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Rebuilding MTS/MTO Rules: How Sophus Helped Mindray Regain Inventory Control

How Mindray Rebuilt Its MTS/MTO Rules to Regain Inventory Control

Mindray is known worldwide as a leader in medical technology. Its product portfolio spans life-support systems, clinical diagnostics, digital ultrasound, and radiology and its growth over the past decade has been rapid and continuous.

But even a strong company can face structural supply chain issues. In Mindray’s case, inventory levels were increasing faster than sales while delivery lead times failed to improve. This created a contradiction that drew executive attention:

If inventory is rising, why are service levels not improving?

The answer came from a deeper examination of how Mindray classified and executed its supply modes (MTS vs. MTO/CTO) across its product families and global markets.

Why Inventory Rose While Service Levels Fell

Mindray offered thousands of product configurations across different markets. In principle, each SKU should fall into one of two planning models:

  • MTS (Make-to-Stock): Finished goods inventory to support fast delivery.
  • MTO/CTO (Make-to-Order / Configure-to-Order): Component or subassembly inventory, with assembly triggered by customer orders.

But in practice, these boundaries had blurred:

  • MTS items maintained finished-goods inventory, yet delivery times barely improved.
  • MTO items held significant component stock, yet assembly cycles were slow and unpredictable.

The company was absorbing the cost of stocking at both ends components and finished goods without gaining the delivery benefits typically associated with either mode.

This created three structural issues:

  • Total inventory increased without improving service levels.
  • Inventory was misallocated across products and regions.
  • The absence of consistent supply mode rules became a growing bottleneck as the company expanded.

The problem was not capacity. It was the lack of a unified, up-to-date logic that matched supply modes to actual product behavior and market expectations.

Rebuilding the System by Fixing the Rules, Not the Symptoms

Sophus worked with Mindray to redesign the underlying supply logic instead of applying surface-level fixes such as raising safety stock or forcing production acceleration.

The transformation focused on three major pillars.

Pillar 1 — Unified Supply Mode Classification Across the Company

Different teams had been applying different interpretations of MTS and MTO. The first step was to align everyone executives, planners, manufacturing, product teams on a clear, consistent classification framework.

Key drivers of this classification included:

  • Demand variability
  • BOM structure and complexity
  • Assembly cycle time
  • Market delivery expectations
  • Customization requirements
  • Cost-to-serve implications 

The alignment produced a stable, company-wide rule:

“This product is MTS for a reason; this product is MTO for a reason.”

This clarity eliminated confusion and created a foundation for consistent planning, stocking, and fulfillment decisions across global operations.

Pillar 2 — Delivery Cycle and Fulfillment Rules Defined by Product Type

Once supply modes were standardized, Mindray redefined delivery expectations by product family. Each category now had:

  • A standard delivery cycle
  • Clear assembly time assumptions
  • Component vs. finished-goods inventory requirements
  • Internal service targets
  • Regional fulfillment rules
  • Exceptions for high-value or highly customized items 

These rules were driven by quantitative data rather than historical habits or subjective assumptions.

By giving every SKU a defined and justified delivery target, Mindray created the structure needed for a more accurate and effective inventory optimization model.

Pillar 3 — Inventory Structure Redesign Across the Network

The final step addressed how inventory was distributed across Mindray’s global supply network. This redesign incorporated:

  • Channel expansion plans
  • Regional demand growth
  • Cost-to-stock and holding cost modeling
  • Safety-stock rules aligned to supply mode
  • Assembly and logistics constraints
  • Cross-border and regulatory considerations

The result was a rational inventory blueprint that struck the right balance between:

  • Total network cost
  • Service performance
  • Capital efficiency
  • Market requirements 

It also created a foundation that could guide future product launches and expansion decisions.

Results: A More Structured, Faster, and Leaner Supply Chain

Mindray’s new supply logic delivered meaningful improvements quickly.

Clear Delivery Cycles for Each Product Category

Service expectations became predictable and aligned with real-world capabilities.

Standardized MTS/MTO Rules Across All Teams

Every team understood which items belonged in which mode and why.

Inventory Reduced by 9% Network-Wide

A leaner structure without compromising delivery performance.

Significant Improvement in On-Time Delivery

Better supply mode alignment led to faster response, fewer delays, and more efficient production planning.

Mindray regained control over both inventory and service performance — not by adding stock, but by correcting the logic behind how stock was planned, positioned, and fulfilled.

Key Lesson: Inventory Problems Are Usually Logic Problems

Mindray’s challenge was not a shortage of capacity or resources. It was a shortage of clarity.

  • Inventory grows when supply modes are inconsistent.
  • Service declines when delivery rules are undefined.
  • Networks become inefficient when product categories lose structure. 

By rebuilding supply mode rules, delivery expectations, and inventory architecture, Mindray restored alignment across its entire supply ecosystem.

The result was a supply chain that is:

  • Lean where it should be lean
  • Fast where speed is required
  • Disciplined where precision matters most

This transformation shows that true supply chain improvement comes not from stocking more, but from thinking more clearly.

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Author

Byron Song
Byron Song has over a decade of experience in supply chain network design and optimization, working with manufacturers, retailers, and 3PLs worldwide. At Sophus.ai, he leads the development of AI-powered tools that help organizations design, simulate, and optimize logistics networks faster and with greater accuracy. His work has enabled clients to cut network-design lead times by 50% and achieve double-digit cost reductions through smarter scenario planning.

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