ChainAlign Role Personas & Decision Archetypes Framework

Version: 1.0 Status: Design Phase (MILESTONE 5.1) Date: 2025-10-22 Purpose: Define persona archetypes that drive adaptive page generation, learning, and decision intelligence

Executive Summary

ChainAlign's secret sauce is a persona-based adaptive intelligence system that:

Identifies which persona best fits each user at registration
Learns how that persona makes decisions (via interaction tracking)
Adapts page generation, headlines, metrics, and actions to that specific persona
Evolves the persona template based on aggregate user behavior
Personalizes without requiring manual configuration

This framework defines the 12 core personas across two decision flows:

S&OP Flow (7 personas): Supply chain and operational planning
Financial Flow (5 personas): Strategic financial management

Part 1: S&OP Decision Flow Personas

Overview

S&OP is about trade-off decisions: Demand vs. Supply vs. Profitability. Each role makes different trade-offs.

Persona 1: S&OP Executive Owner (CEO/COO/GM/VP P&L)

Primary Decision Authority: Final sign-off on all trade-offs

Decision Focus

Demand vs. Supply vs. Profitability trade-offs
Capacity investment vs. lost sales
Market share vs. margin optimization
Cross-functional consensus

Platform Utility

Consensus Plan vs. Financial Budget view (alignment check)
Executive summary: "Are we aligned on the plan? Does it fit the budget?"
Scenario comparison: "What's the financial impact of each option?"
Dissent summary: "Which teams don't agree? Why?"

Page Generation Requirements

Headline Type: Strategic/Trade-off (e.g., "To meet $5B revenue target, require 15% capacity investment")
Hero Section: 3-way waterfall (Demand | Supply Constraint | Financial Impact)
Metrics: Plan variance to AOP, capital required, ROI
Context: Cross-functional readiness, dissents, risk flags
Actions: Approve Plan, Request Changes, Escalate Risk
Detail Level: Executive summary (2-3 KPIs per section)

Interaction Patterns to Track

Spends time on which trade-offs?
Approves with/without scenario review?
Escalates financial or operational risks?
Decision speed (minutes to hours?)

Data Inputs

Consensus status across all functions
Financial plan vs. operating budget
Scenario financial summaries
Risk indicators from each function

Persona 2: VP/Director of Supply Chain & Operations

Primary Decision Authority: Supply constraints, capacity, inventory levels, supplier risk

Decision Focus

Production capacity and scheduling
Inventory targets vs. service levels
Supplier risk and diversification
Lead time and supply variability
Make-vs-buy decisions

Platform Utility

Supply-Demand Gap Analysis (primary chart)
Capacity utilization by product line and time period
Inventory projections with coverage metrics
Supplier risk and concentration analysis
Lead time variability (policy impact)

Page Generation Requirements

Headline Type: Operational (e.g., "Demand exceeds capacity in Q3, requires 2-week production lead extension")
Hero Section: Supply-demand gap chart + coverage metric
Metrics: Gap size, timeline to resolution, inventory days, supplier risk
Context: External factors (policy routing changes, lead time impacts)
Actions: Adjust production plan, Increase inventory, Source alternative supplier, Request demand adjustment
Detail Level: Medium (drill into supplier, SKU, time period)

Interaction Patterns to Track

Focuses on which products/suppliers?
How often reviews lead time impacts?
Decision speed on capacity trade-offs?
Uses external data (weather, policy) in decisions?

Data Inputs

Demand forecast
Capacity by production line
Inventory targets and current levels
Supplier lead times and variability
Policy events and routing impacts
Weather-driven demand shifts

Persona 3: VP/Director of Sales

Primary Decision Authority: Unconstrained sales forecast, market intelligence, sales targets

Decision Focus

Demand forecast accuracy and bias
Market intelligence and competitive threats
Sales target alignment and achievability
Promotional effectiveness
New customer/product potential

Platform Utility

Forecast Accuracy & Bias Metrics (primary dashboard)
Historical forecast vs. actual comparison
Forecast bias by region, customer, product
Competitive win/loss analysis
Promotional impact quantification

Page Generation Requirements

Headline Type: Market (e.g., "Forecast bias in EMEA +8%, suggesting $2.3M upside opportunity")
Hero Section: Forecast variance trend + by-region breakdown
Metrics: Forecast accuracy (MAPE), bias, upside/downside, RMSE
Context: Competitive intelligence, promo effectiveness, market trends
Actions: Adjust forecast, Review by region, Analyze competitive win/loss, Plan promotion
Detail Level: Medium (by region, channel, product family)

Interaction Patterns to Track

Reviews which regions/products most?
Responds to forecast variance alerts?
Adjusts forecasts based on external data?
Decision frequency (daily, weekly)?

