Set a made-to-order furniture lead time by adding four waits together: the queue ahead of the order, supplier delivery for materials, your own build and finishing hours, and shipping. Divide the queue in hours by your real weekly capacity, quote a range rather than a date, and email the customer when the piece changes stage.
AI helps with the sums on your past orders and with writing the updates. It cannot know your supplier is late or that one maker is off sick, so the figures come from your own records, and the dates you promise stay yours. Setting up the sheets takes a three-person workshop about an afternoon.
Four clocks run inside every lead time
When a customer asks "how long?", the honest answer is the total of four clocks that start at different moments and sometimes run at the same time. Most missed dates come from counting only one of them, usually the build.
- Queue time. How long before the piece reaches the bench. If there are 310 hours of confirmed work ahead of it and you produce 88 hours a week, the queue alone is a little over three and a half weeks.
- Supplier time. Timber that must be kiln-dried and delivered, upholstery fabric ordered from a mill, glass cut to size, hardware from a distributor. This clock can run while the queue clock runs, which is why ordering materials early shortens the total.
- Making time. Cutting, joinery, assembly, sanding, upholstery. It is the clock you control best and usually the one you quote.
- Finishing and delivery time. Oil or lacquer needs drying and curing between coats; a courier or freight leg adds days; a large piece may need a booked two-person delivery slot.
The total is not the sum of all four, because supplier time overlaps with queue time. The formula comes later. For now, write the four clocks at the top of a sheet with the headings queue, materials, making and finish-and-delivery, because the next steps measure each one separately.
Count capacity in hours a week, not pieces a week
"We make about three tables a week" sounds like capacity but breaks the moment someone orders a wardrobe. Capacity in hours survives a mix of products, because every order gets an hours estimate and the queue is just a running total.
An illustrative workshop has three makers on a five-day week. On paper that is 3 x 40 = 120 hours. The real number is lower, and finding it is the first job:
| Item | Hours per week |
|---|---|
| Paid hours, 3 makers x 40 | 120 |
| Less: holiday and sickness allowance (about 8%) | -10 |
| Less: machine set-up, tidying, tool care, deliveries | -14 |
| Less: customer calls, measuring, rework | -8 |
| Productive capacity on customer orders | 88 |
Those percentages are placeholders. Replace them with your own by looking at a typical month: how many hours went to jobs that appear on an order, and how many went to everything else? If you keep no time records, a two-week experiment with a simple sheet (maker, date, order number, hours) will tell you more than any estimate.
Estimating hours per product
Each product needs an hours figure for cutting-to-finished. Take it from your history, not from your head. If you have a spreadsheet or invoicing records with start and finish dates, an assistant can help you get the numbers out. A prompt such as this works once the data is in a sheet you can paste or attach:
Here is a table of our last 40 finished orders: order number, product type, date confirmed, date materials arrived, date started, date finished, date delivered, and maker hours logged. For each product type, work out the median and the highest making-hours figure, and the median number of days between "materials arrived" and "finished". Show the table of working. If a product type has fewer than 4 orders, say so instead of giving a figure. Do not estimate anything not in the data.An illustrative reply would show that dining tables took a median of 26 hours (highest 34), bookcases 18 hours, and upholstered chairs 11 hours each, with the chairs' elapsed time dominated by waits for fabric. Notice what the "highest" figure is for: it shows how much a hard job can run over the median, and that gap sets the size of your buffer.
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Buy nowTurn the queue into weeks
Keep one sheet called "Queue" with a row per confirmed order: order number, product, estimated hours, status, and the date materials are due. The sum of hours for orders not yet finished, divided by weekly capacity, is the queue in weeks.
Using the workshop above: confirmed orders total 310 hours, so the queue is 310 / 88 = 3.5 weeks. A new dining table of 26 hours will therefore start in about 3.5 weeks and take just under another 0.3 of a week to build. That alone gives a making-complete date of roughly four weeks out.
