Spotlight: What if reducing your inventory could actually increase your revenue, improve patient satisfaction, and eliminate stockouts? This real-world prosthetics case study we led shows how data-driven SKU optimization and Lean Six Sigma transformed operational performance—unlocking nearly $1M in profit gains.
Prosthetic providers must maintain inventories of numerous component sizes and configurations to support patient-specific prosthetic devices. However, excessive SKU variation and decentralized purchasing often lead to high carrying costs, obsolete inventory, and frequent stockouts of critical components.
This post presents a case study demonstrating how a mid-sized prosthetic services company applied Lean Six Sigma methodology and Pareto-based SKU optimization to redesign its inventory management system. The project resulted in significant improvements in inventory efficiency, reduced component lead times, improved patient comfort through faster fittings, and nearly $1 million in annual profit improvement.
The prosthetic services provider faced significant inefficiencies due to excessive SKU variation, decentralized inventory management, and lack of demand forecasting. These issues resulted in high carrying costs, frequent stockouts, and delayed patient fittings.
By implementing Lean Six Sigma using the DMAIC framework and conducting Pareto-based SKU analysis, the company identified that a small subset of SKUs drove the majority of demand. Strategic interventions—including SKU rationalization, centralized inventory planning, demand forecasting, and regional inventory hubs—enabled a comprehensive transformation.
The results were substantial:
Read the full success story below…
Prosthetic providers must maintain inventories of numerous component sizes and configurations to support patient-specific prosthetic devices. However, excessive SKU variation and decentralized purchasing often lead to high carrying costs, obsolete inventory, and frequent stockouts of critical components.
This post presents a case study demonstrating how a mid-sized prosthetic services company applied Lean Six Sigma methodology and Pareto-based SKU optimization to redesign its inventory management system. The project resulted in significant improvements in inventory efficiency, reduced component lead times, improved patient comfort through faster fittings, and nearly $1 million in annual profit improvement.
The prosthetic services provider faced significant inefficiencies due to excessive SKU variation, decentralized inventory management, and lack of demand forecasting. These issues resulted in high carrying costs, frequent stockouts, and delayed patient fittings.
By implementing Lean Six Sigma using the DMAIC framework and conducting Pareto-based SKU analysis, the company identified that a small subset of SKUs drove the majority of demand. Strategic interventions—including SKU rationalization, centralized inventory planning, demand forecasting, and regional inventory hubs—enabled a comprehensive transformation.
The results were substantial:
- 42% reduction in inventory carrying costs
- 71% decrease in stockouts
- 55% faster component availability
- Nearly $1M increase in annual operating profit
Read the full success story below…
Prosthetic companies face a unique supply chain challenge. Unlike traditional manufacturing environments, prosthetic devices are highly customized medical products built from modular components such as prosthetic knees, feet, pylons, liners, and adapters. Each of these components exists in multiple sizes and mobility levels, creating large SKU catalogs.
To avoid delays during patient fittings, clinics often maintain significant local inventories. Over time this practice leads to three major operational problems such as:
This case study involves a mid-size prosthetics provider with 18 clinics and 1 centralized fabrication lab serving approximately 4,800 patients annually, generating about $18.5M annual revenue. Their inventory included prosthetic knees, feet, pylons, liners, adapters. Components were stocked in multiple sizes and mobility-level variants. Details of the company have been kept anonymous to go with non-disclosure agreements.
The company leadership recognized that their inventory inefficiencies were negatively affecting both financial performance and patient experience and decided to have an Operational Excellence expert advise them.
Operational Problem
Before the operational improvement project began, the company maintained more than 520 component SKUs across clinics and the central warehouse. Inventory planning was largely decentralized, with individual clinics ordering components based on anticipated patient demand.
This approach created several inefficiencies:
Operational Excellence Methodology
The company was recommended to adopt Lean Six Sigma using the DMAIC model (Define, Measure, Analyze, Improve, Control).
