Value Engineering, 360°

1.1 · Value is a ratio

Everything in this course rests on one equation: Value = Function ÷ Cost. Function is what the product must do — its performance, reliability and appeal. Cost is everything it takes to deliver that function across the lifecycle. A product is not "expensive" or "cheap" in isolation; it is good or bad value relative to the function it delivers. Value Engineering is the discipline of improving that ratio deliberately.

The method was born at General Electric in 1947, when Lawrence D. Miles noticed that wartime material substitutions — forced by shortages — often produced parts that worked better and cost less. His insight: teams had been buying parts when they should have been buying functions. Ask "what does it do, and what else could do it?" and whole design spaces open up.

1.2 · Five ways to improve value

• Same function, lower cost — classic cost-out (most VAVE work lives here)
• More function, same cost — value-up for competitiveness
• More function, lower cost — the VE ideal; happens more often than you'd think
• Function grows faster than cost — justified premiumisation
• Trim unvalued function, cut cost sharply — data-backed de-contenting

Note what is not on the list: cutting function customers value to save cost. That is how cost-cutting destroys brands — and it is precisely what the function-first method prevents.

1.3 · VA, VE and VM

Value Analysis (VA) applies the method to products already in production — teardown, question, re-engineer, implement as running changes. Value Engineering (VE) applies it during design, before cost is locked in — and since roughly 80% of lifecycle cost is committed by early design decisions, a euro of effort in design returns what ten return later. Value Management (VM) is the governance layer that makes both permanent: targets, funnels, cadence, capability. Together the practice is called VAVE.

1.4 · The four types of value

• Use value — what the product does; its work-performing functions
• Esteem value — what makes it desirable: brand, finish, perceived quality
• Exchange value — what it can be traded for: price power, resale
• Cost value — the sum of material, labour, overhead and lifecycle cost

Esteem value is real value — customers pay for it. The VE question is never "kill the chrome"; it is "does this chrome deliver more esteem than it costs?"

1.5 · Value, price and cost — untangling the trio

Three words engineers constantly blur: price is what the market will pay — set by competition and perceived value, largely outside your control. Cost is what you spend to deliver — set by your design and supply choices, largely inside your control. Value is the ratio the customer experiences. The market caps the price; physics floors the cost; everything between is margin plus the headroom VE exists to capture. When someone says "we can't afford that feature", translate it: the function's cost exceeds the price the market pays for it — now it's an engineering problem with three levers, not a budget complaint.

1.6 · Why the economics never stop favouring VE

The cost-commitment curve is the discipline's cornerstone: by the time concept design is frozen, roughly 70–80% of lifecycle cost is committed while less than 10% has actually been spent. Every later phase can only optimise inside decisions already made — which is why an hour of function analysis at a design gate outperforms a month of price negotiation after tooling kick-off. Add the arithmetic of scale: a study costing a few person-weeks that removes even 5% from a high-volume product returns its cost hundreds of times over. This is why VE is one of the few methodologies written into law: US federal agencies are required to apply it (OMB Circular A-131), and federal-aid highway projects above statutory thresholds must undergo VE analysis before construction approval.

2.1 · Scope and sponsorship

Every failed VE study fails before it starts. Preparation takes 2–4 weeks and begins with a sponsor — someone senior enough to commit resources and act on decisions — plus a written scope: which product, which cost baseline, what target (typically 10–20% of the addressed cost), what's explicitly out of bounds. An unscoped study wanders; an unsponsored one produces slides.

2.2 · The team

Six to ten people, cross-functional by design: design engineering, manufacturing, purchasing, quality, finance, service — plus key suppliers for commodity insight. Homogeneous teams produce homogeneous ideas; the friction between functions is where the ideas live. The team is led by a trained facilitator who owns the process, not the content — ideally certified (VMA/AVS/CVS, covered in Module 12).

2.3 · The data pack

• Costed BOM — every part with material, process, labour and overhead cost, validated by finance. This is non-negotiable.
• Quality history — warranty Paretos, scrap and rework data, field-failure modes
• Voice of customer — requirements, satisfaction data, feature-usage evidence
• Volumes & forecasts — because every lever's economics depend on quantity
• Competitor samples — procured early; teardown insight feeds every phase
• Physical parts — the actual product in the room. Teams ideate on what they can touch.

2.4 · Choosing the right first product

Programmes live or die on the first study's credibility, so pick the target deliberately. Score candidates on: spend (annual volume × unit cost — enough zeros to matter), design authority (you can actually change it), stability (2+ years of production life left to harvest savings), pain (margin pressure or warranty trouble creates pull), and data (a costed BOM exists or can be built in two weeks). A mid-complexity, high-volume product your own engineers designed is the ideal first target; the flagship with frozen tooling and a defensive chief engineer is the worst.

