Value Engineering, 360°

1.1 · Value is a ratio

Everything in this course is built on one equation: Value = Function ÷ Cost. Function is what the product has to do: how well it works, how reliable it is and how appealing it is. Cost is everything you spend to deliver that function over the product's life. A product isn't expensive or cheap on its own. It's good or bad value compared with what it does. Value Engineering is a way of improving that ratio on purpose.

The method started at General Electric in 1947. Lawrence D. Miles noticed that when wartime shortages forced teams to use different materials, the new parts often worked better and cost less. He realised teams had been buying parts when they should have been buying functions. Ask "what does it do, and what else could do it?" and you find many more possible designs.

In 75 years the method has gone from a wartime workaround to a recognised engineering discipline, with professional societies and certifications, US law, a European standard and now AI tools. Fashionable methods come and go within ten years. Methods that work last for generations. This one works.

1.2 · Five ways to improve value

• Same function, lower cost — the classic cost reduction, and where most VE work happens
• More function, same cost — adds value to help you compete
• More function, lower cost — the ideal result, and more common than you'd think
• Function grows faster than cost — a better product that's worth the higher price
• Remove functions customers don't value, cut cost sharply — dropping features, backed by data

Notice what isn't on the list: cutting functions customers value just to save money. That's how cost-cutting damages brands, and it's exactly what starting from functions prevents.

1.3 · VA, VE and VM

Value Analysis (VA) applies the method to products already in production: take the product apart, question each part, redesign it and bring in the changes during production. Value Engineering (VE) applies it during design, before the cost is fixed. Because 70–80% of lifecycle cost is decided by early design choices, one pound of effort in design is worth ten pounds of effort later. Value Management (VM) is the management system that makes both last: targets, savings funnels, regular reviews and training. Together, the practice is called VAVE.

1.4 · The four types of value

• Use value — what the product does, meaning the functions that do the work
• Esteem value — what makes people want it: brand, finish and how good it feels
• Exchange value — what it can be sold or traded for: pricing power and resale value
• Cost value — the total of material, labour, overhead and lifetime costs

Esteem value is real value, because customers pay for it. The VE question is never "get rid of the chrome". It's "is the chrome worth more to customers than it costs?"

1.5 · Value, price and cost: what's the difference?

Engineers often mix up three words. Price is what the market will pay. Competition and how customers see the product set it, and it's mostly outside your control. Cost is what you spend to make and deliver the product. Your design and supplier choices set it, and it's mostly inside your control. Value is the ratio the customer experiences. The market sets the highest price you can charge, and physics sets the lowest possible cost. Everything in between is your margin plus the savings VE is there to find. When someone says "we can't afford that feature", what they mean is that the feature costs more than the market will pay for it. That's an engineering problem you can solve, not a budget complaint.

1.6 · Why VE always pays

The cost-commitment curve is the key idea behind VE. By the time the concept design is frozen, about 70–80% of lifecycle cost is already decided, but less than 10% has actually been spent. Every later stage can only work within decisions already made. That's why an hour of function analysis at a design review beats a month of price negotiation after tooling has started. Volume helps too: a study that takes a few person-weeks and removes just 5% from a high-volume product pays for itself hundreds of times over. It's also why VE is one of the few methods written into law. US federal agencies must use it (OMB Circular A-131), and federal-aid highway projects above set cost thresholds must have a VE study before construction is approved.

2.1 · Scope and sponsorship

Most failed VE studies fail before they start. Preparation takes 2–4 weeks. It starts with a sponsor: someone senior enough to provide people and money, and to act on the decisions. You also need a written scope: which product, which starting cost, what target (usually 10–20% of the cost being studied) and what's definitely out of bounds. Without a scope, a study drifts. Without a sponsor, it produces slides and nothing else.

2.2 · The team

Six to ten people from different departments: design engineering, manufacturing, purchasing, quality, finance and service, plus key suppliers who know their materials and parts. A team from one department comes up with the same kind of ideas. The best ideas come when people from different departments challenge each other. A trained facilitator leads the team and runs the process, but doesn't supply the answers. Ideally they're certified (VMA or CVS, covered in Module 12).

2.3 · The information pack

• Costed parts list (BOM) — every part with its material, process, labour and overhead cost, checked by finance. You can't do without this.
• Quality history — the most common warranty claims, scrap and rework figures, and how products fail in use
• What customers say — their requirements, satisfaction scores and which features they actually use
• Volumes and forecasts — because whether a saving is worth it depends on how many you make
• Competitor samples — buy them early, because what you learn from taking them apart helps at every stage
• The real parts — the actual product in the room. People have better ideas about things they can hold.

2.4 · Choosing the right first product

Whether a programme succeeds depends on how well the first study goes, so choose the product carefully. Score each option on: spend (yearly volume × unit cost, big enough to matter), design control (you're actually allowed to change it), life left (at least 2 more years of production to collect the savings), pain (margin pressure or warranty problems, so people want it fixed), and data (a costed parts list exists or can be built in two weeks). The ideal first product is a fairly simple, high-volume one that your own engineers designed. The worst is your flagship product, with tooling that can't change and a chief engineer who'll defend every detail.

2.5 · The key roles in a study

• Sponsor — owns the target, comes to the kick-off and Phase 6, and removes obstacles. No sponsor, no study.
• Facilitator — runs the process and keeps time. On purpose, this is NOT the person who knows the product best, because experts tend to defend the current design.
• Finance partner — checks the starting cost and every savings figure. Involving them from day one stops someone saying "those numbers aren't real" in Phase 6.
• Idea champions — one person for each idea that gets through Phase 4, who takes it through development.
• Note-taker — writes down every idea word for word, with the function it relates to. Don't rely on memory.

2.6 · Fix the starting cost, then set the target

Before the workshop, fix a dated starting cost: a specific version of the parts list, a set volume and a currency. Agree how savings will be measured, for example gross or net, and how you'll allow for changes in material prices. Nobody believes savings measured against a starting point that keeps moving. Then base the target on evidence, such as the gap to a competitor, the margin you need, or the mismatches found in an earlier review. Don't just pick a round number.

2.7 · The workshop week: a plan that works

• Day 1: Information and functions — go through the costs, walk the production line and look at the product; start function analysis while it's all fresh
• Day 2: FAST, worth and targets — the FAST diagram, function–cost matrix and Value Index; by the end of the day the team knows exactly where the mismatches are
• Day 3: Creative — a full day generating ideas for the target functions, using brainwriting, SCAMPER and TRIZ. No criticism allowed all day
• Day 4: Evaluation and champions — sort the ideas, screen them and score them in a weighted matrix. Each idea that gets through has a named champion and a review date
• Day 5: Develop and present — draft the business cases, note the risks, and end the week with a decision meeting with the sponsor, recording every decision

For smaller studies, a three-day version works: combine days 1–2 and days 4–5. For complex products, a split format suits better: two days, a two-week break to gather more data, then two more days. The one thing you can't shorten is keeping the phases separate. If you generate ideas and judge them in the same session, both suffer.

3.1 · Know the product inside out

The Information Phase builds the full picture before anyone suggests anything. It's organised around Miles' five questions: What is it? What does it do? What does it cost? What is it worth? What else could do the job? Answering the first four before you touch the fifth is what makes a VE study different from a brainstorm.