Now add the other clocks. Oak boards for the table take 10 working days to arrive, which fits comfortably inside the queue wait, so they add nothing. The hard-wax oil needs 3 days between final coat and packing. The courier leg is 4 days. Total: about 4 weeks of queue and making, plus 3 days of finish, plus 4 days of delivery, which is roughly 5 weeks. A buffer comes next.
Where the buffer comes from
Instead of adding "a bit extra", use your own record of being late. Pull the last 20 to 40 finished orders and compare the date you quoted with the date the customer received the piece.
In an illustrative set of 30 orders, 21 arrived on or before the quoted date, 6 were up to a week late and 3 were up to two weeks late. To have been on time for roughly 27 of the 30 (nine in ten) you would have needed to add a week to every quote. That is a defensible buffer because it comes from your own history. A quote of "5 to 6 weeks" for the table then covers nine orders in ten.
Ask the assistant to do this comparison, and to show the working so you can verify it:
For each order below I have the quoted delivery date and the actual delivery date. Work out how many days late or early each was, then tell me the number of days of buffer I would have needed to have been on time for 80%, 90% and 95% of orders. Show the sorted list of days late so I can check it.The three percentages give you a choice. A buffer for 80 per cent is shorter and more competitive; one for 95 per cent costs you some sales at the checkout but protects your reviews. Pick one for the product page and keep the other as a rule for large or high-value orders.
Supplier lead times: record them, then quote the slowest common case
Supplier waits are the clock that shifts without warning. Keep a "Suppliers" tab and fill it from your own purchase history, not the supplier's website.
| Material | Stated lead time | Actual, last 6 orders | Figure to use |
|---|---|---|---|
| Oak boards, kiln-dried | 5 working days | 4 to 12 days | 10 days |
| Upholstery fabric, standard range | 2 weeks | 10 to 21 days | 3 weeks |
| Cut-to-size glass | 7 working days | 6 to 9 days | 10 days |
| Hinges and drawer runners | 3 working days | 2 to 5 days | 5 days |
This is illustrative, but the pattern is real: the figure to use is close to the slowest of recent deliveries, not the supplier's stated promise. If you place orders as soon as the customer pays rather than when the piece reaches the bench, the supplier clock runs inside the queue clock and costs you nothing, provided you have somewhere to store the materials.
A sensible rule is to order materials the day an order is confirmed, as long as the deposit covers the material cost. If you cannot afford that, say so in the lead time: "ships 6 weeks from the day your fabric arrives" is honest and accounts for the dependency. Comparing suppliers on reliability as well as price is easier with the method in comparing supplier quotes side by side.
The formula, written once
Put the rule into the sheet so every quote uses the same arithmetic.
Start of making = today + queue weeks (queue hours / weekly capacity)
Making complete = start of making + (product hours / weekly capacity)
Materials ready = today + supplier days (use the "figure to use")
Ready to finish = the LATER of making complete and materials ready (if materials are late, the start slips)
Ready to ship = ready to finish + finish and cure days
Delivered = ready to ship + delivery days
Quote range = delivered to delivered + bufferNotice the "later of" line. Materials do not always fit inside the queue. If fabric needs three weeks and the queue is two, the piece waits a week at the bench, and the quote has to follow the longer clock. In a spreadsheet, =MAX(making_complete, materials_ready) does it, and a quote-range column joins the final date and the buffer into text such as "5 to 6 weeks".
If you have the schedule side to build as well, the tutorial on a weekly production schedule for a small manufacturer turns the same queue into who builds what on which day.
What the customer sees: wording on the page and in the confirmation
Customers accept a long lead time far more readily than a broken one. Wording matters because it sets the promise you are judged on.
Product page
Show the range and say what it assumes. For example: "Made to order. Currently 5 to 6 weeks from payment to delivery. Upholstery fabric adds up to 2 weeks if the fabric you choose is not in stock." Avoid "ships in 2 to 3 weeks" if that figure leaves out the queue, because customers read it as the total.
Order confirmation
Repeat the range and name the milestones, so the later updates read as progress rather than surprises. The wording below shows the idea:
Thank you for your order. Here is what happens next.
