Case Study
Modern Data Stack Enables Accurate, Optimized Inventory for a Mid-Sized Manufacturer
Client Overview
The client was a growing SMB manufacturer headquartered in the Pacific Northwest United States, focusing on gears and mechanical assemblies for industrial machinery and renewable energy sectors with about 90 employees. The company was facing increasing complexity in inventory management as product variety and customer demand expanded. Inventory represented one of the company’s largest investments—and one of its biggest operational risks.
Before engaging Lasso, the firm depended on fragmented tools like Excel spreadsheets, a basic QuickBooks setup, and manual paper-based tracking. This resulted in persistent inventory inaccuracies, leading to production halts, inflated holding costs, and missed revenue opportunities.
Problem
The company grappled with several inventory-related hurdles that hampered growth:
Inventory Inaccuracies: Reliance on manual entries and disjointed systems caused discrepancies in stock levels. Shop floor teams frequently encountered unexpected shortages, resulting in lengthy downtime.
Suboptimal Inventory Levels: Without a robust data architecture and analytics foundation, demand forecasting was guesswork, leading to overstocking of low-turnover items and understocking of critical parts.
Data Fragmentation: Information from procurement, production, and sales was isolated, complicating report generation. Managers devoted hours to manual data consolidation, fostering reactive rather than strategic decisions.
These issues collectively diminished profit margins through inefficient working capital and tapered revenue growth opportunities due to an inability to supply products to customers when they needed them.
Solution
To address these challenges, Lasso designed and built a modern data lakehouse purpose-built for this small business’s scale and agility. The lakehouse combined the flexibility of a data lake with the performance and structure of a data warehouse—allowing the company to centralize raw data while enabling fast, reliable inventory analytics.
Single Source of Truth for Inventory: The data lakehouse centralized inventory data from ERP and WMS systems and applied consistent transformation logic to define: On-hand inventory, Allocated and reserved quantities, Available-to-promise (ATP), and Inventory aging and valuation. By resolving discrepancies at ingestion and transformation time, the company eliminated conflicting reports and significantly increased trust in inventory numbers.
Improved Inventory Accuracy: With standardized data pipelines and validation rules in place, inventory accuracy improved rapidly. Automated checks flagged mismatches between systems and highlighted timing issues related to transactions and postings.
Planners and operations teams no longer spent time reconciling numbers—freeing them to focus on planning and execution.
Demand and Consumption Visibility: The data lakehouse enabled the blending of multiple data sources to create a true picture of demand, including: Historical shipments, Open sales orders, and Production consumption. This delivered SKU-level demand trends, usage variability, and seasonality insights that were previously unavailable.
Inventory Optimization Logic: Using the curated lakehouse data, the company implemented practical inventory optimization models, including:, Dynamic safety stock calculations based on demand variability, Data-driven reorder points using actual supplier lead times, and Identification of slow-moving, excess, and obsolete inventory. Because the logic ran directly on data lakehouse tables, results refreshed automatically as new data arrived—without manual spreadsheet updates.
Role-Based Analytics: The lakehouse powered role-specific dashboards tailored to each team:
Planners: stockout risk, reorder alerts, safety stock coverage
Operations: inventory turns, aging, bottlenecks by location
Finance: inventory valuation, working capital impact
All teams worked from the same data foundation, improving alignment and decision-making across the organization.
Outcomes
The data lakehouse transformation delivered measurable gains in GearTech’s inventory management:
Superior Accuracy: Real-time data unification and governance achieved 97% inventory accuracy, up from 78%, reducing reconciliation errors by 88%.
Enhanced Optimization: ML-driven forecasting minimized overstock by 42%, freeing $190,000 in capital. Stockouts decreased by 62%, boosting fulfillment rates to 98%.
Efficiency Improvements: Automated workflows saved 14 hours weekly on data handling, allowing teams to focus on innovation. Inventory turnover increased from 4.5x to 6.5x annually.
Cost Reductions: Annual savings reached $280,000 through lower holding costs, reduced rush orders, and optimized supplier contracts informed by lakehouse insights.
Future-Proof Scalability: The architecture supported advanced analytics, like predictive maintenance integration, paving the way for further efficiencies.
Proven Results
Lasso Case Studies
Turn Your Data Into Better Decisions
Get in touch with our team of data and AI experts to learn how our end-to-end solutions can empower your business to make smarter, data-driven decisions.