2.5 · Roles that make or break the study

• Sponsor — owns the target, attends kick-off and Phase 6, clears roadblocks. No sponsor, no study.
• Facilitator — owns the process and the clock; deliberately NOT the deepest product expert (experts defend designs).
• Finance partner — validates the baseline and every savings claim; involving them from day one prevents the classic "those numbers aren't real" ambush in Phase 6.
• Idea champions — assigned per surviving idea in Phase 4; they carry proposals through development.
• Scribe — captures every idea verbatim with its function tag; memory is not a capture system.

2.6 · Freeze the baseline, then set the target

Before the workshop, freeze a dated cost baseline — a specific BOM revision, volume basis and currency — and agree the measurement rules (gross vs. net, how commodity swings are neutralised). Savings claimed against a moving baseline convince no one. Then set the target from evidence: a benchmark gap, a margin requirement, or the value-mismatch total from a prior scan — not a round number pulled from the ceiling.

3.1 · Know the thing cold

The Information Phase assembles the complete picture before anyone proposes anything. Miles' founding questions structure it: What is it? What does it do? What does it cost? What is it worth? What else could do the job? The discipline of answering the first four before touching the fifth is what separates a SAVE study from a brainstorm.

3.2 · The cost walk

Walk the costed BOM as a team: material, conversion, labour, overhead, logistics, warranty. Then Pareto it — typically 20% of parts carry 80% of cost, and those parts get the analytical firepower. Complement with a process walk: visit the line, watch the assembly, time the operations. Cost that looks reasonable on a spreadsheet often looks absurd on the shop floor.

3.3 · Requirements, not assumptions

Capture what customers actually require — via QFD, Kano analysis or plain structured interviews — and tag each requirement as must-have, performance, or delighter. Later, when someone claims "customers need this", the team checks the evidence, not the volume of the voice.

3.4 · Structuring cost data so it talks

A costed BOM answers "what does each part cost?" — but the Information Phase needs "why does it cost that?" Split every part into material / conversion / labour / overhead, and be suspicious of allocated overhead: standard-costing systems smear indirect cost by labour hours or material value, which can make a simple part look expensive and an expensive process look cheap. Compute cost density metrics — €/kg against material benchmarks, € per function against the worth estimates coming in Phase 2 — because density exposes outliers that absolute Paretos hide.

3.5 · The Kano model in five minutes

• Must-be — expected silently (brakes work). Absence enrages; excellence earns nothing. Deliver at minimum adequate cost.
• Performance — more is better, linearly (fuel economy, capacity). Customers consciously trade money for these — spend here matches price.
• Attractive — unexpected delighters. High esteem-value leverage, but validate with data before spending.
• Indifferent — customers genuinely don't care. The prime de-spec hunting ground.
• Reverse — some customers actively dislike (complexity, extra buttons). Removing these raises value while cutting cost — the perfect VE move.

Mapping requirements to Kano categories converts the vague instruction "don't hurt the customer" into a surgical one: protect must-be and performance, interrogate attractive, attack indifferent and reverse.

4.1 · The heart of the method

This phase is what makes VE, VE. Every element of the product is expressed as a two-word verb + noun function: "transmit torque", "resist corrosion", "convey esteem". Two words only — if you need more, you are describing a solution, not a function. The abstraction is the point: "fasten flange" locks you into bolts; "join components" opens welding, adhesives, snap-fits and integration.

4.2 · Basic vs. secondary

The basic function is why the product exists — remove it and the product is pointless (a kettle's basic function is heat water). Secondary functions are how this particular design happens to work — and that is where most removable cost hides, because secondary functions are design choices, not requirements. All-time functions (ensure safety, convey esteem) act across the whole product and are drawn above the main path.

4.3 · The FAST diagram

Function Analysis System Technique maps function logic on one axis: moving right answers HOW? a function is achieved; moving left answers WHY? it exists. Dashed scope lines bracket the study: the higher-order objective sits outside the left line, assumed functions (like "supply power") outside the right. A FAST diagram that reads correctly in both directions is validated logic; one that doesn't reveals the team never really understood the product.