Data Inputs

Historical demand vs. forecast
Market intelligence (news, competitor actions)
Sales pipeline and conversion rates
Promotional history and effectiveness
Weather/policy context (impacts demand)

Persona 4: VP/Director of Finance (FP&A Leader)

Primary Decision Authority: Financial translation of S&OP plan into budget, profitability analysis, AOP gap

Decision Focus

Plan profitability and margin impact
Budget allocation and expense management
Working capital implications
Annual Operating Plan (AOP) reconciliation
Scenario financial modeling

Platform Utility

Integrated P&L Model linking S&OP decisions to profitability
Cash flow impact of inventory and production decisions
Working capital analysis (receivables, payables, inventory)
Scenario P&L and variance analysis
Financial risk indicators

Page Generation Requirements

Headline Type: Financial (e.g., "Current plan delivers $8.2M EBIT, $1.8M below AOP target")
Hero Section: P&L waterfall (Revenue | COGS | OpEx | EBIT) + AOP comparison
Metrics: EBIT, EBITDA, margin %, working capital, cash conversion
Context: Plan vs. AOP gap, key cost drivers, financial risks
Actions: Adjust plan, Reduce cost, Improve pricing, Escalate gap
Detail Level: Medium (by cost category, business unit)

Interaction Patterns to Track

Focuses on which P&L line items?
How sensitive to working capital?
Reviews cash flow impact?
Decision speed on financial trade-offs?

Data Inputs

S&OP plan (demand, supply, pricing)
Cost structure (COGS, OpEx)
Working capital drivers
AOP targets and assumptions
Scenario financial impacts

Persona 5: Demand Planner / Forecasting Manager

Primary Decision Authority: Statistical forecast generation, historical data management, demand review facilitation

Decision Focus

Forecast methodology and model selection
Historical data quality and adjustments
Seasonal and trend decomposition
Outlier detection and handling
Forecast accuracy improvement

Platform Utility

Rich Statistical Modeling Tools (the core)
Time series decomposition (trend, seasonality, residuals)
Anomaly detection and handling
Model comparison and selection
Forecast accuracy diagnostics (ACF, residuals)
External variable correlation (weather, policy)

Page Generation Requirements

Headline Type: Analytical (e.g., "Temperature correlation explains 52% of demand variance, improves forecast accuracy by 8%")
Hero Section: Time series chart + residuals + external factor overlay
Metrics: Forecast accuracy, bias, MAE, RMSE, external correlation strength
Context: Anomalies, external factors (weather, policy), data quality
Actions: Adjust model, Include external variable, Handle outlier, Update baseline
Detail Level: High (granular: by product, region, time period, statistical measures)

Interaction Patterns to Track

Which statistical methods are preferred?
How often adjusts baseline vs. uses model?
Responds to anomaly alerts?
Incorporates external data into models?

Data Inputs

Historical demand data (clean and detailed)
External data (weather, policy, economic)
Statistical models and accuracy metrics
Anomaly detection results
Outlier handling decisions

Persona 6: Supply Planner / Production Manager

Primary Decision Authority: Detailed capacity planning, executable schedules, inventory targets

Decision Focus

Production schedule feasibility
Inventory target setting by SKU
Machine scheduling and changeover costs
Safety stock calculations
Supply chain lead time management

Platform Utility

Detailed Capacity Utilization Dashboard
Production schedule (by machine, shift, product)
Inventory projections (by SKU, warehouse)
Lead time adherence tracking
Changeover and constraint analysis
Safety stock vs. service level trade-off

Page Generation Requirements

Headline Type: Operational (e.g., "Machine A overloaded 12% in weeks 5-7, recommend pre-production in week 3")
Hero Section: Capacity utilization Gantt + inventory projection
Metrics: Utilization %, stockout risk, inventory days, lead time performance
Context: Constraint location, bottleneck products, lead time impacts
Actions: Adjust production schedule, Pre-produce, Reduce batch size, Request capacity increase
Detail Level: High (by machine, SKU, week)

Interaction Patterns to Track

Focuses on which machines/products?
How often adjusts inventory targets?
Responds to stockout risk alerts?
Lead time variability sensitivity?