1. We order your materials this week. We will tell you when they arrive.
2. Your piece goes on the bench around [week commencing]. We will email you then.
3. After finishing, it cures for 3 days and we book your delivery.
Our current estimate is 5 to 6 weeks from today. If anything changes, you will hear from us before the estimate does.The last sentence is a promise to be proactive, which customers tend to value more than a short date. It is worth including only if you will keep it, which the next section makes routine.
A small update routine, drafted by AI from a sheet row
Updates fail because they depend on someone remembering. Link them to status changes in the queue sheet instead. Five triggers cover most orders:
- Order confirmed. Range and milestones.
- Materials arrived. "Your timber is here and your piece is scheduled for the week of [date]."
- Making started. A photo of the work, if you take one.
- Finished and curing. Firm delivery week; ask for delivery access details.
- Delay. Any change that moves the range by more than a few days.
Each of these can be drafted from a single row by pasting it into an assistant with this instruction:
Write a short, friendly email to a customer from this order row: [paste row]. Status change: [status]. Use the customer's first name only if it is in the row. Do not promise any date the row does not contain. Do not apologise unless the status is DELAY. Keep it under 90 words and end with our workshop signature.An illustrative output for a "materials arrived" row reads: "Hello [first name], your oak boards arrived today and have been checked. Your table is scheduled to go on the bench in the week of 18 January, and we expect it to be finished and delivered within the 5 to 6 week estimate we gave you. I will write again when making starts." You would still read it before sending. The assistant has no way to know that "18 January" in the row was changed by hand yesterday.
When a date slips: before and after
A delay email is where lead-time habits show. A weak version arrives after the missed date, offers no new date and blames a supplier. A better version arrives as soon as you know, gives the cause in a sentence, gives a new range and offers a choice if there is one.
Before: "Hi, just to let you know there have been some delays with your order. We are working on it and will be in touch."
After: "Hello [first name], the fabric for your armchair has been delayed at the mill and is now due on 12 February, not 29 January. That moves your delivery from the week of 1 March to the week of 15 March. If that is too late, I can offer a different fabric from stock, which would keep the original week. Tell me which you would prefer."
The second version has a cause, a new range, a consequence and a choice, and it costs no more to write when an assistant drafts it from the row. The change in the row (new fabric date, new delivery week) must come from you first.
Two more cases: rush orders and mixed-size queues
A rush request ("I need it in three weeks") should be handled by a rule, not a mood. Decide in advance how many rush slots you allow a month, what they cost (a percentage or a fixed fee), and what happens to the order that gets pushed back. Put the rule where your team can see it, then quote rush honestly: "We can fit it into a rush slot for an extra 15 per cent. That puts delivery in the week of 8 March. The standard slot is the week of 29 March."
A queue with a few very large jobs, such as a fitted wardrobe set at 140 hours, behaves differently from one of small pieces. A large job consumes more than a week of total capacity, so give big jobs their own row in the queue, apply the capacity rule to them as well, and add a line to the quote that the range is wider for fitted work because site measurements and access can move the date.
An order that was quoted for the wrong week
This is an illustrative case, and a common one. A maker quoted four weeks for a bookcase because the customer's room was being redecorated and the date mattered. The queue was 2.5 weeks, the build 14 hours, and the stain needed 3 days to cure, which on paper made about four weeks. What the maker had not counted was the glass shelves, ordered on the day of confirmation, which arrived on day 16 and were the wrong thickness. The replacement took another 9 days.
The bookcase went out 12 days after the quote. Nothing about the maker's skill or capacity was wrong; the failure was that the supplier clock was missing from the formula and there was no second-chance rule for parts. Two lines would have changed the outcome: a note in the order row that glass was a risk item, with a check of the delivery the day it arrived; and a "parts arrived and verified" status, so a wrong part is found at day 16, not at assembly.
Checking that your lead times are working
A lead-time method earns trust only if you measure it. Once a month, take the finished orders and work out three numbers, which an assistant can calculate if you paste the table and ask it to show its workings:
- Within-range rate. The share of orders delivered inside the range you quoted. Aim for 90 per cent or more.