Tip: There are over 15 operational excellence models to choose from. And the choice depends on several parameters. You may checkout various OpEx models here and how to choose business process improvement methodology here.
Tip: Checkout more about Lean Six Sigma in my book Revolutionizing Industries with Lean Six Sigma
Coming back to this case study, here Lean Six Sigma methodology was selected for three main reasons:
The project team consisted of:
Measurement Phase
During the measurement phase, the team analyzed three years of historical inventory data.
Key metrics evaluated included:
Key Insights
Pareto Analysis
The SKU Pareto analysis revealed two important insights namely-
The SKU demand distribution showed that a small number of prosthetic component sizes are used far more frequently than others. Prosthetic feet sizes S23–S27 and knee modules M1–M3 accounted for the largest share of demand.
The cumulative demand curve demonstrated that the first 10 SKUs represent roughly 75% of annual demand, while the remaining SKUs contribute relatively little usage.
This pattern is common in prosthetic supply chains because most patients fall within a limited set of common limb sizes and mobility categories.
Root Cause Analysis
The operational analysis identified four root causes of the inventory problem.
First, each clinic maintained independent inventory ordering practices, which created redundant stocking across locations.
Second, the company lacked demand forecasting tools, meaning component purchases were reactive rather than data driven.
Third, the SKU catalog had expanded over time without structured lifecycle management, resulting in unnecessary component variations.
Fourth, there was no centralized inventory visibility system, preventing the redistribution of unused parts between clinics.
Improvement Strategy
The operational improvement program implemented four major changes.
1. SKU Rationalization
The team reduced the total SKU count from 520 to 360, eliminating rarely used component sizes and consolidating similar variants.
2. Centralized Inventory Planning
Inventory planning responsibility was moved from individual clinics to a central supply chain team.
3. Demand Forecasting
Historical patient data was used to forecast component demand by:
Instead of stocking large quantities in each clinic, the company created a regional inventory hub capable of supplying clinics within 24–48 hours.
To avoid delays during patient fittings, clinics often maintain significant local inventories. Over time this practice leads to three major operational problems such as:
- Excess working capital tied up in inventory
- Obsolete components due to design upgrades or low demand
- Stockouts of high-demand sizes despite large inventories
This case study involves a mid-size prosthetics provider with 18 clinics and 1 centralized fabrication lab serving approximately 4,800 patients annually, generating about $18.5M annual revenue. Their inventory included prosthetic knees, feet, pylons, liners, adapters. Components were stocked in multiple sizes and mobility-level variants. Details of the company have been kept anonymous to go with non-disclosure agreements.
The company leadership recognized that their inventory inefficiencies were negatively affecting both financial performance and patient experience and decided to have an Operational Excellence expert advise them.
Operational Problem
Before the operational improvement project began, the company maintained more than 520 component SKUs across clinics and the central warehouse. Inventory planning was largely decentralized, with individual clinics ordering components based on anticipated patient demand.
This approach created several inefficiencies:
- Clinics stocked similar components redundantly
- Rarely used sizes remained unused for long periods
- High-demand sizes frequently ran out of stock
- Technicians often had to delay fittings while waiting for parts
Operational Excellence Methodology
The company was recommended to adopt Lean Six Sigma using the DMAIC model (Define, Measure, Analyze, Improve, Control).
Tip: There are over 15 operational excellence models to choose from. And the choice depends on several parameters. You may checkout various OpEx models here and how to choose business process improvement methodology here.
Tip: Checkout more about Lean Six Sigma in my book Revolutionizing Industries with Lean Six Sigma
Coming back to this case study, here Lean Six Sigma methodology was selected for three main reasons:
- Lean methods help eliminate waste such as excess inventory and redundant SKUs.
- Six Sigma analysis provides data-driven decision-making using demand patterns.
- The DMAIC framework supports structured operational transformation.
The project team consisted of:
- Supply chain manager
- Fabrication lab supervisor
- Clinical prosthetist representative
- Data analyst
- Operational excellence lead
Measurement Phase
During the measurement phase, the team analyzed three years of historical inventory data.