Data Inputs

Production plan and demand forecast
Machine capacity and constraints
Current and projected inventory
Lead times by supplier (with variability)
Changeover costs and times

Persona 7: VP/Director of Marketing

Primary Decision Authority: Demand shaping via promotions, pricing, NPIs

Decision Focus

Promotion planning and ROI analysis
Pricing optimization
New Product Introduction (NPI) timing and volume
Market share and competitive positioning
Campaign effectiveness tracking

Platform Utility

Promotional Impact Quantification
Promotion ROI analysis (lift vs. cost)
Pricing elasticity and optimization models
NPI demand ramp scenarios
Campaign effectiveness tracking
Incremental volume vs. cannibalization

Page Generation Requirements

Headline Type: Market (e.g., "Q2 promotion drives $3.2M incremental revenue, 18% ROI vs. 12% threshold")
Hero Section: Promotion impact chart + ROI comparison
Metrics: Lift %, ROI, incremental revenue, margin impact, cannibalization
Context: Competitive activity, market conditions, customer response
Actions: Approve promotion, Adjust timing, Increase investment, Cancel/pivot
Detail Level: Medium (by promotion, product family, customer segment)

Interaction Patterns to Track

Focuses on which product categories?
How much incremental volume vs. cannibalization?
Promotion speed (how fast decides)?
Sensitivity to competitive activity?

Data Inputs

Promotion plans and ROI models
Historical promotion effectiveness
Demand forecast with/without promotion
Pricing and elasticity data
Competitive activity and pricing
NPI demand ramps

Part 2: Financial Decision Flow Personas

Overview

Financial flow is about capital allocation and value creation. Each role manages different aspects of financial performance and risk.

Persona 8: Chief Financial Officer (CFO)

Primary Decision Authority: Strategic capital allocation, M&A, financing, enterprise-wide profitability and risk

Decision Focus

Strategic capital allocation (CapEx, R&D, M&A)
Enterprise profitability and ROIC
Balance sheet optimization
Cash flow and liquidity management
Investor communication and equity value

Platform Utility

Strategic Financial Dashboard (executive level)
Forward P&L, cash flow, ROIC by business unit
Capital allocation waterfall
Cash flow bridge (operating, investing, financing)
Enterprise-wide risk dashboard
Strategic KPIs (revenue growth, margin, ROIC, FCF)

Page Generation Requirements

Headline Type: Strategic (e.g., "Operating margin declining 40bps YTD due to supply chain inflation, requires $2.1M productivity initiative")
Hero Section: P&L waterfall + ROIC by BU + cash flow summary
Metrics: Revenue growth, EBIT margin, ROIC, FCF, debt ratios, strategic KPIs
Context: Year-over-year trends, guidance vs. actual, strategic risks
Actions: Approve capital plan, Escalate margin pressure, Authorize special spending, Request detailed analysis
Detail Level: Executive summary (by business unit, strategic theme)

Interaction Patterns to Track

Which KPIs does CFO spend most time on?
How often reviews forecast vs. guidance?
Requires drill-down into detail?
Decision speed on capital allocation?

Data Inputs

Enterprise financial plans
Capital project pipeline
Cash flow forecasts
Strategic KPIs and targets
Guidance and market expectations
S&OP operational drivers

Persona 9: VP/Director of FP&A (Financial Planning & Analysis)

Primary Decision Authority: Scenario modeling, budget allocation, what-if financial models linking operational drivers to P&L

Decision Focus

Scenario financial modeling
Budget development and variance analysis
Cost reduction opportunity identification
Operational driver sensitivity (what drives margin?)
Rolling forecasts and AOP updates

Platform Utility

Integrated Financial Models
Operational drivers linked to P&L (e.g., volume → COGS → margin)
Scenario modeling engine (waterfall scenarios to P&L)
Variance analysis (plan vs. actual, forecast vs. guidance)
Cost structure and sensitivity analysis
Rolling forecast and AOP reconciliation

Page Generation Requirements

Headline Type: Analytical (e.g., "COGS inflation $4.2M vs. plan; demand forecast +3% could offset via volume leverage")
Hero Section: Variance waterfall + sensitivity analysis + scenario comparison
Metrics: Variance $, %, driver impact, scenario P&L delta, sensitivity coefficients
Context: Root causes, mitigation levers, scenario assumptions
Actions: Model scenario, Adjust assumptions, Recommend action, Update forecast
Detail Level: High (by cost category, driver, scenario)

Interaction Patterns to Track

Which operational drivers are most sensitive?
How often runs "what-if" scenarios?
Focuses on which variance drivers?
Collaborates with which functions?

