Supplier price history tracker for MOQ, lead time, and cost

What problem a price history tracker actually solves

Purchasing decisions often get made with half the story. The last quote is buried in email, the “latest” spreadsheet sits on someone else’s laptop, and the details that actually change outcomes (MOQ, lead time, shipping terms, payment terms) are scattered across PDFs and chat threads.

That mess matters because quotes aren’t stable. For the same item, suppliers change unit price, MOQ, lead time, packaging, payment terms, and shipping assumptions. If you only see today’s number, you miss patterns like “cheap, but always slips by two weeks” or “price jumped 12% after the first order.”

Bad choices show up later and usually cost more than the price difference between two quotes. A low unit price can turn into stockouts, production delays, rush freight, quality disputes, or quiet margin erosion when you keep expediting to hit dates.

A tracker earns its keep when it answers questions in seconds:

• What did we pay last time for this exact item and quantity?
• How has this supplier’s lead time moved over the last few quotes?
• What’s the real delivered cost at our typical order size (not just unit price)?
• Which supplier is consistently reliable, not just occasionally cheapest?
• What changed versus the previous quote?

Example: you get two quotes for the same component. Supplier A is 8% cheaper but requires a higher MOQ and quotes 6 weeks lead time. Supplier B is slightly higher, the MOQ fits your cash plan, and they usually ship in 2 weeks. Without history, it’s easy to chase the lower price. With history, you can see Supplier A often slips and triggers paid air freight, making them the most expensive option in practice.

What data to capture for each supplier quote

The tracker is only as good as the fields you save. Capture the quote as it was offered (not just the “best” number) so you can explain decisions later and spot drift, like lead times creeping up or fees appearing.

Start with pricing in a way that prevents confusion:

• Unit price
• Price break quantity (often the MOQ)
• Extended price at that break (so you aren’t doing mental math every time)

Lead time also needs two forms:

• The stated lead time (for example, “4-6 weeks”)
• The promised ship or delivery date on the quote

Dates are what planning runs on. The stated range is still useful later when you compare promises to reality.

To make total cost comparisons fair, capture the extras that change the real spend:

• Shipping terms and estimated freight (who pays, how it ships, and the cost)
• Duties/taxes assumptions (if known), destination country/port
• Currency (and the exchange rate used, if you convert)
• Payment terms (Net 30, upfront, deposit split)
• Packaging, labeling, inspection, or tooling notes (one-time or per-order)

Finally, tie performance back to the same supplier and item: late deliveries versus promised date, quality issues (returns/defects), and communication speed. These can be simple tags or counters.

One rule matters most: don’t overwrite old quotes. Treat each quote as a new version with a date, who received it, and the source (email, portal, call). That’s what gives you a real history instead of a constantly edited snapshot.

How to compare quotes fairly (cost and speed rules)

A tracker only helps if every quote is compared using the same rules. Otherwise, the “cheapest” option is often the one with missing costs or an unrealistic delivery promise.

Cost rules that stay fair

Define a simple, consistent “total cost” that purchasing and finance both accept. Don’t stop at unit price. Use a repeatable estimate that includes the common add-ons:

• Unit price at the quoted break
• Freight/shipping (or a placeholder estimate if unknown)
• Duties/taxes/customs fees (if applicable)
• Packaging/labeling/inspection fees
• Payment fees (wire/card/platform) when they’re material

Then normalize the basics before ranking:

• Units of measure (per piece vs per box of 50)
• Pack sizes
• Currency (with the exchange rate rule you’re using)

MOQ is the usual trap. Compare suppliers at your expected order quantity, not at the tier that looks best on paper. If you typically buy 800 units, a quote with MOQ 2,000 should be priced at 2,000 units (because that’s what you’d have to pay) or clearly flagged as not workable for that order.

Speed rules and tie-breakers

For delivery speed, record lead time in days and a concrete readiness/ship date. Lead time ranges can be vague, but a date forces clarity.

Example: Supplier A is $1.90/unit with 30-day lead time and MOQ 500. Supplier B is $2.05/unit with 10-day lead time and MOQ 1,000. If you need 600 units next month, Supplier B’s MOQ forces you to buy 1,000 units. That changes the “real” spend and may erase the speed advantage.

When totals are close, decide tie-breakers ahead of time: on-time reliability, payment terms, and supplier status (preferred/approved). The point is consistency, so buyers aren’t reinventing the logic on every purchase.

