For decades, manufacturing leaders have invested in speed—faster production, faster fulfilment, faster time-to-market. But today, the next real advantage isn’t how fast you make, it’s how fast you plan. 

In an environment where tariffs shift overnight, supply chains reroute in days, and customer demand swings in hours, planning latency, the gap between signal and decision has become the new bottleneck. 

Planning latency drains margin, slows response, and locks up working capital. And in volatile markets, even a few days of delay can cost millions. 

That’s why manufacturers are rethinking their planning model, not just digitising it, but making it continuous, connected, and responsive by design. 

The Trends Driving the Shift 

Recent research from McKinsey, IDC, Forrester, and others highlights how structural change in manufacturing is demanding a faster, smarter planning rhythm: 

• Digital twins are now table stakes. 
  60% of manufacturing leaders have already implemented or planned digital twins to simulate capacity, cost, and carbon trade-offs before committing decisions. (Bain, Gartner) 

• Advanced planning & scheduling is back in focus. 
  IDC forecasts >30% of large Asian manufacturers will deploy new AI-enhanced APS tools by 2027, pushing perfect-order rates over 95%. 

Integrated planning boosts EBIT and resilience. 
McKinsey shows that mature IBP users achieve up to 2 extra EBIT points, 15–20pp higher service levels, and lower capital intensity. 

• Workforce and automation require co-planning. 
  Less than 5% of new factory robots are humanoid or autonomous. Instead, cobots must be planned alongside skills, space, and shift models. 

Why Monthly Planning Creates Latency 

Traditional S&OP cycles anchor decisions to static monthly horizons. But when real-world signals move faster than the planning cadence, manufacturers are left reacting—often too late. 

Legacy planning introduces latency at every level: assumptions are locked too early, response windows shrink, and cross-functional misalignment multiplies. The cost? Slow course correction, excess stock, missed orders, and margin erosion. 

 

The Cost of Planning Latency in Manufacturing 

Slow, batch-based planning cycles don’t just delay decisions—they hit performance and profit. According to industry research: 

Manufacturers with high planning latency can lose up to 10% of EBIT due to excess inventory, missed demand, and late course correction. (McKinsey) 

It takes 5–15 days on average to replan after a disruption—by then, service levels and margin have already eroded. (Gartner) 

Forecast error rates exceed 50% in sectors still locked into monthly planning cadences. (IDC) 

20–30% of inventory in discrete manufacturing is classified as “avoidable buffer stock”—a direct cost of misaligned plans. (BCG) 

Planning latency isn’t just inefficiency—it’s a financial and operational risk. 

 

A Continuous-Planning Playbook 

In David Marmer’s recent blog, he outlined how the future of planning must be continuous—an active, decision-driven discipline that closes the gap between insight and action. The Product and Tech perspectives in this series have also shown how this shift is becoming achievable at scale. 

Here’s how that continuous approach reduces planning latency across the enterprise: 

 

Challenge	Latency reduction
Footprint moves & tariff changes	Location-aware scenarios auto-replan when duty codes or incentive
Digital-twin proliferation	Physics-based twins feed live parameters (energy, risk, throughput)
Cobots + human upskilling	Capacity tables integrate robot ramp-up curves and skills gaps to trigger upskilling or hiring.
Margin Squeeze	IBP “control tower” aligns operational decisions to a live P&L, revealing value-impact instantly.

 

Getting Started: Cutting Planning Latency in 90 Days 

You don’t need to transform the entire business to reduce latency. The fastest gains come from focusing on one volatile, decision-heavy value stream. 

Start by: 

• Connecting a digital twin to model cost, capacity, and lead-time dynamics. 

• Running rolling scenarios that simulate trade-offs under constraint or disruption. 

• Measuring agility—how quickly can your plan adapt when a new constraint or signal hits? 

The goal isn’t more complexity—it’s less latency between decision need and execution confidence. 

Final Thought 

Manufacturing used to win on scale, then efficiency. Today, it wins on adaptability—and that makes planning latency the next metric that matters.