Data Inputs

Operational plans (S&OP demand, supply, pricing)
Cost structure and historical actuals
Budget allocations
Financial assumptions and sensitivities
Variance drivers and analysis

Persona 10: Controller / Chief Accounting Officer

Primary Decision Authority: Financial accuracy, internal controls, compliance with regulations, data integrity and audit trail

Decision Focus

Financial data accuracy and completeness
Internal controls and compliance
Regulatory reporting requirements
Audit readiness
Data governance and integrity

Platform Utility

Data Integrity & Audit Trail Dashboard
Data source validation and reconciliation
Control testing and exception tracking
Regulatory compliance checklist
Audit workpaper support
Data lineage and traceability

Page Generation Requirements

Headline Type: Compliance (e.g., "Data completeness for EMEA region 94%, 3 exceptions in Q3 forecast")
Hero Section: Data quality scorecard + exceptions and remediation
Metrics: Data completeness %, validation pass rate, exception count, audit risk
Context: Exception details, root causes, remediation plans, timeline
Actions: Request remediation, Escalate audit risk, Approve as-is with note, Adjust scope
Detail Level: Medium-high (by data source, control, exception)

Interaction Patterns to Track

How sensitive to data quality thresholds?
Requires detailed exception investigation?
Responds to audit risk alerts?
Decision speed on remediation?

Data Inputs

Data source validation results
Control test results
Regulatory requirement checklist
Audit exception list
Data lineage and reconciliation

Persona 11: Treasurer / Treasury Manager

Primary Decision Authority: Liquidity management, working capital optimization, FX and debt risk management

Decision Focus

Cash flow forecasting and management
Working capital optimization (DPO, DSO, DIO)
Foreign exchange hedging
Debt management and refinancing
Short-term investment and liquidity reserve

Platform Utility

Working Capital & Cash Flow Dashboard (granular)
Daily/weekly/monthly cash position and forecast
Working capital by component (AR, AP, inventory)
FX exposure and hedging requirements
Debt schedule and covenant monitoring
Liquidity and stress scenarios

Page Generation Requirements

Headline Type: Financial (e.g., "Cash position tight in March (-$52M), FX headwind +$3.2M, requires $45M working capital improvement")
Hero Section: Cash flow bridge + working capital by component
Metrics: Cash position, WC %, DSO/DPO/DIO, FX exposure, covenant ratios
Context: Seasonality, FX movements, capital expenditure timing
Actions: Optimize working capital, Adjust payment terms, Execute FX hedge, Request credit facility increase
Detail Level: High (daily/weekly cash, by currency, by component)

Interaction Patterns to Track

Focuses on which WC levers?
How sensitive to FX movements?
Hedging frequency and strategy?
Cash forecasting accuracy review?

Data Inputs

Sales and receivable forecast
Purchase and payable schedules
Inventory projections
Capital expenditure schedule
FX positions and rates
Debt schedule

Persona 12: Head of Investor Relations

Primary Decision Authority: External financial communication and narrative, investor expectations, equity value story

Decision Focus

Quarterly earnings narrative
Investor guidance and expectations management
Key performance indicator (KPI) story and messaging
Historical vs. forecast reconciliation
Risk narrative and mitigation story

Platform Utility

KPI Dashboard & Investor-Ready Reporting
Key metrics tracking vs. guidance
Historical data (5-year trends)
Forecast vs. guidance reconciliation with narrative
Peer benchmarking
Risk indicator dashboard
Management narrative support

Page Generation Requirements

Headline Type: Narrative (e.g., "Q2 earnings beat guidance by $0.08, FY24 track to $2.35 EPS vs. $2.30 guidance, supply chain normalization key driver")
Hero Section: KPI waterfall (Guidance | Forecast | Actual) + key message
Metrics: EPS, revenue growth, margin, ROIC, guidance vs. actual
Context: Key drivers of performance, peer comparison, risk mitigation
Actions: Approve messaging, Request detail for investor call, Escalate gap, Update guidance
Detail Level: Medium (investor-ready narrative, not too much detail)

Interaction Patterns to Track

Which KPIs matter most to investors?
Frequency of guidance reviews?
Requires detailed reconciliation?
Investor meeting preparation timing?

