Outcome Backcasting Engine

Reverse-engineer your path from desired futures to present actions

Author

Derek M D Chan — Creator and maintainer

Co-developed with Claude (Anthropic)

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Overview

The Outcome Backcasting Engine is a strategic planning tool that helps you work backwards from a desired future outcome to identify the specific steps, resources, and dependencies needed to achieve your goals. Unlike traditional forward planning, backcasting starts with your end goal and creates a reverse roadmap to get there.

Key Concepts

What is Backcasting?

Backcasting is a planning methodology that:

1. Starts with the end in mind - Define your desired future state
2. Works backwards - Identify what needs to happen to reach that state
3. Creates actionable steps - Break down the path into concrete actions
4. Identifies dependencies - Understand what must happen before other things
5. Monitors progress - Track advancement and adjust the plan dynamically

When to Use Backcasting

• Complex projects with multiple phases and dependencies
• Long-term goals (6 months to several years)
• Strategic initiatives requiring coordination of many elements
• Constraint-heavy scenarios where certain requirements must be met
• Innovation projects where the path isn't immediately obvious

Features

Core Capabilities

• Outcome Definition - Clearly specify your desired end state
• Automatic Step Generation - Create template structures with major phases
• Dependency Management - Track which steps must be completed before others
• Progress Tracking - Monitor completion status across all steps
• Risk Analysis - Identify and plan for potential obstacles
• Resource Planning - Track required resources (time, money, skills, tools, people)
• Critical Path Analysis - Identify bottlenecks and longest dependency chains
• Next Actions View - See what's ready to be worked on right now

Step Types

1. Milestone 🎯 - Major checkpoints marking significant progress
2. Action ⚡ - Concrete tasks to be executed
3. Decision 🤔 - Choice points requiring evaluation
4. Dependency 🔗 - External requirements or prerequisites
5. Risk Mitigation 🛡️ - Steps to reduce or eliminate risks

Priority Levels

• Critical 🔴 - Must be done, no alternatives
• High 🟠 - Very important, high impact
• Medium 🟡 - Important but not urgent
• Low ⚪ - Nice to have, low priority

Status Tracking

• Not Started - Step hasn't been begun yet
• In Progress - Currently being worked on
• Completed - Successfully finished
• Blocked - Cannot proceed due to dependencies
• Skipped - Decided not to pursue this step

Installation

Prerequisites

• Python 3.7 or higher
• Linux environment (tested on openSUSE)

Setup

1. The engine is located at:

   /home/panda/Documents/PythonScripts/OutcomeBackcasting/
   

2. Make the scripts executable:

   chmod +x /home/panda/Documents/PythonScripts/OutcomeBackcasting/*.sh
   chmod +x /home/panda/Documents/PythonScripts/OutcomeBackcasting/*.py
   

3. Run the launcher:

   ./run_backcast.sh
   

Usage Guide

Quick Start

1. Launch the application:

   ./run_backcast.sh
   

2. Create a new plan (Option 1):

   • Enter your outcome title (e.g., "Launch SaaS Product")
   • Describe what success looks like
   • Define success criteria (specific, measurable goals)
   • List constraints (budget, time, resources)
   • Set timeline (e.g., "9 months")

3. Generate template steps (optional):

   • Choose how many major phases (default: 5)
   • The engine creates a structured template
   • Customize the generated steps to fit your needs

4. Customize your plan:

   • Add specific steps for your project
   • Update descriptions and success criteria
   • Define dependencies between steps
   • Add resources and risk information

5. Execute and track:

   • View "Next Actions" to see what's ready to work on
   • Update step statuses as you make progress
   • Run analysis to get insights and suggestions
   • Export your plan to share or reference

Menu Options

Plan Management

1. Create new plan - Start a fresh backcasting plan with wizard guidance

2. Load existing plan - Open a previously saved plan

3. View plan overview - See high-level summary, progress, and outcome details

View Steps

4. View all steps - List every step in the plan with status indicators

5. View step details - Deep dive into a specific step's information

6. View next actions - Show only steps that are ready to be worked on (all dependencies met)

7. View critical path - Identify the longest chain of dependencies (bottleneck analysis)

Edit Plan

8. Add new step - Create a custom step with full details

9. Update step status - Mark steps as completed, in progress, blocked, etc.

10. Delete step - Remove a step (dependencies are automatically cleaned up)

Analysis

11. Analyze plan - Get comprehensive analysis:

• Progress metrics (% complete, breakdown by status)
• Risk analysis (high-priority risks identified)
• Resource summary (grouped by type)
• Optimization suggestions (parallelization, bottlenecks)
• Current blockers (what's preventing progress)

