📊 Download: Is-Is-Not Analysis Template
Professional Is-Is-Not Analysis Excel template with real-world examples, structured 4-dimension analysis, and step-by-step guidance.
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What is an Is-Is-Not Analysis?
Is-Is-Not Analysis – Real-World Examples
1. Pizza Production
Pizza Restaurant Chain
Complete Is-Is-Not analysis industry example for food service. Shows structured problem definition applied to pizza quality issues: “Pizzas sometimes undercooked in the center”. Systematic comparison across WHAT, WHERE, WHEN, EXTENT categories identified critical differences: only thick-crust affected, only Oven #2, only peak hours. Root cause analysis pointed to dough thickness, oven overloading, and operator training gaps.
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2. Automotive / Manufacturing
Paint Shop Operations
Complete Is-Is-Not analysis industry example for automotive manufacturing. Shows IATF 16949-compliant problem analysis for paint defects: “Orange peel texture and dust inclusions in clear coat finish”. Structured comparison revealed: only vertical surfaces affected, only Paint Booth #3, only after equipment idle. Root causes identified: paint viscosity changes, HVAC filtration issues, and temperature stabilization time.
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3. Pharmaceutical / Life Sciences
Tablet Manufacturing
Complete Is-Is-Not analysis industry example for pharmaceutical production. Shows GMP-compliant deviation analysis for tablet quality: “Tablet hardness out of specification (below 8 kp minimum)”. Systematic comparison across categories: only Metformin 500mg affected, only Press #2, only night shift first 2 hours. Critical causes: binder concentration, punch wear pattern, and humidity sensitivity.
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4. Service Industry
IT Service Desk
Complete Is-Is-Not analysis industry example for service operations. Shows systematic problem analysis for service quality: “First-call resolution rate dropped from 75% to 52%”. Structured comparison revealed: only password resets and VPN issues affected, only US East Coast team, only after Azure AD migration. Root causes: system complexity, training gaps, and SSO timeout configuration.
💡 Want to create your own? Contact me for the free template.
5. Information Technology
Software Deployment / DevOps
Complete Is-Is-Not analysis industry example for IT operations. Shows structured incident analysis for performance issues: “API response times exceed 2-second SLA (averaging 4.5 seconds)”. Systematic comparison: only Order and Inventory APIs affected, only US-East production, only after v2.5.0 release. Technical causes: ORM upgrade introducing N+1 queries, connection pool exhaustion, and index fragmentation.
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Why Use Is-Is-Not Analysis
Every hour spent on the wrong cause is wasted money.
Is-Is-Not Analysis reduces search time by eliminating irrelevant areas before the deep dive begins.
Without it: Teams chase symptoms, not causes. With it: Focus shifts to what actually matters.
The method pays off especially when internal failure costs are high – rework, scrap, and delayed shipments add up fast.
When to Use Is-Is-Not Analysis
Use Is-Is-Not Analysis when a problem costs time and money, but the cause isn’t obvious.
Typical triggers:
- A defect appears only in certain batches, shifts, or locations
- Multiple teams point to different root causes
- Quick fixes didn’t work – the problem returned
In the 8D process, Is-Is-Not belongs to Step D2 (Problem Description) –before tools like Ishikawa Diagram or 5-Why Analysis.
The rule:
- First narrow down WHERE the problem is.
- Then search for WHY.
Without clear boundaries, teams waste weeks analyzing the wrong areas.
8D Report (Step D2: Problem Description)
In 8D problem-solving, D2 requires a precise problem definition BEFORE root cause analysis begins. “We have a defect – but where exactly? When? On which products?” Is-Is-Not forces disciplined boundaries. Vague problems lead to vague solutions; sharp definitions lead to root causes.
Field Failures / Warranty Claims
When products fail in the field, Is-Is-Not scopes the affected population. “Only units from Plant B fail, Plant A units are fine – what’s different?” Geographic, temporal, and production-based contrasts reveal failure patterns. Field failure isolation → Targeted containment.
A3 / PDCA Problem Solving (Plan Phase)
In A3 reports, the limited space demands sharp problem definitions. “Define the problem in one box.” Is-Is-Not compresses complex situations into clear contrasts. A3 discipline requires precision; Is-Is-Not provides it.