3.2 · The cost walk

Go through the costed parts list as a team: material, processing, labour, overhead, logistics and warranty. Then rank it with a Pareto chart. Usually 20% of the parts make up 80% of the cost, and those are the parts to focus on. Then do a process walk: visit the production line, watch the assembly and time each step. A cost that looks reasonable on a spreadsheet often looks ridiculous on the factory floor.

3.3 · Requirements, not assumptions

Find out what customers actually need, using QFD, Kano analysis or simple structured interviews. Label each requirement as a must-have, a performance feature or a nice surprise. Later, when someone says "customers need this", the team checks the evidence instead of listening to whoever is loudest.

3.4 · Organising cost data so it tells you something

A costed parts list tells you what each part costs, but the Information Phase needs to know why it costs that. Split every part into material, processing, labour and overhead, and be wary of how overhead is shared out. Standard costing systems spread indirect costs by labour hours or material value. That can make a simple part look expensive and an expensive process look cheap. Work out cost density: £ per kg compared with typical material prices, and £ per function compared with the worth figures you'll set in Phase 2. Density shows up odd costs that a simple Pareto ranking hides.

3.5 · The Kano model in five minutes

• Must-be — taken for granted (the brakes work). Customers are furious if it's missing, but doing it brilliantly earns nothing. Deliver it at the lowest cost that does the job.
• Performance — the more the better (fuel economy, capacity). Customers knowingly pay more for these, so spending here is reflected in the price.
• Attractive — nice surprises customers don't expect. They add a lot of esteem value, but check the data before spending money on them.
• Indifferent — customers really don't care. The best place to look for things to remove.
• Reverse — some customers actively dislike these (complexity, extra buttons). Removing them raises value and cuts cost at the same time, which is the perfect VE result.

Sorting requirements into Kano categories turns the vague rule "don't hurt the customer" into a precise one: protect must-be and performance features, question attractive ones, and go after indifferent and reverse ones.

3.6 · Gathering the facts you already have

Most of what the Information Phase needs already exists in the business. It's just scattered, nobody owns it, and it's never been put together. The most useful sources are: warranty claim text (what actually fails, in the customer's own words, and the best description of functions you have); spare-part sales (the parts people buy as spares are the parts that break, a reliability ranking nobody has put together); rework and scrap records (where the design causes problems in the factory every day); customer return comments (problems with looks and ease of use that never reach engineering); spend analysis (different prices paid for almost identical parts across factories and suppliers); and past study records (ideas set aside last time are already partly checked and ready to reuse). During preparation, give each source an owner and a one-week deadline. This kind of digging is exactly what AI tools now speed up most: they can group ten thousand warranty claims by function in an afternoon (Module 11 shows how). A team that arrives with six sources put together doesn't argue about opinions. It argues about which fact to tackle first.

4.1 · What is a function? (Start here)

Look at the pen on your desk. Most people see a thing: a tube, a spring, a clip, some ink. A value engineer sees jobs: the ink makes marks, the spring stores energy, the clip secures the pen to a pocket. A function is the job a thing does — not the thing itself. That one shift in seeing is the heart of Value Engineering.

Why does it matter so much? Because customers never buy parts — they buy jobs done. Nobody wants a heating element; they want hot water. Nobody wants six screws; they want two parts held together. The moment you describe a product as jobs instead of parts, you free yourself to ask the most powerful question in engineering: "what else could do this job — better, or cheaper?"

Remember Lawrence Miles' insight from Module 1: teams were buying parts when they should have been buying functions. Phase 2 is where that idea becomes a working method.

4.2 · The two-word rule: active verb + measurable noun

Value Engineering writes every function as exactly two words: an active verb (a doing word) plus a measurable noun (a thing you can count, weigh, or test). "Heat water." "Transmit torque." "Support load." Two words. Always.

Why so strict? Three reasons, and each one earns money:

• Two words force clarity. If you need a sentence, you haven't understood the job yet. "It provides a secure mounting interface for the assembly" becomes, honestly: position component.
• Two words remove the solution. "Fasten flange" secretly assumes bolts. "Join components" opens welding, adhesives, snap-fits — or redesigning so there is nothing to join. The vaguer the verb–noun pair sounds, the wider the door to better ideas.
• Two words can be measured. "Conduct current" — you can measure amps. "Support load" — you can measure newtons. If you can measure it, you can put a cost and a worth on it. If you can't measure it, you can't engineer it.

The acid test for any function statement: could two engineers independently agree how to measure whether the job is being done? "Emit light" — yes, lumens. "Provide illumination solutions" — no. Rewrite it.

4.3 · The banned verbs (and how to spot a fake function)

Some verbs sound professional but say nothing. Ban them: provide, allow, enable, facilitate, ensure, support (as in "support the user"), improve, assist. They fail the measurement test, and they usually smuggle a part into the sentence. "Provide mounting" is just a bracket wearing a disguise. "Enable operation" is the whole product wearing a disguise.

Fake functions come in four flavours — learn to spot each one:

• The banned verb — "provide heat" → say generate heat.
• The hidden solution — "fasten with screws" → the screws are one answer, not the job. Say join components.
• The unmeasurable noun — "create convenience" → convenience isn't measurable. What actually happens? reduce effort, save time.
• The product in disguise — "boil kettle" → the kettle is the product, not the job. Say heat water.

4.4 · The function family — every type, one by one

Not all functions are the same, and the differences show you where the savings are. Here are all the types, each with a simple test you can do in seconds:

• Basic function — the reason the product exists. Test: remove it — is the product now pointless? A kettle that cannot heat water is a jug. There is exactly one basic function per study scope; if you find three, you are really looking at three studies.
• Secondary functions — jobs that exist only because of how this particular design achieves the basic function. The kettle's element must resist corrosion only because we chose a metal element immersed in water. Choose a different design and many secondary functions simply disappear. That's why most of the cost you can remove is here.
• Required secondary functions — demanded by law, codes or safety standards: suppress interference, resist flame. Test: would a regulator object if it vanished? You can't remove these, but you can do them more cheaply. You must have them, but how you do them is up to you.
• Esteem functions — jobs that make people want the product: convey quality, please eye. The soft-close hinge, the satisfying click, the gloss finish. Test: would the product still work without it, but sell worse? Esteem value is real value, so measure it with market data, not opinions.
• Unwanted functions — jobs the design does that nobody asked for: the motor generates heat, the pump creates noise. Each unwanted function usually needs extra parts to deal with it (heat sinks, insulation, dampers), and they all add cost. Get rid of the unwanted function and those extra parts go too.
• Unnecessary functions — functions that don't support anything else. These are the easiest wins: remove them and nothing is lost. They're usually left over from an older design that nobody questioned.
• All-time functions — jobs performed constantly across the whole product, like protect user and convey esteem. On a FAST diagram they float above the main path.
• Higher-order & assumed functions — the objective your product serves (prepare beverage) sits outside the left scope line; things you take as given (supply power) sit outside the right. They mark the edges of the diagram but aren't studied.

4.5 · The FAST diagram, built step by step

FAST stands for Function Analysis System Technique. It's a map that arranges your functions by how they connect, not by where the parts are. It uses two simple questions: moving right asks HOW? a function is done, and moving left asks WHY? it's needed. If the chain makes sense in both directions, your team really understands the product. If it doesn't, a card is in the wrong place, and you've found a misunderstanding before it cost you money.

Press Next step to build a real FAST diagram for an electric kettle:

Two practical points. First, when a function needs several other functions at the same time, stack them on top of each other (an AND). When there are different ways to do it, draw them as branches (an OR). Second, there are two kinds of FAST: the technical FAST you just built, and the customer FAST, which starts from what customers need (assure dependability, assure convenience). The customer FAST is useful when you're studying a whole product rather than one part of it.

