When faced with inconsistent consumption and outdated inventory practices, I built a reliable system that transformed guesswork into algorithm. By redesigning the Min/Max calculation, modeling price break thresholds, and integrating VOI/VMI decision logic, I helped reduce stockouts, free up capital, and align pricing with real usage.

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Situation

A large manufacturing company was facing operational inefficiencies, missed savings opportunities, and margin erosion across its supply chain due to outdated inventory practices. Despite strong production capabilities, the organization was plagued by inconsistent stocking levels: some critical components were consistently overstocked while others experienced dangerous stockouts, risking unplanned downtime and late customer deliveries.

The root of the issue lay in disconnected and manual inventory management practices. Procurement teams often relied on static reorder points, anecdotal usage assumptions, and supplier-set minimum order quantities. These methods failed to reflect real consumption patterns or adapt to changes in demand. To make matters worse, the company’s supplier-customer pricing model was misaligned: items were being miscategorized as supplier-owned (VMI) or customer-invoiced upon use (VOI) without understanding how usage patterns impacted risk, price breaks, and cash flow. This created margin loss on both sides.

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Task

The objective was to build an intelligent, data-backed inventory model that could:

• Dynamically calculate Min/Max levels using historical usage trends
• Recommend whether items should remain VMI (vendor-managed inventory) or shift to VOI (vendor-owned inventory) based on consistency of consumption and inventory turns
• Evaluate trade-offs between cost-savings from bulk purchasing and financial risk of holding inventory
• Prevent stockouts while reducing procurement noise and inventory waste
• Empower plant managers and finance controllers with scenario-based decision-making tools
• Align pricing tiers with true usage patterns and vendor contract discounts

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Action

The first step was to collect and analyze 12-month rolling consumption data across hundreds of stocked part numbers. I classified each item based on its usage regularity; for example, if an item was consumed in 6 or more months within a year, it was considered VOI-eligible. These items, typically fast-moving or high-turn, could be purchased in bulk by the supplier and invoiced per unit as used by the customer. This arrangement allowed for competitive pricing without increasing holding risk for the customer.

Next, I built an automated Excel-based Min/Max tool that offered flexible stocking strategies, either 60-day or 90-day coverage, based on the manufacturing site’s preference. The tool calculated optimized Min/Max levels using monthly usage averages and rolling trends, ensuring right-sized orders, one-shot pricing, and improved inventory health. I also modeled inventory turn rates and simulated the financial impact of holding 6 vs. 12 months of stock, helping teams visualize how purchasing decisions affected not just price breaks, but also carrying costs, crib/warehouse space, and cash flow.

Collaboration with the customer was critical. I met with plant managers to understand risk tolerances and operational preferences, and worked closely with financial controllers to evaluate the impact of moving from VMI to VOI across product groups. This helped reframe the conversation from transactional purchasing to long-term strategy and empowered plant teams to choose their inventory path confidently.

Simultaneously, I addressed pricing inefficiencies. I audited vendor contracts and discovered the company was overcharging for items lacking documented discount agreements, and undercharging for items where prior quantity fluctuations misaligned with tiered pricing logic. I partnered with IT to enhance our Power BI “Customer Portal” that exposed these patterns transparently. The portal displayed:

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Result

The impact of this project was both operational and strategic:

• Eliminated stockouts for high-use materials through dynamic auto-replenishment
• Reduced procurement noise by 40%, streamlining reorders and improving buyer efficiency
• Recovered over $200K in tied-up working capital through right-sized inventory holding
• Enabled strategic stocking decisions based on plant-specific risk thresholds and inventory turns
• Improved VOI pricing transparency, which strengthened trust and collaboration between supplier and customer
• Exposed pricing misalignments, prompting corrections to prevent future margin loss
• Elevated procurement from reactive to proactive, linking price, quantity, and risk to usage data
• Created a scalable, repeatable solution now regarded as a best-practice framework across multiple facilities

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Key Skills

• Inventory Forecasting & Consumption Modeling
• Min/Max Optimization Logic
• Procurement Cost-Benefit Analysis
• Excel Automation with VBA & Logic Flows
• Inventory Turns & Holding Cost Justification
• Stakeholder Alignment (Operations + Finance + Procurement)
• Power BI Dashboarding & Transparency Tools
• Strategic Supply Chain Management

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Disclaimer: The company name and proprietary business information have been intentionally omitted from this case study. All details are presented strictly for academic, professional, and educational purposes only. The solutions, data representations, and outcomes described are shared to illustrate process improvement methodologies, technical skills, and strategic problem-solving, not to disclose or represent any confidential or proprietary information.