4.4 · Function–cost matrix and the Value Index

Now load economics onto the logic. Allocate every part's cost across the functions it serves — rows sum to component cost, columns to function cost. Then estimate each function's worth: the lowest cost that could still reliably achieve it, benchmarked against the cheapest known way in any industry. The Value Index = Cost ÷ Worth ranks the mismatches:

• VI ≤ 1.2 — healthy; leave it alone
• VI 1.2–2.0 — watch list; challenge opportunistically
• VI > 2.0 — attack; this function is a creative-phase target

Example: a kettle's "indicate status" function costing €0.60 against a €0.15 worth (a simple neon lamp achieves it) has VI = 4.0 — a €0.45-per-unit opportunity found by arithmetic, not opinion. Multiply by a million units and this one row funds the whole study.

4.5 · Writing verb–noun pairs that actually work

Use an active, measurable verb and a measurable noun: "transmit torque", "conduct current", "limit temperature". Ban the weasel verbs — provide, allow, enable, facilitate — they smuggle solutions in and measure nothing ("provide mounting" is a bracket in disguise; "position component" is a function). The acid test: could two engineers independently agree how to measure whether the function is performed? If not, rewrite it.

4.6 · The full function taxonomy

• Basic — the reason the product exists. Exactly one per scope, or your scope is wrong.
• Secondary — how this design happens to achieve the basic function. The main cost pool.
• Required secondary — imposed by regulation or codes ("suppress interference", "resist flame"). Non-negotiable in existence, fully negotiable in how.
• Esteem — functions of desirability ("convey quality"). Real value; measure with market data.
• Unnecessary — supports nothing on the FAST path. Pure gold: delete without loss.
• Higher-order / assumed — outside the scope lines left and right; they anchor the logic but aren't studied.

4.7 · Building the FAST diagram, step by step

• 1. Write every function on a card (random function determination — no order yet).
• 2. Find the basic function; place it just inside the left scope line.
• 3. For each card ask HOW is this achieved? — its answer sits to its right; WHY does it exist? — to its left.
• 4. Where one function is achieved by several together, stack them vertically (AND); alternatives branch (OR).
• 5. Hang all-time functions (ensure safety, convey esteem) above the main path; one-time functions (facilitate assembly) below.
• 6. Draw the scope lines; the higher-order objective sits outside-left, assumed functions outside-right.
• 7. Read the whole diagram aloud in BOTH directions. Any sentence that sounds wrong is a misplaced card.

Note there are two flavours: the technical FAST above, and the customer FAST, which starts from customer-need verbs (assure convenience, assure dependability) — useful when the study scope is a whole product rather than a subsystem.

4.8 · Estimating worth without fooling yourself

Worth is the study's moral compass, so estimate it honestly, four ways: (1) cheapest existing solution — the lowest cost any industry pays to achieve this function (a neon lamp indicates status for €0.15, whatever your current PCB does); (2) physics floor — minimum material and energy the function fundamentally requires; (3) historical best — the cheapest your own company ever achieved it; (4) expert triangulation when data is thin. Always function-level, never part-level, and always with the assumption written down. Worth set equal to current cost is the classic self-deception — it guarantees every VI equals 1.0 and the study finds nothing.

5.1 · Quantity breeds quality

The Creative Phase generates the maximum number of ways to perform each targeted function — not tweaks to the existing design. Osborn's rules are law: defer all judgement, welcome wild ideas, go for quantity, build on others' ideas ("yes, and…"). A strong workshop produces 200–400 raw ideas; since only about one in eight survives evaluation, volume is not vanity — it is the math.

5.2 · The technique arsenal

• Classic brainstorming — facilitated, time-boxed rounds per function. Fast, social, but vulnerable to loud voices.
• Brainwriting 6-3-5 — 6 people write 3 ideas in 5 minutes, pass the sheet, build on what arrives: 108 ideas in 30 silent minutes; introverts contribute equally.
• SCAMPER — Substitute, Combine, Adapt, Modify/Magnify, Put to other use, Eliminate, Reverse — a mutation checklist for any part or process.
• TRIZ — Altshuller's theory of inventive problem solving: model the contradiction ("stiffer but lighter"), apply the contradiction matrix and the 40 inventive principles distilled from millions of patents.
• Morphological analysis — decompose into sub-functions, list every solution option per sub-function, recombine columns into thousands of concept permutations.
• Analogy & biomimicry — how does another industry, or nature, deliver this function for less?

5.3 · AI-assisted ideation

The newest tool in the kit: prime an LLM with the FAST model, the BOM and the constraints, and it will generate and cluster hundreds of candidate ideas in minutes — including cross-industry solutions no one in the room has seen. The division of labour is clear: machines diverge, humans judge. AI raises the floor of idea volume; the team still owns feasibility and selection.