4.6 · The function library — your verb & noun toolbox

Stuck for the right two words? Use this library. Pick a category, borrow the verb and noun, and change the noun to fit your product. Every entry is written the way a function should be: an active verb and a measurable noun.

4.7 · Adding the costs: function–cost matrix, worth and the Value Index

The logic on its own doesn't save money, so now we add the costs. Take every part's cost and split it across the functions that part does. Each row adds up to the part's cost, and each column adds up to the function's total cost. For the first time, you can see what you're actually paying for each job, and it's always a surprise.

Next, estimate each function's worth: the lowest cost that could still reliably do the job. Estimate it honestly in one of four ways: (1) the cheapest existing solution in any industry (a neon lamp shows status for £0.15, whatever your current circuit board does); (2) the physical minimum: the least material and energy the job really needs; (3) your best result in the past; (4) asking several experts and comparing their answers, when data is thin. Never set worth equal to the current cost. That's a classic way of fooling yourself, and it guarantees the study finds nothing.

Finally divide: Value Index = Cost ÷ Worth. The VI turns gut feeling into a ranked list of targets:

• VI ≤ 1.2 — healthy; leave it alone
• VI 1.2–2.0 — keep an eye on it and question it at the next design change
• VI > 2.0 — act now; this function goes straight to the Creative Phase

Example: a kettle's "indicate status" function costs £0.60, but a simple neon lamp does the same job for £0.15 — VI = 4.0. At a million units a year, that one line of arithmetic is worth £450,000. VE decides where to focus by doing the maths, not by arguing.

5.1 · The most fragile moment in the whole job plan

You have just finished Phase 2. You know exactly which functions cost too much — the kettle's "indicate status" function costs £0.60 and is worth £0.15. Now comes the question the whole study exists for: "how else could we do this?" And here is the uncomfortable truth: the answer is fragile. New ideas arrive weak, half-formed and easy to kill. A raised eyebrow kills one. A quick "we tried that in 2019" kills another. The boss speaking first kills ten, because everyone quietly rearranges their thinking around what the boss said. The Creative Phase isn't about being talented. It's about creating a safe space where fragile ideas last long enough to develop.

One more mindset shift before the techniques: you are not looking for the answer. A strong workshop produces 200–400 raw ideas, and only about one in eight survives evaluation. That isn't failure, it's just the maths. If you want 25 implemented improvements, you need a couple of hundred raw ideas to start with. Quantity is the strategy, and every technique in this module is simply a different way of producing lots of ideas.

5.2 · Osborn's four rules for the room

Alex Osborn, the advertising executive who invented brainstorming in the 1940s, wrote four rules. They sound soft, but they're not. They're what makes this phase work, and breaking one in front of everyone puts the room back into judging mode for the rest of the day.

• 1 · Defer all judgement. No criticism, no praise either — "great idea!" tells everyone else theirs wasn't. Evaluation has its own phase; it can wait a day. In the room it sounds like: an engineer says "make the bracket from cardboard", and nobody laughs — someone writes it down.
• 2 · Welcome wild ideas. The silly idea is rarely the answer, but it widens the search and helps you find it. "Cardboard bracket" is absurd — but it provokes "how much load does this bracket actually see?", and that question finds the 40% weight saving.
• 3 · Go for quantity. Set a number and chase it: "ten more ideas for 'contain water' before the break." The first five ideas are the obvious ones everyone already had. The best ideas usually come between numbers 15 and 30, after the easy answers run out.
• 4 · Build on ideas — "yes, and…". Ideas are starting points, not final answers. Someone says "snap-fit instead of screws"; someone else adds "yes, and if it snaps from the top, the robot can assemble it in one motion." Most breakthroughs come from combining ideas.

5.3 · Aim every technique at a function, never at a part

This is the difference between VE ideation and ordinary cost-down brainstorming. Ask a room "how do we make this shaft cheaper?" and you get thin answers: cheaper steel, looser tolerance, ask the supplier for 5%. Everyone is stuck on the existing design. Ask instead "how else can we transmit torque?" Name the function and keep the part out of sight on purpose, and the room comes up with splines, press-fits, polygon shafts, adhesives, friction welds and overmoulds. That's ten completely different approaches, not three discounts. Use every technique below on the functions with the worst Value Index from Phase 2, one function at a time, written at the top of the board.

5.4 · Classic brainstorming: how to make it work

Everyone thinks they know brainstorming; most meetings called "brainstorms" are just discussions. Done properly, it has clear rules. A facilitator enforces the four rules and doesn't add ideas. There's one function on the board at a time. You work in 25-minute bursts with proper breaks, because people run out of ideas after about half an hour. Every idea is written down word for word, with the function it relates to, because rewording loses the unusual parts. And the most senior person speaks last in every round, because whatever they say first shapes everyone else's thinking. When ideas dry up, the facilitator throws in a challenge ("how would IKEA do this?", "what if it had to cost nothing?", "what would a £2 toy that does this look like?") and the ideas start again. Brainstorming is fast, sociable and energising, but it favours loud voices. That's exactly why the next technique exists.

5.5 · Brainwriting 6-3-5: a fair chance for quiet people

Six people, three ideas each, five minutes — in silence. Each person writes three ideas for the target function on a sheet, then passes the sheet to the neighbour. Next round, you read what arrived and write three more — new ideas, or better versions of what your colleague started. After six rounds every sheet has visited everyone: 6 × 3 × 6 = 108 ideas in thirty silent minutes.

Watch one idea travel: round 1, Priya writes "snap-fit cover". Round 2, Marek reads it and adds "snap-fit with moulded-in hinge — lid never gets lost". Round 3, Chen adds "make the hinge the cable guide too — deletes a clip". One idea turned into a way of using fewer parts that nobody had thought of at the start. Because it's silent, the loudest person adds exactly three ideas per round, the same as the quietest graduate, who often knows the product best. Start every creative session with 6-3-5: it guarantees lots of ideas, gives everyone an equal say, and gets everything written down before any discussion.

5.6 · SCAMPER: a checklist for changing a design

SCAMPER is seven questions you ask about an existing design, one letter at a time: Substitute, Combine, Adapt, Modify or magnify, Put to other use, Eliminate, Reverse. It's mechanical on purpose: when the room is tired and ideas have stopped, the checklist keeps them coming. Ask each question about the part, the material, the process, even the packaging. The strongest letter in VE is E — eliminate: "what if this part simply didn't exist? which neighbour could do its job?" Try each letter on a torch in the explorer below, then ask the same questions about your own product.

5.7 · TRIZ in twenty minutes

TRIZ is based on a study of millions of patents. Its main idea is that inventive problems are contradictions, and the same 40 or so principles solve them in every industry. A technical contradiction is "improving A makes B worse": stiffer but heavier, faster but hotter. Look up the two properties in the contradiction matrix and it shows the principles inventors have used before. 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. The goal to aim for is the Ideal Final Result. It asks: "how could this function happen by itself, with no part, no cost and no harm?" Snap-fits are the ideal result for joining parts, and just asking that question produces ideas.

