thinking-kepner-tregoe（思考凯普纳·特雷戈）

Kepner-Tregoe Rational Process

Overview

The Kepner-Tregoe (KT) methodology, developed by Charles Kepner and Benjamin Tregoe in the 1950s, provides four integrated analytical processes for rational thinking. Unlike heuristic approaches, KT offers rigorous frameworks for separating fact from speculation and making defensible decisions.

Core Principle: Separate what you know from what you assume. Use structured comparison to reveal truth.

When to Use

• Complex engineering problems with multiple potential causes
• High-stakes decisions requiring documented rationale
• Root cause analysis when 5 Whys yields ambiguous results
• Evaluating alternatives with competing criteria
• Post-implementation risk assessment
• Incident response requiring systematic triage
• Architecture decisions with long-term implications

Decision flow:

Complex problem? → yes → Multiple concerns/unclear priority? → yes → Start with SA
                                                            ↘ no → Known single problem? → yes → PA
                                                                                         ↘ no → Decision needed? → yes → DA
                                                                                                                 ↘ no → Implementation risk? → yes → PPA
               ↘ no → Simpler frameworks may suffice

The Four Processes

Process 1: Situation Analysis (SA)

Use when facing multiple concerns, unclear priorities, or overwhelm.

Purpose

Break complex situations into manageable components, set priorities, and plan approach.

Steps

Step 1: List All Concerns

Brainstorm everything that needs attention:

Concerns:
- Production API latency increased 3x
- New feature deployment blocked
- Team velocity dropped 40%
- Customer complaints about checkout errors
- Database connection pool exhaustion
- Unclear requirements for Q2 roadmap

Step 2: Separate and Clarify

For each concern, ask: "Is this one issue or multiple?"

"Production performance issues" →
  - API latency (response time)
  - Database connections (resource exhaustion)
  - Memory usage (potential leak)

Step 3: Set Priority

Use Timing, Impact, Trend (TIT):

Timing: When must action be taken? Impact: What's the consequence of inaction? Trend: Is it getting better, worse, or stable?

Step 4: Plan Approach

For each prioritized concern, determine:

• Which KT process applies (PA, DA, PPA)?
• Who should be involved?
• What information is needed?

SA Template

# Situation Analysis: [Context]
Date: [Date]

## Concerns Inventory
| # | Concern | Clarification Needed? |
|---|---------|----------------------|
| 1 | [Concern] | [Yes/No - details] |

## Priority Matrix
| Concern | Timing | Impact | Trend | Priority | Next Process |
|---------|--------|--------|-------|----------|--------------|
| | | | | | SA/PA/DA/PPA |

## Action Plan
| Priority | Concern | Process | Owner | Deadline |
|----------|---------|---------|-------|----------|
| P0 | | | | |

Process 2: Problem Analysis (PA)

Use when you need to find root cause of a deviation from expected performance.

Purpose

Systematically identify the true cause by comparing what IS happening vs. what IS NOT.

Key Concept: The IS/IS-NOT Matrix

The power of PA lies in specification through contrast. A problem exists in a specific context—understanding boundaries reveals cause.

Steps

Step 1: State the Problem

Be precise about the deviation:

BAD: "The system is slow"
GOOD: "API response time increased from 200ms to 800ms for /checkout endpoint starting Monday 9 AM"

Step 2: Specify the Problem (IS/IS-NOT)

Step 3: Identify Distinctions

For each IS/IS-NOT pair, ask: "What's unique or distinctive about the IS side?"

Distinctions identified:
- Only /checkout endpoint (payment processing)
- Only US-East region (specific DB replica)
- Only during business hours (load-related?)
- Only ~30% of requests (specific query pattern?)
- Started Monday 9 AM (deployment? config change?)

Step 4: Identify Changes

What changed in, on, around, or about the distinctions?