12. Export plan - Save to different formats:

• Markdown (.md) - Rich formatting with emoji status indicators
• Text (.txt) - Simple plain text for universal compatibility
• CSV (.csv) - Spreadsheet format for Excel/Google Sheets

Example Workflow

Scenario: Launching a New Product

OUTCOME: "Launch MVP of SaaS Analytics Platform"
TIMELINE: 6 months
SUCCESS CRITERIA:
  - 100 beta users signed up
  - Core features working (data ingestion, dashboards, alerts)
  - Payment processing integrated
  - Security audit passed

GENERATED PLAN (working backwards from launch):
  [Phase 5] Public Launch
    → Beta testing complete
    → All critical bugs fixed
    → Marketing materials ready

  [Phase 4] Beta Testing
    → 50 beta users onboarded
    → Feedback loop established
    → Rapid iteration process

  [Phase 3] Core Features Complete
    → Dashboard system working
    → Data pipeline stable
    → Alert system functional

  [Phase 2] Technical Foundation
    → Database architecture finalized
    → API endpoints created
    → Authentication system built

  [Phase 1] Project Setup
    → Tech stack chosen
    → Development environment configured
    → Team assembled

Advanced Features

Dependency Management

Steps can depend on other steps. The engine automatically:

• Prevents working on steps with incomplete dependencies
• Shows "Next Actions" that are ready to start
• Identifies blocked steps and what's blocking them
• Calculates the critical path (longest dependency chain)

Example:

Step 5: "Deploy to Production"
  Dependencies: [3, 4]  # Requires Step 3 and 4 to be done first

Step 4: "Security Audit"
  Dependencies: [2]     # Requires Step 2

Step 3: "Beta Testing"
  Dependencies: [2]     # Requires Step 2

Step 2: "Feature Development"
  Dependencies: [1]     # Requires Step 1

Step 1: "Project Setup"
  Dependencies: []      # No dependencies, can start immediately

Resource Tracking

Each step can specify required resources:

Resource Types:

• Time - How long it takes
• Money - Budget required
• Skill - Expertise needed
• Tool - Software, hardware, or equipment
• Person - Specific individuals or roles

Example:

Step: "Build Mobile App"
Resources:
  - Mobile Developer (person) - 2 developers
  - React Native (tool) - License required
  - 8 weeks (time) - Full-time work
  - $40,000 (money) - Contractor budget
  - iOS/Android expertise (skill) - Required

Risk Management

Identify and mitigate potential problems:

Risk Fields:

• Description - What could go wrong
• Probability - Low, Medium, High
• Impact - Low, Medium, High
• Mitigation - How to prevent or handle it

Example:

Step: "Migrate to New Database"
Risk:
  Description: "Data loss during migration"
  Probability: Medium
  Impact: High
  Mitigation: "Create full backup, test migration on staging environment first,
               have rollback plan ready"

Analysis Tools

Progress Metrics:

• Overall completion percentage
• Breakdown by status (completed, in progress, blocked)
• Visual indicators in CLI

Critical Path Analysis:

• Identifies longest dependency chain
• Highlights steps that will delay the entire project if delayed
• Helps prioritize work on bottlenecks

Optimization Suggestions:

• Identifies independent steps that could be parallelized
• Finds bottleneck steps with many dependents
• Flags steps without clear success criteria
• Highlights high risks without mitigation plans

Blocker Detection:

• Shows steps marked as "blocked"
• Lists which incomplete dependencies are causing the block
• Helps focus effort on unblocking critical work

File Format

Plans are saved as JSON files in:

/home/panda/Documents/PythonScripts/OutcomeBackcasting/data/

File Structure:

{
  "outcome": {
    "title": "...",
    "description": "...",
    "success_criteria": [...],
    "constraints": [...],
    "timeline": "..."
  },
  "steps": [
    {
      "id": 1,
      "title": "...",
      "description": "...",
      "type": "action",
      "status": "not_started",
      "priority": "high",
      "dependencies": [2, 3],
      "resources_needed": [...],
      "risks": [...],
      "success_criteria": [...]
    }
  ]
}

Tips and Best Practices

Defining Good Outcomes

DO:

• Be specific and measurable
• Include quantifiable success criteria
• Set realistic timelines
• Define what "done" looks like clearly

DON'T:

• Use vague language ("improve things")
• Set impossible goals without constraints
• Forget to define success criteria
• Skip the constraints section

Creating Effective Steps

DO:

• Use action verbs (Build, Deploy, Test, Design)
• Make success criteria objective and testable
• Estimate durations realistically
• Identify all dependencies upfront
• Document risks as you think of them