5.8 · Morphological analysis: finding new combinations

When a product has several sub-functions, break it into them and list every known solution for each — then combine. For the torch: provide light (LED array, single high-power LED, laser diode, electroluminescent panel), store energy (AA cells, rechargeable li-ion, supercapacitor, hand-crank dynamo), control output (push switch, twist bezel, proximity sensor). That little table already holds 4 × 4 × 3 = 48 possible torch concepts — including combinations nobody has built, like "single LED + hand-crank + twist bezel" (the emergency torch that never needs batteries). Most combinations make no sense. You're looking for the three or four new ones worth taking into evaluation. Morphological analysis is the slowest technique but the most thorough — use it when the function structure is rich and you suspect the winning combination has simply never been tried.

5.9 · Analogy and biomimicry: borrow from other industries and nature

Someone, somewhere, already delivers your function for less — often in a different industry, sometimes in nature. The questions are simple: "which industry does this function at one-tenth of our cost?" and "how does nature do it?" The track record is famous. Velcro came from burrs stuck to a dog's fur. The Shinkansen bullet train's nose copies the kingfisher's beak — a bird that enters water without a splash — which cut the train's tunnel boom and its energy use. Drag-reducing surfaces copy shark skin. And at workshop scale: a £2 toy transmits torque with a moulded polygon and no key at all; a drinks carton "contains fluid" for pennies; a bicycle brake controls huge friction forces with simple pressed-metal parts. Pick your function, ask "who else has this problem, and solves it more cheaply?", and spend twenty minutes looking at how they do it.

5.10 · Using AI to generate ideas

This is the newest tool. Give an AI assistant the FAST diagram, the parts list and the constraints, and it will produce and group hundreds of possible ideas in minutes, including solutions from other industries nobody in the room has seen. A working prompt looks like: "Here are our functions and costs. For the function 'dissipate heat', currently a £2.10 aluminium heatsink: list 20 alternative ways to deliver this function, grouped by physics, including approaches from consumer electronics, automotive and aerospace." The roles are clear: the AI generates ideas, and people judge them. AI means you'll always have plenty of ideas, but the team still decides what's feasible, checks what's new and makes the choice.

5.11 · The techniques are a toolkit, not a menu

Use them in order: start with brainwriting 6-3-5 (silent, fair and quick to produce ideas), then brainstorming on each function to build on what's been written, then SCAMPER for the difficult functions. Bring in TRIZ when a real contradiction is in the way, morphological analysis when a detailed function structure might hide untried combinations, and finish with analogy rounds ("how does a £2 toy solve this? how does nature?"). Targets help: "ten more ideas for 'contain water' before lunch" usually produces eight average ideas and two great ones, which is exactly what you want.

6.1 · The morning after: 380 sticky notes and a decision to make

The creative day went well. The wall holds 380 ideas: brilliant ones, silly ones, duplicates, and a few that would get someone fired. Now what? Two failure modes wait for you here. Analysis paralysis: trying to deep-study all 380, running out of steam by idea 60, and quietly picking the familiar ones. Gut-feel sorting: the chief engineer walks along the wall and keeps what they already liked, which makes the whole creative phase pointless. The Evaluation Phase avoids both with a funnel of four rounds. The first rounds are quick and cheap, and each one gets more careful as the list gets shorter. About one idea in eight makes it to development, and that's a healthy rate, not a disappointing one.

6.2 · Round 1: quick sort into go, grow or park

First pass is fast and shallow — about a minute per idea, done as a group. Three piles only:

• Go — clearly feasible, clearly valuable. "Replace six screws with two snap-fits" goes straight through.
• Grow — promising but immature. "Some kind of integrated hinge…?" needs shaping before it can be judged. It goes through, marked for improvement, often by combining it with similar ideas.
• Park — not now. Wrong timing, wrong volumes, needs a platform change. Parked doesn't mean deleted. The parked list is the starting point for next year's study, and half of today's "obvious" winners were parked in an earlier round.

Two rules keep this sort fair. Ideas are judged as written, and kindly: you're removing what can't work, not what's unfamiliar. And nobody says "that will never work here". At this stage, the only reasons to reject an idea are physics, safety or the law. An hour of sorting usually cuts 380 ideas to about 120.

6.3 · Round 2: group, merge and screen out

Before any scoring, group the survivors by theme — fastening, material, architecture, packaging. Duplicates merge (eight versions of "use fewer screws" become one strong idea with eight authors), and half-ideas combine into whole ones: "snap-fit cover" plus "moulded hinge" plus "cable guide in the lid" is one integrated-cover concept worth three times any single note. Then apply the must-pass checks: the things no score can outweigh, such as safety and legal compliance, requirements that define the brand, and promises in customer contracts. A £3 saving that breaks a homologation rule doesn't get a low score. It's out. Grouping usually leaves 60–80 separate, checked ideas.

6.4 · Round 3a: the effort–impact grid, in ten minutes

Now the first ranking tool, which is rough on purpose. Ask two questions about each idea: how big is the benefit? (impact: savings, quality, weight) and how hard is it to get? (effort: engineering hours, tooling cost, validation time). Plot every idea on the 2×2:

• Quick wins (high impact, low effort) — do these first; they show early on that the programme works. Example: delete the second label nobody reads — £0.04 per unit, one drawing change.
• Big bets (high impact, high effort) — the strategic projects. Example: integrate the bracket into the housing moulding — £0.80 per unit but new tooling and full revalidation.
• Fill-ins (low impact, low effort) — do them when convenient, batch them into running changes.
• Money pits (low impact, high effort) — park them, politely, for good.

The grid takes ten minutes with sticky notes and shows at a glance what your programme looks like. A wall full of fill-ins means the creative phase played it too safe; a wall full of big bets means this year's savings target is in trouble.

6.5 · Round 3b: the weighted decision matrix, with real numbers

For the ideas that matter (the quick wins and big bets), move from rough to precise. A weighted matrix has five steps. One: agree the criteria (four to six, no more). Say: net savings, technical risk, time to implement, investment. Two: weight them to total 100% — say 40 / 30 / 20 / 10. Three: score every concept 1–9 on every criterion. Four: multiply and add. Five: read the ranking, then question it. That discussion is where the real value is.

Worked run on "transmit torque": the spline shaft scores 8 on savings, 7 on risk (proven), 9 on speed, 8 on investment → weighted 7.9. The integrated forging scores 9 on savings but 4 on risk, 3 on speed, 4 on investment → weighted 5.8. The spline wins today. Now test the sensitivity: give savings 55% and risk 15%, and the spline still wins (8.05 against 6.55). The forging only edges ahead if savings carries about 85% of the weight. That's a robust result, and it's worth saying so to the sponsor. When a ranking flips after a small change in the weights, the decision really depends on how much risk you'll accept, and that's the discussion to have. Try it live in the lab below.

6.6 · Setting fair criteria

The matrix is only as honest as its criteria, so agree them before anyone sees the ideas. Criteria chosen afterwards get bent to favour someone's pet idea. Keep to four to six. Any more and the weights stop meaning anything. Watch for double counting: "annual savings" and "payback" both measure money, so pick one and pair it with investment. Ask finance to own the savings scale, so nobody can say "those numbers aren't real" in Phase 6. And never score your own idea: swap ideas with a colleague.

6.7 · Round 3c: the Pugh matrix, when exact scores would be guesswork

Sometimes you can't honestly score 1–9 because early ideas are too vague. The Pugh matrix gives up precision to stay honest. Pick a reference design (the datum), normally the current one, and compare every idea with it, criterion by criterion, using just three marks: better (+), same (S) or worse (−). Reference design: the current keyed shaft. The spline scores +cost, +assembly, S-risk, −tooling, giving a net +1. The plastic overmould scores ++cost, −risk, −validation, giving a net 0, but with the biggest single benefit on the board. The real strength of Pugh is the second round: combine the best parts. Ask of every minus, "can this idea borrow a plus from another one?" Can the spline's tooling problem be solved with the overmould's approach? The ideas get better by combining, instead of just being ranked. Use Pugh to choose between early ideas, and the weighted matrix when the numbers can be trusted.