Data Inputs

Financial forecast and actual
Investor guidance and expectations
Peer benchmarking data
Risk indicators
Management narrative points
Market sentiment and news

Part 3: Persona System Architecture

Persona Definition Schema

interface Persona {
  // Identity
  id: string;                          // "supply_chain_director"
  name: string;                        // "VP/Director of Supply Chain & Operations"
  flow: "sop" | "financial";           // Decision flow
  priority: number;                    // For new user matching (1-12)

  // Decision Authority & Focus
  primaryAuthority: string;            // "Production capacity, inventory levels, supplier risk"
  decisionFocus: string[];             // Array of key decisions

  // Platform Needs
  primaryDashboard: string;            // "supply_demand_gap"
  keyMetrics: string[];                // ["gap_size", "timeline", "inventory_days"]
  detailLevel: "executive" | "medium" | "high";

  // Page Generation
  headlineType: string;                // "operational"
  heroChartType: string;               // "supply_demand_gap"
  contextFactors: string[];            // ["policy_routing", "weather", "lead_time_variability"]
  actions: string[];                   // Available actions

  // Data Inputs
  requiredDataSources: string[];       // What data needed
  externalDataNeeds: string[];         // Weather, policy, economic

  // Learning Profile
  interactionPatterns: {
    focusAreas: string[];              // What does this persona focus on?
    decisionSpeed: string;             // "minutes" | "hours" | "days"
    dataPreferences: string[];         // External data usage, detail level
    collaborationPatterns: string[];   // Which other personas do they interact with?
  };

  // Persona Evolution
  defaultProfile: PersonaProfile;      // Initial learning model
  templates: PageTemplate[];           // Role-specific page templates
}

interface PersonaProfile {
  // Behavioral patterns for this persona
  detailPreference: number;            // 0-100: Executive summary to detailed
  externalDataUsage: number;           // 0-100: Ignores external data to data-driven
  decisionSpeed: number;               // 0-100: Deliberative to fast
  collaborationFrequency: number;      // 0-100: Independent to collaborative

  // Learned interactions
  metricPreferences: Map<string, number>;    // Which metrics does this persona care about?
  actionPreferences: Map<string, number>;    // Which actions does this persona take?
  dataSourcePreferences: Map<string, number>; // Which data sources do they use?

  // Confidence & metadata
  sampleSize: number;                  // How many users contributing to profile?
  lastUpdated: Date;
  version: string;
}

interface PageTemplate {
  id: string;
  personaId: string;
  contextType: string;                 // "pre_read" | "workbench" | "dashboard"
  layout: string;                      // Component layout structure
  defaultChartType: string;
  actionButtons: string[];
  detailSections: string[];
}

Part 4: User Onboarding & Persona Matching

Initial User Registration

Step 1: Role Selection

User selects from 12 roles. Backend maps to persona_id.

SELECT DISTINCT persona_id, persona_name, flow
FROM personas
ORDER BY flow, priority

Step 2: Initial Profile Creation

INSERT INTO user_profiles (
  user_id,
  persona_id,
  persona_profile,  -- Clone default persona profile
  custom_preferences,
  created_at
)
VALUES (...)

-- At this point, use DEFAULT persona profile

Step 3: First Page Load

When user first accesses dashboard:
Fetch user_profiles[user_id].persona_profile (default template)
Generate page using that persona's preferences
Start tracking interactions

Part 5: Persona Learning & Evolution

How Personas Learn

Individual User Learning (Fast - Real Time)

Every interaction updates that user's profile:

UPDATE user_profiles
SET persona_profile = jsonb_set(
  persona_profile,
  '{metric_preferences}',
  metric_preferences || newPrefs
)
WHERE user_id = $1

Persona Template Evolution (Slow - Weekly/Monthly)

Scheduled job that analyzes all users in a persona:

SELECT persona_id, COUNT(*) as user_count
FROM user_profiles
WHERE persona_id = 'supply_chain_director'
  AND created_at > NOW() - INTERVAL '1 month'
GROUP BY persona_id

FOR EACH persona:
  -- Aggregate learnings across all users
  avgDetailPreference = AVG(persona_profile->>'detail_preference')
  avgExternalDataUsage = AVG(persona_profile->>'external_data_usage')
  ...