Intermittent / Sporadic Quality Problems
When defects occur randomly – sometimes yes, sometimes no – Is-Is-Not reveals patterns. “Why do Monday shifts have problems but Tuesday shifts don’t?” The contrast between IS and IS NOT points directly at the cause. Sporadic problems have hidden patterns; Is-Is-Not finds them.
Multi-Line / Multi-Machine Production Issues
When the same product runs on multiple lines but only some have problems. “Line 3 produces defects, Line 1 and 2 don’t – what’s different?” Is-Is-Not systematically compares conditions. Same product, different results = difference holds the key.
Supplier Quality Issues
When supplier material causes problems, Is-Is-Not defines the scope. “This batch fails, but previous batches passed – what changed?” Compare failing vs. passing batches to identify supplier-side differences. Supplier investigation starts with clear problem boundaries.
Process Drift Investigation
When a stable process suddenly produces out-of-spec results. “We were fine yesterday, today we’re failing – what happened?” Is-Is-Not compares the “before” state with the “after” state. Process drift has a cause; Is-Is-Not finds what changed.
Customer Complaints with Unclear Scope
When a customer reports a problem, the complaint often lacks precision. “Your parts are defective!” – but which parts? Which shipments? Is-Is-Not separates affected from unaffected. Turn vague complaints into actionable problem statements. Customer frustration → Clear investigation focus.
CAPA (Corrective and Preventive Action)
CAPA systems require documented problem definitions before corrective actions. “What exactly is the nonconformance?” Is-Is-Not provides the structured documentation auditors expect. Regulatory compliance requires precision; Is-Is-Not delivers it.
Audit Findings / Nonconformance Reports
When audits identify issues, Is-Is-Not defines the exact scope. “The auditor found a deviation – but how widespread is it?” Determine if the finding is isolated or systemic before planning response. Audit response requires scope before solutions.
Equipment / Machine Failures
When machines fail unpredictably, Is-Is-Not identifies patterns. “Machine fails on Product A but not Product B – what’s the difference?” Equipment-product interactions reveal hidden stresses. Machine problems have product-specific triggers.
Pharmaceutical Deviation Investigations
GMP requires thorough deviation investigations with documented scope. “Which batches are affected? Which aren’t?” Is-Is-Not meets regulatory expectations for investigation rigor. FDA expects systematic analysis; Is-Is-Not provides the structure.
Automotive IATF 16949 Problem Solving
IATF 16949 requires disciplined problem-solving methods. “Show your problem definition methodology.” Is-Is-Not demonstrates systematic thinking to auditors. Customer-specific requirements often mandate Is-Is-Not explicitly.
IsIs-Not Analysis Principles
Principles of Is-/Is-Not Analysis are:
Identification: Describe problem or improvement opportunity in detail.
The identification process is the first and most important step in the Is-Is-Not method. It requires a clear specification of the problem at hand. This stage needs the understanding of symptoms and severity of relevance.
It is important to begin with proper identification as only then would problem solving be effective. It avoids the regular pitfall of treating signs and symptoms as a substitute of the problem.
For example, in a manufacturing scenario, if a machine is producing defective parts, you need the error to be quantified in as specific terms possible such as the nature of defect and its frequency and also under what conditions does this happen. Having this level of clarity grounds any further analysis and helps to make sure the right problem is addressed.
Differentiation: Contrasting What Is Occurring with What Is Not
Differentiation is a systematic way of comparing and contrasting the discovery (what is) with non-discovery (what is not). Scout:
This principle seeks to scrutinize the problem within different contexts and conditions to identify its limits and nature The opposing answer is you have to differentiate; this helps narrow down what exactly your problem may be and will help tell you the specifics that separate that generic condition from its unique conditions only relevant to a problem time frame.
If a defect of any kind only happens during one shift, or with such and such materials, these are key observations for example. This principle helps in both eliminating common causes while searching for the root cause of the problem by down to distinct difference leading contributors part of the problem.
Analysis: Examining the Differences to Understand the Problem Better
This is the phase where actual problem-solving starts which is nothing but Analysis. Other than defining the problem and not-to-be-confused-with statement, this is the deep dive into data and observations collected.
This phase is about pulling apart the differences and similarities found in differentiation to essentially get at what the root problem could be. It is usually an iterative analysis, which involves several rounds of looking and hypothesizing. It requires a lot of critical thinking and working with data.
