MILESTONE 5: Role-Based Adaptive Intelligence - Strategic Overview
Status: Just Launched M5.1 Total Phases: 5 (Foundation → Intelligence → Personalization) Timeline: 8-10 weeks to production-quality Impact: This is ChainAlign's "secret sauce"
What Makes This Your Secret Sauce
Most BI/planning tools show the same dashboard to everyone. ChainAlign shows different pages to different people because it understands:
- WHO is viewing (S&OP Director vs. CFO vs. Demand Planner)
- WHEN they're viewing (Pre-read vs. workbench vs. dashboard)
- WHAT they care about (Different metrics per role)
- HOW they decide (Speed, detail level, external data usage)
- WHY it matters (Business impact quantified)
And it learns continuously - the persona templates improve as more users interact.
The Complete Vision
┌─────────────────────────────────────────────────────────────────┐
│ CHAINALIGN INTELLIGENCE SYSTEM │
├─────────────────────────────────────────────────────────────────┤
│ │
│ USER REGISTRATION │
│ ├─ Select Role (12 options) │
│ ├─ Initial Persona Assignment (from default template) │
│ └─ First Page Load (using persona defaults) │
│ ↓ │
│ PERSONA-DRIVEN PAGE GENERATION │
│ ├─ Anomaly Detection (2+ std dev) │
│ ├─ Impact Quantification ($, %, timeline) │
│ ├─ Headline Generation (data-driven, role-aware) │
│ ├─ Hero Section Selection (Executive-focused) │
│ ├─ Detail Section Selection (persona detail level) │
│ ├─ Action Recommendations (learned from similar users) │
│ └─ Render in Persona Template │
│ ↓ │
│ INTERACTION TRACKING & LEARNING │
│ ├─ Track: Headline views, chart interactions, actions taken │
│ ├─ Individual Learning: Update this user's profile │
│ ├─ Aggregate Learning: Weekly persona template evolution │
│ └─ Persona Matching: Adjust persona if user behavior diverges │
│ ↓ │
│ CONTINUOUS IMPROVEMENT │
│ ├─ User Profile → More personalized pages │
│ ├─ Persona Template → Better defaults for new users │
│ └─ Feedback Loop → System gets smarter weekly │
│ │
└─────────────────────────────────────────────────────────────────┘
Current State vs. Future State
CURRENT (Post MILESTONE 4)
User Logs In
↓
Page Renderer
├─ Generic layout
├─ All users see similar structure
└─ Some component customization
↓
Generic Dashboard
FUTURE (After MILESTONE 5)
User Logs In
↓
Get User Persona Profile
├─ Assigned role (S&OP Director, CFO, etc.)
├─ Learned preferences (detail level, metrics, external data)
└─ Historical actions (patterns of decision-making)
↓
Generate Adaptive Page
├─ Detect anomalies relevant to this role
├─ Quantify business impact for this role
├─ Generate headline (data-driven, role-specific)
├─ Create hero section (chart type based on role)
├─ Select detail elements (level matches persona)
├─ Recommend actions (from similar users' patterns)
└─ Use persona template (layout, colors, language)
↓
McKinsey-Style Page
├─ Headline (data-backed statement)
├─ Hero (chart + key metric + context)
├─ Details (only what this role cares about)
├─ Actions (with impact estimates)
└─ Audit trail (external factors, decisions)
↓
Track Interactions
└─ Every view, click, action feeds learning loop
The 12 Personas at a Glance
S&OP Flow (7 Personas)
| # | Role | Primary Decision | Page Focus | Hero Chart |
|---|---|---|---|---|
| 1 | S&OP Executive | Trade-offs: Demand vs. Supply vs. Profit | Strategic alignment & approval | 3-way waterfall |
| 2 | Supply Chain Dir | Production capacity, inventory, suppliers | Supply-demand gap | Gap + coverage |
| 3 | Sales VP | Demand forecast accuracy & bias | Forecast vs. actual | Forecast variance trend |
| 4 | Finance VP (FP&A) | Plan profitability & AOP gap | Financial impact | P&L waterfall |
| 5 | Demand Planner | Statistical forecasting & modeling | Model accuracy & anomalies | Time series + residuals |
| 6 | Supply Planner | Detailed capacity & schedules | Capacity utilization | Gantt + inventory |
| 7 | Marketing VP | Promotions & demand shaping | Promo ROI & effectiveness | Promotion impact |
Financial Flow (5 Personas)
| # | Role | Primary Decision | Page Focus | Hero Chart |
|---|---|---|---|---|