  -- Update DEFAULT template
  UPDATE personas
  SET default_profile = {
    detail_preference: avgDetailPreference,
    external_data_usage: avgExternalDataUsage,
    ...
  }
  WHERE id = persona_id

Interaction Tracking for Learning

What to Track

Event	Data Collected	Used For
`headline_viewed`	time_spent, scrolled_to_detail	Understanding attention patterns
`chart_interacted`	chart_type, zoom_level, drill_down	Chart preference learning
`metric_focused`	metric_id, time_spent, compared_to	Metric importance scoring
`external_data_used`	data_source, triggered_action	External data utility
`action_taken`	action_id, outcome, decision_time	Action effectiveness
`collaborated_with`	other_persona, communication_pattern	Cross-persona workflows

Learning Engine Logic

FUNCTION update_persona_profile(user_id, event_type, event_data):

  // 1. Get current user profile
  profile = DB.user_profiles[user_id]

  // 2. Update individual preferences
  CASE event_type:
    WHEN "metric_focused":
      profile.metricPreferences[event_data.metric] += 10
    WHEN "action_taken":
      profile.actionPreferences[event_data.action] += 5
      record_outcome(user_id, action, success/failure)
    WHEN "external_data_used":
      profile.externalDataUsage += 2

  // 3. Normalize (keep 0-100 scale)
  profile = normalize(profile)

  // 4. Save
  DB.user_profiles[user_id] = profile

  // 5. Check if profile changed enough to warrant persona re-evaluation
  IF distance(profile, current_persona.default_profile) > THRESHOLD:
    // User behavior diverging from persona - investigate
    // Or offer persona switch

Part 6: Page Generation Powered by Personas

The Persona-Driven Page Generation Pipeline

User Request
    ↓
[1] Get User Persona Profile
    ├─ persona_id (from registration)
    └─ persona_profile (learned behaviors)
    ↓
[2] Get Context
    ├─ page_context (pre_read, workbench, dashboard)
    ├─ user_role (supply chain vs financial)
    └─ data_context (what data available)
    ↓
[3] Data Gathering & Anomaly Detection
    ├─ Fetch all relevant data
    ├─ Detect anomalies (2+ std dev)
    ├─ Quantify impact ($, %, timeline)
    └─ Enrich with external data (weather, policy, econ)
    ↓
[4] Headline Generation
    ├─ Filter anomalies for persona interest
    ├─ Rank by persona metric_preferences
    ├─ Select top 3-5 anomalies
    └─ Generate LLM headline for each
    ↓
[5] Hero Section Selection
    ├─ Pick anomaly with highest impact
    ├─ Select chart type based on:
    │   ├─ persona detailLevel
    │   ├─ persona chartPreferences
    │   └─ data_type
    └─ Build hero with headline + chart + metric
    ↓
[6] Detail Section Selection
    ├─ Filter detail elements by:
    │   ├─ persona detailLevel
    │   ├─ persona dataSourcePreferences
    │   ├─ contextual relevance
    │   └─ external data (weather/policy)
    └─ Organize by persona actionPreferences
    ↓
[7] Actions Selection
    ├─ Get persona's typical actions
    ├─ Filter for current context
    ├─ Add action impact estimates
    └─ Rank by effectiveness (from learning)
    ↓
[8] Render Page
    ├─ Use persona's preferred template
    ├─ Inherit colors, fonts, layout from persona
    └─ Track rendering metrics
    ↓
Page Delivered

Part 7: Business intelligence-Style Page Examples by Persona

Example 1: Supply Chain Director Pre-Read

┌─────────────────────────────────────────────────────────────┐
│ HEADLINE (Data-Driven)                                      │
│ Frankfurt warehouse overstock: 23% above target,            │
│ $2.3M working capital at risk                               │
└─────────────────────────────────────────────────────────────┘

┌─────────────────────────────────────────────────────────────┐
│ HERO SECTION                                                │
│                                                             │
│ [Waterfall Chart]                  │ Key Metric: -$2.3M    │
│  Target: 5,200 units               │ Coverage: 8.2 weeks   │
│  Actual: 12,450 units              │ (target: 4.5 weeks)   │
│  Excess: 7,250 units               │                       │
│  Cost: $2.3M                        │ Driver: Tariff        │
│                                     │ hoarding 3 weeks ago  │
│ [Inventory Trend Line with Spike]  │                       │
└─────────────────────────────────────────────────────────────┘

┌─────────────────────────────────────────────────────────────┐
│ ROOT CAUSE SECTION                                          │
│                                                             │
│ External Factor: US tariff increase announced Jan 15        │
│ Impact: 3-week hoarding behavior (Feb 1-21)                │
│ Supplier: ABC Corp diversifying away from China            │
│ Timeline: Routing change 45 days (policy lead time)        │
└─────────────────────────────────────────────────────────────┘

┌─────────────────────────────────────────────────────────────┐
│ ACTIONS                                                     │
│                                                             │
│ [Primary] Reduce inventory by 7,250 units in 4 weeks       │
│ [Secondary] Accelerate demand planning for Q2              │
│ [Tertiary] Adjust supplier routing per FTA settlement      │
└─────────────────────────────────────────────────────────────┘