A simple data model that stays usable as you grow

The tracker stays trustworthy when the data model is boring, consistent, and hard to “almost” fill out. Store every quote the same way, then connect it to what happened later.

Five core records cover most teams:

• Products/SKUs: SKU code, name, key specs, unit of measure, approved suppliers
• Suppliers: legal name, contacts, region, default currency, default terms
• Quotes: supplier, product, unit price, MOQ, lead time, quote date, validity window, key assumptions
• Orders/Shipments: what you placed and received (dates, quantities, paid price, delivery outcome)
• Attachments/Audit log: quote PDFs/emails/screenshots, plus who edited what and when

Keep Quotes separate from Orders. Quotes are promises. Orders are reality. Linking them lets you measure the gap between quoted lead time and actual delivery, or quoted price and invoice price.

A few small choices prevent chaos as volume grows:

• Use a unique ID for every quote
• Store dates as real dates (quoted on, valid until, expected ship)
• Keep currency explicit, and store an FX rate if you convert
• Treat MOQ and lead time as numbers (avoid free-text where possible)
• Lock edits after approval, but allow comments

Step by step: build the tracker workflow

The workflow has one job: adding a new quote should be faster than hunting through email.

Start with a single “New Quote” form that forces the fields people tend to skip: supplier, SKU, currency, unit price, MOQ, lead time, quote date, and expiry date. Add shipping and fixed fees if you have them.

After saving, auto-calculate total cost at a few quantities that match how you buy (for example: MOQ, typical order size, and a bulk quantity). This prevents the classic mistake where Supplier A looks cheaper until you remember their MOQ forces you to buy far more than you need.

For each SKU, show a simple ranked view based on rules you choose (for example, lowest total cost at typical quantity, then fastest delivery as a tie-breaker).

Two guardrails keep rankings honest:

• Expired quotes are clearly flagged (and can trigger a refresh task)
• If someone chooses a non-top-ranked supplier, they enter a short reason (quality, stock risk, terms, relationship)

That single “reason” field turns a gut call into a decision you can review later.

Getting existing quote history into the system

History only helps if it goes in cleanly. Start with the sources you already trust: spreadsheets, ERP exports, and email threads. You don’t need perfection on day one; you need enough history to see pricing patterns and lead time drift.

For CSV imports, keep one file per batch (for example, one month of RFQs). Normalize units and currencies before you import. “$12 per box of 10” and “$1.20 per unit” shouldn’t land as two unrelated prices.

Email and call quotes need a fast manual path. A short form usually beats copying into a spreadsheet. Stick to the fields that change decisions: supplier, SKU, date, price, currency, MOQ, lead time, validity window, and shipping terms.

Duplicates are common when the same quote is forwarded or re-sent. A practical unique check is supplier + SKU + quote date + MOQ (and shipping terms, if they meaningfully change cost). If a likely duplicate is detected, let the user choose: update the existing record or save a new revision.

Store enough “source context” to verify later: reference number, email subject/thread name, and the attachment file name.

Before you trust the imported data, run a few quick checks for the common failure modes:

• Lead time missing or written as “ASAP”
• MOQ off by 10x compared to the supplier’s usual range
• Currency doesn’t match the supplier’s normal billing
• Price entered without a unit (per piece vs per carton)
• Expired quotes imported as if they’re current

Example: if a buyer enters “14” for lead time, make them choose days or weeks. That one prompt prevents weeks of misleading comparisons.

Reports and views people actually use day to day

Views should answer real questions quickly: “Should I reorder now?”, “Who’s slipping on lead time?”, “Is this quote really cheaper once we factor everything in?” Build a small set of screens people come back to.

Start with these:

• Per-SKU price trend: unit price over time, plus total cost per unit (unit + freight + duties + other fees)
• Per-SKU quote timeline: each quote with supplier, MOQ, lead time, validity, and key notes
• Supplier performance summary: on-time rate, average lead time by lane/region, count of price increases
• Side-by-side comparison: filter by region, MOQ range, currency, and recency, then sort by total cost or delivery speed
• Last decision snapshot: winner, runner-up, and the recorded reason

Alerts work best when they’re specific and editable by category. For example: “Unit price up more than 5% vs last accepted quote,” or “Lead time drifted by more than 7 days across the last 3 quotes.”

Saved views make the tool feel fast. Two that usually stick are “Reorder this month” (SKUs below reorder point with valid quotes) and “New supplier review” (suppliers with limited history).

Common mistakes that make the tracker misleading

Most “bad rankings” happen because the system loses context, then people trust the output anyway.