| 8 | CFO | Capital allocation, strategic investments | Enterprise profitability & ROIC | P&L + ROIC by BU |
| 9 | FP&A VP | Scenario modeling & budgeting | Variance analysis & drivers | Variance waterfall |
| 10 | Controller | Data integrity & compliance | Data quality & controls | Data quality scorecard |
| 11 | Treasurer | Cash flow & working capital | Liquidity management | Cash bridge + WC by component |
| 12 | Head of IR | Investor narrative & KPIs | Earnings story & guidance | KPI vs. guidance waterfall |
McKinsey-Style Page Structure
Every page follows this structure (adapted per persona):
┌──────────────────────────────────────────────────────────────┐
│ 1. HEADLINE (Data-Driven, Role-Specific) │
│ ✓ Metric + Direction + Impact │
│ Example: "Frankfurt inventory 23% above optimal, │
│ costing $2.3M in working capital" │
└──────────────────────────────────────────────────────────────┘
┌──────────────────────────────────────────────────────────────┐
│ 2. HERO SECTION (Chart + Key Metric) │
│ ┌────────────────────┐ ┌──────────────────────┐ │
│ │ [Primary Chart] │ │ Key Metrics: │ │
│ │ (Waterfall, Trend, │ │ • $2.3M impact │ │
│ │ Gap, etc.) │ │ • 8.2 weeks (vs 4.5) │ │
│ │ │ │ • Root: Tariff │ │
│ │ │ │ • Timeline: 45 days │ │
│ └────────────────────┘ └──────────────────────┘ │
│ │
│ Context: "Spike driven by tariff-induced hoarding 3 wks ago"│
└──────────────────────────────────────────────────────────────┘
┌──────────────────────────────────────────────────────────────┐
│ 3. ROOT CAUSE & CONTEXT │
│ • External Driver: Policy event (tariff 20%→25%) │
│ • Secondary Driver: Weather spike drove demand (Feb 3) │
│ • Timeline: Tariff announced Jan 15, effective Mar 15 │
│ • Supplier Impact: ABC Corp routing from China→Vietnam │
│ • Lead Time: 45 days to complete rerouting │
└──────────────────────────────────────────────────────────────┘
┌──────────────────────────────────────────────────────────────┐
│ 4. DETAIL SECTIONS (Persona Detail Level) │
│ • Executive: 2-3 KPIs per section │
│ • Medium: Drill by product/region/supplier │
│ • High: Granular (by machine, SKU, week, driver) │
└──────────────────────────────────────────────────────────────┘
┌──────────────────────────────────────────────────────────────┐
│ 5. ACTIONS & RECOMMENDATIONS │
│ [Primary] Reduce inventory by 7,250 units in 4 weeks │
│ [Secondary] Accelerate demand planning for Q2 │
│ [Tertiary] Adjust supplier routing per FTA settlement │
│ │
│ Impact: $2.3M working capital freed, inventory normalized │
│ Timeline: Can execute in 4 weeks (before policy effective) │
└──────────────────────────────────────────────────────────────┘
┌──────────────────────────────────────────────────────────────┐
│ 6. AUDIT TRAIL (Transparency) │
│ Detected: 2025-10-20 (anomaly detection) │
│ Policy Event: US tariff 20%→25% (Jan 15 announced) │
│ External Data: Temperature spike Feb 1-3 (demand driver) │
│ Recommended Action: 2025-10-22 │
│ Owner: Supply Chain Director │
│ Previous Similar Case: Aug 2024 (action effective in 6d) │
└──────────────────────────────────────────────────────────────┘
How Pages Differ by Persona
Same Data, Different Pages
Data: Frankfurt inventory spike (+7,250 units, $2.3M cost)
Supply Chain Director sees:
HEADLINE: "Frankfurt warehouse overstock 23%, $2.3M at risk"
HERO: Inventory waterfall + coverage metric (8.2 vs 4.5 weeks)
DETAIL: Supplier routing impact, lead time to normalize
ACTIONS: Reduce inventory, adjust production schedule
S&OP Executive sees:
HEADLINE: "Inventory spike creates $2.3M working capital headwind,
offsetting operational gain"
HERO: 3-way waterfall (Demand | Supply | Financial)
DETAIL: Trade-off analysis (inventory vs. service level vs. cash)
ACTIONS: Approve inventory reduction, authorize financing if needed
CFO sees:
HEADLINE: "Current plan delivers $148M EBIT vs $150M target,
$2M gap driven by working capital drag"
HERO: P&L waterfall with working capital impact
DETAIL: Cash flow impact, balance sheet implications
ACTIONS: Approve liquidity facility, adjust forecast
Demand Planner sees:
HEADLINE: "Forecast bias +3% in Frankfurt, demand exceeded plan.
