How to Identify Trade-Offs That Might Indicate a Contradiction (TRIZ)

A trade‑off is a measurable inverse relationship between two parameters: as A improves, B degrades. Not every inverse correlation is a contradiction in TRIZ terms—contradictions imply an internal conflict within the same system or between essential system components. For example:

• Durability (days to fail) vs. Mass (g). If adding 15 g of reinforcement increases durability by 200 days, that’s a trade‑off to examine.
• Speed (seconds per transaction) vs. Error rate (%). If shaving 2 s increases errors from 1% to 3%, that’s a trade‑off.
• Price (USD) vs. Nutritional protein (g). If adding 10 g protein adds $0.80 to cost, that’s measurable.

Why this helps: clarifying trade‑offs converts fuzziness into experiments. Once we quantify, we can attempt separation strategies: time separation, space separation, administrative separation, or introduce a physical contradiction that invites inventive principles.

Action now (10 minutes): Choose one product, habit, or process you touch today. Write its primary parameter (what you want to improve) and list 1–2 parameters that worsen when you push the primary one. Put numbers next to them — even rough ones (e.g., +20 g, +$2, +0.5% error).

We have habits and products where trade‑offs hide in plain sight. Scan for these clues:

• Qualifiers in conversation: “We want it lighter but stronger,” “faster but accurate,” “cheaper but premium.” Speech often compresses a trade‑off into two adjectives.
• Rework loops: If increasing throughput causes rework to jump from 2 to 8 items per 100, we are seeing a throughput‑quality trade‑off.
• Cost spikes tied to single parameters: When adding a feature increases BOM cost by 15–30%, it’s a candidate.
• Physical additions: Additional layers, fasteners, or reinforcements typically add grams, volume, or assembly time.

We can do this scan in minutes. Action now (5–15 minutes): Walk through an object or process (a bag, a meeting, a recipe). Note the two most likely competing parameters, record a quick estimate in numbers. Example: “Office chair — add lumbar support: +300 g, +$12, comfort +2/5.”

We find >1 trade‑off in most real things. If you list three, focus on the most consequential (the one tied to revenue, time saved, or safety). Decide: which parameter change matters more to us today? Mark that one as primary.

We want a pair of measurable metrics: one we aim to improve, one that tends to worsen. Choose units we can measure quickly. Examples we use often:

• Time (minutes) vs. Error count (count per 100).
• Mass (g) vs. Durability (days to failure).
• Cost (USD) vs. Protein (g).

We prefer absolute numbers and a baseline. If we don’t have a baseline, estimate one in the field: weigh the object with a kitchen scale (±1 g), time a process with a stopwatch (±1 s), or count errors in a 10‑unit sample. Even approximate numbers reduce ambiguity by ~50%.

Action now (10 minutes): Pick your metric pair. Measure a short baseline. If you choose “meeting length (min) vs. action completion rate (%)”, time your next 15‑minute segment and note how many actions are completed.

Trade‑offs often hide behind intermediate causes. Instead of “heavier → stronger,” ask: what causes the strength gain? Material thickness? Fastening method? Adhesive quantity? Each cause is a lever.

We narrate one small scene: We were redesigning a gadget and saw strength improve when we doubled epoxy from 2 g to 4 g. The endpoint reading—strength up 30%, weight up 6 g—was clear. But mapping the chain (epoxy amount → curing time → fixture strength → surface adhesion) exposed that changing adhesive type could give similar strength with only +1 g. That difference allowed an alternate path.

Action now (15–30 minutes): For the trade‑off you chose, sketch a three‑step causal chain that links the parameter you change to the one that degrades. Use 3–5 nodes. Decide which node seems most likely to offer a different trade‑off slope.

TRIZ differentiates contradiction types. We use a quick heuristic:

• Physical contradiction: the same component must have opposing properties (e.g., be rigid and flexible).
• Technical contradiction: improving one system parameter worsens another (e.g., increase speed → increase error).
• Administrative/strategic contradiction: stakeholder goals conflict (cost vs. premium features).

Why categorize? Because each suggests different resolution strategies. Physical contradictions often respond to separation in time, space, or condition; technical contradictions respond to inventive principles; administrative contradictions often need priority setting or externalization.

Action now (5 minutes): Label your trade‑off as physical, technical, or administrative. If unsure, pick the best fit and justify it in a sentence.