For instance in a services industry, when customer complaints are high during certain periods of the day — mechanisitic diagnostic approach would help us analyse if it is due to staffing levels or flow of customers conversation compared to off-peak times.
How to Conduct an Is-Is-Not Analysis
1. Define the problem
In the Is-Is-Not method, this is called Problem Definition; in other words, explain what your problem is as simply and specifically as possible.
This definition should be broad enough to capture the essence of the problem without being too specific. A good problem statement will be more descriptive — where and when does the problem tend to occur, how severe is it, what specific impacts you care about. As an instance: instead of exclaiming “Production is going badly,” it would be a more accurate description on the problem level to say “The percentage of OK-parts has fallen 2% month by month in production line X”.
2. List ‘Is’-Factors
Listing ‘Is’ involves describing how the problem shows up, going over exactly what are the features of the condition in which the problem is happening.
This step includes collecting relevant observations (data) of the present problem such as environment conditions, timings, processes involved, resources used and other necessary factors.
For example, if you are dealing with customer complaints in a service industry your ‘Is’ factors could include when the complaints tend to happen, what do customers actually complain about and which services they use etc. The big idea is to take a snapshot of the problem as it exists today, with all relevant context, such that an effective comparison can be made.
3. List ‘Is-Not’-Factors
Using the same identified & standardised characteristics, identify & document scenarios where this problem is not observed.
This means that they will look at circumstances other than the problem but without this. Through identifying these ‘Is-Not’ components, you can then get a sense for what are those factors or conditions that seem to be allowing the problem in these situations.
This could mean that ‘Is-Not’ factors for a software application, which will often crash under certain user conditions, would involve conditions where the application does not crash. For example, it could be different user actions, system environments, or the times people are using them. This provides contrast and segregates variables that might be contributing to the issue.
4. Compare and Analyze
Examining the “Is” and “Is-Not” elements will help one find trends, variations, and commonalities. Finding the underlying cause of the issue depends much on this comparison study.
It is searching for hints in the variations between the “Is” and “Is-Not” situations. This stage may call for statistical analysis, brainstorming meetings, or other analytical tools and usually calls for critical thought. By use of the juxtaposition of these elements, one seeks insights that would generate theories regarding the underlying cause.
If machine failures mostly on specific days, for instance, comparing operating, personnel, and maintenance practices on those days with others helps to identify underlying reasons. Developing workable answers to the found issue depends on this careful study.
5. Derive Actions
After the Is and Is-Not factors are analyzed, the next crucial step is to derive targeted actions aimed at addressing the identified root causes and preventing recurrence. This phase involves transforming analytical insights into practical, actionable steps.
The goal is to eliminate the root causes identified in the Is-Is-Not Analysis by implementing corrective and preventive actions. Additionally, these actions should enhance process stability and minimize the risk of similar issues arising in the future.
- Review Is-Is-Not Findings:
Start by revisiting the Is-Is-Not Analysis, focusing on:
- Differences and peculiarities identified between the IS and IS NOT scenarios.
- Potential Root Causes listed for each question.
- Changes in the Timeframe that might indicate triggers or contributing factors.
2. Identify Action Types:
- Corrective Actions: Directly address the root causes to eliminate the current problem.
- Preventive Actions: Implement process changes to prevent recurrence or occurrence in other areas.
- Verification Actions: Verify the effectiveness of implemented solutions.
3. Specify Clear Objectives:
Each action should have a clear objective related to:
- Fixing a specific root cause.
- Strengthening process controls.
- Reducing the probability of recurrence.
4. Define Action Details:
For each action, clearly define:
- Description: What exactly needs to be done?
- Responsible Team: Who is accountable for executing the action?
- Priority Level: How urgent is the action? (High, Medium, Low)
- Due Date: When should the action be completed?
- Verification Method: How will the effectiveness of the action be verified?
How to Combine Is-Is-Not Analysis with Other Quality Tools
Is-Is-Not is a PROBLEM DEFINITION tool – it belongs at the START of root cause analysis. Here’s how it integrates:
5W2H Method
5W2H expands on Is-Is-Not with HOW and HOW MUCH dimensions. “Is-Is-Not defined the problem; 5W2H adds implementation details.” Complementary frameworks for complete problem understanding.
Flow Chart
Is-Is-Not identifies WHERE in the process problems occur. “IS: After Step 5. IS NOT: Before Step 5.” Process mapping validates location hypotheses.
