Case Studies

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Global Manufacturing company

Overview:

A global manufacturing enterprise operating three production facilities and five international warehouses managed an inventory portfolio exceeding $40M. The engagement aimed to establish end-to-end inventory visibility, strengthen inventory governance, and unlock working capital through data-driven decision intelligence.

Challenge:

Inventory data was fragmented across multiple locations, limiting enterprise-wide visibility and obscuring where capital was tied up. Inventory turnover remained below industry benchmarks, excess and slow-moving stock accumulated, and a growing share of capital was locked in obsolete items. Manual reporting cycles spanning several weeks further delayed executive decision-making and constrained proactive inventory control.

Solution:

I architected and deployed a global inventory analytics ecosystem by extracting ERP data and developing Power BI dashboards delivering global-to-bin-level transparency across all manufacturing and warehouse sites.

The solution framework included:

Enterprise-wide inventory intelligence dashboards providing real-time visibility of stock distribution, process-stage inventory, and value concentration
ABC classification modeling, isolating high-impact items representing 80% of total consumption value
Strategic inventory policy redesign, introducing optimized safety stock thresholds and reordering logic for A, B, and C categories
Inventory ageing analytics, systematically identifying obsolete and slow-moving stock in collaboration with procurement teams
Automated reporting architecture, replacing multi-week manual reporting cycles with instant, self-service analytics

This shifted inventory management from reactive oversight to predictive, control-driven operations.

Result:

The transformation delivered substantial financial and operational gains:
• $4M reduction in global inventory holdings (10%), releasing significant working capital
• End-to-end management visibility, from executive-level summaries down to bin-level stock positioning
• Accelerated inventory velocity, achieving turnover rates of 2.8 for A-class and 4.1 for B-class items
• Obsolete inventory recovery, salvaging 70% of obsolete stock items and 40% of total obsolete inventory value
• Reporting cycle compression, reducing a three-week manual effort to real-time, on-demand insight delivery

ERP : Microsoft Dynamics ERP

Data Modeling & Visualization : Power BI

Fashion Retail brand

Overview:

A global fashion retail brand sourcing raw materials from China and operating production facilities in Bangladesh and Vancouver managed over 12,000 SKUs. The engagement aimed to enhance demand forecasting accuracy, optimize inventory management, and streamline the end-to-end planning process to support timely product launches and maximize working capital efficiency.

Challenge:

The company faced significant operational and planning inefficiencies:
• Inaccurate demand forecasts for raw materials (fabrics) resulted in frequent finished goods stockouts and occasional excess inventory
• Existing forecasting tools could not accommodate rapid changes in low-level items triggered by trend shifts, leading to misaligned procurement
• Lack of integration between PLM and ERP systems caused frequent inaccuracies in order quantities, as design updates and BOM changes were not reflected in material planning
• The large SKU portfolio (12,000+ SKUs) compounded forecast errors, inventory inefficiencies, and planning delays
These challenges constrained product availability, slowed seasonal launches, and tied up working capital unnecessarily.

Solution:

I implemented a holistic, analytics-driven demand planning framework, combining advanced forecasting, system integration, and proactive scenario modeling:

SKU Rationalization: Identified slow-moving and redundant SKUs to streamline inventory and focus on high-impact item
Advanced Forecasting Models: Leveraged historical sales, seasonality, and trend signals to generate accurate, actionable demand forecasts
ERP–PLM Integration:Synchronized design updates, BOM revisions, and low-level SKU changes with material planning to ensure correct order quantities
Scenario-Based Planning: Simulated shifts in market trends and product demand to enable proactive adjustments in procurement and production
Trend Sensing Inputs: Integrated merchandising and retail insights to anticipate demand shifts and enhance forecast precision
Redefined Reordering Policies: Adjusted reorder points and safety stock levels based on updated SKU classifications, design changes, and BOM adjustments.

This solution shifted planning from reactive and error-prone processes to predictive, data-driven decision-making.

Result:

The transformation delivered measurable operational and financial results:
• 30% reduction in fabric overbuy, freeing significant working capital
• 15% decrease in finished goods stockouts, improving product availability
• Zero material stockouts and improved inventory turns across all SKU categories
• Higher on-time launch rate for seasonal collections, enhancing responsiveness to market trends
• Single source of truth for materials and SKU data, reducing planning errors and decision delays

ERP : Salesforce

PLM : BlueCherry Fashion

Data Modeling & Visualization : Power BI