┌─────────────────────────────────────────────────────────────┐
│ AUDIT TRAIL                                                 │
│                                                             │
│ Detected: 2025-10-20 (anomaly detection)                   │
│ Policy Event: US tariff 20%→25% (Jan 15)                   │
│ External Data: Weather spike drove demand (Feb 3)          │
│ Recommended Action: 2025-10-22                             │
│ Owner: Supply Chain Director                                │
└─────────────────────────────────────────────────────────────┘

Example 2: CFO Strategic Dashboard

┌─────────────────────────────────────────────────────────────┐
│ HEADLINE (Strategic)                                        │
│ Operating margin declining 40bps YTD due to supply chain    │
│ inflation, $2.1M productivity initiative required           │
└─────────────────────────────────────────────────────────────┘

┌─────────────────────────────────────────────────────────────┐
│ HERO SECTION                                                │
│                                                             │
│ [P&L Waterfall]                    │ Key Metrics:          │
│  Revenue: $1,250M                  │ Target EBIT: $150M    │
│  COGS: ($875M) - inflation +$12M   │ Current: $148M        │
│  OpEx: ($180M)                     │ Gap: -$2.1M           │
│  EBIT: $195M                        │                       │
│                                     │ ROIC: 12.3%           │
│ [Margin Trend (40bps down YTD)]    │ (Target: 13%)         │
└─────────────────────────────────────────────────────────────┘

┌─────────────────────────────────────────────────────────────┐
│ DRIVERS & MITIGATION                                        │
│                                                             │
│ Supply Chain Inflation: +$12M (+1.3% of COGS)              │
│  └─ Mitigation: Supplier renegotiation ($4M), sourcing      │
│                 alternative ($5M), automation ($3M)         │
│                                                             │
│ Demand Forecast: -3% vs. plan ($8M revenue impact)         │
│  └─ Mitigation: Market recovery Q4 ($5M), new products     │
│                 ($3M), promotion timing ($2M)               │
└─────────────────────────────────────────────────────────────┘

┌─────────────────────────────────────────────────────────────┐
│ ACTIONS                                                     │
│                                                             │
│ [Approve] Productivity Initiative ($2.1M commitment)        │
│ [Escalate] Demand forecast ($8M risk) to VP Sales          │
│ [Monitor] Margin recovery timeline (next 4 weeks)          │
└─────────────────────────────────────────────────────────────┘

Part 8: Implementation Roadmap

Phase 1: Persona System Foundation (2-3 weeks)

Tasks:

Create personas table with schema from Part 3
Create user_profiles table (user + persona mapping + learning)
Implement persona assignment in registration flow
Build initial persona templates for all 12 roles
Implement basic profile cloning (default → user)

Deliverables:

Personas database with all 12 roles
User registration flows to role selection
User profile initialization

Phase 2: Interaction Tracking & Learning (2-3 weeks)

Tasks:

Define interaction event schema (Part 5)
Implement event tracking in frontend (UserInteractionTracker enhancement)
Build persona profile update logic
Create persona evolution job (weekly aggregation)
Build visualization of persona learning over time

Deliverables:

Complete interaction tracking
Individual user learning
Persona template evolution

Phase 3: Headline Generation & Hero Section (2-3 weeks)

Tasks:

Build anomaly detection service
Build impact quantification service
Build headline generator (LLM + rule-based)
Build HeroSection component (Business intelligence-style)
Integrate with page generation pipeline

Deliverables:

Data-driven headlines
Hero section component
Integration into page pipeline

Phase 4: Persona-Driven Page Generation (2-3 weeks)

Tasks:

Build AdaptivePageGenerator service
Implement context router (pre-read vs. workbench vs. dashboard)
Build detail section selector (persona-aware)
Build action recommender (persona-aware)
Integration with existing page renderer

Deliverables:

Persona-driven pages
Context-aware layouts
Business intelligence-style structure

Phase 5: External Data Integration (1-2 weeks)

Tasks:

Link M3 external data (weather, policy, econ) to page generation
Build external data enrichment service
Integrate into headline and detail generation
Add context badges for external factors

Deliverables:

External data in page generation
Enhanced headlines with external context

Part 9: Success Metrics

How We Know This Works

Metric	Target	Measures
User Engagement	Pages viewed per user > 2x	Are personas driving discovery?
Decision Speed	Average decision time < 10 min	Are pages helping users decide faster?
Persona Accuracy	User satisfaction with persona > 85%	Did we match the user correctly?
Headline Relevance	User acts on top headline > 40%	Are headlines driving action?
External Data Impact	Policy/weather in 60% of decisions	Is external data integrated?
Learning Effectiveness	Persona template converges < 6 months	Do personas evolve quickly?
Page Personalization	Page variance across users > 60%	Are pages truly personalized?

