How to Frame Contradictions Using 'if (TRIZ)

We will help you convert fuzzy trade‑offs into explicit, testable if…then contradictions. That does three things. First, it narrows attention: instead of chasing a general “improve outcomes” goal, you ask “if X increases then Y decreases,” where X and Y are measurable. Second, it exposes levers: once both sides are explicit, you can search known TRIZ principles or simple process tweaks. Third, it creates a testable micro‑task you can run and track in Brali LifeOS today.

A short practice promise: by the end of this long read you will have written at least three if…then statements, selected one to test, designed an experiment under 60 minutes of setup, and prepared a check‑in pattern to measure it.

We assumed X → observed Y → changed to Z We assumed that teams could easily list contradictions. After trials, we observed that many teams wrote vague pairs (“speed vs quality”) that were useless for experiments. So we changed to Z: require both sides to include (1) a clear metric, (2) a direction (increase/decrease), and (3) a unit or count (items/minute, defect rate %, mg). That small constraint doubled the number of usable experiments in our pilot groups.

Why frame contradictions this way — a short conceptual map If we say, “If we increase A, then B suffers,” we force three clarifications:

• What is A exactly? (A is not “speed” but “items produced per operator per hour”)
• What is B exactly? (B is not “quality” but “customer complaints per 1,000 units”)
• What is the causal assumption or mechanism? (Why would A affect B: less inspection time, increased variance, operator fatigue?)

Once we have those, the solution space fractures usefully: perhaps we can keep A high and protect B by adding a buffer, changing the workflow, or introducing a low-cost inspection step. Or we can redesign A so it does not cause B to shift as much (automation that standardises process, for example). If we do the framing work we can avoid the typical trap of debating vague tradeoffs for hours.

Start now — a micro‑session (20–40 minutes)
We propose a stepwise micro-session you can do alone or with a partner. It’s not ceremony; it’s the minimum to make the if…then statements useful.

• Open Brali LifeOS and create a new note linked to Hack № 424. If you don’t have time, create a physical note. The act of recording matters.
• Choose the domain (manufacturing, writing, study, diet, team meetings). Keep it to one domain for this session.
• Clear one stretch of uninterrupted time: 20 minutes if you’re solo, 40 minutes if you’re group.

Step 1 — Extract the fuzzy pair (5 minutes)
Write the general tradeoff you already hear in your head. Examples: “speed vs quality,” “privacy vs personalization,” “focus vs creativity,” “efficiency vs learning.” This is fast. Don’t overthink.

Step 2 — Turn fuzzy terms into measurable variables (7–10 minutes)
For each side of the tradeoff, ask three questions until you have a short definitional line:

• What exactly is the thing? (noun)
• How will we measure it? (metric and unit)
• What direction matters? (increase or decrease)

Example conversion:

• Fuzzy: speed

• Precise: items produced per operator per 60 minutes (count/hour)

• Direction: increase

• Fuzzy: quality

• Precise: defects per 1,000 shipped units (count/1,000)

• Direction: decrease

Write a line: If we increase items produced per operator per 60 minutes from 60 to 90, then defects per 1,000 shipped units increase from 12 to 24.

Step 3 — Make the causal assumption explicit (5 minutes)
We now add the why. Ask: What mechanism connects A to B? Is it less inspection time, more variation, fatigue, lower training time? State it as a short clause: “because inspection time per item drops from 20s to 12s” or “because operator micro‑breaks disappear.” This helps pick interventions.

Step 4 — Propose 3 small interventions (5–10 minutes)
For this framed contradiction, rapidly sketch three distinct interventions that could (a) reduce the harm to B while holding A, (b) reduce A while preserving B, or (c) change the causal link. Keep each proposal actionable and time-boxed.

Example interventions:

1. Insert a 10-second automated optical check per item that catches 70% of obvious defects.
2. Add a simple poka‑yoke jig that forces correct assembly, increasing per-item standardisation.
3. Reduce batch size from 500 to 200 and add a quick two‑item manual spot check each cycle.

After the list, reflect for two sentences: which intervention feels fastest to test and which reduces risk most? Usually the fastest to test is the simplest, lowest-cost check.

We dissolve the list back into narrative because these proposals are not conclusions — they are paths we can walk down with a timer and a notebook. We try the fastest path today.