5.4 · TRIZ in twenty minutes, not twenty days

TRIZ's core claim, distilled from studying millions of patents: inventive problems are contradictions, and the same ~40 principles resolve them across every industry. A technical contradiction is "improving A worsens B" — stiffer but heavier, faster but hotter. Look up the two parameters in the contradiction matrix and it returns the principles inventors historically used. Example: strength vs. weight of a moving part points to Principle 1 (Segmentation — ribbed or hollow structures), 8 (Anti-weight — counterbalance), 15 (Dynamics — make it adaptive) and 40 (Composite materials). A physical contradiction is "A must be X and not-X" (hot AND cold, present AND absent) — resolved by separation: in time (hot during brazing, cold in use), in space (rigid here, flexible there), or by condition. And the north star is ideality — the Ideal Final Result asks: "how would this function perform itself, with no part, no cost, no harm?" Snap-fits are the IFR of fastening; that question alone generates ideas.

5.5 · The techniques are a toolkit, not a menu

Sequence them: open with brainwriting 6-3-5 (silent, equal, fast volume), move to function-prompted brainstorming to build on the written seeds, apply SCAMPER to the stubborn functions, bring TRIZ when a genuine contradiction blocks progress, and close with analogy rounds ("how does a €2 toy solve this? how does nature?"). Quotas help: "ten more ideas for 'contain water' before lunch" reliably produces eight mediocre ideas and two gems — which is exactly the deal you want.

5.6 · Facilitating for volume

The facilitator's creative-phase job is protecting divergence: enforce the no-judgement rule visibly the first time it's broken (park the criticism on a wall, thank the critic, move on); make the most senior person speak last in every round; run 25-minute sprints with real breaks; and capture every idea verbatim with a function tag — paraphrasing kills the wild ones, and the wild ones stretch the space where the practical winners live.

6.1 · Coarse screen first

Never deep-analyse 300 ideas. First pass is go / grow / park: clearly feasible ideas advance, promising-but-immature ones get grouped and strengthened, and the rest are parked (never deleted — parked ideas seed future waves). This typically cuts the list by two-thirds in an hour.

6.2 · Structured ranking

Survivors get scored on weighted criteria — savings potential, technical risk, investment, time-to-implement, customer impact. The weighted evaluation matrix makes trade-offs explicit; the Pugh matrix compares concepts against the current design (the datum) with simple +/−/S scores, ideal for concept selection; the effort–impact grid sorts quick wins from strategic projects.

6.3 · Scenarios and champions

Ideas interact — a material substitution may enable a process change that unlocks a part integration. Cluster compatible ideas into implementation scenarios, and give every surviving idea a champion: a named owner who carries it into development. Ideas without owners are opinions.

6.4 · Designing evaluation criteria that don't lie

Four to six criteria, agreed with the sponsor before anyone sees the idea list — criteria chosen after seeing ideas get bent toward someone's favourite. Watch for double counting: "annual savings" and "payback" both measure money; pick one plus investment. A workable default: net savings (40%) · technical risk (30%) · time-to-implement (20%) · investment (10%), with finance owning the savings scale so Phase 6 can't be ambushed with "those numbers aren't real".

6.5 · Pugh in practice — a two-minute worked run

Datum: the current keyed shaft. Candidates: spline, polygon shaft, plastic overmould. Score each against the datum per criterion as better (+), same (S) or worse (−): the spline runs +cost, +assembly, S-risk, −tooling → net +1; the overmould runs ++cost, −risk, −validation → net 0 but with the biggest upside. Pugh's real power is the second pass: hybridise — can the spline's tooling problem borrow the overmould's approach? Concepts converge upward instead of merely being ranked.

6.6 · From ranked list to executable scenarios

Ideas interact. Run a quick compatibility check (a material substitution may kill a process idea, or enable it), then bundle survivors by implementation vehicle: running changes this year, the mid-cycle refresh, the next-generation platform. Finally the honesty test: count the engineering hours the scenarios need against hours that actually exist. A shortlist beyond your capacity is a wish list — better to implement eight ideas than approve thirty.

7.1 · From idea to proposal

Champions convert shortlisted ideas into implementable value proposals. Each answers five questions: what exactly changes, what it saves (validated by finance), what it costs to implement (tooling, validation, engineering hours), what could go wrong, and when it lands. An idea with those five answers is a business decision; without them it's still a sticky note.

7.2 · Engineering validation

De-risk before you commit: CAE simulation to verify a thinner wall or relaxed safety factor, design FMEA to expose new failure modes, a DVP&R (design verification plan & report) scaled to the change's risk. For sourcing ideas, a should-cost model or supplier quotation validates the savings claim. Regulated industries add the change-control question early: does this need re-certification, re-validation, PPAP?

