This episode demystifies how to determine true production capacity and build data-driven work standards—so planners can schedule with confidence and frontline teams know exactly what “good” looks like. You’ll hear how digital twins and scenario-based simulations expose the hidden impact of micro-stoppages, staffing changes, and layout tweaks, while real-time data replaces theoretical guesses with hard evidence. A trio of case snapshots—a mechatronics line that freed up 18 % more capacity, a food-packaging cell that slashed shift-to-shift variability by 25 %, and a furniture plant that finally forecasted seasonal demand without ballooning inventory—prove the approach works in the real world.
Without a robust capacity-and-standards framework, manufacturers typically wrestle with:

• Chronic over- or under-loading of resources that leads to overtime one week and idle assets the next.
  
• Production plans grounded in optimistic “nameplate” rates instead of actual, disturbance-adjusted throughput.
  
• Invisible micro-stoppages and cycle-time drift that silently erode line capability.
  
• Fragmented work instructions that produce wide performance swings between operators or shifts.
  
• Costly cap-ex decisions made without knowing whether existing assets still have untapped headroom.
  

Tune in for a step-by-step playbook—from capturing real cycle data and building a digital twin to stress-testing “what-if” scenarios and locking in fair, transparent standards—that turns capacity planning from educated guesswork into a competitive edge.

👉 Read the full article:
🔗 Blog

👉 Book a free session with a DBR77 expert and see how our platform works in action.
📅 Let’s talk: Book a meeting