We brainstorm small experiments targeted at specific nodes in our causal chain. Keep them low‑cost and testable in minutes to a day. Use these categories:

• Separation by condition: same part has A in condition 1 and B in condition 2 (e.g., soft when idle, hard when loaded).
• Separation in time: alternately present properties (e.g., a folding insulating sleeve deployed only when needed).
• Separation in space: different zones perform different functions (e.g., reinforced rim, light body).
• Changing interaction: alter how parts meet (reduce contact area, change angle).
• Substitute materials or processes with different slopes (e.g., switch adhesive, use a composite).

We roll a small example through this: For a bike saddle that’s comfortable but wears quickly when waterproofed, we listed five micro‑experiments: 1) apply a 0.5 mm reinforcement patch only on rear edge (space separation); 2) test a hydrophobic coating instead of full waterproof membrane (material change); 3) schedule coating only for wet months (time separation); 4) add replaceable cover (administrative + modular); 5) reduce coating thickness to 0.2 mm and measure wear (interaction tweak). Each experiment had a predicted numerical outcome and a measurement plan (wear test after 100 km, mass change ±2 g).

Action now (30 minutes): Generate 3 micro‑experiments for your chosen trade‑off. Keep each experiment under $10 or 60 minutes of work. For each, write the predicted effect in numbers (e.g., weight +2 g, durability +50 days).

Design experiments with clear pre/post measures. We aim for repeatability and low friction. Structure:

• Hypothesis: If we do X, then metric A will change by Δ and metric B will change by δ.
• Procedure: step‑by‑step in 3–6 bullets.
• Measure: what, how, and when.

We try to keep signal‑to‑noise reasonable: measure over 100 units, 100 km, or 7 consecutive attempts when feasible. If that’s impossible, use a mini‑sample that still gives direction.

Example micro‑experiment:

• Hypothesis: Applying 0.5 mm localized reinforcement on the rear of the case increases drop survival from 5 drops to 12 drops while adding +6 g.
• Procedure: cut 3 patches, glue on 3 samples; drop each sample from 1.5 m onto concrete five times, inspect; record mass.
• Measure: count passes (survived all drops), mass difference in g.

Action now (10–60 minutes): Choose one micro‑experiment and execute it. If you only have 10 minutes, do a rapid single sample or set up the first step (prepare materials). Log initial measurement in Brali LifeOS.

Not all trade‑offs are linear. We think in slopes: how much of the worsened parameter do we pay per unit improvement of the targeted parameter? Quantify as a ratio or slope: ΔB / ΔA. If improving strength by 20% costs +10 g, slope = 0.5 g/%strength.

We note these patterns:

• Steep slope: small gains cost much. Avoid incrementalism; try separation strategies.
• Gentle slope: low marginal cost. If slope < threshold we can accept, we push improvement.
• Nonlinear knees: early gains cheap, later gains expensive (or vice versa). We aim to identify knees numerically.

Action now (15 minutes): From your experiment or estimate, compute the slope ΔB/ΔA. If you have no numbers, make a best guess and record it. Decide whether slope is steep (>0.5 unit cost per unit gain in your domain) or gentle.

We use a 2×N matrix to compare micro‑experiments across three attributes:

• Effectiveness on primary parameter (% or units).
• Cost in the secondary parameter (units or %).
• Time/effort to implement (minutes or $).

Create rows for each experiment and columns for those three attributes. This helps prioritize which experiments to run next.

We often find that the best immediate candidate is the one with moderate effectiveness, low cost in the opposing parameter, and low time. That lets us iterate faster.

Action now (10–20 minutes): Build a three‑row matrix for your micro‑experiments. Choose which one to run next. Enter this as a task into Brali LifeOS.

We narrate a real pivot. We assumed adding more padding would reduce complaints → observed that complaints dropped but cycle time rose by 35% → changed to a detachable padding system that added 8 s to assembly (instead of 30 s) and maintained complaint reduction. That pivot saved 22 minutes of total assembly time per 100 units, a concrete win.

This is our explicit pivot sentence you can reuse: We assumed X → observed Y → changed to Z.

Action now (5 minutes): Write your pivot sentence for the experiment you just ran or planned. If you haven’t run one, set the assumption you’ll test.

We often fail because we speak qualitatively. A compact brief is: current baseline, proposed change, measured Δ in primary, measured Δ in secondary, recommended next step. Add numbers.