7.3 · The business case

The universal arithmetic: annual saving = per-unit saving × volume; payback = one-time cost ÷ annual saving. Most running-change proposals should pay back in under 12 months; well-run VE studies return better than 10:1 overall. Present savings honestly: gross vs. net of implementation cost, and dated — a saving that lands in 18 months is worth less than one landing this quarter.

7.4 · The validation ladder

Match rigour to risk, not to habit: desk calculation → CAE/simulation → rig test → line trial → field pilot. A packaging spec change may need one step; a safety-factor reduction needs all five. Two rules: climb only as high as the risk class demands (over-validation quietly kills payback), and start long-lead items (tooling, supplier qualification) in parallel behind clear kill-gates rather than in series after every test passes.

7.5 · FMEA in ten minutes

Every VE change can create failure modes the current design never had. Score each candidate mode on Severity × Occurrence × Detection (1–10 each; the product is the RPN) — but score the delta against the current design, not absolutes: a thinner wall that raises occurrence from 2 to 4 on a severity-8 mode needs an action; one that goes 2 to 3 on severity-2 needs a note. High-severity or regulated systems escalate to a full cross-functional DFMEA — that's not bureaucracy, it's what makes the saving safe to bank.

7.6 · Money mechanics finance will sign

Net saving = gross piece-cost saving − amortised one-time costs (tooling, validation, engineering hours, scrapped inventory, requalification). Payback = one-time ÷ annual net — the right metric for stable-volume running changes; use NPV at the company hurdle rate when volumes ramp or decay. And date everything: savings start at the implementation date, not the approval date — a proposal without a date has a business case of zero.

8.1 · The decision meeting

Present to the sponsor and decision board: the value logic (functions and mismatches), the numbers (savings, cost, payback), the risks and their mitigations, and the asks (resources, decisions, dates). The output is a decision log — explicit go/no-go per proposal. A presentation that ends without decisions is a rehearsal, not a Phase 6.

8.2 · The savings funnel

Approved ideas enter a staged funnel: L1 Idea → L2 Validated → L3 Approved → L4 Implemented. Each stage has entry criteria (L2 requires finance-verified savings; L4 requires the change in production with PPAP/validation complete). The funnel is reviewed monthly: every idea has an owner, a stage and a date — or it is killed. Savings only "count" at L4, audited against the frozen baseline so inflation and mix changes can't blur the result.

8.3 · Close the loop

Post-implementation, feed the lessons back: update design guidelines, cost standards, preferred-parts lists and the idea bank. This institutional memory is what makes wave two cheaper and faster than wave one — and what eventually turns VE from a project into a capability.

8.4 · The one-pager that wins decisions

Boards decide on one page per proposal: the function and its value mismatch → what changes (picture beats paragraph) → net saving, one-time cost, payback → top risk and its mitigation → the ask, the owner, the date. Then pre-wire: walk the two most sceptical stakeholders through it before the meeting; surprises make executives defensive, and defensive executives say no. The meeting itself produces a decision log — proposal, verdict, owner, date, signature. Applause is not an output.

8.5 · Savings accounting that survives audit

Rules agreed in Phase 0, applied ruthlessly here: measure against the frozen baseline; neutralise commodity and volume swings (index-adjust so a copper spike doesn't erase a real design saving); report the gross → net bridge openly; count only at L4 — implemented in production and audited by finance; and reconcile with purchasing's ledger so the same euro isn't claimed twice. Every credible programme's reputation rests on this paragraph.

8.6 · Closing the loop

Feed every implemented idea back into the system: update design rules (so the next programme never re-adds the deleted bracket), refresh cost standards with the new should-costs, tag the idea bank (parked ideas seed the next wave), and run a 30-minute retro per wave: what stalled, why, and which stage of the funnel leaked. Wave two should always be cheaper and faster than wave one — if it isn't, the loop isn't closing.

9.1 · Six lever families

• Product design — part-count reduction, DFMA, material substitution, tolerance optimisation, de-speccing, platforms, safety-factor right-sizing
• Materials — grade optimisation, recyclates, near-net-shape blanks, nesting & yield, scrap monetisation
• Manufacturing — process substitution, automation, cycle-time/OEE, yield, tooling strategy, make-vs-buy, footprint
• Sourcing — should-cost negotiation, LPP, competitive RFQs, bundling, best-cost-country, supplier VAVE, indexation
• Packaging & logistics — pack spec, returnables, cube utilisation, mode shift
• Complexity & lifecycle — SKU rationalisation, commonality, warranty-cost design

Rule of thumb: where the cost sits decides which family bites. BOM-heavy products (like most automotive) → sourcing + design; labour-heavy → DFMA + automation; volatile-material products → indexation first.