Key metrics evaluated included:
- Annual SKU usage
- Stockout frequency
- Inventory turnover
- Carrying cost
- component lead times
Key Insights
- Approximately 20% of component sizes accounted for about 65% of total usage.
- The Pareto demand analysis revealed that many SKUs were rarely used.
Pareto Analysis
The SKU Pareto analysis revealed two important insights namely-
The SKU demand distribution showed that a small number of prosthetic component sizes are used far more frequently than others. Prosthetic feet sizes S23–S27 and knee modules M1–M3 accounted for the largest share of demand.
The cumulative demand curve demonstrated that the first 10 SKUs represent roughly 75% of annual demand, while the remaining SKUs contribute relatively little usage.
This pattern is common in prosthetic supply chains because most patients fall within a limited set of common limb sizes and mobility categories.
Root Cause Analysis
The operational analysis identified four root causes of the inventory problem.
First, each clinic maintained independent inventory ordering practices, which created redundant stocking across locations.
Second, the company lacked demand forecasting tools, meaning component purchases were reactive rather than data driven.
Third, the SKU catalog had expanded over time without structured lifecycle management, resulting in unnecessary component variations.
Fourth, there was no centralized inventory visibility system, preventing the redistribution of unused parts between clinics.
Improvement Strategy
The operational improvement program implemented four major changes.
1. SKU Rationalization
The team reduced the total SKU count from 520 to 360, eliminating rarely used component sizes and consolidating similar variants.
2. Centralized Inventory Planning
Inventory planning responsibility was moved from individual clinics to a central supply chain team.
3. Demand Forecasting
Historical patient data was used to forecast component demand by:
- limb type
- patient mobility classification
- prosthetic configuration
Instead of stocking large quantities in each clinic, the company created a regional inventory hub capable of supplying clinics within 24–48 hours.
The graphs below show a quick recap of improvements that happened after implementing the Lean Six Sigma operational excellence program.
The graphs below show a quick recap of improvements that happened after implementing the Lean Six Sigma operational excellence program.
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Inventory Waste Breakdown (Before and After Improvement)
The Inventory Waste Breakdown identifies the largest cost drivers and helps prioritize improvement initiatives both current and future.
Inventory Waste Breakdown (Before and After Improvement)
The Inventory Waste Breakdown identifies the largest cost drivers and helps prioritize improvement initiatives both current and future.
Operational Excellence Dashboard
What the dashboard shows operationally?
Supply Chain Efficiency
Financial Impact
The reduction in excess inventory and improved component availability had a measurable financial impact.
Supply Chain Efficiency
- Inventory turnover increased significantly.
- Carrying cost dropped substantially.
- Stockouts fell dramatically.
- Lead time for prosthetic components improved.
- Faster fittings improved patient satisfaction.
Financial Impact
The reduction in excess inventory and improved component availability had a measurable financial impact.
The profit increase resulted from:
- reduced inventory costs
- higher clinic throughput
- faster patient fittings
Faster access to the correct prosthetic components allowed clinicians to complete fittings more quickly and with fewer rescheduled appointments.
Strategic Benefits
Beyond financial results, the project created several strategic advantages.
First, the company gained real-time visibility into component demand patterns, enabling more accurate supply planning.
Second, centralized inventory management improved supply chain resilience, ensuring that critical components remained available.
Third, the simplified SKU catalog reduced operational complexity for technicians and clinicians.
Finally, faster fitting cycles allowed clinics to treat more patients annually without increasing staff levels.
Conclusion
Inventory management is one of the most significant operational challenges facing prosthetic providers due to the large number of component sizes and configurations required for patient-specific devices.
This case study demonstrates how applying Lean Six Sigma principles combined with SKU Pareto analysis can significantly improve both the company’s profitability and patient satisfaction.
The table below summarizes the Operational Impact of the Transformation
Strategic Benefits
Beyond financial results, the project created several strategic advantages.
First, the company gained real-time visibility into component demand patterns, enabling more accurate supply planning.