The biggest mistake is overwriting old quotes. If you replace last month’s quote with today’s number, you lose the trend and you can’t explain why a supplier suddenly looks “better” or “worse.”

Another trap is comparing unit price only. A lower unit price can be irrelevant if MOQ forces extra inventory, or if freight and duties push the landed cost above the alternative.

Normalization errors also destroy trust. If one buyer enters “per kg” and another enters “per piece,” the math can look precise but be wrong. Add missing validity dates and you end up using expired pricing in today’s decisions.

Finally, rankings drift when you ignore actual delivery performance. If Supplier A promises 10 days but delivers in 18, your tracker should learn that, or it will keep recommending the wrong option.

Practical fixes:

• Store every quote as a new record with timestamp and source
• Compare total landed cost, including MOQ impact and freight
• Normalize currency, units, and pack sizes before ranking
• Require validity dates and clearly flag expired quotes
• Record promised versus actual delivery and use performance in scoring

Quick checklist before you trust the rankings

Before you send a “best option” summary to a manager, run a quick sanity check. It takes minutes and prevents choices based on incomplete data.

Make sure each quote record is complete and comparable:

• SKU/part number, supplier, quote date, unit of measure, currency, validity/expiry
• MOQ is captured, and you’re comparing at a clearly chosen quantity (for example, 500 units)
• Lead time is in days and (when possible) you also have a promised ship/ready date
• Total cost includes the items you actually pay (freight, packaging, tooling, bank/broker fees where relevant)
• Your ranking rule is written down and applied the same way every time

Then check consistency. If one supplier quotes per 1,000 pieces and another per piece, the ranking will be wrong unless you normalize units. Same for currency: pick one exchange-rate rule (spot rate on quote date, or a monthly rate) and stick to it.

Also be realistic about recency. A quote from 10 months ago might be useful for a trend line, but it rarely reflects today’s market.

Example: choosing between low price and fast delivery

You need to replenish a fast-moving SKU: 1,000 units per month. You have 10 days of stock left, and a stockout costs about $800 per day in missed sales and expediting.

Two suppliers reply:

Supplier A offers a lower unit price: $4.50, but the MOQ is 3,000 units and lead time is 30 days. Shipping is $600 per order.

Supplier B is pricier at $5.10, but the MOQ is 1,000 units and lead time is 10 days. Shipping is $400 per order.

If you compare unit price only, A wins. But total landed cost per unit for the order you actually have to place looks like this:

• Supplier A: (3,000 x $4.50 + $600) / 3,000 = $4.70 per unit, plus cash tied up in extra inventory
• Supplier B: (1,000 x $5.10 + $400) / 1,000 = $5.50 per unit

Now add timing. With only 10 days of stock, Supplier A arrives in 30 days, meaning roughly 20 days of stockout unless you find a bridge option. At $800 per day, 20 days is about $16,000 in stockout impact. That dwarfs the $800 difference in unit cost between these two orders.

So the best choice today is likely Supplier B, even though the unit price is higher. Next month, when you have 40 days of coverage, Supplier A might become the better option.

When you approve a quote, capture a short decision note so future reviews don’t rely on memory:

• Stock on hand and expected run rate
• The “must arrive by” date you used
• Any assumed stockout cost or expediting option
• What would change the decision next time (coverage, MOQ flexibility)

Next steps: roll it out without slowing purchasing down

Treat rollout like a pilot, not a big system change. The tracker only helps if buyers can use it during real purchasing work.

Start with a small, high-impact slice: about 20 top SKUs (or the parts that cause the most pain) and around 5 suppliers. That keeps the first pass clean, makes gaps obvious, and lets you tune the comparison rules before everyone depends on the ranking.

Agree early on two things: one scoring method, and one set of required fields. If people can save a quote without lead time, MOQ, currency, and validity date, the database fills up fast but the outputs won’t be trustworthy.

A light rollout plan:

• Week 1: capture new quotes only for pilot SKUs and suppliers
• Week 2: review results with buyers and fix confusing fields or rules
• Week 3: add approval only where it matters (high spend or new supplier)
• Week 4: expand the SKU list based on what the team actually orders

Reminders for expiring quotes, alerts for lead time spikes, and a weekly summary of “best current options” can keep momentum without adding busywork.

If you’re building the tracker as an internal app, AppMaster (appmaster.io) is one way to create the database, forms, and dashboards without writing code, while still generating production-ready backend, web, and mobile apps when you need them.