9.2 · Anatomy of a should-cost

A quoted price is one opaque number; a should-cost model decomposes it into negotiable layers: raw material (mass × market price ÷ yield), process & machine (cycle time × machine-hour rate), direct labour, scrap & yield, overhead (driven by plant utilisation), SG&A, and a fair margin. Each layer is built from physics and market rates, and each is a separate conversation with the supplier. Fact-based negotiation with a cleansheet typically recovers 5–15% on quoted prices — without squeezing the supplier's legitimate profit.

9.3 · Linear Performance Pricing

When you buy a family of similar parts, regress price against the dominant cost driver (mass, power, area). Parts above the regression line are paying more per unit of driver than their siblings — a ready-made negotiation target list, produced from data you already own.

9.4 · Building a cleansheet in seven steps

• 1 · Scope — part drawing, material spec, annual volume, sourcing region. Cost is meaningless without these four.
• 2 · Material — net mass ÷ process yield = gross mass, × market price from a commodity index (never a list price).
• 3 · Process route — the operations a competent maker would use, with cycle times from physics and machine specs, not folklore.
• 4 · Machine rate — (depreciation + energy + floorspace + maintenance) ÷ (annual hours × OEE). Utilisation assumptions move this ±30%.
• 5 · Labour — operators per machine × regional loaded wage; automation level is the design variable.
• 6 · Overhead & SG&A — regional percentage benchmarks, sanity-checked; the layer where idle plants hide.
• 7 · Margin — a fair commodity-typical profit. Then sanity-check the total against LPP curves and real quotes.

9.5 · LPP with real numbers

Plot your eight stamped brackets: price (y) against mass (x). Regression: price ≈ €0.85 + €1.9/kg. Six parts hug the line; bracket D sits 18% above it, bracket F 25%. Before celebrating, check specs — F has a special coating (justified); D doesn't (target). One hour with data you already own produced a negotiation list worth more than a month of meetings. That's LPP: cheap, fast, and brutally objective.

9.6 · Negotiation choreography

Never email a cleansheet with "explain the gap" — that's how you get a defensive supplier and a worse relationship. Instead: share the method, invite them to a joint workshop, walk the layers together, and split the conversation three ways — raw material gets indexed in the contract (nobody negotiates the copper price), conversion gets engineered together (cycle times, yields, packaging), margin gets respected. Close with a gain-share clause so their ideas keep coming. Suppliers who feel audited hide information; suppliers who feel partnered volunteer it.

10.1 · The five-step teardown

• 1 · Acquire & document — photograph, weigh and measure everything before the first screw turns
• 2 · Systematic disassembly — level by level, capturing time: assembly sequence in reverse is assembly cost in disguise
• 3 · Digital BOM capture — every part logged: material, mass, process, supplier marks, tooling clues, fastener count
• 4 · Should-cost each part — cleansheet the competitor BOM to estimate their cost position line by line
• 5 · Harvest & transfer ideas — side-by-side function comparison feeds the Creative Phase: adopt, adapt or leapfrog

10.2 · Six dimensions of benchmarking

Cost benchmarking is one lens of six: functional (performance per euro — torque/€, lumens/€), design (architecture, integration, material mix), process (how the best plants make it), feature (feature sets vs. price ladders — who over-serves, who under-prices), and patent/IP (expired art you can use freely; active art to design around). Benchmarking also answers VE's hardest question — what is this function worth? — with the best evidence there is: a competitor delivering it for less.

10.3 · The cost walk

Teardown output converges into a cost walk: current cost at the left, target at the right, and quantified lever blocks in between (design −8, material −4, manufacturing −4, sourcing −5, logistics & complexity −2, on a 100 base). Real walks are built bottom-up from validated ideas — each block has owners and dates, or it is decoration.

10.4 · Running a proper teardown lab

You need less than you think: a bench, calibrated scales, torque drivers, callipers, a lightbox with a scale reference in every photo, and — non-negotiable — a data schema agreed before the first screw: part ID, assembly level, mass, material (verified, not guessed), process evidence (parting lines, tool marks), fastener count and type, disassembly time. Photograph every state before changing it. Keep the "wall of parts" — laying both products out side by side, level by level, teaches more in an hour than the spreadsheet does in a week. Scan or CT anything you'll want to measure later; destructive steps are one-way doors.