Changes near Monday 9 AM:
- Payment provider SDK updated (Sunday night deploy)
- Database index rebuild scheduled (Sunday maintenance)
- New fraud detection rules enabled (Monday 8:45 AM)

Step 5: Generate Possible Causes

Combine distinctions and changes:

Possible causes:
1. Fraud detection rules causing additional DB queries
2. Payment SDK making synchronous external calls
3. Index rebuild affected checkout-related queries

Step 6: Test Against Specification

For each possible cause, verify it explains ALL IS and IS-NOT:

Step 7: Verify True Cause

Design verification to confirm or rule out:

Verification plan for "Fraud detection rules":
1. Check timing: Rules enabled 8:45 AM (matches)
2. Check scope: Rules only on checkout (matches)
3. Test: Disable rules in canary, measure latency
4. Examine: Query logs for fraud check queries

IS/IS-NOT Template

# Problem Analysis: [Problem Statement]
Date: [Date]

## Problem Specification

### What
| Question | IS | IS NOT | Distinction |
|----------|-----|---------|-------------|
| What object has the problem? | | | |
| What specifically is wrong? | | | |

### Where
| Question | IS | IS NOT | Distinction |
|----------|-----|---------|-------------|
| Where is the problem observed? | | | |
| Where on the object is it? | | | |

### When
| Question | IS | IS NOT | Distinction |
|----------|-----|---------|-------------|
| When first observed? | | | |
| Any pattern to occurrence? | | | |

### Extent
| Question | IS | IS NOT | Distinction |
|----------|-----|---------|-------------|
| How many/much affected? | | | |
| Is it changing? | | | |

## Distinctions Summary
1. [Unique characteristic]
2. [Unique characteristic]

## Changes Near Distinctions
| Change | When | What Changed |
|--------|------|--------------|
| | | |

## Possible Causes
| # | Cause | Based on |
|---|-------|----------|
| 1 | | Distinction + Change |

## Cause Testing
| Cause | Explains IS | Explains IS-NOT | Verdict |
|-------|-------------|-----------------|---------|
| | | | |

## Verification Plan
- [ ] [Test to confirm/rule out most likely cause]

## Confirmed Root Cause
[Cause with evidence]

Process 3: Decision Analysis (DA)

Use when choosing among alternatives with multiple criteria.

Purpose

Make systematic, defensible decisions by separating MUSTS from WANTS and scoring alternatives objectively.

Steps

Step 1: Clarify the Decision

State the decision clearly:

"Select a message queue system for order processing"
"Choose deployment strategy for the new auth service"

Step 2: Develop Objectives

List what the decision must accomplish:

Objectives:
- Handle 10K messages/second throughput
- Provide at-least-once delivery guarantees
- Support multiple consumer groups
- Minimize operational overhead
- Stay within $5K/month budget
- Integrate with existing monitoring

Step 3: Classify as MUST vs WANT

MUST: Non-negotiable requirements (pass/fail) WANT: Desirable attributes (weighted scoring)

Step 4: Generate Alternatives

List viable options:

Alternatives:
A. Apache Kafka (self-managed)
B. AWS SQS + SNS
C. RabbitMQ (self-managed)
D. Amazon MSK (managed Kafka)

Step 5: Screen Against MUSTs

RabbitMQ and MSK eliminated—don't meet MUSTs.

Step 6: Score Against WANTs

Rate each alternative 1-10 on each WANT:

Step 7: Calculate Weighted Scores

SQS+SNS scores higher on weighted WANTs.

Step 8: Assess Risks (→ feeds into PPA)

Before final decision, consider adverse consequences:

Step 9: Make Decision

Consider scores AND risks to make final choice. Document rationale.

DA Template

# Decision Analysis: [Decision Statement]
Date: [Date]
Decision Maker: [Name]

## Objectives
| Objective | MUST/WANT | Weight |
|-----------|-----------|--------|
| | | |

## Alternatives
1. [Option A]
2. [Option B]
3. [Option C]

## MUST Screening
| Alternative | MUST 1 | MUST 2 | MUST 3 | Pass/Fail |
|-------------|--------|--------|--------|-----------|
| | | | | |

## WANT Scoring
| WANT (Weight) | Alt A | Alt B | Alt C |
|---------------|-------|-------|-------|
| (w) | score | score | score |
| **Weighted Total** | | | |

## Risk Assessment
| Alternative | Risk | L | S | Mitigation |
|-------------|------|---|---|------------|
| | | | | |

## Decision
**Selected:** [Alternative]
**Rationale:** [Why this choice given scores and risks]

Process 4: Potential Problem Analysis (PPA)

Use after making a decision or before implementation to anticipate and prevent problems.

