Exhibits

01

Most plants run well below their capacity

Exhibit 1 · The capacity you already own

US manufacturing runs 2.5 points below its own long-run average

Capacity utilization, US manufacturing, selected readings, percent of capacity, seasonally adjusted

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US manufacturing runs 2.5 points below its own long-run average (Capacity utilization, US manufacturing, selected readings, percent of capacity, seasonally adjusted)
Item	Value	Unit	Cite
1988–89 high	85.5	percent of capacity	`CAP-003`
1994–95 high	84.6	percent of capacity	`CAP-003`
Long-run average, 1972–2025	78.2	percent of capacity	`CAP-003`
1990–91 low	77.2	percent of capacity	`CAP-003`
August 2026 (preliminary)	75.7	percent of capacity	`CAP-001`
2009 low	63.4	percent of capacity	`CAP-003`

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Note: August 2026 is a preliminary estimate. Manufacturing as defined by the Federal Reserve. Highs and lows are cycle readings shown in the release.

Source: Board of Governors of the Federal Reserve System, G.17 Industrial Production and Capacity Utilization, release of September 18, 2026, summary and summary table (CAP-001, CAP-003).

From: The capacity you already own, Exhibit 1

Exhibit 2 · The capacity you already own

The average plant operates 66 of the 168 hours in a week

Average plant hours per week in operation, US manufacturing, Q2 2026, hours; shift references assume 8-hour shifts, five days a week

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The average plant operates 66 of the 168 hours in a week (Average plant hours per week in operation, US manufacturing, Q2 2026, hours; shift references assume 8-hour shifts, five days a week)
Item	Value	Unit	Cite
Average plant, hours operated	66	hours per week	`CAP-006`
Hours in a week	168	hours per week	`calendar`
Hours not operated	102	hours per week	`TC-D-001`

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Note: Standard error 0.9 hours. Continuous-process industries (for example petroleum refineries, paperboard mills) report 168 hours. Shift references assume 8-hour shifts, 5 days a week.

Source: U.S. Census Bureau, Quarterly Survey of Plant Capacity Utilization, 2026 Quarter 2, Table 1 (CAP-006). Hours not operated: Trapped Capacity derived figure TC-D-001; shift references are arithmetic.

From: The capacity you already own, Exhibit 2

Exhibit 3 · The capacity you already own

Fourteen of 19 industries run below their own long-run averages

Capacity utilization by manufacturing industry, August 2026 (preliminary) against each industry's 1972–2025 average, percent of capacity, seasonally adjusted