10.5 · Costing a competitor's BOM honestly

Cleansheet their parts at their assumptions, not yours: their likely region, their volumes, their process choices as evidenced by the parts. Band every estimate (±15% is honest) and document assumptions. The output that matters is the delta table: their door hinge €1.10 vs. yours €2.30 is a finding even if both carry error bars — deltas are robust where absolutes are fragile.

10.6 · Ethics and legality — the bright lines

Teardown of products bought on the open market is lawful, standard industry practice — an entire industry exists to do it. The bright lines: no misappropriated confidential information (a competitor's drawing from a shared supplier is poison — refuse it), and patents are not secrets — they're public documents you should actively read — but copying a patented solution needs a licence or a design-around. Learn freely; copy carefully; involve counsel when adopting, not when analysing.

11.1 · The digital cost stack

• AI should-cost engines — feature-based tools read 3D CAD, simulate the manufacturing process and output cycle time, tooling and piece cost per region before any supplier quote
• CT-scan & 3D teardown — industrial computed tomography digitises competitor products non-destructively: wall thicknesses, hidden joints, internal architecture
• Spend cubes — classified spend joined with BOM data exposes price variance for identical parts across plants and suppliers
• Generative design & topology optimisation — algorithms explore thousands of geometry variants at minimum material
• Additive manufacturing — part consolidation, tool-less low volumes, conformal-cooled tooling
• Digital twin / CAE — simulation-validated margin removal; virtual DOE replaces physical trials

11.2 · LLMs in the workshop

Large language models now assist across the job plan: mining warranty text and quotes in the Information Phase, generating and clustering ideas in the Creative Phase, drafting business cases in Development, and powering cost knowledge graphs that link functions ↔ parts ↔ costs ↔ suppliers so no lesson is lost between waves. The practical stance: AI compresses analysis and divergence; humans own judgement, validation and decisions. Teams that treat AI output as a starting draft move roughly twice as fast; teams that treat it as an answer ship mistakes faster.

11.3 · What doesn't change

No tool replaces the discipline: functions before solutions, worth before ideas, validation before implementation, P&L before applause. The stack multiplies a good method — it cannot rescue a skipped one.

11.4 · Inside an AI should-cost engine

Feature-based engines work in four stages: geometry extraction (the CAD model is parsed into manufacturing features — holes, bends, bosses, pockets), routing selection (the engine picks a feasible process chain for the material and geometry), physics simulation (cycle times computed from tonnage, melt volumes, tool paths), and regional economics (machine rates, wages and overheads from maintained data libraries — aPriori, for instance, maintains 92 regional cost libraries). Two consequences: quality-in decides quality-out (a sloppy CAD model costs wrongly), and outputs are directional — perfect for ranking design options and preparing negotiations, never a substitute for the final quote.

11.5 · Prompt patterns that work in VAVE

The reliable ideation pattern: role + function + constraints + quantity + ranking — "You are a cost engineer. Generate 25 ways to 'transmit torque' in a 40 Nm automotive application, manufacturable at 200k/year, ranked by likely unit cost; flag any that typically fail durability." Use the same structure to cluster teardown notes, draft business cases, and translate warranty text into function language. Two hard rules: verify every number the model outputs (LLMs are fluent, not calibrated), and never paste confidential BOMs or drawings into public tools — use your company's approved instances.

11.6 · Cost and carbon — one lever, two savings

The same physics that drives cost drives CO₂e: mass, energy intensity, scrap, logistics distance. Add a kgCO₂e column to the cleansheet and most cost levers reveal a carbon dividend — lightweighting, yield improvement, recycled content, nearshoring. With carbon border mechanisms and customer scope-3 reporting spreading, the VE study that quantifies both is answering next year's question this year.

12.1 · From event to operating system

One-off studies decay; programmes compound. The operating system: a monthly funnel review (owners, stages, dates), quarterly wave planning (next products, next teardowns), an annual value roadmap per product line tied to margin plans, and continuous capability building. KPIs that keep it honest: funnel coverage (% of product cost under active study, target ≥60%), idea conversion rate, cost transparency (% of BOM with a should-cost, target ≥80%), savings velocity, average payback, and the reuse index.

12.2 · What to expect

A first structured wave on an unworked product typically finds 8–15% of product cost; mature programmes sustain 3–5% annually; study ROI routinely exceeds 10:1. The biggest failure mode is not weak ideas — it is weak follow-through, which is exactly what the funnel governance exists to prevent.

12.3 · Standards and certification

The SAVE International Value Methodology Standard defines the job plan used worldwide; EN 12973 codifies Value Management in Europe. Practitioners certify through SAVE as VMA (Value Methodology Associate — entry level), AVS (Associate Value Specialist) and CVS (Certified Value Specialist — the professional benchmark for leading studies). TRIZ has its own certification levels; both credentials signal a practitioner who runs the method, not just the meeting.