DON'T:

• Create steps that are too large (break them down)
• Leave success criteria as "Define criteria"
• Forget to update status as work progresses
• Ignore potential risks

Managing Dependencies

Best Practices:

• Review the critical path regularly
• Focus on unblocking high-priority items
• Look for opportunities to parallelize work
• Update dependencies as the plan evolves

Using Next Actions

• Start each work session by checking "Next Actions"
• Focus on critical and high-priority items first
• Update status immediately when completing steps
• This keeps the plan current and useful

Regular Analysis

Weekly Review:

• Run "Analyze plan" to check progress
• Review high-priority risks
• Check for new blockers
• Update step statuses

Monthly Review:

• Revisit the outcome - still accurate?
• Review optimization suggestions
• Adjust timeline if needed
• Clean up completed steps

Command Reference

Keyboard Shortcuts

• Ctrl+D or END - End multiline input
• Ctrl+C - Exit the program
• Enter - Continue after viewing results

Input Formats

Step IDs:

• Single number: 5
• Multiple (comma-separated): 1,3,5,7

Duration Format:

• Use human-readable: 3 days, 2 weeks, 4 months

Priority/Status:

• Select by number from menu

Troubleshooting

Common Issues

"No plan loaded" error:

• Create a new plan (Option 1) or load existing (Option 2) first

Can't see next actions:

• Check if dependencies are completed
• Review step statuses (might all be done or blocked)

Plan file not found:

• Check filename includes .json extension
• Verify file exists in data directory
• Use Option 2 to see list of available plans

Dependencies not working:

• Ensure step IDs are valid (match existing steps)
• Check for circular dependencies
• Verify dependent steps are actually completed

Getting Help

• Re-read this README for detailed guidance
• Check the ai_plugin_concepts.md for design philosophy
• Review example workflows in this document

Architecture

Core Components

backcast_engine.py - Core engine with data models and algorithms

• Data classes (Outcome, Step, Resource, Risk, BackcastPlan)
• BackcastEngine class (CRUD operations, analysis)
• BackcastAnalyzer class (risk analysis, optimization suggestions)

backcast_cli.py - Interactive command-line interface

• BackcastCLI class (menu system, user interaction)
• Colored output for better UX
• Wizard-style plan creation
• Multiple view modes

run_backcast.sh - Launcher script

• Sets up environment
• Handles errors gracefully
• Unix line endings (LF)

Data Flow

User Input → CLI → Engine → Data Storage (JSON)
                 ↓
             Analysis ← Analyzer
                 ↓
            Display Results

Design Philosophy

Based on concept #9 from ai_plugin_concepts.md:

Key Principles:

1. Outcome-first thinking - Always start with the end goal
2. Reverse planning - Work backwards to find the path
3. Constraint awareness - Acknowledge limitations upfront
4. Dynamic adjustment - Plans evolve as work progresses
5. Dependency clarity - Make relationships explicit
6. Risk consciousness - Identify problems before they occur

Future Enhancements

Potential Features

• AI-powered step generation - Use LLM to suggest realistic steps
• Timeline visualization - Gantt chart style display
• Collaborative planning - Multi-user support
• Integration with task managers - Sync with Asana, Notion, etc.
• Template library - Pre-built plans for common goals
• Monte Carlo simulation - Probability-based timeline estimates
• Notification system - Alerts for blockers and deadlines
• Mobile companion app - Check status on the go
• Web dashboard - Visual progress tracking

Integration Ideas

• Project management tools - Asana, Jira, Monday.com APIs
• Calendar integration - Auto-schedule steps based on dependencies
• Team chat - Slack/Discord notifications for status changes
• Time tracking - Toggl, Harvest integration for actual vs estimated
• Document management - Link steps to relevant files/docs

Contributing

This is a personal utility built for local use. If you want to extend it:

1. Core engine is in backcast_engine.py
2. CLI interface is in backcast_cli.py
3. Data is stored in data/ directory as JSON
4. Follow the existing code style (dataclasses, type hints, docstrings)

Version History

v1.0 - 2025-11-21

• Initial release
• Core backcasting functionality
• Interactive CLI
• JSON storage
• Analysis tools
• Export to Markdown, Text, CSV

License

MIT License - see LICENSE for details.

Author & Credits

Derek M D Chan — Creator and maintainer

• GitHub: @NET-OF-BEING

Co-developed with Claude (Anthropic) using AI-assisted development

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Built with the Outcome Backcasting methodology

Strategic planning tool that reverse-engineers paths from desired future outcomes to present actions.