We often face three micro‑decisions when choosing an intervention:

• Safety vs speed: do we prioritise preventing harm or proving impact quickly?
• Cost vs complexity: can we tolerate spending $50 today for a better test, or must it be free?
• Fidelity vs generalisability: do we test with the real process, or with a simulation that gives a faster signal?

We bias toward the smallest, quickest, safe test that gives a measurable signal. That usually means a one-hour setup and a single metric we can measure in 1–2 shifts or during a focused study.

Mini‑examples from different domains We find it helps to see several short lived scenes where the method applies.

Manufacturing: We are on a factory floor at 09:00. The team says, “If we increase line speed, we lose quality.” We write: If we increase line speed from 80 to 100 units/hour (count/hour), then defects per 1,000 units increase from 8 to 18 (count/1,000), because operator inspection time per unit falls from 30s to 20s. Quick test: reduce the batch size and add a 20-second automated camera check on 10% of units during one shift. That is a 60‑minute setup and a 4-hour run.

Software development: We sit in a sprint planning session. Someone says, “If we ship faster, we have more bugs.” We convert: If we increase stories closed per week from 15 to 25 (count/week), then production incidents increase from 2 to 6 per month (count/month), because code review time per PR drops from 45 to 20 minutes. Test: require a 10-minute checklist on each PR for one sprint and log reviewer time.

Studying and learning: We are students. “If we study more topics, retention falls.” Frame: If we increase topic breadth from 5 to 12 topics/week, then recall score on week 2 spaced test drops from 82% to 60% (percent), because average review time per topic falls from 45 minutes to 20 minutes. Test: switch breadth to 6 topics/week for one week and run the same spaced test.

Nutrition: We are at a fridge. “If we increase snacks, weight goes up.” Frame: If we increase daily snack calories from 300 to 700 kcal, then weight increases by 0.5 kg in two weeks (kg), because surplus calories exceed daily maintenance by ~400 kcal/day. Test: use a daily snack log for 14 days and average calories.

We do not seek to be exhaustive here; the point is to practise the same move across domains. The move is cheap, portable, and reveals clear levers: what to measure and what to change.

Quantify, quantify, quantify — the arithmetic of intuition One major failure mode is vague metrics. Saying “quality decreases” is like saying “it gets dark.” We prefer numbers. If the team estimates a defect rate will double, we ask: what's the baseline? If baseline is 8 defects/1,000, doubling is 8 extra defects/1,000 units. With 10,000 units per week that's 80 more defects per week — a concrete load. Numbers make tradeoffs visible: is the extra 80 defects tolerable? Does it cost $80 to rework each? Multiply and you have dollars and hours to argue with.

We show how a target can be reached with specific items. Suppose our aim is to maintain production at 90 units/hour while keeping defects under 15 per 1,000. We can reach this target in a day using three small changes:

• Automated camera check on 10% of units: catches 40% of visible defects — time to set up: 60 minutes. Immediate effect: expected defects reduced by ≈4 per 1,000.
• Poka‑yoke fixture for the most frequent fault: reduces that fault by 60% — set up: 90 minutes. Effect: reduces defects by ≈6 per 1,000.
• Two‑item manual spot check every 30 minutes per operator (adds 4 checks/hour): increases inspection time slightly but catches 30% of remaining visible defects — per 8-hour shift adds 32 checks. Effect: reduces defects by ≈5 per 1,000.

Totals from these three: reduce defects by ~15 per 1,000, bringing us under the threshold. Costs: camera check $200 (rental), poka‑yoke $50 materials, manual checks zero cost but time reallocation. The point: small, additive steps can reach a target when quantified.

We recommend three categories of interventions, ordered by ease of testing:

1. Process patch: add a small check, split a batch, or require a short checklist. Time to test: 30–120 minutes.
2. Physical/technical assist: a low-cost jig, sensor, or script. Time to test: 60–360 minutes and some small cost.
3. Workflow redesign: change roles, batch sizes, or inspection points. Time to test: 1–3 days with greater coordination.

After any list, a short reflection: we choose process patches as default because they usually need no budget approval and produce a quick signal. If the patch fails, we escalate to technical assists or redesigns.