Ishikawa Diagram
Is-Is-Not boundaries prevent Ishikawa from going too broad. “The problem is X, not Y – focus causes on X only.” Bounded problems enable focused brainstorming. Scope first → Brainstorm second.
5-Why Analysis
Is-Is-Not defines WHAT the problem is; 5-Why finds WHY it happens. Is-Is-Not first, then 5-Why. Without clear definition, 5-Why chases the wrong cause. “What’s the problem?” (Is-Is-Not) → “Why does it happen?” (5-Why). Definition → Causation.
Pareto Chart
When Is-Is-Not reveals multiple problem types, Pareto prioritizes which to solve first. “Is-Is-Not shows 5 defect types – which causes 80% of impact?” Is-Is-Not categorizes; Pareto prioritizes.
Correlation / Scatter Diagrams
Is-Is-Not may reveal variables to investigate; Scatter plots test relationships. “Is temperature correlated with the IS/IS NOT pattern?” Hypothesis from Is-Is-Not → Validation through correlation.
Control Chart
Is-Is-Not Analysis uses control chart data as evidence. “Control chart shows when the shift happened.” Timestamps from SPC support Is-Is-Not WHEN questions. Control chart data → Is-Is-Not evidence.
SIPOC Diagram
SIPOC shows process context; Is-Is-Not pinpoints problem location within it. “Supplier X, Process Y, Output Z – Is-Is-Not says problem is in Process Y.” Process context → Problem isolation.
Poka Yoke
Is-Is-Not reveals where error-proofing is needed. “Operator B never has defects – what does Operator B do differently?” The IS NOT condition often contains the Poka-Yoke solution.
MSA / Gage R&R
Before trusting Is-Is-Not data, verify measurements. “Are the IS/IS NOT differences real or measurement noise?” Measurement validation → Reliable Is-Is-Not conclusions.
Action Management
Is-Is-Not Analysis generates actions: “Investigate LINE 3 differences” – track in Action Management. Every contrast discovered needs follow-up. Findings → Actions → Closure.
FMEA Review
When FMEA predicted risks become actual problems, Is-Is-Not validates assumptions. “FMEA said this could happen – Is-Is-Not confirms the scope.” Cross-reference Is-Is-Not findings with FMEA failure modes. Prediction validation → FMEA improvement.
Lessons Learned
Completed Is-Is-Not analyses feed Lessons Learned databases. “Problem pattern X was solved by finding Y difference.” Future problems with similar patterns have head starts. Problem solving → Organizational learning.
Quality Alert
Is-Is-Not scopes Quality Alerts. “Alert for affected products only – Is-Is-Not defined which.” Targeted containment, not broad recalls. Precise alerts → Minimized disruption.
Check Sheets (Tally Sheets)
Is-Is-Not identifies WHAT data to collect. “We need to track by shift, machine, and operator.” Check Sheets then gather the evidence. Problem boundaries define data requirements.
8D Report
Is-Is-Not IS D2 of the 8D process. D1 (Team) → D2 (Is-Is-Not) → D3 (Containment) → D4 (Root Cause). D2 must be completed before D4 begins. 8D structure requires Is-Is-Not; it’s not optional.
Histogram
Histograms visualize differences between IS and IS NOT populations. “Affected parts have different distribution than unaffected parts.” Overlay histograms for visual contrast. Statistical visualization supports Is-Is-Not conclusions.
Control Plan
FMEA identifies risks; Control Plans implement controls. High-RPN characteristics from FMEA become Control Plan items with specifications, methods, and reaction plans. FMEA → Control Plan is a one-way flow: risk drives control.
Gemba Walk / Go-See
Is-Is-Not identifies WHERE to look during Gemba. “IS locations vs. IS NOT locations – visit both.” Direct observation confirms or challenges Is-Is-Not hypotheses. Desk analysis → Floor verification.
CAPA Management
Recurring out-of-control signals, even after correction, indicate systemic issues requiring formal CAPA. The Control Chart provides trend evidence; CAPA addresses the underlying system failure. Pattern of signals → CAPA initiation.
Fault Tree Analysis (FTA)
Is-Is-Not defines the TOP EVENT in fault trees. “What is the failure we’re analyzing?” Clear top event = logical decomposition. Vague top event = messy tree.