6.8 · Round 4: check the winners for risk

The short list, about one raw idea in eight (around 45 of the 380), gets one last quick check before development spends real money on it: what could go wrong, and would we see it coming? Ask three questions about each winner. Does it affect safety or homologation? (If so, the testing cost has just tripled, so score it again.) Does it depend on one supplier, one machine or one person? (If so, you need a backup plan, with its cost included.) And what would it cost to undo if it fails in use? A £0.30 saving that could lead to a recall isn't a saving. This is ten minutes of FMEA-style thinking, not a full FMEA. The point is that no idea reaches Phase 5 with a hidden problem that could stop it.

6.9 · From a ranked list to options you can act on

Ideas affect each other. Changing a material might rule out a process idea, or make it possible. So run a quick compatibility check, then group the remaining ideas by when they can be brought in: changes during production this year, the mid-life update, or the next-generation product. Related ideas are bundled together too, so the 45 or so survivors usually become about 15 proposals, each with its own business case. Many teams present these groups as three options (cautious, balanced and ambitious), so the sponsor chooses how far to go, not a list of parts. Finally, be honest: compare the engineering hours needed with the hours actually available. A shortlist you can't resource is just a wish list. It's better to deliver eight ideas than approve thirty.

6.10 · Champions: every idea needs an owner

The phase ends with names, not lists. Every idea that gets through has a champion: one person who takes it into development, reports on its progress, and argues for it when priorities clash. Not a committee, but one named person. The champion gets the idea's file (the function it serves, its score, its risks and its worth), a date for the Phase 5 development review, and the authority to get help. Experience shows it clearly: ideas with a champion get developed, and ideas given to "the team" turn up again, untouched, in next year's workshop.

7.1 · From idea to proposal

Champions turn shortlisted ideas into value proposals that can be put into practice. Each one answers five questions: what exactly changes, what it saves (checked by finance), what it costs to bring in (tooling, testing, engineering hours), what could go wrong, and when it happens. With those five answers, an idea becomes a business decision. Without them, it's still a sticky note.

7.2 · Engineering checks

Reduce the risk before you commit. Use CAE simulation (computer modelling) to check a thinner wall or a smaller safety margin. Use a design FMEA to find new ways the product could fail. And use a DVP&R (design verification plan and report) sized to how risky the change is. For sourcing ideas, a should-cost model or a supplier quote confirms the saving. In regulated industries, ask early whether the change needs re-certification, re-testing or PPAP (customer approval of production parts).

7.3 · The business case

The basic sums are always the same: annual saving = saving per unit × volume, and payback = one-time cost ÷ annual saving. Most changes to products already in production should pay back in under 12 months, and well-run VE studies return more than 10:1 overall. Be honest about savings: show them before and after the one-time costs, and give a date. A saving that arrives in 18 months is worth less than one that arrives this quarter.

7.4 · The testing ladder

Match the amount of testing to the risk, not to habit: desk calculation → CAE/simulation → rig test → production-line trial → field trial. A packaging spec change may need one step, while reducing a safety margin needs all five. Two rules: only go as far up the ladder as the risk requires, because too much testing slowly destroys the payback. And start anything with a long lead time (tooling, approving a supplier) at the same time as testing, with clear stop points, rather than waiting until every test has passed.

7.5 · FMEA in ten minutes

Any VE change can create new ways for the product to fail that the current design never had. Score each possible failure on Severity × Occurrence × Detection (1–10 each; multiply them to get the RPN, the risk priority number). But score the change from the current design, not the absolute figure. A thinner wall that raises occurrence from 2 to 4 on a failure with severity 8 needs action. One that goes from 2 to 3 on a failure with severity 2 just needs a note. For serious or regulated systems, move up to a full DFMEA with the whole team. That isn't red tape. It's what makes the saving safe to count.

7.6 · The money: sums finance will sign off

Net saving = the saving per part, minus the one-time costs spread over time (tooling, testing, engineering hours, scrapped stock and re-approval). Payback = one-time cost ÷ annual net saving. That's the right measure when volumes are steady. When volumes are rising or falling, use NPV (net present value) at the company's minimum required return. And put a date on everything: savings start on the day the change is made, not the day it's approved. A proposal without a date has a business case of zero.

8.1 · The decision meeting

Present to the sponsor and the decision board: the value logic (functions and mismatches), the numbers (savings, cost, payback), the risks and how you'll handle them, and what you need (people, decisions, dates). The result is a decision log: a clear yes or no for each proposal. A presentation that ends without decisions is just a rehearsal, not Phase 6.

8.2 · The savings funnel

Approved ideas go into a funnel with stages: L1 Idea → L2 Validated → L3 Approved → L4 Implemented. Each stage has rules for getting in: L2 needs savings checked by finance, and L4 needs the change in production with approval and testing complete. The funnel is reviewed every month. Every idea has an owner, a stage and a date, or it's dropped. Savings only "count" at L4, checked against the fixed starting cost, so inflation and changes in product mix can't blur the result.

8.3 · Learn from each study

Once the changes are made, feed the lessons back: update design guidelines, cost standards, preferred-parts lists and the idea bank. This shared memory is what makes the second round cheaper and faster than the first, and what eventually turns VE from a one-off project into a skill the company keeps.

8.4 · The one-pager that wins decisions

Boards decide using one page per proposal: the function and its value mismatch → what changes (a picture is better than a paragraph) → net saving, one-time cost and payback → the biggest risk and how you'll handle it → what you need, who owns it and the date. Then brief people in advance: take the two most sceptical people through it before the meeting. Surprises make senior managers defensive, and defensive managers say no. The meeting itself produces a decision log: the proposal, the decision, the owner, the date and a signature. Applause isn't a result.

8.5 · Counting savings in a way that stands up to checking

Apply the rules agreed during preparation (Module 2), strictly: measure against the fixed starting cost; remove the effect of changes in material prices and volumes (adjust using an index, so a rise in copper prices doesn't hide a real design saving); show openly how you get from gross to net saving; only count savings at L4, when they're in production and checked by finance; and match your figures with purchasing's records so the same pound isn't claimed twice. The reputation of every believable programme depends on these rules.

8.6 · Feeding lessons back into the system

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, label the idea bank (parked ideas are the starting point for the next round), and hold a 30-minute review after each round: what got stuck, why, and at which stage of the funnel ideas were lost. The second round should always be cheaper and faster than the first. If it isn't, the lessons aren't being fed back.

8.7 · Why approved ideas don't happen: the five causes

This is the uncomfortable truth this phase is here to deal with: in poorly managed programmes, 40–60% of approved workshop ideas never reach production. They don't fail dramatically. They just fade away. Here are the five causes and how to prevent each one. No owner: an idea given to "the team" belongs to nobody, so name one champion per idea in Phase 4. No engineering time: approval without engineering hours is just a wish, so the decision meeting books the hours, not just the intention. A late surprise from change control: re-approval, PPAP or re-certification costs appear late and wipe out the payback, so ask about change control during Development, not after approval. A moving starting cost: nobody can prove the saving happened, so finance stops believing it; fix the starting cost during preparation. The champion leaves: the only person who knew the idea walks out of the door, so the funnel review gives ownerless ideas a new owner within a month. None of these is bad luck. All five are management failures with known fixes. That's why the monthly funnel review, boring as it sounds, is where programmes really succeed.