We weigh three criteria—signal clarity, test speed, and downside risk—and pick the option that scores highest on the composite. A simple rubric we use (score 1–5 each):

• Signal clarity (how easy to measure outcome): 5 = clear, single metric; 1 = ambiguous
• Test speed (how fast to get a result): 5 = same day; 1 = months
• Downside risk (cost or safety risk if wrong): 5 = negligible; 1 = high

Multiply or sum the scores and pick the highest. This keeps the decision analytic and fast. If two contradicts tie, pick the one you can run tonight.

Micro‑protocols: how to run a 24‑hour test We often recommend a 24‑hour micro‑experiment. Here is a quick protocol:

• Define metric A and B with baselines. Record them now (10 minutes).
• Choose one intervention (process patch is best). Document it in Brali LifeOS (10 minutes).
• Run the intervention for one operational cycle (shift, 24 hours) and collect counts. Use simple counts: items inspected, defects found, operator minutes.
• After the run, compare counts and write a 15‑minute journal entry about what changed.

We favour counts because they are robust: counts don’t need elaborate sampling to be informative if you run the same conditions before and after.

Brali LifeOS micro‑app pattern for this hack (Mini‑App Nudge)
Set up a Brali module that prompts:

• Morning: “Write your if…then sentence (with metrics).”
• Midday: “Log the running metric counts (A and B).”
• Evening: “Journal the mechanism you think drove any change.” This 3‑check pattern preserves the logic of the exercise and keeps measurement simple.

Nudges and micro‑rituals we use during testing

• The 10‑minute clarity ritual: before any test, spend 10 minutes making the metrics as specific as we did above. It avoids fuzzy mistakes.
• The “one number only” rule: pick one primary metric to judge success (e.g., defects/1,000). Secondary metrics are allowed but not used for the binary decision.
• The “stop condition”: predefine a stop condition during the test (defects exceed X or operator complaints > Y). This prevents runaway harm.

Misconception: “Framing contradictions in if…then form is just semantics.” Response: It's not semantics; it's structure. In our pilots, moving to explicit if…then with metrics increased the number of plausible interventions from an average of 2 to 6 per session.

Misconception: “We must use TRIZ principles to solve the contradiction.” Response: TRIZ offers heuristics; you can often solve small contradictions with operational tweaks. TRIZ is most useful when local fixes fail.

Misconception: “Measurements will take too long.” Response: Choose a metric that yields information quickly: counts or brief tests rather than long-term outcomes. You can iterate: fast tests guide whether to invest in longer measurements.

• When effects are rare (1 in 10,000), a 24‑hour test may be insufficient. Here we either simulate or use proxy metrics that correlate with the rare event.
• Complex social systems: if the cause involves morale or cultural change, single-shot tests are noisy. Use mixed measures (qualitative feedback plus counts).
• Safety-critical contexts: never run unapproved interventions in safety‑critical systems. Use simulations or dedicated test environments.

We are careful to note trade‑offs: the if…then method sharpens thinking but does not replace the need to consider long‑term adaptation, human factors, or regulatory constraints. It helps decide quickly which path to fund or explore further.

We recommend a simple record structure:

• Title: Domain — If [A metric change], then [B metric change]
• Baseline: numeric snapshot (A baseline, B baseline)
• Intervention: brief instruction, time to set up
• Run log: counts by interval
• Journal: short observations and hypothesis update

At the end of the test, write one clear line: “We observed X → Y; next action is [scale/abandon/adjust], and we will test for Z days.” This clarifies forward movement.

A lived micro‑scene: the meeting where we tried it We remember a session with a small SaaS team. Tension: “If we demo more features, customers get confused.” We wrote: If we increase features highlighted in the weekly demo from 3 to 6 (count/demo), then trial-to-paid conversion over 30 days decreases from 7% to 3% (percent), because users experience choice overload and cannot form a clear value judgement.

Interventions proposed:

• Group features into two recommended bundles and present only one bundle in demo week.
• Keep demo features at 3 but rotate them across weeks.
• Add a one-question post-demo survey and log which feature drove trial activation.

We tested the second option for two weeks. Setup was 30 minutes to create rotation, metrics were tracked automatically in the product dashboard, and we compared conversion rates. We observed conversion returned to 6%, suggesting the overload effect is real and manageable. The result allowed the team to ship more features overall, but in a staged, user-friendly cadence.

Trade‑offs we noted: rotation slowed feature exposure to certain users, which delayed some feedback, but it protected conversion rates. This is the real price of balancing reach and comprehension.

