cloudless-retail/RETAILCLOUDDROP6.md

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**Drop6 Roadmap & ROI: Your First 90 Stores**
*Series capstone: “Edge Renaissance—putting compute (and the customer) back where they belong.”*
---
### ☕ Executive espresso (60second read)
* **Three phases, ninety stores, one playbook.** Pilot (≤5), Prove (≤30), Scale (≤90).
* **ROI is real—and fast.** 100ms faster + higher uptime → +\$XM revenue; hardware amortizes in \~18months.
* **Govern once, repeat everywhere.** Immutable builds, GitOps, patch waves. Treat each store like a tiny region.
* **Message to the board:** “Were not reinventing—just putting compute back where customers are.”
---
## 1⃣ The 053090 rollout map
```
PHASE 0: Prep (46 weeks)
Goal: Tooling, golden images, KPIs defined
Outputs: Git repo, CI/CD, BOM finalized, latency baseline
PHASE 1: Pilot 5 (68 weeks)
Goal: Prove CX & $ impact with 2 workloads (e.g., /inventory API + image cache)
KPIs: TTFB ↓100 ms, conversion ↑≥5%, no POS downtime during WAN blips
Deliverables: Exec brief, ops runbooks, security signoff
PHASE 2: Prove 30 (812 weeks)
Goal: Add 23 more edge workloads (vision AI, promos), test patch waves
KPIs: Cache hit ≥90%, alert MTTR <15 min, patch drift ≤N1
Deliverables: Audit evidence automation, SLO dashboard, budget ask for scale
PHASE 3: Scale 90 (1216 weeks)
Goal: Industrialized rollout kit; 3 waves of 30 stores
KPIs: 95% stores on latest bundle, <0.1% rollback rate
Deliverables: Playbook v1.0, board update, expansion roadmap (300+)
```
---
## 2⃣ Quickmath ROI you can defend
**Inputs (example):**
* Online revenue = \$500M/year, base conversion 2.5%
* Latency cut = 100ms → +78% conversion (industry studies)
* Capex per store cluster (Good/Better tier) = \$56k (4yr life)
* CDN/egress avoided = \$0.045/GB × 100TB/mo ≈ \$4.5k/mo
**Backofnapkin:**
```
Revenue lift: $500M × 7% = +$35M/year
Hardware: 90 × $6k = $540k (=$135k/yr amortized)
CDN savings: ~$54k/yr (after expansion)
ROI (Year 1): (~$35M + $54k) / $675k ≈ 51×
```
*Swap in your numbers; the order of magnitude rarely changes.*
---
## 3⃣ People & process: who does what?
```
CORE EDGE PLATFORM SQUAD (810 FTE)
• Platform Lead (1) roadmap, budget, exec comms
• SRE/Automation (23) CI/CD, GitOps, patch waves
• Security/Compliance Eng (12) policy as code, audits
• Edge App Owners (23) /inventory API, promos, vision AI
REGIONAL / STORE TECH PARTNERS (asneeded)
• “Smart hands” for racking, swaps
• Local networking tweaks
BUSINESS STAKEHOLDERS
• Digital Product, Merch, Ops define the CX wins
• Finance track realized savings & uplift
```
> **Rule:** Central team builds the pattern, sites pull it. No snowflakes.
---
## 4⃣ Operate like a cloud, even if its a closet
**Your top 6 SLOs (track weekly):**
1. **TTFB p95 (ms)** for key endpoints (PDP image, `/inventory`)
2. **Cache hit rate (%)** on Varnish/Nginx
3. **POS/API latency (ms)** during WAN events
4. **Patch drift (# stores on N2 or older)**
5. **Backup success rate (%)** (PBS snapshots)
6. **MTTR for node failure (mins/hours)**
Dashboards: Grafana/Loki summaries pushed centrally nightly; alerts on trends.
---
## 5⃣ Boardready narrative (5 slides)
1. **Why now:** Latency is a silent tax; were overspending on cloud/CDN; Amazon wins on speed.
2. **What were doing:** Put compute and delivery where customers are—stores & DCs—using Proxmox on commodity gear.
3. **Proof:** Pilot results—100ms faster → +X% revenue, \$Y saved in egress, 0 outages on WAN loss.
4. **Plan & risk:** 053090 rollout, governance pillars, automated compliance.
5. **Ask:** \$Z capex, 10 FTE crossfunctional team, payback <18 months.
---
## 6⃣ Risk register (and the automitigations)
| Risk | Mitigation baked in |
| ----------------------- | ------------------------------------------ |
| Node dies in peak hours | Ceph 3× replica + livemigration |
| Patch breaks store | Canary waves + fast rollback via Git tag |
| WAN outage | Local DNS/APIs, queued sync jobs |
| Audit fails | Automated evidence exports; policyascode |
| Drift / config sprawl | Immutable bundles, Git as SOoT |
---
## 7⃣ Timeline & dollars (visual you can paste)
```
Q1 Prep & Pilot (5 stores) $ 75k (gear + labor)
Q2 Prove @ 30 stores $ 180k
Q3 Scale wave 1 (30 stores) $ 180k
Q4 Scale wave 2 (30 stores) $ 180k
-----------------------------------------
YEAR 1 CAP-EX / OPEX ≈ $615k
Projected Year-1 Upside $35M+ revenue lift + $54k cost saved
```
*(Adjust “gear” line if you choose GPU nodes or higher tiers.)*
---
## 8⃣ This weeks action list
1. **Finalize KPIs & baselines** (latency, conversion, uptime).
2. **Draft the board deck** with the 5slide narrative above.
3. **Lock the pilot scope**: 2 workloads, 5 stores, 8week clock.
4. **Stand up the repo & CI/CD** (even if empty) to anchor governance.
5. **Book the Phasegate reviews** now (Pilot exit, 30store exit).
---
### 🎁 Series wrap & whats next
* **Appendix pack (on request):** BOM spreadsheet, CI pipeline YAML, audit evidence script.
* **Office hours / webinar?** If theres interest, Ill host a live walkthrough.
* **Spinoffs:**
* Edge ML Ops: packaging & shipping models to 500 sites
* From closets to parking lots: EV chargers & edge compute
* Design patterns for zerotrust store networks
> **Thank you for riding along.** This wasnt 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 dont pay rent on distance.
*Hit follow, drop questions in the comments, or DM for the appendix. Your closets are ready to scale.*