9.1 · Why should-cost: one number isn't enough

With every part you buy, the supplier knows far more than you do. They know what they pay for material, their cycle times, their scrap rate, how busy their factory is and their margin. You know one number: the quote. Negotiating like that is guesswork. Push too little and you overpay for years. Push too hard without the facts and you force a good supplier into a loss, then inherit their quality problems. Should-costing evens things up. You rebuild the part's cost from physics and published market rates: what the material must weigh, what the machine must cost per hour, and what a fair margin looks like. Then you go into the negotiation knowing roughly what the supplier knows. The conversation changes straight away, from "give us 5%" (random, and resisted) to "your quote suggests your press shop is only 55% busy. Is that right?" (specific, and something they can answer). One is haggling. The other is engineering.

9.2 · Six groups of cost levers

• 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 most of the cost is decides which levers work best. When most cost is in bought-in parts (as in most cars) → sourcing and design. When it's mostly labour → DFMA (design for manufacture and assembly) and automation. When material prices swing a lot → link prices to an index first.

9.3 · What's inside a should-cost

A quoted price is one number you can't see inside. A should-cost model breaks it into layers you can negotiate: raw material (mass × market price ÷ yield), process & machine (cycle time × machine-hour rate), direct labour, scrap & yield, overhead (driven by how busy the plant is), SG&A (sales, general and admin costs), and a fair margin. Each layer is built from physics and market rates, and each is a separate conversation with the supplier. Negotiating with the facts from a cleansheet usually saves 5–15% on quoted prices, without cutting into the supplier's fair profit.

9.4 · Linear Performance Pricing

When you buy a family of similar parts, plot their price against the main thing that drives cost (weight, power or area) and draw a best-fit line. Parts above the line cost more per kilo (or per watt, or per square metre) than similar parts. That gives you a ready-made list of parts to negotiate, from data you already have.

9.5 · 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 guesswork.
• 4 · Machine rate — (depreciation + energy + floorspace + maintenance) ÷ (annual hours × OEE). How busy you assume the machine is can move this by ±30%.
• 5 · Labour — operators per machine × the local wage including on-costs; the level of automation is what you can change.
• 6 · Overhead & SG&A — typical local percentages, sense-checked; this is where the cost of a half-empty factory hides.
• 7 · Margin — a fair profit, typical for this kind of part. Then sense-check the total against LPP curves and real quotes.

9.6 · LPP with real numbers

Plot your eight stamped brackets: price (y) against mass (x). Regression: price ≈ £0.85 + £1.9/kg. Six parts sit close to the line. Bracket D is 18% above it and bracket F is 25% above. Before celebrating, check the specs: F has a special coating (justified), but D doesn't (a target). One hour with data you already have produced a negotiation list worth more than a month of meetings. That's LPP: cheap, quick and completely objective.

9.7 · How to run the negotiation

Never email a cleansheet saying "explain the gap". That makes the supplier defensive and damages the relationship. Instead, share your method, invite them to a joint workshop, go through the layers together, and split the discussion three ways. Raw material is linked to an index in the contract (nobody negotiates the price of copper). Processing costs are worked on together (cycle times, yields, packaging). Margin is respected. Finish with a gain-share clause, so they share the savings and keep sending ideas. Suppliers who feel they're being checked up on hide information. Suppliers who feel like partners offer it.

9.8 · AI in should-costing: from days to minutes

The seven layers haven't changed, but how fast you can build them has. Feature-based AI engines read a 3D CAD model, recognise its manufacturing features, pick a workable process route and return a regional should-cost in under a minute (Module 11 looks at these in detail). Spend analysis software runs LPP automatically across a whole category of parts, flagging every odd one out instead of the eight parts you had time to plot. AI writing tools draft the negotiation fact pack (the layer table, the questions, the counter-arguments) from your cleansheet in minutes. What AI does not change: the model is only as good as its assumptions, and a negotiation is still won by the engineer who understands the physics behind every layer. Learn the manual cleansheet first (this module), and the AI tools will multiply what you can do, instead of being a black box you can't explain to the supplier.

10.1 · Why teardown matters

Phase 2 taught you to ask "what is this function worth?" — and the honest answer is hard to find from inside your own company. Your cost systems tell you what things do cost, not what they could cost. Your engineers defend designs they created. Your suppliers quote as much as the market will pay. The one piece of evidence nobody can argue with is a competitor's product doing the same job for less, sitting on your bench, taken apart, weighed and costed. That's what teardown gives you: not opinion, but physical proof of what's possible.

And here's the good news: every product on the market is an open book. Once a product is on sale, anyone can buy one and study it, and a whole industry exists to do exactly that. Your competitors are almost certainly studying your products this way. The only question is whether you study theirs.

10.2 · What it costs, and what it returns

Teardown feels expensive. Buy competitors' products just to take them apart? Do the sums before deciding. A medium-sized teardown study (two competitor products, four weeks of a small team, plus lab time) might cost £30,000 to £60,000 in total. If it finds ideas worth even £1.50 per unit on a product made at 200,000 units a year, that's £300,000 every year: five to ten times the cost in the first year, and again every year after. That's why carmakers pay millions for teardown database subscriptions. It's not even close. Choose what to study carefully: the market leader (what does "best" cost?), the price disruptor (how is it so cheap?), and the closest rival (where exactly do we lose?). One of each teaches more than three of the same.

10.3 · Step 1 — Acquire and document

The units arrive. The most important rule of the whole process applies now, before any tool comes out: you get exactly one first teardown. Every state you change is gone forever. So the first day is cameras and scales, not screwdrivers: photograph every face and label, weigh the complete product, measure the overall dimensions, record serial numbers, build dates and market specification. Professional labs take photos against a plain background with a ruler or scale in every shot, so any measurement can be taken again from the photo months later. Boring? Completely. But every question the study will ever face ("are you sure that's the EU version?", "was that seal already broken?") is answered by these records.

10.4 · Step 2: take it apart step by step, and time it

Taking it apart isn't smashing it up. It's watching their assembly in reverse. Work one level at a time: covers off, inside out, systems separated, subassemblies opened, exactly as the exploded view above shows. And time every step, because the stopwatch tells you about their factory. A product that comes apart in 48 seconds went together in about a minute of paid labour. One that takes 92 seconds and three tool changes cost its maker twice as much to assemble. Record every fastener (how many, what type, what size, because each different fastener is one more thing to buy), every clip, and every "why is this here?" moment. The record of taking it apart is the competitor's process sheet, rebuilt.

10.5 · Step 3: record every part (the digital BOM)

Now the pile of parts becomes data. Every part is recorded using a template agreed before the first screw came out: part ID, assembly level, weight, material (confirmed with a spark test, XRF analyser or burn test, never guessed), signs of how it was made (parting lines, tool marks and draft angles tell you how many moulds and what machines were used), supplier markings, number of fasteners and time to remove. Photograph every part on the lightbox. This digital parts list (BOM) is what the whole study is built on. Findings get forgotten and opinions differ, but a complete costed parts list of a competitor's product keeps answering questions for years. Keep the physical wall of parts too: both products laid out side by side, level by level, teach you more in an hour than the spreadsheet does in a week.