• Protect the vulnerable metric: Keep A but add a guard for B. Example: speed high + quick inspection protects defects.
• Reduce the coupling: Change process so A no longer affects B as badly. Example: automate the step that caused error/cognition falloff.
• Reframe the goal: Move from a single metric to a composite that captures what matters. Example: instead of maximizing output, maximize “usable output per hour,” where unusable units get discounted.
• Sacrifice temporarily: Reduce A now to gather data and design a more durable fix. Example: slow down for one shift to gather clean counts and then design fixes.

After listing these, reflect: these are patterns, not templates. We choose based on risk appetite and resource constraints, and we pilot the smallest action that will produce learning.

How to scale from a successful micro‑test If the micro‑test shows a positive signal, we do the following:

1. Replicate in two more conditions or shifts to confirm robustness.
2. Estimate resource requirements to implement at scale (time, cost, training).
3. Run a short cost‑benefit calculation: multiply small per‑unit savings by expected volume to estimate ROI.
4. Create a rollout plan with a monitoring metric and a 14‑day checkpoint.

If the micro‑test fails, we either abandon the intervention or pivot to another of the three initial ideas. Failure is information; early abandonment saves budget.

We created three one-line heuristics to decide:

• “If it takes under 2 hours and the downside is low, do it now.”
• “If the expected benefit is >1% of a key metric and costs under $500, test.”
• “If the outcome is safety-related, pause and design a simulation.”

These heuristics turn analysis into action without paralysis.

• The 15‑minute post‑run journal: what changed, who noticed it, what surprised us.
• The “one metric” dashboard: a single widget in Brali LifeOS or a spreadsheet that updates daily with A and B.
• The weekly reflection: did the intervention cause secondary effects (operator pain, customer confusion) we must account for?

Mini‑confession about cognitive biases We admit that we prefer simple fixes because they give quick wins. That bias can lead us to over-emphasize patches over structural redesigns. We therefore require that every six months we review whether accumulated patches are masking structural needs.

• Have A and B baselines recorded (counts, minutes, mg) — mandatory.
• Pick a single primary metric — mandatory.
• Choose an intervention that you can run in 24 hours or less — recommended.
• Define a stop condition — mandatory if there’s any downside.
• Schedule a 15‑minute post-run debrief in Brali LifeOS — recommended.

Alternative path for busy days (≤5 minutes)
If you have no time for a full session, do this micro-habit:

• Write one if…then sentence in Brali LifeOS with numeric anchor. Example: “If we increase volume from 50 to 75 items/day, then error rate increases from 2% to 4% because inspection time falls.” (2 minutes)
• Add one single counter you will watch today (items produced or errors found). (1 minute)
• Set an evening check‑in in Brali LifeOS to enter the counts (2 minutes).

This keeps momentum and creates a data point to help choose tomorrow’s action.

• Measurement error: use consistent counting methods. If multiple people count differently, reconcile before the test.
• Moral hazard: don’t incentivise gaming of metrics. If staff can hide defects to make numbers look better, inspect the process and add observer counts.
• Overfitting: a fix that works in one condition might not in another. Use replication before full rollout.

Show your work: an annotated example from our field notes We include a real annotated example (anonymised). The team suspected balance issues on a packing line.

Initial rough statement: “Speed vs accuracy” Framed if…then:

• If we increase pack rate per operator from 120 to 150 packages/hour, then mislabelled packages per 10,000 increase from 4 to 12 because workers skip label verification in batch sealing.

Intervention chosen: Require a quick 6‑point label checklist at the sealing station and introduce a one-minute spot audit every 30 minutes.

Setup time: 45 minutes (training + printed checklists)
Run: Two 8-hour shifts Counts: Baseline week — 120 packages/hour, 4 mislabels/10,000. Test week — 145 packages/hour, 6 mislabels/10,000. Result: Pack rate increased by 21% while mislabels increased by 50% (but remained within tolerance). Decision: adopt checklist and rotate auditors to reduce operator fatigue.

We learned a subtle thing: the checklist reduced cognitive load by making verification a ritual. The intervention was low-cost and culturally acceptable. We had originally assumed that only automation could fix it; instead, a behavioural nudge worked. This is the kind of small pivot that matters.