Conclusion

This Role Personas & Decision Archetypes Framework is ChainAlign's secret sauce because it:

Understands the human - Each role has different priorities, decisions, and contexts
Adapts intelligently - Pages change based on who is viewing and how they decide
Learns continuously - Persona templates improve as more users interact
Scales personalization - One persona template serves many users
Drives action - Pages are optimized for each persona's decision authority and focus

The result: Business intelligence-quality pages that feel built just for you, continuously getting smarter about how you make decisions.

Next Steps: Ready to implement Phase 1 (Persona System Foundation)?

Executive Summary​

Part 1: S&OP Decision Flow Personas​

Overview​

Persona 1: S&OP Executive Owner (CEO/COO/GM/VP P&L)​

Persona 2: VP/Director of Supply Chain & Operations​

Persona 3: VP/Director of Sales​

Persona 4: VP/Director of Finance (FP&A Leader)​

Persona 5: Demand Planner / Forecasting Manager​

Persona 6: Supply Planner / Production Manager​

Persona 7: VP/Director of Marketing​

Part 2: Financial Decision Flow Personas​

Overview​

Persona 8: Chief Financial Officer (CFO)​

Persona 9: VP/Director of FP&A (Financial Planning & Analysis)​

Persona 10: Controller / Chief Accounting Officer​

Persona 11: Treasurer / Treasury Manager​

Persona 12: Head of Investor Relations​

Part 3: Persona System Architecture​

Persona Definition Schema​

Part 4: User Onboarding & Persona Matching​

Initial User Registration​

Part 5: Persona Learning & Evolution​

How Personas Learn​

Interaction Tracking for Learning​

Part 6: Page Generation Powered by Personas​

The Persona-Driven Page Generation Pipeline​

Part 7: Business intelligence-Style Page Examples by Persona​

Example 1: Supply Chain Director Pre-Read​

Example 2: CFO Strategic Dashboard​

Part 8: Implementation Roadmap​

Phase 1: Persona System Foundation (2-3 weeks)​

Phase 2: Interaction Tracking & Learning (2-3 weeks)​

Phase 3: Headline Generation & Hero Section (2-3 weeks)​

Phase 4: Persona-Driven Page Generation (2-3 weeks)​

Phase 5: External Data Integration (1-2 weeks)​

Part 9: Success Metrics​

How We Know This Works​

Conclusion​

Executive Summary

Part 1: S&OP Decision Flow Personas

Overview

Persona 1: S&OP Executive Owner (CEO/COO/GM/VP P&L)

Persona 2: VP/Director of Supply Chain & Operations

Persona 3: VP/Director of Sales

Persona 4: VP/Director of Finance (FP&A Leader)

Persona 5: Demand Planner / Forecasting Manager

Persona 6: Supply Planner / Production Manager

Persona 7: VP/Director of Marketing

Part 2: Financial Decision Flow Personas

Overview

Persona 8: Chief Financial Officer (CFO)

Persona 9: VP/Director of FP&A (Financial Planning & Analysis)

Persona 10: Controller / Chief Accounting Officer

Persona 11: Treasurer / Treasury Manager

Persona 12: Head of Investor Relations

Part 3: Persona System Architecture

Persona Definition Schema

Part 4: User Onboarding & Persona Matching

Initial User Registration

Part 5: Persona Learning & Evolution

How Personas Learn

Interaction Tracking for Learning

Part 6: Page Generation Powered by Personas

The Persona-Driven Page Generation Pipeline

Part 7: Business intelligence-Style Page Examples by Persona

Example 1: Supply Chain Director Pre-Read

Example 2: CFO Strategic Dashboard

Part 8: Implementation Roadmap

Phase 1: Persona System Foundation (2-3 weeks)

Phase 2: Interaction Tracking & Learning (2-3 weeks)

Phase 3: Headline Generation & Hero Section (2-3 weeks)

Phase 4: Persona-Driven Page Generation (2-3 weeks)

Phase 5: External Data Integration (1-2 weeks)

Part 9: Success Metrics

How We Know This Works

Conclusion