10.6 · Step 4: should-cost their parts, honestly

Build a cleansheet for the competitor's parts using Module 9's method, but with their assumptions, not yours: where they probably make it, their volumes (you can estimate these from sales data), and the processes their parts show they used. Give every estimate an honest range (±15%) and write down your assumptions. What matters is the difference table: their door hinge at £1.10 against yours at £2.30 is a reliable finding, even though both numbers could be a bit out. Differences stay reliable even when the exact figures are uncertain. Rank the differences, and the study's priorities become obvious.

10.7 · Step 5: turn findings into ideas

A teardown that ends with just a report is called teardown tourism: an interesting visit that changes nothing. What you really want is ideas in your funnel. Every important difference becomes a starting point for the Creative Phase ("they do this with a snap-fit, so what would that take on our product?"). Every clever solution gets a decision: copy it, adapt it or go one better. And the costed findings feed the cost walk from Module 7, with today's cost on the left, the target on the right, and steps proven by the teardown in between. The work on the bench is only finished when the findings have owners, dates and a place in the savings funnel.

10.8 · Six ways to benchmark: it's about more than part cost

• Cost — the difference table: where do they spend less, part by part? The £1.20 hinge gap.
• Functional — performance per pound: torque/£, lumens/£, litres of boot space per £. The value ratio, not the raw figure.
• Design — architecture choices: their one-piece moulded chassis versus your four-part welded one; integration versus modularity.
• Process — what the parts say about how the best plants make them: weld counts, tool marks, cycle-time clues.
• Feature — features compared with prices: who gives customers more than they need, and who charges for things customers ignore (Kano from Module 3, applied to competitors).
• Patent / IP — expired patents are free ideas; active ones are constraints to design around. Both are readable in the parts.

Price on its own tells you almost nothing: a cheaper competitor might be selling at a loss to win market share. Looking at all six together tells you why the numbers are what they are, and benchmarking answers VE's hardest question (what is a function worth?) with the best evidence there is.

10.9 · Running the physical lab

You need less than you think: a solid bench, calibrated scales, torque drivers, callipers, a lightbox, and the agreed data schema. Photograph every state before changing it; scan or CT-scan anything you might want to measure before any step that destroys it, because you can't undo those. Label bags and boxes by assembly level from the start. Three hours of care saves three weeks of "which bracket was this?" later.

10.10 · AI in the teardown lab

AI is changing teardown faster than almost any other part of VE, because teardown produces huge amounts of exactly the kind of data AI is good at. Computer vision now recognises photographed parts and fills in parts-list lines that an engineer just checks. CT scanning with AI records a complete assembly without removing a screw: internal shapes, wall thicknesses and materials go into a 3D model, so the "one first teardown" doesn't have to destroy anything. AI language tools sort thousands of parts-list lines into categories and draft the write-ups. Automatic should-cost software takes a part's shape and produces cycle times and cost estimates in minutes rather than days. And digital teardown libraries mean you can benchmark twenty vehicles from a laptop before buying one. The roles are the same as ever: computers count and estimate, and engineers decide what it means and what to do.

10.11 · Ethics and the law: where the limits are

Taking apart products bought on the open market is legal and standard practice across industry. The limits are clear. Never use confidential information obtained improperly: a competitor's drawing passed on by a shared supplier will contaminate the whole study, so refuse it and say why. Patents aren't secrets: they're public documents you should read. But copying a patented solution needs a licence or a different design. Learn freely, copy carefully, and involve your lawyers when you adopt an idea, not when you're just studying it.

11.1 · The digital cost tools

• AI should-cost engines — software that reads a 3D CAD model, simulates how it would be made, and gives cycle time, tooling and part cost for each region, before any supplier quotes
• CT scanning and 3D teardown — industrial CT scanners record competitors' products without taking them apart: wall thicknesses, hidden joints and how the inside is laid out
• Spend analysis ("spend cubes") — your purchasing data sorted and linked to parts lists, showing different prices paid for the same part across factories and suppliers
• Generative design and topology optimisation — software tries thousands of shapes to find the one that uses the least material
• Additive manufacturing (3D printing) — combining several parts into one, making small volumes without tooling, and moulds with cooling channels that follow the part's shape
• Digital twin / CAE — removing excess safety margin once simulation shows it's safe; virtual experiments replace physical trials

11.2 · AI language tools (LLMs) in the workshop

Large language models (LLMs), the AI behind tools like chat assistants, now help throughout the job plan. They dig through warranty text and quotes in the Information Phase, produce and group ideas in the Creative Phase, and draft business cases in Development. They also power linked cost databases that connect functions, parts, costs and suppliers, so no lesson is lost between rounds. The practical view: AI speeds up analysis and idea generation, and people are responsible for judgement, checking and decisions. Teams that treat AI output as a first draft move about twice as fast. Teams that treat it as the answer just make mistakes faster.

11.3 · What doesn't change

No tool replaces the method: functions before solutions, worth before ideas, testing before implementation, and real savings before celebration. The tools make a good method more powerful, but they can't rescue one that's been skipped.

11.4 · Inside an AI should-cost engine

These engines work in four stages. Reading the shape: the CAD model is broken down into manufacturing features such as holes, bends, bosses and pockets. Choosing the process: the engine picks a workable sequence of processes for the material and shape. Simulating the physics: cycle times are calculated from press force, the volume of melted material and tool paths. Applying local costs: machine rates, wages and overheads come from regularly updated data libraries (aPriori, for example, keeps 92 regional cost libraries). Two things follow. The result is only as good as what you put in, so a careless CAD model gives the wrong cost. And the results are a guide: ideal for ranking design options and preparing for negotiations, but never a replacement for the final quote.

11.5 · How to ask AI tools good questions for VAVE

A reliable way to ask for ideas is: role + function + limits + how many + ranking. For example: "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 group teardown notes, draft business cases and turn warranty text into function language. Two strict rules. Check every number the AI gives you, because AI tools sound confident even when they're wrong. And never paste confidential parts lists or drawings into public tools. Use your company's approved versions.

11.6 · Cost and carbon: one change, two savings

The same physics that drives cost also drives carbon emissions (CO₂e): weight, energy use, scrap and transport distance. Add a kgCO₂e column to the cleansheet and most cost savings turn out to cut carbon too: making parts lighter, reducing scrap, using recycled material and making things closer to home. Carbon taxes on imports are spreading, and customers increasingly report the emissions in their supply chains ("scope 3"). A VE study that measures both is answering next year's question this year.

11.7 · AI across the job plan, phase by phase

• Phase 1 · Information — AI groups thousands of warranty claims and service notes by function and ranks them; spend analysis builds the starting cost in days, not weeks
• Phase 2 · Function Analysis — a first draft of the function tree from the parts list in minutes. The team's job becomes correcting it, which is quicker and better than starting from nothing
• Phase 3 · Creative — 25 ranked ideas per function as a warm-up before the human session; cross-industry analogy search on demand
• Phase 4 · Evaluation — duplicate ideas grouped automatically and a first check on what's feasible; people still do the scoring
• Phase 5 · Development — drafted business cases with the arithmetic pre-filled; AI should-cost for instant savings estimates per idea
• Phase 6 · Presentation — live funnel dashboards; auto-drafted decision logs and savings-audit trails

Notice the pattern: in every phase, AI does the drafting, counting and grouping, and the team does the judging, deciding and owning. The phase reviews, who makes decisions, and the record of what was decided stay exactly where the six-phase job plan put them.

