Supply Sentry Case Study
How All Printing Resources Inc Applied the Tier Framework to Build Custom Customer Inventory Management
Challenge: Track customer inventory across multiple locations with RFID technology
Framework Application: Classified as Tier 1-2, verified Level 2 data readiness, assessed internal team capacity
Decision: Build custom solution instead of $400K-$600K/year vendor platform (Blue Yonder)
Result: Supply Sentry deployed at $150K-$250K total cost with full control and customization
The Business Problem
As a printing resources distributor, All Printing Resources Inc supplies consumable materials (inks, plates, anilox rolls) to flexographic printing operations. Many customers maintain inventory at multiple production facilities and struggle to track consumption rates, reorder timing, and stock levels across locations.
Customer pain points:
- •Inventory visibility gaps across 5-15 production facilities per customer
- •Manual stock counts consuming 10-20 hours per week per location
- •Stockouts causing production delays (cost: $5K-$50K per incident)
- •Overstocking tying up $50K-$200K in excess inventory per customer
- •No consumption analytics to optimize reorder quantities
The opportunity: If All Printing Resources Inc could provide real-time inventory visibility and automated reorder recommendations, customers would reduce carrying costs while eliminating stockouts—and All Printing Resources Inc would capture more consistent reorder business.
The Initial Proposal
Install RFID readers at customer facilities to automatically track inventory levels. Build a cloud platform for customers to view stock across all locations, receive reorder alerts, and analyze consumption trends.
Step 1: Classify the Technology Tier
Using the AI Technology Tiers framework, we classified each component of the proposed solution:
Tier 0: Automation
- ✓RFID data ingestion (automated reads)
- ✓Threshold-based alerts (if stock < X, alert)
- ✓Dashboard visualizations (charts, tables)
Cost Range: $5K-$50K
Tier 1-2: ML
- ✓Consumption trend analysis (time series forecasting)
- ✓Predictive reorder recommendations (learning from history)
- ✓Anomaly detection (unusual consumption patterns)
Cost Range: $100K-$500K
Framework Verdict: Tier 1-2 Solution
Core functionality is Tier 0 (data ingestion, alerts, dashboards). Differentiating value comes from Tier 1-2 (consumption forecasting, predictive reordering). This classification determined both technical approach and budget range.
Step 2: Assess Data Readiness
The Data Readiness framework defines 5 levels (0-4). We assessed our existing data infrastructure:
| Data Level | Requirement | All Printing Resources Inc Status |
|---|---|---|
| Level 1 | Centralized CRM/ERP system | ✓ Epicor ERP with customer/product master data |
| Level 2 | Clean historical data (1+ years) | ✓ 3+ years order history, RFID pilot data (6 months) |
| Level 2 | Data governance processes | ✓ Defined product hierarchies, customer segmentation |
| Level 3 | Real-time data pipelines | ⚠ Need to build RFID → cloud pipeline |
Framework Verdict: Level 2 Data Readiness
Strong Level 2 foundation with clean historical data. Level 3 real-time infrastructure needed for RFID feeds, but this is a build task, not a data cleanup problem. According to the framework: Level 2 data confidently supports Tier 0-2 AI—which matches our Tier 1-2 classification. ✅ Data readiness confirmed.
Step 3: Evaluate Team Requirements
The Team & Skills framework defines requirements by tier. For a Tier 1-2 project:
Required Skills (Tier 1-2)
- 1.
Backend Developers (2 FTEs)
API development, RFID data ingestion, PostgreSQL
- 2.
Frontend Developer (1 FTE)
Dashboard UI, mobile-responsive design
- 3.
Data Analyst (0.5 FTE)
Consumption forecasting, statistical modeling
- 4.
DevOps/Infrastructure (0.5 FTE)
Cloud hosting, CI/CD, monitoring
Total: 3-4 FTEs (framework estimate: 2-5 FTEs for Tier 2) ✓
Internal Capacity (All Printing Resources Inc IT)
- ✓
2 Senior Developers
Experienced with C#, Python, SQL, API development
- ✓
1 Frontend Developer
React, Vue.js, responsive design experience
- ⚠
Data Analysis Skills
Gap: Needed to hire or upskill for forecasting
- ✓
IT Director
Infrastructure, DevOps, project management
Decision: 80% capacity in-house, hire 1 data analyst contractor
Framework Verdict: Internal Team Viable
We had 3 of 4 required roles internally. One data analyst contractor ($75/hr × 6 months) filled the gap. Team capacity aligned with framework guidance for Tier 1-2 projects. ✅ Build option feasible.
Step 4: Build vs. Buy Cost Analysis
Using the ROI Calculator and Implementation Roadmap, we compared custom build vs. commercial vendor options:
Blue Yonder Platform
VendorImplementation
$400K - $600K
6-9 months timeline
Annual Subscription
$400K - $600K/year
Based on SKU count + locations
3-Year Total Cost
$1.6M - $2.4M
Pros:
- ✓ Proven enterprise platform
- ✓ Advanced ML forecasting
- ✓ Vendor support & SLAs
Cons:
- ✗ Extremely high annual cost
- ✗ Vendor lock-in
- ✗ Limited customization for our vertical
- ✗ Overkill for our use case (enterprise-scale features we don't need)
Custom Build (Supply Sentry)
BuildDevelopment Cost
$150K - $250K
6-9 months timeline
Annual Operating Cost
$30K - $50K/year
Cloud hosting + maintenance
3-Year Total Cost
$240K - $400K
Pros:
- ✓ 85-90% cost savings vs. Blue Yonder
- ✓ Full control & customization
- ✓ No vendor lock-in
- ✓ Tailored to flexo printing industry
Cons:
- ✗ Internal development risk
- ✗ No vendor SLA guarantees
- ✗ Requires ongoing internal maintenance
Framework Verdict: Build Custom Solution
Tier 1-2 classification + Level 2 data + internal team capacity = Build is viable.
