Tooling Cost of Ownership: What to Track and the Report

Key takeaways

• The cost of owning a tool is its purchase price plus regrinds, presetting, machine downtime at every change, scrap caused by wear, storage and disposal, minus any scrap credit.
• Tooling cost per part = total tooling cost ÷ good parts. Count edges or tool lives used, not boxes issued.
• In the worked example below, a €6 insert costs €0.177 per part and an €11 insert costs €0.131, because the cheap one needs 20 more changes per 3,000 parts.
• You need six data sources: item master, issue transactions, regrind orders, part counts per tool from the machine, changeover events and scrap reasons.
• The most common mistake is a report that shows purchase spend only. In the example, purchase was just 28% of the cheap insert's real cost.

What tooling cost of ownership means

Tooling cost of ownership is everything a cutting tool, die insert or gauge costs you from the day it arrives until the day it leaves the plant.

This guide covers inserts, drills, end mills, taps, die and mould inserts and gauges in machining and metalworking plants.

Purchasing sees the invoice. Production sees the machine standing still while an operator indexes an insert and checks the first part.

Only a report that joins both answers the real question: what does this tool cost us per good part?

If you are still designing where tools are stored and issued, start with our tool crib data model. This article builds the cost layer on top of it.

The full cost stack of a tool over its life

Each element below is a separate line in your data. If one is missing, the report quietly favours the tool that is cheapest to buy.

Carbide scrap usually has a resale value, so collect worn inserts and solid carbide tools separately. The credit reduces the total, but rarely changes which tool wins.

The formulas: per part and per machine hour

Use two formulas. They answer different questions, and each one can mislead on its own.

Tooling cost per part

Tooling cost per part = (purchase + regrind + presetting + changeover downtime + tool-caused scrap + handling − scrap credit) ÷ good parts

This is the number to compare two tools on the same operation. It includes machine time, so it rewards long tool life.

Tooling cost per machine hour

Direct tooling cost per run hour = (purchase + regrind + presetting − scrap credit) ÷ machine run hours

Controllers use this one to build the machine hour rate for quoting. Leave changeover downtime out of it, because a machine rate calculated over productive hours already recovers that time.

For dies and moulds, divide by output instead of hours: cost per part = total die cost over its life ÷ (strokes × parts per stroke, or shots × cavities).

Worked example: the cheap insert that costs more

A CNC lathe turns a steel shaft. The team compares a budget insert with a premium grade for one month of 3,000 good parts.

The numbers are round and illustrative, and presetting, handling and scrap credit are left out to keep the math short. Handling grows with issues (25 inserts against 15), so adding it would widen B's lead.

Step 1: edges and inserts used

• A: 3,000 ÷ 60 = 50 edges, so 50 ÷ 2 = 25 inserts.
• B: 3,000 ÷ 100 = 30 edges, so 30 ÷ 2 = 15 inserts.

Step 2: purchase cost

• A: 25 × €6.00 = €150, or 150 ÷ 3,000 = €0.050 per part.
• B: 15 × €11.00 = €165, or 165 ÷ 3,000 = €0.055 per part.

On purchase alone, B costs 10% more per part. This is where most tooling reports stop.

Step 3: changeover downtime

• A: 50 changes × 4 min = 200 min × €1.50 = €300.
• B: 30 changes × 4 min = 120 min × €1.50 = €180.

Step 4: scrap caused by tool changes and wear

• A: 50 ÷ 5 = 10 scrapped parts × €8 = €80.
• B: 30 ÷ 5 = 6 scrapped parts × €8 = €48.

Step 5: total cost per part

The premium insert saves €137 a month on this one operation, or €1,644 a year at the same volume. It also frees 80 machine minutes a month, 16 hours a year.

If this lathe is the bottleneck, those minutes are worth lost output, not just the machine rate, and the gap is larger. The example also holds cutting data equal, while a premium grade often runs faster and shortens the 1.5 minute cycle too.

Step 6: the same tools per machine hour

Run time for 3,000 parts is 3,000 × 1.5 = 4,500 min, or 75 hours, for both inserts.

• A: €150 ÷ 75 h = €2.00 per run hour.
• B: €165 ÷ 75 h = €2.20 per run hour.

Per run hour, A still looks cheaper. That is why you compare tools per good part and use the hourly figure only for the machine rate.

