---
name: sop-scenario-modeler-59af414fb33e
description: S&OP scenario modeling skill for demand-supply-financial plan alignment with what-if analysis
allowed-tools:
  - Read
  - Write
  - Glob
  - Grep
  - Bash
metadata:
  specialization: supply-chain
  domain: business
  category: demand-forecasting
  priority: medium
graph:
  domains: [domain:supply-chain]
  specializations: [specialization:supply-chain-optimization]
  skillAreas: [skill-area:procurement-management, skill-area:capacity-planning-ops, skill-area:quantitative-modeling]
  workflows: [workflow:vendor-onboarding, workflow:vendor-evaluation]
  roles: [role:supply-chain-analyst, role:procurement-manager, role:strategic-planner]
---

# S&OP Scenario Modeler

## Overview

The S&OP Scenario Modeler enables comprehensive Sales and Operations Planning through multi-scenario modeling, demand-supply-financial plan alignment, and what-if analysis. It supports the monthly S&OP cycle by facilitating scenario comparison and consensus plan development.

## Capabilities

- **Multi-Scenario Demand Planning**: Create and compare multiple demand scenarios
- **Supply Constraint Modeling**: Model capacity, material, and resource constraints
- **Financial Impact Calculation**: Revenue, margin, and cost impact analysis
- **Capacity Utilization Optimization**: Balance capacity across scenarios
- **Inventory Investment Modeling**: Working capital implications by scenario
- **Gap Analysis and Reconciliation**: Identify and resolve demand-supply gaps
- **Executive Summary Generation**: Automated scenario comparison reports
- **Consensus Plan Tracking**: Monitor adherence to agreed plans

## Input Schema

```yaml
sop_scenario_request:
  base_scenario:
    demand_plan: object
    supply_plan: object
    financial_targets: object
  alternative_scenarios: array
    - name: string
      demand_adjustments: object
      supply_adjustments: object
  constraints:
    capacity_limits: object
    material_availability: object
    financial_bounds: object
  planning_horizon: integer
  granularity: string
```

## Output Schema

```yaml
sop_scenario_output:
  scenarios: array
    - name: string
      demand_plan: object
      supply_plan: object
      financial_projection: object
      gaps: array
      feasibility_score: float
  comparison_matrix: object
  recommendations: array
  executive_summary: string
  consensus_plan: object
```

## Usage

### Demand Scenario Comparison

```
Input: Optimistic, base, pessimistic demand scenarios
Process: Model supply response and financial impact for each
Output: Side-by-side comparison with recommendation
```

### Capacity Constraint Analysis

```
Input: Demand plan exceeding capacity in Q3
Process: Model capacity addition, demand shaping, outsourcing options
Output: Feasible scenario with cost-service tradeoffs
```

### Financial Plan Alignment

```
Input: Operations plan vs. financial budget targets
Process: Reconcile volume, price, cost assumptions
Output: Aligned operational-financial plan
```

## Integration Points

- **Planning Platforms**: o9 Solutions, Kinaxis, SAP IBP connectors
- **ERP Systems**: SAP, Oracle for master data and constraints
- **Financial Systems**: Budget and forecast integration
- **BI Tools**: Visualization and reporting

## Process Dependencies

- Sales and Operations Planning (S&OP)
- Demand Forecasting and Planning
- Capacity Planning and Constraint Management

## Best Practices

1. Define clear scenario naming conventions
2. Establish assumption documentation standards
3. Include sensitivity analysis on key drivers
4. Track scenario accuracy over time
5. Maintain version control on scenarios
6. Ensure cross-functional alignment on assumptions


---
Source attribution: a5c-ai
Original project: https://github.com/a5c-ai/babysitter
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Mega AI Hub.

## Upstream license notice

MIT License

Copyright (c) 2026 Gabriel Moreira

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.



# Mega AI Hub resource metadata

Creator / publisher: a5c-ai
Original project: https://github.com/a5c-ai/babysitter
Attribution basis: Upstream repository explicitly recorded in package mirror path

Fields of work: Marketing, Operations, Product Management, Sales
Roles: Business Development, Marketing Analyst, Operations Manager, Product Manager, Supply Chain Manager
Specialties: API
Tools: Kinaxis, Oracle, Sap, O9 Solutions, Sap Ibp
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: f367e94fcf046f47feabaebada8b22602b1b74e5a35b4dce155f69a5d3e2abd6