11.8 · Adopting the tools in order: crawl, walk, run

Crawl: start with the data you already have. Spend analysis and LPP across your biggest categories need no new tools, find the odd ones out within weeks, and pay for everything that follows. Walk: trial an AI should-cost engine on your top 50 bought-in parts. Compare it with real quotes for three months before you trust it, and add a digital teardown-library subscription if you benchmark regularly. Run: approved AI assistants built into the workshop itself, CT scanning for teardowns that don't destroy the product, and a linked cost database connecting functions, parts, costs and suppliers, so no lesson is lost between rounds. The mistake to avoid is buying the "run" tools first. Tools make a working method stronger. Added to no method at all, they just produce nicely formatted guesses. Build the skills first, then buy the software.

11.9 · Ground rules: keeping AI-assisted cost work checkable

Four rules make the difference between AI being a big help and a big risk. Check every number: AI tools sound confident even when they're wrong, so any figure that goes into a business case is checked by a person against a real source. Protect the data: parts lists, drawings and supplier prices never go into public tools. Use your company's approved versions, and treat pasting a drawing like emailing it. Keep a person's name on it: every AI-drafted business case, should-cost and decision log is signed by a named engineer who checked it. "The AI said so" won't stand up to any audit. Watch for drift: compare AI cost estimates with real quotes regularly, because rate data goes out of date and AI models change without warning. Teams that follow these four rules from the start move fast and keep finance's trust, and as Module 8 showed, that's the only kind of speed that counts.

12.1 · From a one-off event to the way you work

One-off studies fade. Programmes keep building on themselves. Here's how to run one: a monthly funnel review (owners, stages, dates), planning every quarter (which products and teardowns are next), a yearly value plan for each product line linked to its margin plan, and training people all the time. These KPIs keep it honest: funnel coverage (% of product cost being actively studied, target ≥60%), how many ideas get implemented, cost visibility (% of the parts list with a should-cost, target ≥80%), how fast savings arrive, average payback, and how often ideas are reused.

12.2 · What to expect

A first proper round on a product nobody has studied before usually finds 8–15% of product cost. Established programmes keep finding 3–5% a year, and studies often return more than 10:1. Programmes rarely fail because the ideas are weak. They fail because nobody follows through, and that's exactly what the funnel reviews are there to prevent.

12.3 · Standards and certification

The SAVE International Value Methodology Standard sets out the job plan used worldwide, and EN 12973 (revised in 2020) sets out Value Management in Europe, and EN 1325 defines its terms. SAVE International certifies at two levels: VMA (Value Methodology Associate, the entry level) and CVS (Certified Value Specialist, the professional standard for leading studies). The older AVS and VMP titles were retired in 2016. In the UK, the Institute of Value Management certifies practitioners against EN 12973. TRIZ has its own certification levels. Both show that someone can run the method properly, not just the meeting.

12.4 · Ways to organise a programme that can grow

There are three main ways to organise it. A central team of experts (a "centre of excellence") has deep skills, tools and standards, but becomes a bottleneck. Champions in every product line can grow, but their skills get spread thin. The mix of both works best in practice: a small central value office that owns the method, training, tools and the savings records, with certified champions in every product line. Make sure it reports to someone neutral. A value office under engineering focuses on design and ignores sourcing, and one under purchasing does the opposite. Having the finance director or operations director as sponsor keeps both sides balanced.

12.5 · Targets and rewards

Work back from the market: product-line margin plans → yearly cost plans → targets for each part of the design at each design review. Treat cost as a requirement, as important as weight. Be careful with rewards. Reward savings that are checked, net and actually in production, or people will chase big numbers that only exist on paper. Give credit to the people who came up with ideas as well as those who carried them out. And never set a savings target higher than the engineering time you've actually paid for. A target with no one to deliver it just destroys morale.

12.6 · Developing people's skills step by step

A clear path for building skills is better than relying on a few heroes: awareness (everyone, which is this course's job) → practitioner (does the analysis on their own product) → facilitator (leads studies, with a learner helping to run each round) → certified specialist (VMA → CVS through SAVE International). Add a group that meets every month to share experience, and a yearly internal "value day" where teams present the ideas they've put into practice. Recognition is the cheapest way to keep good people.

13.1 · Preventing cost is better than removing it

Everything so far in this course removes cost that's already there. This module is about never letting it in. By the time a concept design is frozen, 70–80% of lifecycle cost is already decided, but almost none has been spent. So the cheapest VAVE workshop is the one you never needed because you set a target. Target costing (Toyota's genka kikaku, developed from the 1960s) turns the usual thinking upside down. The traditional "cost-plus" approach asks: what will it cost, and what price does that mean we have to charge? Target costing asks: what will the market pay, and what cost does that allow?

The sum is surprisingly simple: Allowable cost = target selling price − required margin. Use the price the company actually receives: take VAT and any dealer or retailer margin off the shop price first. The hard part of target costing is people, not maths: sticking to the number when engineering says "it can't be done for that", and splitting the number up so every team owns a piece of it.

13.2 · Allowable cost, drifting cost and the value gap

Three numbers drive the process. The allowable cost comes from the market, working down from the price. The drifting cost is today's estimate of what the design as drawn would actually cost, built up from the parts. It changes with every design decision. The difference between them is the value gap. Closing it is design work: function analysis, DFMA, questioning specs and designing with suppliers. It's the whole toolkit from Modules 4–11, used on a new design instead of an existing one.

The discipline comes from the cardinal rule (Cooper & Slagmulder), the one rule you never break: the target cost can never be exceeded. If a feature has to be added, its cost must be saved somewhere else: another feature cut back, a design simplified, or a make-or-buy decision changed. What the rule really stops is the slow creep where every team adds "just 2%" and the product ends up well over budget. If the gap really can't be closed, the honest options are to change what's in the product or not launch it. Don't pretend the margin will turn up later.

13.3 · Splitting the target: from vehicle to part to supplier

One number for the whole product is a wish. Splitting it down is a plan. The allowable cost for the vehicle is divided between systems (body, powertrain, chassis, electrical and so on), based on how much each function is worth and on benchmark data. It's then split into subsystems and parts, and finally into target prices for suppliers, shared openly with them along with the cost model behind them. Two habits help the plan survive in real life: keep a small reserve at each level that isn't given to anyone (because designs change), and make a named engineer, not a department, the owner of every target.

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

Aircraft programmes track weight with an owner for each subsystem, an up-to-date status at every design review, and a recovery plan as soon as anyone goes over their share. Design-to-cost uses exactly the same approach for cost. Cost is a requirement, as important as weight, performance or safety. At each design review, the drifting cost is reported against the target, and a review with a red cost status and no recovery plan doesn't pass. Useful tools: trade-off curves (cost against performance for each concept, so decisions are made on purpose, not by accident); tolerance–cost curves (tightening a tolerance usually makes machining cost rise steeply, so question every decimal place); and a cost model that updates with the CAD, so engineers see the £ effect of a design choice in the same week they make it, not at the next round of quotes.

13.5 · After production starts: handing over to kaizen costing

Target costing ends when production starts (SOP, start of production). Kaizen costing takes over from there: small, continuous reductions against a yearly cost-reduction target on the product in production. The two work as one system. Target costing decides where the cost starts, and kaizen costing brings it down further. A mature product company does both, plus regular VAVE rounds (Module 12) when a big change is needed. One warning: kaizen targets applied blindly year after year are how quality starts to spiral downwards. Cost reductions need the same function and quality checks as in the Development Phase (Module 7).

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