- Average days of slippage on late orders. A rate of 90 per cent with two-day slips is fine; 90 per cent with five-week slips needs a closer look.
- Update-before-miss rate. Of the late orders, how many were warned about before the original range ended? This shows whether the delay routine is being followed.
If the within-range rate stays below your target for two months, do not just widen the quote. Look at which clock is the cause (queue, supplier, making or finishing) using the dated columns in your sheet, and fix that one. If your queue consistently runs longer than you can sell comfortably, the question becomes whether to take the next order at all, which the capacity-planning tutorial walks through.
Keeping the numbers honest over time
Capacity changes: a new hire, a broken machine, a month of holidays. Update the weekly capacity figure whenever the team changes, not once a year. Supplier figures drift, so refresh the "figure to use" column every quarter from the last six orders. And treat the product-hours table as a living document; when a maker finds a faster way to do something, log it, because a table of old hours will make you quote too long and lose orders.
If you would like someone to look over your own order history and build the sheet with you, that is what my AI implementation consultation is for. Whatever route you take, keep the principle: the date you promise should come from your records, and the assistant's job is to do the arithmetic and write the update, not to guess when the oak will arrive.
Questions makers ask about quoting delivery dates
Should I quote a date or a range of weeks?
Quote a range of one to two weeks on the product page and in the order confirmation, then narrow it to a firm week once the timber, fabric or hardware has arrived and the piece is on the bench. A single date set at checkout is usually broken by something outside your control.
How much buffer should I add to my lead time?
Start with the gap between your own quoted and actual dates over the last 20 to 40 orders. If you were late on one order in five, quote far enough out that you would have been on time for four out of five and then a bit more. A flat 10 to 20 per cent is a starting guess, not a rule.
Can AI predict my lead times without any data?
No. It can organise your history, spot patterns and draft the messages, but the numbers come from your orders and your suppliers. With fewer than ten finished orders, treat any estimate as a guess and widen the range.
What if a customer needs the piece by a fixed date?
Work backwards from their date using your real queue, and say plainly whether it fits. If it only fits with a rush slot, name the rush arrangement and any extra cost before they pay, and write it into the order confirmation.
Further reads
- AI Furniture Visualisation for Makers: Show a Piece in the Room — Help customers commit to a piece while the date is still fresh.
- Best AI for Furniture Designers: Sketches, Cut Lists and Quotes — Get cut lists and hours estimates that feed the queue sheet.
- Best AI for Supply Chain: Delay Alerts and Reorders Without an ERP — Get warned when a supplier shipment runs late.
- AI Supplier Management: Track Prices, Lead Times, and Risk — Track supplier prices, lead times and risk over time.
- Shopify Pre-Order App: Which to Pick, Then Set Ship Dates With AI — Wording for ship dates on an online store.
- How to Price Handmade Items: Materials, Hours and Fees With AI — Price the piece so the hours you promised are paid for.
- AI for Furniture Stores: What a Chatbot Must Know Before Launch — A launch checklist for a furniture store chatbot: delivery windows, dimensions, fabric care and returns, plus 21 test questions and a go-live gate.
- Best AI for Cabinet Making: Supplier Orders, Lead Times and Chasers — How a small cabinet shop can use AI to chase board, hardware and door orders: a lead-time tracker, chaser prompts and acknowledgement checks.
- Joinery Quotation Template: Bespoke Items, Finishes, Lead Times With AI — A joinery quotation template: item schedule, finish options, staged deposit, lead-time triggers and install exclusions, drafted with AI and checked by you.
- Window Installation Quote Template: Specs, Lead Times, Exclusions With AI — A window quote built from a worked eight-window schedule: spec lines, U-value wording, lead-time dates, making-good exclusions and an AI prompt with checks.
- AI Tools and AI Development: The Complete 2026 Guide — the AI hub, including every tutorial in the AI-for-business series.
Sources: the maker's own order records (illustrative figures throughout); general capacity-planning practice. No vendor data is quoted.