Regrindable tools add one more line. Our tool crib data model shows how three regrinds cut an end mill's cost per part by more than half.

The data you need and where each field comes from

You cannot calculate the example above from the purchasing system. You need six sources joined on tool, machine and part number.

Two values come from finance: the machine rate per hour and the value of a scrapped part at each operation. Agree them once and store them with a valid-from date.

The tool change record

The piece most plants lack is a record per tool change. It is what links the crib, the machine and quality.

change_reason matters as much as the count. A tool changed at 94 parts with END_OF_LIFE is normal wear, and one changed at 40 because it broke is a problem.

How to get tool life counts

Tool life is the denominator of the whole report. Pick the most automatic method your machines allow, and use operator entry only as the fallback.

1. The machine's own tool life counter

Many CNC controls have a tool life management function that counts parts or cutting minutes per tool number. When the operator resets it at a change, the count before the reset is the tool's life.

Read that value at every reset, through the control's data interface or by writing it down with the change.

2. PLC signals

Count the cycle complete signal and record every stop. A stop coded "tool change" with the tool number closes that tool's life and opens the next, while the other turret tools keep counting.

Our guide to CNC machine monitoring explains which signals to take and how to connect older machines.

3. Operator entry at the tool change

The operator scans the machine's QR code, picks the tool and the change reason, and the system reads the machine's part counter and subtracts the reading at that tool's last change.

Keep it under 20 seconds. A long form at the machine gets skipped, and then the report is built on guesses.

The monthly tooling cost report

One report, one month, the same layout every time. Build it from the joined data, not from the invoice total.

Report sections

Rank the top 10 by total €, not by € per part. A small job with a high rate matters less than a high-volume job a few cents over.

A Pareto chart of those ten rows usually shows where to look first.

KPIs for tooling cost

Report every KPI with its numerator and denominator. A percentage on its own hides whether it came from 5 tools or 500.

Tool-related downtime also sits inside your OEE. Tool changes reduce availability, and tool scrap reduces quality, as our OEE calculation guide shows.

To shorten the change itself, apply SMED: preset holders off the machine so the stop covers only the swap and the first-part check.

Common mistakes in tooling cost tracking

1. Counting only the purchase price

The worked example shows it. A report of spend per insert picked the tool that cost 35% more per part (€0.177 against €0.131).

2. Not linking tool changes to machine time

If tool changes are not recorded as stops with a reason, their minutes disappear into general downtime.

Give tool change and tool breakage their own downtime reason codes, so the minutes land on the right tool.

3. Averaging tool life across materials

Say one insert makes 100 parts per edge in C45 steel and 40 in stainless, on 2,000 and 1,000 parts a month.

The plain average of 70 parts per edge predicts 3,000 ÷ 70 = 43 edges. The real need is 2,000 ÷ 100 + 1,000 ÷ 40 = 20 + 25 = 45 edges, so set std_life per material.

4. Counting boxes, not tool lives

Issues show that ten inserts left the crib. They do not show whether each was used on both edges or thrown away after one.

Divide parts by tool lives from the change records, not by issues.

5. Using today's price for last year

Store the cost on the transaction. A report that multiplies old consumption by today's price rewrites your history.

6. Ignoring scrap that the tool caused

If scrap has no tool reason, it lands on the operator or the material. Link it to the tool change record with the scrap rate formula you already use.

Where tooling software fits

Dedicated tool management systems handle presetting data, tool assemblies and crib issues. Our roundups of tool crib management software and tool and mould management software compare them.

The cost report above still needs machine data: part counts, stops and scrap. That is the half most crib systems do not collect.

How Fabrico helps

Fabrico is an OEE platform with a full CMMS built in. It does not have a tool life management module, it does not build the tooling cost report for you, and it does not reorder stock automatically.

What it does provide is the machine half of the data. Through PLC connections, IoT sensors and AI cameras it records part counts and every stop, including short ones, and your team can give tool changes their own stop reasons.

It calculates OEE in real time, so tool change minutes show up in availability. Export the machine data to Excel and join it with your crib, regrind and scrap records to build the report above.

Its inventory holds inserts and tools with min and max levels, records what each maintenance work order consumes, and uses QR codes on machines and parts.

Inserts issued to production still need your crib log. Your team places the orders.

Want to see your tool change minutes measured at the machine? Book a 30 minute demo with a Fabrico consultant, no commitment, or contact us with your questions.