Drop 6 initial commit.
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@ -0,0 +1,160 @@
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**Drop 6 – Roadmap & ROI: Your First 90 Stores**
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*Series capstone: “Edge Renaissance—putting compute (and the customer) back where they belong.”*
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---
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### ☕ Executive espresso (60‑second read)
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* **Three phases, ninety stores, one playbook.** Pilot (≤5), Prove (≤30), Scale (≤90).
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* **ROI is real—and fast.** 100 ms faster + higher uptime → +\$X M revenue; hardware amortizes in \~18 months.
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* **Govern once, repeat everywhere.** Immutable builds, GitOps, patch waves. Treat each store like a tiny region.
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* **Message to the board:** “We’re not reinventing—just putting compute back where customers are.”
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---
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## 1️⃣ The 0‑5‑30‑90 rollout map
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```
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PHASE 0: Prep (4–6 weeks)
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Goal: Tooling, golden images, KPIs defined
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Outputs: Git repo, CI/CD, BOM finalized, latency baseline
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PHASE 1: Pilot 5 (6–8 weeks)
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Goal: Prove CX & $ impact with 2 workloads (e.g., /inventory API + image cache)
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KPIs: TTFB ↓100 ms, conversion ↑≥5%, no POS downtime during WAN blips
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Deliverables: Exec brief, ops runbooks, security sign‑off
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PHASE 2: Prove 30 (8–12 weeks)
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Goal: Add 2–3 more edge workloads (vision AI, promos), test patch waves
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KPIs: Cache hit ≥90%, alert MTTR <15 min, patch drift ≤N‑1
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Deliverables: Audit evidence automation, SLO dashboard, budget ask for scale
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PHASE 3: Scale 90 (12–16 weeks)
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Goal: Industrialized rollout kit; 3 waves of 30 stores
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KPIs: 95% stores on latest bundle, <0.1% rollback rate
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Deliverables: Playbook v1.0, board update, expansion roadmap (300+)
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```
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---
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## 2️⃣ Quick‑math ROI you can defend
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**Inputs (example):**
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* Online revenue = \$500 M/year, base conversion 2.5%
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* Latency cut = 100 ms → +7–8% conversion (industry studies)
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* Cap‑ex per store cluster (Good/Better tier) = \$5–6 k (4‑yr life)
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* CDN/egress avoided = \$0.045/GB × 100 TB/mo ≈ \$4.5 k/mo
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**Back‑of‑napkin:**
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```
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Revenue lift: $500M × 7% = +$35M/year
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Hardware: 90 × $6k = $540k (=$135k/yr amortized)
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CDN savings: ~$54k/yr (after expansion)
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ROI (Year 1): (~$35M + $54k) / $675k ≈ 51×
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```
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*Swap in your numbers; the order of magnitude rarely changes.*
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---
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## 3️⃣ People & process: who does what?
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```
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CORE EDGE PLATFORM SQUAD (8–10 FTE)
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• Platform Lead (1) – roadmap, budget, exec comms
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• SRE/Automation (2–3) – CI/CD, GitOps, patch waves
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• Security/Compliance Eng (1–2) – policy as code, audits
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• Edge App Owners (2–3) – /inventory API, promos, vision AI
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REGIONAL / STORE TECH PARTNERS (as‑needed)
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• “Smart hands” for racking, swaps
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• Local networking tweaks
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BUSINESS STAKEHOLDERS
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• Digital Product, Merch, Ops – define the CX wins
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• Finance – track realized savings & uplift
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```
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> **Rule:** Central team builds the pattern, sites pull it. No snowflakes.
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---
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## 4️⃣ Operate like a cloud, even if it’s a closet
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**Your top 6 SLOs (track weekly):**
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1. **TTFB p95 (ms)** for key endpoints (PDP image, `/inventory`)
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2. **Cache hit rate (%)** on Varnish/Nginx
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3. **POS/API latency (ms)** during WAN events
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4. **Patch drift (# stores on N‑2 or older)**
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5. **Backup success rate (%)** (PBS snapshots)
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6. **MTTR for node failure (mins/hours)**
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Dashboards: Grafana/Loki summaries pushed centrally nightly; alerts on trends.
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---
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## 5️⃣ Board‑ready narrative (5 slides)
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1. **Why now:** Latency is a silent tax; we’re over‑spending on cloud/CDN; Amazon wins on speed.
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2. **What we’re doing:** Put compute and delivery where customers are—stores & DCs—using Proxmox on commodity gear.
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3. **Proof:** Pilot results—100 ms faster → +X% revenue, \$Y saved in egress, 0 outages on WAN loss.
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4. **Plan & risk:** 0‑5‑30‑90 rollout, governance pillars, automated compliance.
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5. **Ask:** \$Z cap‑ex, 10 FTE cross‑functional team, payback <18 months.
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---
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## 6️⃣ Risk register (and the auto‑mitigations)
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| Risk | Mitigation baked in |
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| ----------------------- | ------------------------------------------ |
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| Node dies in peak hours | Ceph 3× replica + live‑migration |
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| Patch breaks store | Canary waves + fast rollback via Git tag |
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| WAN outage | Local DNS/APIs, queued sync jobs |
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| Audit fails | Automated evidence exports; policy‑as‑code |
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| Drift / config sprawl | Immutable bundles, Git as SOoT |
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---
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## 7️⃣ Timeline & dollars (visual you can paste)
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```
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Q1 Prep & Pilot (5 stores) $ 75k (gear + labor)
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Q2 Prove @ 30 stores $ 180k
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Q3 Scale wave 1 (30 stores) $ 180k
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Q4 Scale wave 2 (30 stores) $ 180k
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-----------------------------------------
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YEAR 1 CAP-EX / OPEX ≈ $615k
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Projected Year-1 Upside $35M+ revenue lift + $54k cost saved
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```
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*(Adjust “gear” line if you choose GPU nodes or higher tiers.)*
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---
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## 8️⃣ This week’s action list
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1. **Finalize KPIs & baselines** (latency, conversion, uptime).
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2. **Draft the board deck** with the 5‑slide narrative above.
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3. **Lock the pilot scope**: 2 workloads, 5 stores, 8‑week clock.
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4. **Stand up the repo & CI/CD** (even if empty) to anchor governance.
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5. **Book the Phase‑gate reviews** now (Pilot exit, 30‑store exit).
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---
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### 🎁 Series wrap & what’s next
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* **Appendix pack (on request):** BOM spreadsheet, CI pipeline YAML, audit evidence script.
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* **Office hours / webinar?** If there’s interest, I’ll host a live walkthrough.
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* **Spin‑offs:**
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* “Edge ML Ops: packaging & shipping models to 500 sites”
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* “From closets to parking lots: EV chargers & edge compute”
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* “Design patterns for zero‑trust store networks”
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> **Thank you for riding along.** This wasn’t about “new tech”—it was about rediscovering what made the Internet (and great retail) work: put value as close to the customer as you can, and don’t pay rent on distance.
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*Hit follow, drop questions in the comments, or DM for the appendix. Your closets are ready to scale.*
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