Case Study

StockPilot V2: Advanced Multi-Location Procurement Intelligence

An advanced engineering expansion of the StockPilot ecosystem, adding deep vendor performance scoring, predictive restocking calendars, and variance analytics.

Industry

Supply Chain & Retail

Deployment

Cloud-Native Expansion Module

Problem & its Solution

What frustrates enterprises most and how we solve it with precision engineering

Problem

Inefficient Purchasing & Vendor Blindness. Procurement teams lacked the data to negotiate effectively with unreliable vendors and carried bloated inventory to survive seasonal demand spikes.

45%

Dead Stock Rate (-%)

Solution

Predictive Procurement Intelligence. Brihat deployed advanced AI to build predictive, Just-in-Time restocking calendars and automated vendor performance scoring to ruthlessly optimize supply chain margins.

25%

Carrying Costs Cut

Problem

Inefficient Purchasing & Vendor Blindness

Procurement teams lacked the data to negotiate effectively with unreliable vendors and carried bloated inventory to survive seasonal demand spikes.

45%

Dead Stock Rate (-%)

Solution

Predictive Procurement Intelligence

Brihat deployed advanced AI to build predictive, Just-in-Time restocking calendars and automated vendor performance scoring to ruthlessly optimize supply chain margins.

25%

Carrying Costs Cut

How We Helped

A detailed look at the approach, delivery decisions, and execution details.

Driving Predictability Deep into the Supply Chain

Following the successful deployment of the core StockPilot inventory engine, the client aimed to push operations from 'synchronized' to 'highly predictive.' While multi-location stockouts were heavily minimized, the procurement department still essentially operated manually when evaluating vendor negotiations and reacting to seasonal demand shifts. The client needed a profound intelligence upgrade to minimize carrying costs and mathematically optimize supplier relationships.

Brihat engineered StockPilot V2, an intelligent expansion module focusing strictly on Procurement Intelligence and Vendor Analytics. We augmented the existing architecture with advanced machine learning models designed to forecast demand months in advance and grade supplier reliability mercilessly.

Vendor Performance & Variance Analytics

We built a quantitative grading system for the supply chain.
Key engineering additions include:

Automated Vendor Scoring: The system passively calculates 'On-Time-In-Full' (OTIF) metrics for every PO, fundamentally grading vendors on reliability.
Procurement Cost Variance: Algorithmic tracking of historical pricing for 1,000+ SKUs, alerting procurement managers instantly if a vendor drifts above standard market rates.

Predictive Restocking Calendar

To optimize carrying costs, Brihat deployed a complex predictive scheduling engine. The system analyzes lead times against projected sales velocity, generating an automated, visual restocking calendar. This tells the purchasing department exactly *when* to execute a PO (down to the day) to ensure stock arrives just before the reorder threshold fails, essentially creating a 'Justin-Time' (JIT) workflow for multi-warehouse retail.

Delivery Process

This system was delivered through a structured, governance-first approach designed for enterprise environments — balancing speed with stability, and innovation with accountability.

Discovery & System Mapping

Deep analysis of existing systems, workflows, data ownership, and decision paths to define clear system boundaries and risks.

Architecture & Governance Design

Designed a modular architecture with role-based access, auditability, and security controls embedded from day one.

Iterative Build & Validation

Delivered in controlled iterations with continuous validation against real workflows, data integrity, and operational needs.

What Changed After Delivery

A phase-by-phase view of operational change, business outcomes, and measurable progress after implementation.

Vendor Database RefactoringData Granularity: High

Re-engineered the backend to track micro-KPIs (date ordered vs. date received) for every vendor.

Enabled the creation of the ruthless OTIF grading scale.

Predictive Engine TrainingCarrying Costs: -25%

Trained the JIT algorithms on historical seasonality and adjusted vendor lead times.

Allowed the firm to push carrying costs down without risking stockouts.

Variance AlertsMargin Protection: Active

Implemented the financial cross-checking logic to audit incoming prices against historical means.

Protected the firm's profit margins from silent 'pricing creep'.

Business Impact & ROI

Measurable results demonstrating how intelligent systems replaced legacy processes with speed, accuracy, and accountability.

Carrying Costs Cut (%)

Dead Stock Rate (-%)

Just-In-Time Efficiency (+%)

Problem

Fear of stockouts caused the team to over-purchase massively prior to busy seasons.

Legacy Process

✓

Solution

Built predictive models to delay PO execution until mathematically required.

Modern System

Post-Implementation Highlights

Optimized Carrying CostsTransitioned the enterprise to a Just-In-Time procurement model, drastically reducing capital locked in warehouse storage.
Data-Backed NegotiationsProcurement managers now leverage hard AI data regarding historical delays and price variance during vendor contract negotiations.