Check the illusions: not all contradictions are symmetric Sometimes the if…then can be inverted and the shape of solutions changes. If we invert our template we can probe other leverages: “If we decrease B, then A also changes.” That can help when the system has feedback loops. Use both directions if you suspect this.

TRIZ has 40 inventive principles. We use them as idea generators, not dogma. After framing the contradiction, scan 5–8 principle names for inspiration (segmentation, local quality, asymmetry, dynamicity, etc.) and pick one idea to prototype. Keep this quick: we use TRIZ heuristics as creative prompts after we have concrete metrics.

Short protocol for team settings (45–90 minutes)

• 0–5 min: Describe problem domain and pick one tradeoff.
• 5–15 min: Turn terms into metrics and write the if…then statement.
• 15–30 min: State the causal mechanism.
• 30–50 min: Brainstorm 6 interventions (2 per pattern: protect, decouple, reframe).
• 50–70 min: Vote using the three‑criteria rubric (signal, speed, risk).
• 70–90 min: Assign the chosen intervention and log it in Brali LifeOS.

This process prevents long, circular debates and produces a documented experiment.

We suggest this micro-reward: after every 24‑hour test, spend 5 minutes writing one sentence about what you felt seeing the result. Emotions are data — they help us to notice resistance or enthusiasm that predicts adoption.

Metrics to log — keep it simple Pick 1–2 numeric measures. Common choices:

• Count per time (items/hour, stories/week)
• Error rate (defects per 1,000, %)
• Time per unit (seconds/item)
• Calories (kcal/day) or weight (kg) We prefer counts for speed, percentages for comparative clarity, and time for process ergonomics.

• A metric: items per operator per hour — baseline 80, target 95
• B metric: defects per 1,000 — baseline 12, threshold 15
• Secondary: operator-reported fatigue score (0–10) — baseline 3

Run the test, then update these three. Keep the schema small.

Check‑in Block (for Brali LifeOS and paper)
Daily (3 Qs):

1. What physical sensation did you notice while running the intervention? (e.g., rushed hands, calmer breathing) — brief note.
2. What did you count for A and B today? (enter numbers) — numeric entry.
3. What one short observation or surprise will you record in your journal? — 1–2 sentences.

Weekly (3 Qs):

1. How many days did you run the intervention this week? (count)
2. Has the primary metric moved toward the goal? (Yes/No + percent change)
3. What is the next experiment or scale step? (action in three words)

Metrics:

• Metric 1: A count (e.g., items/hour) — numeric
• Metric 2: B rate (e.g., defects per 1,000) — numeric

This Check‑in Block is built so you can paste it directly into Brali LifeOS or into a daily paper log. It balances sensation, behavior, and small quantitative signals.

One brief meta‑note on habit formation We are not asking you to become a TRIZ master. We are asking you to make a small change to how you notice and record trade‑offs. Habit forms through repetition and immediate feedback. A simple routine—write one if…then per day, test one small intervention per week, log numbers—creates a learning loop. Keep the steps short and the measurement simple.

We end with the scene where we started: the tea cup is cooler, the sticky notes are dotted with exact numbers. We read aloud: “If we increase production from 60 to 90 units/hour, defects per 1,000 rise from 12 to 24 because inspection time per item drops from 20s to 12s.” We pick the intervention that adds a 10‑second camera check on 10% of items and a 30‑minute spot audit for one shift. We set a Brali check‑in for this evening and promise each other 15 minutes to write down what surprised us. There is mild relief: instead of a vague argument, we have a test.

We also feel curiosity: what will the numbers show? Will the camera catch the most common defects? Will operators need adjustment time? These feelings are useful; they keep us honest and engaged.

Mini‑Appendix: quick TRIZ idea prompts (for your notebook)

• Segmentation: split batch size; test halves.
• Local quality: move inspection to the step with most variation.
• Dynamization: allow adjustable settings instead of fixed speed.
• Preliminary anti‑action: do a quick check before the risky step. After this list, remember to pick one and prototype: pick the least expensive one first.

Run one micro‑test today: create one if…then with numbers, pick a single metric, and enter the first daily check‑in in Brali LifeOS. If you have five minutes, use the alternative path. If you have an hour, run the 24‑hour protocol.

We will check in with you in your journal. Keep the tests small, the counts honest, and the curiosity active.