Alibi — Logged & Verified
Trust-aware inventory system. Treats inventory as transitions + provenance, not a static list.
What’s wrong with conventional inventory systems?
Traditional inventory systems record totals and assume the ledger reflects reality. Real supply chains are messier — items are mis‑labeled, paperwork lags behind physical movements, and staff take shortcuts.
The result is “ghost inventory” / “phantom inventory,” where the system shows items in stock that aren’t physically there. Phantom inventory creates stock‑outs, extra purchasing, rushed orders, and long‑running distrust in the numbers.
A 2021 study by Zebra Technologies (as cited in Retail Aware) reported phantom inventory at roughly 8% of inventory losses, costing about $8 per $100 in inventory. Causes include theft, counting errors, tracking gaps, and shrink.
Reframing the problem
Alibi models inventory as a stream of state transitions with uncertain provenance. Instead of treating records as ground truth, it treats them as evidence with associated confidence.
Each item has a provenance trail — receipts, transfers, adjustments — allowing the system to surface discrepancies and trust gradients. This mirrors event‑sourcing approaches where every change is logged and auditors can reconstruct how and why counts diverged.
System design choices
- Transitions over totals: Rather than static counts, Alibi tracks every movement. By analyzing events (receipts, transfers, consumption), the system can pinpoint where discrepancies arise.
- Anomalies as signal: Suspicious patterns — unexpected deltas, mismatched lots, missing paperwork — are surfaced for review to reduce the “silent drift” that creates phantom inventory.
- Designed for drift: Alibi assumes messiness. It tolerates uncertainty and lag without losing meaning, reducing the need for shadow systems created when teams lose trust in official records.
Hiring signal
This case study shows how to build trust‑aware systems for compliance, verification, and supply‑chain truth.
It demonstrates design thinking that accounts for human behavior, drift, and failure — and builds tools that help teams recover confidence in their operational reality.
Contact
Quick demo: open the app. Deeper context: use this case study + the full Case Studies hub.