12.4 · Operating models that scale

Three archetypes: a central CoE (deep expertise, tools, standards — but becomes a bottleneck), fully embedded champions in each product line (scales, but skills dilute), and the hybrid that wins in practice — a small central value office owning method, training, tooling and the savings ledger, with certified champions embedded in every line. Report it neutrally: a value office under engineering optimises design and ignores sourcing; under purchasing, the reverse. CFO or COO sponsorship keeps both levers honest.

12.5 · Targets, cascades and incentives

Cascade market-back: product-line margin plans → annual cost roadmaps → subsystem targets at design gates — cost as a spec with the same authority as mass. Incentives need care: reward audited, net, implemented savings (or people will game gross paper numbers), credit idea originators as well as implementers, and never let the savings target exceed the engineering capacity you actually funded — a target without capacity is a morale programme, in reverse.

12.6 · Growing practitioners deliberately

Capability ladders beat hero dependence: awareness (everyone — this course's job) → practitioner (runs analysis in their own product) → facilitator (leads studies; each wave co-facilitated by a learner) → certified specialist (VMA → AVS → CVS through SAVE International). Add a community of practice that meets monthly and an internal "value day" each year where teams present implemented ideas — recognition is the cheapest retention tool a programme has.

13.1 · Prevention beats correction

Everything so far in this course removes cost that already exists. This module is about never letting it in. By the time a concept design is frozen, 70–80% of lifecycle cost is committed while almost none has been spent — so the cheapest VAVE workshop is the one the target made unnecessary. Target costing (Toyota's genka kikaku, developed from the 1960s onward) inverts the traditional logic. Cost-plus asks: what will it cost, and what price does that force? Target costing asks: what will the market pay, and what cost does that allow?

The equation is disarmingly simple: Allowable cost = target market price − required margin. Everything hard about target costing is organisational, not arithmetic: holding the line when engineering says "it can't be done for that", and cascading the number so every team owns a piece of it.

13.2 · Allowable cost, drifting cost, and the value gap

Three numbers run the process. The allowable cost comes from the market, top-down. The drifting cost is the current bottom-up estimate of what the design as drawn would actually cost — it moves with every design decision. The distance between them is the value gap, and closing it is a design activity: function analysis, DFMA, spec challenge, supplier co-design — the entire toolkit of Modules 4–11 pointed forward instead of backward.

Discipline comes from the cardinal rule (Cooper & Slagmulder): the target cost may never be exceeded. If a feature must be added, its cost must be found elsewhere — a different feature trimmed, a design simplified, a make-buy changed. What the rule really bans is the quiet drift where every team adds "just 2%" and the product arrives 15% over. If the gap truly cannot close, the honest moves are to change the product's content or not launch — not to pretend the margin will appear later.

13.3 · The cascade: from vehicle to part to supplier

A single top-level number is a wish. A cascade is a plan. The vehicle-level allowable cost is decomposed to systems (body, powertrain, chassis, electrical…), weighted by function value and benchmark data, then to subsystems and parts, and finally into supplier target prices — shared openly with sourcing partners along with the cost model behind them. Two practices make the cascade survive contact with reality: give each level a small unallocated management reserve (concepts change), and make every target's owner a named engineer, not a department.

13.4 · Design-to-cost: run cost like weight

Aerospace programmes track mass with an owner per subsystem, a live status at every gate, and a recovery plan the moment an allocation is exceeded. Design-to-cost applies exactly that machinery to cost: cost is a specification with the same authority as mass, performance or safety. At each design gate the drifting cost is reported against the target; a gate with a red cost status and no recovery plan does not pass. Practical instruments: trade-off curves (cost vs. performance per concept, so decisions are chosen, not discovered), tolerance–cost curves (tightening a tolerance typically raises machining cost exponentially — challenge every decimal place), and a cost model that updates with the CAD, so engineers see the € consequence of a design choice the week they make it, not at the next quote round.

13.5 · After SOP: the kaizen handoff

Target costing ends at start of production; kaizen costing picks up from there — continuous, incremental reduction against a yearly cost-down target on the running product. The two are one system: target costing decides where the cost curve starts; kaizen costing bends it further down. A mature product organisation runs both, plus periodic VAVE waves (Module 12) when step-change is needed. One warning: kaizen targets applied blindly, year after year, are how quality death-spirals begin — audited function and quality gates apply to cost-downs exactly as they did in the Development Phase (Module 7).