Kepner-Tregoe Problem Analysis
The Is-Is-Not Analysis is a CORE COMPONENT of Kepner-Tregoe methodology. KT adds “DISTINCTION” and “CHANGE” columns to standard Is-Is-Not. Advanced method for complex problems.
Benefits of Is-Is-Not Analysis
Focus: Narrows down the problem area
An important aspect of Is-Is-Not is that it’s able to narrow its focus to the specific problematic function(s). This approach can assist in reducing the scope of an investigation by separating what is and what not belongs to the problem.
This narrow scope is useful for complex environments since all the variables are visible, and we understand what might be really important. In a factory, concentrating on machines and processes that are linked to fail products (not plural) is more beneficial for solving the problem than reviewing all machinery and process.
Clarity: Provides clear understanding
The Is-Is-Not sheet helps to frame the problem-solving process. By systematically going through differentiation and analysis of the problem, it reveals in details what nature or scope the problem might assume as or how big or deep the problem can go which pushes us towards a comprehensive solution devising.
It helps turn fuzzy or difficult problems into clearer, doable pieces. Clarity provides the foundation to make decisions.
Efficiency: Saves time by eliminating irrelevant factors
A key advantage of the Is-Is-Not method is its efficiency, which comes from removing all the data that does not correlate. That it focused to the issues at hand, not wasting time analyzing irrelevant data.
This speed is especially crucial in domains where resolution must be fast. In software troubleshooting, examining into the system configurations/users actions (that lead to an error) would help in approaching a problem rather than scanning through the whole system. By doing so, resources and efforts are poured into underling issues instead of scattered about the many less relevant areas.
Limitations of Is-Is-Not Analysis
Complexity: Can be less effective for extremely complex Problems
The Is-Is-Not method does have the drawback of being less able to deal with truly complex problems. For a problem with differing inter-variables or the influence of myriad factors, this binary delineation of ‘Is’ versus ‘Is-Not’ approach of the method might be superficial.
One may argue that in systemic issues like with organizational change or market dynamics, it might be impossible to solve the issue without a deep understanding of all the layers of factors, while the Is-Is-Not approach does not provide this capability.
Data Dependency: Requires accurate and sufficient data
The Is-Is-Not method is most effective when you have the right kind and quality of data. The method focuses on “Is” and “Is-not” factors based on data analysis. In cases where data is sparse, biased or simply wrong, the analysis could suggest false interpretations.
For example, if the data production defects are not reliably and systematically gathered or are incomplete, an Is-Is Not analysis may fail to identify vital considerations which are then ignored as potential solutions.
Subjectivity: Risk of biased analysis
The other limitation of the Is-Is-Not approach is that it may introduce subjectivity and bias. The ‘Is’ or ‘Is-Not’ factor differentiation can also be influenced by personal perception, the context of experiences and differences. This subjectivity can bias the analysis towards symptoms, rather than root cause.
For instance, a team studying customer feedback may overlook data that does not confirm their prior beliefs or newly developed hypotheses about what matters most to customers and why.
Best Practice Is-Is-Not Analysis
Be Specific: Use Specific Descriptions Rather Than General Statementsc
One of the most important tools you have in effective problem-solving is to be specific. This should help keep us focused on what the actual issue and its attributes are as clarity in specificity is key to knowing where the problem lies.
An example is as opposed to simply stating an issue such as "sales declined", the better statement describes exactly how much sales declined, when it declined, and which product lines and market segments are affected.
Involve the Team: Collaborative Analysis Often Yields Better Results
One of the useful tips for the Is-Is-Not callout method is to include a mix group of people in the discussion process. Collaborative analysis, combining different views, tools, and experiences makes the solution much more better.
Is-Is-Not Analysis Example: Pizza Quality
Situation:
Zero Pizza Defect get customer complaints about their pizza’s baking quality, with some pizzas being overcooked or undercooked.
1. Define the problem
Zero Pizza Defect is experiencing a 30% increase in inconsistent pizza baking quality, with 25% of pizzas being overcooked and 15% undercooked, leading to customer complaints and potential revenue loss.