Excess inventory suggests bias, not supply risk."
HERO: Forecast vs actual trend line with residuals
DETAIL: Statistical analysis, decomposition by driver
ACTIONS: Adjust model, review bias methodology
Implementation Phases
Phase 1: Persona System Foundation (2-3 weeks)
✓ Build personas table (12 roles) ✓ Build user_profiles table (learning data) ✓ Implement role selection in registration ✓ Create default persona templates
Deliverable: Users assign to persona at registration
Phase 2: Interaction Tracking & Learning (2-3 weeks)
✓ Implement event tracking (headline views, chart interactions, actions) ✓ Build individual learning (update user profile in real-time) ✓ Build persona evolution (weekly aggregation of all users) ✓ Create persona matching re-evaluation
Deliverable: System learns from interactions
Phase 3: Headline Generation & Hero Section (2-3 weeks)
✓ Build AnomalyDetector service (2+ std dev) ✓ Build ImpactQuantifier service ($, %, timeline) ✓ Build HeadlineGenerator (LLM + rules) ✓ Build HeroSection component (Executive-focused)
Deliverable: Data-driven headlines + visual hierarchy
Phase 4: Persona-Driven Page Generation (2-3 weeks)
✓ Build AdaptivePageGenerator service ✓ Build DetailSectionSelector (persona-aware) ✓ Build ActionRecommender (learned from similar users) ✓ Integrate into existing PageRenderer
Deliverable: Pages adapt to persona
Phase 5: External Data Integration (1-2 weeks)
✓ Link M3 weather/policy/econ data to page generation ✓ Add external data enrichment to headlines ✓ Add context badges (weather, policy, economic impact)
Deliverable: Pages explain "why" with external context
Why This Is Your Secret Sauce
Competitors Do This
- ✗ Generic dashboards (same for everyone)
- ✗ Static reports (pre-built templates)
- ✗ Data access (tables and charts)
- ✗ Notifications (alerts when thresholds hit)
ChainAlign Does This
- ✓ Persona-driven pages (different for each role)
- ✓ AI-generated headlines (data tells the story)
- ✓ Executive-focused structure (headline → hero → details)
- ✓ Intelligent context (weather, policy, economic factors)
- ✓ Learning & adaptation (system gets smarter each week)
- ✓ Quantified impact (always know the "why" and "$")
- ✓ Transparent audit trail (see how we got here)
Result: Pages that feel built just for you, making your job easier.
Demo Opportunity
When you demo this, you'll show:
-
Same data, different personas
- Supply Chain Director pre-read (focus: supply-demand gap)
- CFO dashboard (focus: financial impact)
- Demand Planner workbench (focus: model accuracy)
-
Data-driven headlines
- "Frankfurt inventory 23% above optimal, $2.3M at risk"
- Not: "Inventory update for Frankfurt"
-
Executive-focused structure
- Headline that leads to action
- Hero section that visualizes the insight
- Details only relevant to the persona
-
External intelligence
- Explains inventory spike via tariff policy
- Shows weather impact on demand
- Links policy routing decisions to timeline
-
Learning & evolution
- "This persona learned that Supply Chain Directors spend 60% of time on lead time issues"
- "Template updated based on 12 users in this role"
Success = This Conversation
Sales Demo:
- Prospect: "So each role sees a different page?"
- You: "Yes, optimized for how you make decisions"
- Prospect: "And it learns what I care about?"
- You: "Exactly. The more you use it, the smarter it gets"
- Prospect: "And the external context (weather, policy)?"
- You: "Baked into every page, explaining why the numbers changed"
That's the sale.
Next: M5.2 - Headline Generation Engine
Ready to start Phase 1, or want to review any persona definitions first?
I recommend:
- Review the 12 personas (especially the ones for your demo)
- Confirm the page structure matches your vision
- Then we'll launch Phase 1 (Persona System Foundation)
What would you like to adjust or confirm?