Problem-Solving Methodology

Solve It Now! implements a systematic approach to problem-solving based on proven troubleshooting frameworks used in professional settings worldwide. This methodology helps you move from problem symptoms to root causes through structured analysis. The framework provides significant value on its own, with optional AI enhancement available to provide additional perspectives.

The Framework

1. Problem Statement

Start with a clear, specific problem statement using the “Object/Defect” format:

  • Object: What thing has the problem?
  • Defect: What exactly is wrong with it?

Good Examples:

  • “My car does not start on cold mornings”
  • “The website login page returns error 500”
  • “Assembly line produces 10% defective units”

Poor Examples:

  • “Nothing works” (too vague)
  • “Performance is bad” (not specific)
  • “Users are complaining” (focuses on symptoms, not root problem)

2. IS/IS NOT Analysis

This critical step helps define problem boundaries by documenting what IS part of the problem and what IS NOT.

WHAT Dimension

  • WHAT IS: List objects that have the problem and describe exactly what’s wrong
  • WHAT IS NOT: List similar objects that don’t have the problem or alternative symptoms not seen

WHERE Dimension

  • WHERE IS: Geographic location, physical position on object, system location
  • WHERE IS NOT: Similar locations where you’d expect the problem but don’t see it

WHEN Dimension

  • WHEN IS: First occurrence, patterns in timing, lifecycle stage
  • WHEN IS NOT: Times when problem could occur but doesn’t

EXTENT Dimension

  • EXTENT IS: How many affected, severity level, trends over time
  • EXTENT IS NOT: How many could be affected but aren’t, severity levels not seen

Why This Framework Works

Systematic Approach

  • Prevents jumping to conclusions
  • Ensures comprehensive data collection
  • Reveals patterns not obvious initially
  • Builds understanding incrementally

Boundary Definition

  • IS NOT analysis is often more revealing than IS analysis
  • Exclusions point toward root causes
  • Helps eliminate false leads early
  • Narrows investigation scope efficiently

Professional Validation

This methodology has proven effective across industries:

  • Manufacturing: Quality control and defect analysis
  • Technology: System troubleshooting and debugging
  • Healthcare: Diagnostic procedures and medical analysis
  • Aviation: Incident investigation and safety analysis
  • Scientific Research: Hypothesis testing and experimental design
  • Engineering: Root cause analysis and system optimization

The structured approach works independently of any AI assistance, providing reliable results through systematic thinking.

AI-Enhanced Analysis (Optional)

While the systematic methodology provides value on its own, AI enhancement can accelerate and enrich your analysis:

Solution Generation

Once you complete the structured analysis, AI can:

  • Identify patterns in your collected data
  • Suggest potential root causes based on your IS/IS NOT analysis
  • Recommend solutions to validate systematically
  • Provide alternative perspectives on your structured findings

Diagnostic Questions

AI generates follow-up questions to:

  • Fill gaps in your systematic analysis
  • Test hypotheses about causes systematically
  • Guide further data collection within the framework
  • Validate or eliminate theories through structured investigation

Iterative Refinement

The AI-enhanced process follows the same systematic approach:

  1. Complete initial structured analysis
  2. Review AI suggestions against your systematic findings
  3. Answer diagnostic questions within the framework
  4. Refine problem understanding systematically
  5. Generate better solutions through continued structured analysis

Example Application: Donut Factory

Problem Statement

“Donuts from the factory are mis-shaped when producing at scale”

IS/IS NOT Analysis

WHAT IS:

  • Plain donuts are mis-shaped
  • Problem occurs during high-volume production
  • Shapes are consistently deformed (not random)

WHAT IS NOT:

  • Luxury topping donuts are not affected
  • Problem doesn’t occur during low-volume production
  • Donuts are not burned, undercooked, or have other quality issues

WHERE IS:

  • Problem occurs at the main production facility
  • Affects the final product coming off production lines

WHERE IS NOT:

  • No reports from other facilities
  • Raw materials and ingredients are not the source
  • Storage and transport are not factors

WHEN IS:

  • Started when production scaled up
  • Occurs consistently during high-volume periods
  • Pattern shows regular intervals of good vs. bad donuts

WHEN IS NOT:

  • Doesn’t happen during low-volume production
  • No problems during initial production setup
  • Doesn’t correlate with shift changes or time of day

EXTENT IS:

  • Affects approximately 10% of plain donut production
  • Problem is stable (consistent percentage)
  • Only one product line affected

EXTENT IS NOT:

  • Not affecting luxury donut lines
  • Not increasing over time
  • Not 100% of production (pattern suggests specific cause)

Root Cause Analysis

The IS NOT analysis reveals the key insight: luxury donuts are unaffected, suggesting different equipment. The 10% rate and consistent pattern points to one faulty machine among multiple plain donut machines.

Solution

Individual machine testing and quality checks at each production stage, rather than only final product inspection.

Getting Started

  1. Start Simple: Begin with clear, observable problems
  2. Be Thorough: Take time to complete all IS/IS NOT sections
  3. Use Examples: Learn from the built-in case studies
  4. Iterate: Use AI suggestions to refine your analysis
  5. Validate: Test solutions systematically

The methodology becomes more intuitive with practice, leading to faster and more accurate problem resolution.

Understanding systematic problem-solving leads to better solutions