2. List ‘Is’-Factors
To address the issue, the company forms a cross-functional team to identitfy the Is-factors:
| Aspect | Is | Is Not |
|---|---|---|
| What | Inconsistent baking quality (some pizzas overcooked or undercooked) | Not related to ingredient quality or dough preparation consistency |
| Where | Primarily occurs during lunch and dinner rush hours, especially with larger orders | Does not occur during low-volume periods or smaller orders |
| When | Happens intermittently, mostly on weekends or high-traffic days | Not a daily or continuous issue during off-peak hours |
| Who | Affects pizzas baked by less experienced staff using Oven 2 | Does not affect pizzas baked by senior staff or using Oven 1 |
| How Much | Affects roughly 15% of pizzas during rush hours | Does not impact more than 15-20% of total pizzas made per day |
3. List ‘Is-Not’-Factors
In the next step they detemine the Is-Not factors:
| Aspect | Is | Is Not |
|---|---|---|
| What | Inconsistent baking quality (some pizzas overcooked or undercooked) | Not related to ingredient quality or dough preparation consistency |
| Where | Primarily occurs during lunch and dinner rush hours, especially with larger orders | Does not occur during low-volume periods or smaller orders |
| When | Happens intermittently, mostly on weekends or high-traffic days | Not a daily or continuous issue during off-peak hours |
| Who | Affects pizzas baked by less experienced staff using Oven 2 | Does not affect pizzas baked by senior staff or using Oven 1 |
| How Much | Affects roughly 15% of pizzas during rush hours | Does not impact more than 15-20% of total pizzas made per day |
4. Compare and Analyze
The teams discovers the following areas which may impact pizza quality:
Staffing and Training:
The issue occurs more frequently when less experienced staff are operating the ovens during rush hours, suggesting possible training gaps in temperature management and baking times.
Equipment:
The problem is more prominent with pizzas baked in Oven 2, which may indicate a calibration or maintenance issue with this specific oven.
Timing/Volume:
The inconsistency happens during busy times, suggesting that high-volume production may lead to rushed or improper baking processes.
5. Derive Actions
Investigate Oven 2:
Check if Oven 2 is properly calibrated and inspect for any performance issues.
Staff Training:
Focus on training newer staff to handle high-volume orders more efficiently, with emphasis on temperature control and timing.
Process Improvement:
Introduce a more structured approach during rush hours, such as staggered baking or assigning senior staff to oversee operations.
Results
Resolving the inconsistent baking issue led to a 40% decrease in customer complaints, a 25% increase in repeat orders, and a 15% boost in overall customer satisfaction scores
FAQ Is-Is-Not Analysis
What is an Is-Is-Not Analysis in quality management?
An Is-Is-Not Analysis, also known as an Is-Is-Not Sheet or Matrix, is a structured tool used in quality management to define, clarify, and gain a deep understanding of a subject or problem by identifying what it is, what it is not, and areas of ambiguity.
Where is the Is-Is-Not Analysis typically used?
Is-Is-Not Analysis is widely used across industries such as manufacturing, healthcare, and software development to facilitate problem-solving, brainstorming, and requirement clarification processes.
What's the primary objective of using the Is-Is-Not Analysis?
The primary goal is to achieve clarity and consensus by highlighting essential characteristics (Is), non-essential characteristics (Is-Not), and areas where clarity is needed.
What is the history behind the Is-Is-Not Analysis in quality management?
The Is-Is-Not Analysis has its roots in problem-solving methodologies like Six Sigma and Lean Manufacturing, and it emerged as a structured tool during the mid-20th century.
What are the key principles of creating an Is-Is-Not Analysis?
The key principles involve distinguishing between essential characteristics (Is) and non-essential characteristics (Is-Not) to define boundaries and scope.
How is an Is-Is-Not Analysis performed?
The steps of an Is-Is-Not Analysis include
- defining the subject
- listing Is and Is-Not characteristics
- organizing the characteristics in a matrix or table, and
- validating with team discussions.
What are the benefits of using an Is-Is-Not Analysis in quality management?
Benefits include improved clarity, alignment among team members, enhanced problem-solving, and support for informed decision-making.
Are there any limitations to using the Is-Is-Not Analysis?
Yes, limitations may include subjectivity in interpretation, potential complexity, and the time required for its creation.
What are best-practices for Is-Is-Not Analysis?
Best practices include keeping it concise, using visuals for clarity, and regularly updating it as the project evolves.
What is an example of how the Is-Is-Not Analysis is used in quality improvement?
In a manufacturing setting, a team may use it to address product defects, listing characteristics like “meets specifications” (Is) and “fails to meet specifications” (Is-Not) to identify areas for process improvement and enhance quality.