Second, centralized inventory management improved supply chain resilience, ensuring that critical components remained available.
Third, the simplified SKU catalog reduced operational complexity for technicians and clinicians.
Finally, faster fitting cycles allowed clinics to treat more patients annually without increasing staff levels.
Conclusion
Inventory management is one of the most significant operational challenges facing prosthetic providers due to the large number of component sizes and configurations required for patient-specific devices.
This case study demonstrates how applying Lean Six Sigma principles combined with SKU Pareto analysis can significantly improve both the company’s profitability and patient satisfaction.
The table below summarizes the Operational Impact of the Transformation
By reducing unnecessary SKU variation, implementing demand forecasting, and centralizing inventory management, the prosthetic provider achieved:
Equally important, the operational improvements enhanced patient comfort by enabling faster prosthetic fittings and reducing appointment delays.
This case study demonstrates that inventory complexity—not just inventory volume—is a primary driver of inefficiency in prosthetic supply chains. By leveraging Lean Six Sigma principles and Pareto-driven SKU optimization, organizations can simultaneously reduce costs, improve service levels, and enhance patient outcomes.
The key takeaway is clear: operational excellence in prosthetics organizations and healthcare supply chains requires a shift from reactive inventory practices to data-driven, centralized, and strategically optimized systems.
If your organization is struggling with excess inventory, stockouts, or long lead times, it’s time to rethink your supply chain strategy. Start by analyzing your SKU demand patterns and exploring Lean Six Sigma methodologies to unlock measurable performance gains.
Reach out today to assess your inventory system and identify immediate opportunities for cost reduction and service improvement.
- 42% reduction in inventory carrying cost
- 71% reduction in stockouts
- 55% faster component availability
- nearly $1 million increase in annual operating profit
Equally important, the operational improvements enhanced patient comfort by enabling faster prosthetic fittings and reducing appointment delays.
This case study demonstrates that inventory complexity—not just inventory volume—is a primary driver of inefficiency in prosthetic supply chains. By leveraging Lean Six Sigma principles and Pareto-driven SKU optimization, organizations can simultaneously reduce costs, improve service levels, and enhance patient outcomes.
The key takeaway is clear: operational excellence in prosthetics organizations and healthcare supply chains requires a shift from reactive inventory practices to data-driven, centralized, and strategically optimized systems.
If your organization is struggling with excess inventory, stockouts, or long lead times, it’s time to rethink your supply chain strategy. Start by analyzing your SKU demand patterns and exploring Lean Six Sigma methodologies to unlock measurable performance gains.
Reach out today to assess your inventory system and identify immediate opportunities for cost reduction and service improvement.
Disclaimer: This article reflects observed industry trends and professional perspectives and does not constitute regulatory, legal, or operational advice. Read full disclaimer here.
About the author:
Dr. Shruti Bhat is an Advisor in Operational Excellence and Business Continuity Across Pharma and MedTech Value Chains (end-to-end).
Keywords and Tags:
#LeanSixSigma #SupplyChainOptimization #InventoryManagement #HealthcareOperations #Prosthetics #OperationalExcellence #ProcessImprovement #ParetoAnalysis #DMAIC #HealthcareInnovation #CostReduction #PatientExperience #DataDrivenDecisions
Categories: Operational Excellence Case Studies | Life Science Industry | Lean Six Sigma
Follow Shruti on YouTube, LinkedIn
About the author:
Dr. Shruti Bhat is an Advisor in Operational Excellence and Business Continuity Across Pharma and MedTech Value Chains (end-to-end).
Keywords and Tags:
#LeanSixSigma #SupplyChainOptimization #InventoryManagement #HealthcareOperations #Prosthetics #OperationalExcellence #ProcessImprovement #ParetoAnalysis #DMAIC #HealthcareInnovation #CostReduction #PatientExperience #DataDrivenDecisions
Categories: Operational Excellence Case Studies | Life Science Industry | Lean Six Sigma
Follow Shruti on YouTube, LinkedIn
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