Rethinking Staffing Through the Lens of Operations Models

Operations management in mechanical systems—like machines on an assembly line—is built on well-understood principles. Variability is the enemy, and demand forecasting is vital for maximizing throughput and capturing market share.
These same principles can—and should—be applied to people-centric operations. While it's inherently more complex to quantify the capacity and output variability of human teams, doing so opens the door to using proven manufacturing models to optimize staffing.

I often reflect on the critical role of hiring, training, and onboarding in meeting rising demand. Organizations that excel at these processes are better positioned to grow with their markets and avoid the pitfalls of reactive staffing.
Unfortunately, many staffing models are shaped by averages and current-state thinking. This creates a dangerous blind spot: the lag between recognizing increased demand and onboarding productive team members. Without proactive forecasting and planning, organizations risk falling behind and ceding market share to more agile competitors.

Imagine a scenario where demand steadily increases. An organization that only responds after the increase becomes visible—even if it ramps up successfully—will likely suffer a revenue loss due to its delayed reaction. The market doesn't wait.

That’s why leaders should intentionally operate people systems below full capacity—ideally in the 75% to 85% utilization range. This buffer shouldn't be seen as inefficiency but rather as strategic headroom. It enables continuous improvement, supports innovation, and—most importantly—provides the flexibility to scale rapidly when demand spikes unexpectedly.

Consider a simple operations model at https://lnkd.in/erCxwVdd. If we increase the initial processing speed of our team (e.g., "box1" and "box2") to 12 units per cycle, how does this impact backlog and revenue under the same demand growth conditions? The results speak volumes. More capacity upfront directly correlates to greater revenue potential and a reduced risk of being caught off guard.

The lesson: apply the discipline of manufacturing operations to human systems. Forecast demand, build scalable onboarding engines, and design for agility—not just efficiency.

Adil Bahadoor, April 2025

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