Blue Yonder is a Tier 2-3 enterprise platform designed for global distributors with 50K+ SKUs across 100+ facilities. All Printing Resources Inc's use case (5K SKUs, 30 customer facilities) doesn't justify $400K-$600K/year. Framework guidance: "Don't over-engineer. Match solution tier to problem tier."
Decision: Build custom Supply Sentry platform. 3-year savings: $1.2M - $2.0M. ✅
Step 5: Implementation Roadmap
We followed the 6-phase Implementation Roadmap from the framework:
Phase 0: Discovery & Problem Definition
6 weeks- ✓Interviewed 8 customers to validate pain points and prioritize features
- ✓Mapped data flows: RFID readers → cloud → customer dashboard
- ✓Defined MVP scope: real-time inventory tracking + basic reorder alerts
Phase 1: Proof of Concept
8 weeks- ✓Deployed RFID system at 1 pilot customer location (3 readers, 150 SKUs)
- ✓Built basic API to ingest RFID reads and store in PostgreSQL database
- ✓Created simple dashboard showing real-time stock levels
- ✓Result: Proved technical feasibility. Customer validated 95%+ read accuracy.
Phase 2: Pilot Implementation
5 months- ✓Expanded to 3 customer facilities (total 12 readers, 500 SKUs tracked)
- ✓Built full-featured dashboard: multi-location views, consumption charts, alert configuration
- ✓Implemented Tier 1 analytics: 30-day consumption trends, reorder point recommendations
- ✓Hired data analyst contractor to develop forecasting models (6-month contract)
- ✓Result: Customers reported 30% reduction in manual stock counts, zero stockouts during pilot.
Phase 3: Production Deployment
4 months- ✓Onboarded 8 additional customers (total 30+ facilities, 2,500 SKUs)
- ✓Hardened infrastructure: load balancing, automated backups, 99.9% uptime SLA
- ✓Added Tier 2 features: predictive reorder alerts using time series forecasting (Prophet model)
- ✓Launched customer-facing mobile app for on-the-go inventory checks
Phase 4: Monitoring & Optimization (Ongoing)
$3K-$5K/mo- ✓Continuous monitoring: API performance, RFID read rates, forecast accuracy
- ✓Monthly model retraining using latest consumption data
- ✓Quarterly feature releases based on customer feedback
Total Timeline: 8 months (Discovery → Production)
Framework estimate for Tier 1-2 production deployment: 6-12 months. ✅ On target.
Results & Lessons Learned
Business Results
For All Printing Resources Inc
- ✓$100,000+ saved in development and operational costs
- ✓85 customers using Supply Sentry as a value-added service
- ✓18% increase in reorder consistency from customers using platform
- ✓Competitive differentiation: only flexo distributor with integrated inventory tech
For Customers
- ✓30-40% reduction in time spent on manual inventory counts
- ✓22% reduction in stockout incidents (measured across 8 pilot customers)
- ✓15% reduction in inventory carrying costs (reduced overstocking)
- ✓Real-time visibility across all facilities (previously impossible)
Key Lessons from the Framework
1. Tier Classification Prevents Over-Engineering
By classifying this as Tier 1-2 (not Tier 3-4), we avoided the trap of "we need deep learning" and focused on statistical forecasting that actually solves the problem. Blue Yonder's Tier 2-3 platform was overkill.
2. Data Readiness Determines Feasibility
Level 2 data readiness gave us confidence we could build Tier 1-2 ML features. If we'd only had Level 1 (ad-hoc spreadsheets), we would have needed to invest in data infrastructure first—changing the ROI entirely.
3. Team Assessment Reveals Build Viability
The framework's team sizing (2-5 FTEs for Tier 2) matched our internal capacity. If it had required 12-30 FTEs (Tier 4), we would have known immediately that build was not viable—saving us from a failed project attempt.
4. Cost Ranges Enable Confident Decision-Making
Framework estimate: Tier 2 implementation costs $100K-$500K. Our actual cost: $180K (within range ✓). This gave us confidence our estimates were realistic, not fantasy. Blue Yonder's $400K-$600K/year pricing was clearly enterprise-tier, not mid-market.
5. When Build Makes Sense vs. When It Doesn't
Build made sense for us because:
- • Tier 1-2 complexity (not Tier 4-5 which requires research-level expertise)
- • Level 2 data readiness (clean historical data available)
- • Internal team capacity matched requirements (3 of 4 roles in-house)
- • Industry-specific needs (flexo printing) that generic platforms don't address
- • Vendor pricing was 5-10x our internal cost (extreme savings justified build risk)
If any of these had been false (e.g., Tier 4 complexity, Level 0 data, no internal devs), buying would have been the right choice.
The Bottom Line
The tier-based framework turned a vague "should we build or buy inventory software?" question into a methodical, evidence-based decision process. By classifying the technology tier, assessing data readiness, evaluating team capacity, and comparing costs systematically, we made a confident build decision that saved $1.2M+ over 3 years.
Without the framework, we likely would have either: (1) bought an overpriced enterprise platform we didn't need, or (2) attempted a build without understanding requirements and failed. The framework prevented both outcomes.
Apply This Framework to Your Decision
Whether you're evaluating inventory management, demand forecasting, quality inspection, or any other AI/automation opportunity, this framework provides the structured approach you need.