Purpose

Identify what could go wrong with a planned action and develop preventive/contingent actions.

Steps

Step 1: State the Plan

Describe what will be implemented:

Plan: Migrate order service from monolith to microservice
Timeline: 4 weeks
Key changes: New service, message queue, database split

Step 2: Identify Potential Problems

Walk through the plan and ask "What could go wrong?":

Potential problems:
1. Message queue loses orders during migration
2. New service has undiscovered bugs in production
3. Database sync fails, causing data inconsistency
4. Rollback needed but unclear how to reverse
5. Performance degradation under load
6. Team lacks Kafka operational knowledge

Step 3: Assess Each Potential Problem

Rate probability (P) and seriousness (S):

Step 4: Identify Likely Causes

For high P×S problems, determine probable causes:

Problem: Message queue loses orders
Likely causes:
- Consumer crashes before acknowledgment
- Queue overflow during peak
- Network partition between services
- Misconfigured dead letter queue

Step 5: Develop Preventive Actions

Actions to reduce probability of cause occurring:

Step 6: Develop Contingent Actions

Actions to reduce impact IF problem occurs:

Step 7: Build Monitoring/Triggers

Define how you'll detect problems and when to activate contingent actions.

PPA Template

# Potential Problem Analysis: [Plan Name]
Date: [Date]

## Plan Summary
[Brief description of what will be implemented]

## Potential Problems
| # | Problem | P (H/M/L) | S (H/M/L) | Priority |
|---|---------|-----------|-----------|----------|
| 1 | | | | |

## High-Priority Problem Analysis

### Problem: [Name]
**Likely Causes:**
1. [Cause]

**Preventive Actions:**
| Cause | Action | Owner | Due |
|-------|--------|-------|-----|
| | | | |

**Contingent Actions:**
| Trigger | Action | Owner |
|---------|--------|-------|
| | | |

## Monitoring Plan
| What to Monitor | Threshold | Alert | Response |
|-----------------|-----------|-------|----------|
| | | | |

## Review Schedule
- Pre-implementation review: [Date]
- Post-implementation check: [Date]

Integrating the Four Processes

Typical flow for complex situations:

1. SA: "We have multiple issues after the release"
   → Separate concerns, prioritize
   → P0: Production errors (needs PA)
   → P1: Architecture decision (needs DA)
   → P2: Future release risks (needs PPA)

2. PA: Investigate production errors
   → IS/IS-NOT analysis
   → Identify root cause
   → Feeds solution options into DA

3. DA: Choose solution approach
   → Define objectives
   → Score alternatives
   → Select best option
   → Risk assessment feeds PPA

4. PPA: Plan implementation
   → Identify what could go wrong
   → Preventive and contingent actions
   → Monitoring plan

Integration with Other Thinking Skills

Verification Checklist

• Used appropriate KT process for the situation type
• SA: All concerns listed, separated, and prioritized with TIT criteria
• PA: IS/IS-NOT fully specified across all four dimensions
• PA: Each possible cause tested against specification
• DA: MUST/WANT clearly separated, MUSTs are truly non-negotiable
• DA: Weighted scores calculated, not just intuition
• PPA: High P×S problems have both preventive and contingent actions
• PPA: Triggers defined for contingent action activation
• Analysis documented for future reference and team alignment

Key Questions by Process

Situation Analysis

• "What are ALL the concerns we're facing?"
• "Is this one problem or several?"
• "What's the timing, impact, and trend?"
• "Which process should we use for each concern?"

Problem Analysis

• "What specifically IS happening vs IS NOT?"
• "What's unique about where/when this occurs?"
• "What changed in, on, or around the distinctions?"
• "Does this cause explain BOTH the IS and IS-NOT?"

Decision Analysis

• "What are the MUST-have requirements?"
• "How important is each WANT relative to others?"
• "How well does each alternative satisfy each objective?"
• "What risks come with each alternative?"

Potential Problem Analysis

• "What could go wrong with this plan?"
• "What would cause each problem?"
• "How can we prevent the cause?"
• "If it happens anyway, how do we minimize damage?"