---
name: ddmrp-buffer-manager-30928d4e1cb1
description: Demand-Driven MRP buffer positioning and management skill with dynamic adjustment
allowed-tools:
  - Read
  - Write
  - Glob
  - Grep
  - Bash
metadata:
  specialization: supply-chain
  domain: business
  category: inventory
  priority: future
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:operations-analyst]
---

# DDMRP Buffer Manager

## Overview

The DDMRP Buffer Manager implements Demand-Driven Material Requirements Planning methodology for inventory management. It handles strategic buffer positioning, zone calculations, dynamic adjustments, and execution prioritization to create flow-based material planning.

## Capabilities

- **Strategic Decoupling Point Identification**: Optimal buffer location selection
- **Buffer Profile Assignment**: Categorize items by lead time and variability
- **Buffer Level Calculation**: Green, yellow, red zone determination
- **Dynamic Adjustment Factors**: Planned and recalculated adjustments
- **Net Flow Position Calculation**: Real-time inventory position
- **Execution Visibility and Prioritization**: Color-coded supply priorities
- **Buffer Health Monitoring**: On-target percentage tracking
- **Lead Time Compression Analysis**: Identify lead time reduction opportunities

## Input Schema

```yaml
ddmrp_request:
  items: array
    - sku_id: string
      average_daily_usage: float
      decoupled_lead_time: integer
      minimum_order_quantity: integer
      variability_factor: string    # low, medium, high
      lead_time_factor: string      # short, medium, long
  bom_structure: object
  planned_adjustments: array        # Promotions, seasonality
  current_positions: array
  calculation_scope: string         # positioning, sizing, execution
```

## Output Schema

```yaml
ddmrp_output:
  buffer_positions: array
    - sku_id: string
      is_decoupling_point: boolean
      rationale: string
  buffer_levels: array
    - sku_id: string
      buffer_profile: string
      zones:
        green: integer
        yellow: integer
        red: integer
        red_safety: integer
      total_buffer: integer
  execution_priorities: array
    - sku_id: string
      net_flow_position: integer
      net_flow_equation: string
      priority_color: string
      on_hand: integer
      on_order: integer
      qualified_demand: integer
  buffer_health: object
```

## Usage

### Buffer Positioning Analysis

```
Input: BOM structure, lead times, demand variability
Process: Identify strategic inventory positioning points
Output: Recommended decoupling points with rationale
```

### Buffer Sizing Calculation

```
Input: ADU, lead time factors, variability factors
Process: Calculate zone sizes using DDMRP formulas
Output: Green, yellow, red zone levels by buffer
```

### Execution Priority Management

```
Input: Current inventory, orders, qualified demand
Process: Calculate net flow position, assign priority color
Output: Prioritized replenishment recommendations
```

## Integration Points

- **DDMRP Platforms**: Demand Driven Technologies, Replenishment+
- **ERP Systems**: BOM, inventory, demand data
- **Planning Systems**: Qualified demand, supply orders
- **Tools/Libraries**: DDMRP algorithms, flow optimization

## Process Dependencies

- Demand-Driven Material Requirements Planning (DDMRP)
- Inventory Optimization and Segmentation
- Safety Stock Calculation and Optimization

## Best Practices

1. Start with pilot categories before full rollout
2. Validate decoupling point selection with operations
3. Monitor buffer health daily during transition
4. Train planners on net flow execution
5. Review dynamic adjustment factors seasonally
6. Track lead time compression progress


---
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: Operations
Roles: Business Process Analyst, Operations Manager, Supply Chain Manager
Specialties: Database
Tools: Ddmrp Platforms, Erp Systems, Planning Systems
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: 47fc629485fad39322363ee69f4154448160198415b58124059e5d683db976fe