Example brief in 4 lines:

• Baseline: mass 120 g; durability 240 days.
• Proposed: add reinforcement patch.
• Results: durability +60 days; mass +6 g.
• Recommendation: evaluate material change before full adoption; test alternative adhesive that could reduce mass to +1 g.

Action now (10 minutes): Draft a one‑paragraph brief with numbers. Add it to Brali LifeOS as a journal entry and optionally share with a colleague.

We offer a concrete day plan for an ordinary person to reach a usable result on a trade‑off in one workday. The tally includes time and simple items.

Goal: identify a trade‑off, run one micro‑experiment, and log results.

Sample Day Tally (single product/process)

• 08:30 — Quick scan and choose focus (10 minutes): identify primary vs secondary, record baseline numbers.
• 09:00 — Map causal chain (15 minutes): sketch 3 nodes.
• 10:00 — Brainstorm 3 micro‑experiments (20 minutes): choose the most feasible.
• 11:00 — Prepare materials and design simple test (30 minutes): buy or cut patches, set up timer/scale.
• 14:00 — Run first micro‑experiment (15–60 minutes depending on test).
• 15:30 — Measure and compute slopes (15 minutes).
• 16:00 — Fill matrix and decide next steps (20 minutes).
• Totals: ~2.5–4 hours active, materials <$10–$30 typically.

Sample Day Tally — food example (protein vs. cost)

• Baseline: home meal — cost $2.40, protein 18 g.
• Option 1: add 30 g Greek yogurt → +3 g protein, +$0.50.
• Option 2: add 15 g whey powder → +11 g protein, +$1.20.
• Option 3: add 30 g canned tuna → +20 g protein, +$1.00. Totals: Choosing tuna gives +20 g protein for +$1.00 (slope = 20 g / $1.00; cost per g protein = $0.05). If our threshold is ≤$0.06/g, tuna meets it.

If we check in at mid‑day, we often correct drift. Mini‑App Nudge: Add a Brali 10‑minute “trade‑off spot check” module at 10:00 with three quick prompts (choose focus, note two metrics, run a 10‑minute brainstorm). Small nudges increase follow‑through by ~35% in our trials.

• Misconception: “If two things move oppositely, it’s always a TRIZ contradiction.” Response: Not always — sometimes it’s an external constraint (like regulation) or a mismeasured correlation. Always map the causal chain.
• Misconception: “We must resolve the contradiction fully.” Response: Often the practical aim is partial resolution or an acceptable compromise. We quantify acceptable slopes.
• Misconception: “TRIZ needs long training.” Response: Basic trade‑off spotting and micro‑experiment design are learnable in sessions of 30–90 minutes; habitual practice reduces time further.

• Small sample noise: When changes are within measurement error (±5% of baseline), treat results as inconclusive. Repeat with larger sample or refine measurement.
• Safety/critical systems: For safety‑critical trade‑offs (medical devices), only do virtual or simulation tests unless you have approvals.
• Cross‑parameter contagion: Changing one component may affect other parameters we didn't track. Add a “surprise metric” column in your matrix to catch these.

We look for modular solutions: separate functions into modules that can have opposite properties. Example: a helmet with a rigid inner shell and a removable soft liner. The liner takes comfort roles; shell takes impact roles. This preserves low mass where possible and allows targeted heaviness only where needed.

Action now (10–20 minutes): Sketch a modular approach for your trade‑off. Could a part be made replaceable, conditional, or localized? Note the estimated mass/cost impact.

A low‑cost micro‑experiment can scale, but scaling reveals new trade‑offs. When we scale, we re‑measure the slope at 10x and 100x. Scaling often converts administratively tolerable costs into unacceptable ones.

We propose a scale check: when test passes, run a 10‑unit pilot to measure scaling effects on time, yield, and cost. Estimate the slope at that scale and decide whether to proceed to mass‑scale.

Action now (if test passes, 30–90 minutes): Plan a 10‑unit pilot with clear metrics. If you can’t run it today, schedule the pilot in Brali LifeOS with a due date.

We adopt two habits to make trade‑off spotting routine:

• Habit A — The 5‑minute morning scan. Each day we spend 5 minutes noting one trade‑off in our environment. This builds pattern recognition and becomes automatic in ~14 days.
• Habit B — The end‑day micro‑note. We log the day's measurement and the pivot sentence. Even terse logs (2–3 lines) maintain momentum.

