metric Trap

We tend to rely on aggregate metrics like Revenue per FTE or Employee Lifetime Value (we are actually building some benchmarks on this right now—stay tuned).

While these metrics are intuitive at a macro level, they can fail at the individual level.

• Revenue per FTE works for Sales
• CSAT works for Customer Success

But how do you measure the efficiency of an Engineer? Your HR team? Your Leadership?

For the vast majority of roles, measurement is indirect. It’s based on perception ("I feel like they are working hard") rather than the reality of how work gets done.

To fix this, we need to start looking at time allocation.

The Mental Model: Overall Equipment Efficiency (OEE)

Let’s borrow a concept from manufacturing.

Imagine you manage a plant that produces egg cartons.

You have a machine with a theoretical capacity of 100%. But in reality, that machine is never running at 100%.

1. Availability Loss: The machine needs maintenance (–40%).
2. Performance Loss: Human supervision gaps slow it down (–40% of the remaining time).
3. Quality Loss: Some cartons come out torn or defective (–2%).

The Reality: Your expensive machine actually has an efficiency rating of about 35%.

This is called Overall Equipment Efficiency (OEE). It calculates the truly productive time after stripping away all the friction.

The Shift: Calculating Labor Efficiency

We are not machines, but the logic holds.

We can view an employee's time through the lens of Productive vs. Ambiguously Productive activity.

Let’s look at a Software Engineer with an 8-hour workday.

• Direct Value Add (~40%): Writing code. This is the "core" work.
• Indirect Value Add (~15%): Meetings with UX/Product teams. Necessary, but not "building."
• The "Friction Stack" (~45%): "Settling in," answering non-critical company-wide emails, context switching, bathroom breaks, and yes, staring at the snack wall.

When you add it up, perhaps only 55% of the day is directly linked to outcomes.

The rest is lost to systemic friction.

Why This Matters

This isn't about policing bathroom breaks or turning humans into robots.

It is about identifying inefficiency.

If 45% of your payroll is going toward "Ambiguously Productive" time, you have a massive opportunity for optimization.

From the Company side:

• Can you move the All-Hands to bi-weekly instead of weekly?
• Can you create a culture of "speedy meetings" to shave 10 minutes off every hour?
• Can you invest in better documentation to reduce "seeking information" time?

The ROI:

If you can generate just a +12.5% increase in productivity per employee by removing friction, you aren't just saving time. The saved time immediately becomes your investment into productive activities.

The Takeaway

Stop measuring efficiency based on "vibes."

Start measuring it based on time composition.

When you understand where the time actually goes, you stop blaming the people, and you start fixing the system.

The question is, are you brave enough to start go through the exercise?

Konstantin

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Whenever you’re ready, there are 2 ways I can help you:

#1

If you’re still looking to get started in People Analytics, I recommend starting with my affordable course:

​Practical People Analytics: Build data-driven HR programs to 10x your professional effectiveness, business impact, and career. This comprehensive course will teach you everything from building an HR dashboard for business results to driving growth through more advanced analytics (i.e., regression). Join your peers today! ​
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#2

If you are looking for support in your human capital programs, such as engagement, retention, and compensation & benefits, and want to take a more data-driven approach, contact me at Tskhay & Associates for consulting services. Or simply reply to this email!