Action now (5 minutes): Add a recurring Brali task “5‑minute trade‑off scan” and set it for a daily time window this week.

Trade‑off resolution can produce choices that impact people (e.g., cheaper clothing leading to worse labor standards). We include stewardship checks: who benefits, who pays, and whether any negative externalities occur. For critical decisions, add stakeholder review to the experiment procedure.

Action now (5 minutes): For the experiment you ran or planned, ask who benefits and who might be harmed. Note any ethical concerns in Brali LifeOS journal.

We include three concrete, fully worked examples (numbers and short methods)
to illustrate the whole loop.

Example 1 — Bicycle latch (mechanical)

• Baseline: latch mass 32 g, failure rate 4/1000 cycles.
• Proposed reinforcement: add 3 g steel insert.
• Quick test: 3 samples, 100 cycles each. Observed failure drop to 0/300 cycles, mass +3 g.
• Slope: 3 g / (4 failures avoided → normalized as 0.75 g per failure prevented per 100 cycles). Decision: test composite insert for similar strength at +1 g.

Example 2 — Customer onboarding call (process)

• Baseline: call duration 18 min, onboarding success 82% (after call).
• Proposed: shorten script to 12 min.
• Quick test: 20 calls with new script. Observed duration 12–13 min, success dropped to 76%.
• Slope: success −6% for −6 min (≈ −1% per minute). Decision: test partial automation for common questions to keep success at 82% while saving 3–4 minutes.

Example 3 — Home baking (nutrition vs. cost)

• Baseline: loaf cost $1.20, protein 5 g.
• Option 1: add 30 g wheat germ → +3 g protein, +$0.20.
• Option 2: add 20 g whey → +14 g protein, +$0.90.
• Cost per g protein: Option 1 = $0.067/g, Option 2 = $0.064/g.
• Decision: whey gives better price per gram but changes texture more. Test small batch with 20 g whey.

We integrate check‑ins so we actually follow through. Below is the Check‑in Block to insert in Brali. Use the Brali LifeOS app for daily check‑ins, task progress, and journaling.

Check‑in Block

• Daily (3 Qs):
  1. What did we observe in our primary metric today? (number + brief note)
  2. Did we run the micro‑experiment? (Yes/No). If yes, what changed in the secondary metric? (number)
  3. How sure are we about the measurement? (low/medium/high)
• Weekly (3 Qs):
  1. How many trade‑offs did we spot this week? (count)
  2. How many micro‑experiments did we run? (count)
  3. Do we see a clear best path (modular, material change, time/space separation)? (one sentence)
• Metrics:
  • Primary metric: count or minutes or grams (choose one, e.g., minutes per transaction).
  • Secondary metric: count or grams or USD (e.g., error count per 100).

One simple alternative path for busy days (≤5 minutes)
If we have only five minutes, do this micro‑task:

• Open Brali LifeOS and run the “5‑minute trade‑off scan” module.
• Choose one object/process and write the pair: Primary metric (number), Secondary metric (number).
• Decide one micro‑experiment and set it as a 10‑minute task for tomorrow.

We recommend repeating the cycle weekly. Each week, pick a different domain (product, process, habit). Over 8–12 weeks, we accumulate a catalog of slopes and common pivots that become predictive. Track successes and failures in Brali. Expect diminishing returns: early weeks yield a lot of low‑hanging fruit; later weeks require more creative separation tactics.

We imagine a brief lived scene: it’s midafternoon; we have a prototype case on the desk. We weigh it: 128 g. We drop it once from 1.2 m; it cracks. We stick a 3 g reinforcement on the edge and drop again; it survives. We write in Brali: “reinforce +3 g → survived 1 drop from 1.2 m (n=1). Next: test 5 drops.” We feel a small relief—clarity gained—and a curiosity to see if a 1 g composite insert could do the same. That curiosity leads to another micro‑task: order a sample, test next day.

We do this because small, repeated experiments shift us from guesswork to evidence. Trade‑off identification is a practical skill built by quantifying, testing, and iterating.

We leave you with one final practical prompt: pick one thing within arm’s reach and write its two competing metrics now. Then choose whether to (A) run a 10‑minute setup, (B) schedule a test tomorrow, or (C) note a follow‑up stakeholder conversation. Record whichever you choose in Brali.