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Fourteen of 19 industries run below their own long-run averages (Capacity utilization by manufacturing industry, August 2026 (preliminary) against each industry's 1972–2025 average, percent of capacity, seasonally adjusted)
Item	Value	Unit	Cite
Furniture, August 2026	67.2	percent of capacity	`CAP-026`
Furniture, 1972-2025 average	77.4	percent of capacity	`CAP-026`
Furniture, gap	-10.2	percentage points	`TC-D-005-01`
Printing, August 2026	68.6	percent of capacity	`CAP-026`
Printing, 1972-2025 average	78.7	percent of capacity	`CAP-026`
Printing, gap	-10.1	percentage points	`TC-D-005-02`
Primary metals, August 2026	67.3	percent of capacity	`CAP-026`
Primary metals, 1972-2025 average	77.1	percent of capacity	`CAP-026`
Primary metals, gap	-9.8	percentage points	`TC-D-005-03`
Paper, August 2026	78.0	percent of capacity	`CAP-026`
Paper, 1972-2025 average	86.5	percent of capacity	`CAP-026`
Paper, gap	-8.5	percentage points	`TC-D-005-04`
Plastics and rubber, August 2026	75.2	percent of capacity	`CAP-026`
Plastics and rubber, 1972-2025 average	81.6	percent of capacity	`CAP-026`
Plastics and rubber, gap	-6.4	percentage points	`TC-D-005-05`
Motor vehicles and parts, August 2026	69.0	percent of capacity	`CAP-026`
Motor vehicles and parts, 1972-2025 average	74.4	percent of capacity	`CAP-026`
Motor vehicles and parts, gap	-5.4	percentage points	`TC-D-005-06`
Textiles, August 2026	72.1	percent of capacity	`CAP-026`
Textiles, 1972-2025 average	77.3	percent of capacity	`CAP-026`
Textiles, gap	-5.2	percentage points	`TC-D-005-07`
Chemicals, August 2026	71.7	percent of capacity	`CAP-026`
Chemicals, 1972-2025 average	76.9	percent of capacity	`CAP-026`
Chemicals, gap	-5.2	percentage points	`TC-D-005-08`
Wood, August 2026	72.2	percent of capacity	`CAP-026`
Wood, 1972-2025 average	76.7	percent of capacity	`CAP-026`
Wood, gap	-4.5	percentage points	`TC-D-005-09`
Apparel and leather, August 2026	71.3	percent of capacity	`CAP-026`
Apparel and leather, 1972-2025 average	75.7	percent of capacity	`CAP-026`
Apparel and leather, gap	-4.4	percentage points	`TC-D-005-10`
Food, beverage and tobacco, August 2026	77.3	percent of capacity	`CAP-026`
Food, beverage and tobacco, 1972-2025 average	80.2	percent of capacity	`CAP-026`
Food, beverage and tobacco, gap	-2.9	percentage points	`TC-D-005-11`
Computers and electronics, August 2026	75.8	percent of capacity	`CAP-026`
Computers and electronics, 1972-2025 average	77.1	percent of capacity	`CAP-026`
Computers and electronics, gap	-1.3	percentage points	`TC-D-005-12`
Miscellaneous, August 2026	76.2	percent of capacity	`CAP-026`
Miscellaneous, 1972-2025 average	77.1	percent of capacity	`CAP-026`
Miscellaneous, gap	-0.9	percentage points	`TC-D-005-13`
Fabricated metals, August 2026	78.3	percent of capacity	`CAP-026`
Fabricated metals, 1972-2025 average	78.5	percent of capacity	`CAP-026`
Fabricated metals, gap	-0.2	percentage points	`TC-D-005-14`
Aerospace and other transportation, August 2026	73.4	percent of capacity	`CAP-026`
Aerospace and other transportation, 1972-2025 average	73.4	percent of capacity	`CAP-026`
Aerospace and other transportation, gap	0.0	percentage points	`TC-D-005-15`
Petroleum and coal products, August 2026	87.9	percent of capacity	`CAP-026`
Petroleum and coal products, 1972-2025 average	85.5	percent of capacity	`CAP-026`
Petroleum and coal products, gap	2.4	percentage points	`TC-D-005-16`
Electrical equipment, August 2026	85.8	percent of capacity	`CAP-026`
Electrical equipment, 1972-2025 average	81.7	percent of capacity	`CAP-026`
Electrical equipment, gap	4.1	percentage points	`TC-D-005-17`
Machinery, August 2026	83.4	percent of capacity	`CAP-026`
Machinery, 1972-2025 average	78.2	percent of capacity	`CAP-026`
Machinery, gap	5.2	percentage points	`TC-D-005-18`
Nonmetallic minerals, August 2026	82.5	percent of capacity	`CAP-026`
Nonmetallic minerals, 1972-2025 average	74.0	percent of capacity	`CAP-026`
Nonmetallic minerals, gap	8.5	percentage points	`TC-D-005-19`

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Note: August 2026 is preliminary and subject to annual revision on November 24, 2026. Industries sorted by gap; the count is unweighted, so a small industry counts the same as a large one.

Source: Board of Governors of the Federal Reserve System, G.17 Industrial Production and Capacity Utilization, release of September 18, 2026, Table 7 (CAP-026). Gaps and count: Trapped Capacity derived figures TC-D-004, TC-D-005.

From: The capacity you already own, Exhibit 3

02

Why plants run below capability

1 exhibit · Read the chapter

Exhibit 4 · The capacity you already own

Plants cite weak orders first, then labor and equipment

Reasons for operating below full production capability, US manufacturing plants, Q2 2026, percent of plants citing at least one reason

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Plants cite weak orders first, then labor and equipment (Reasons for operating below full production capability, US manufacturing plants, Q2 2026, percent of plants citing at least one reason)
Item	Value	Unit	Cite
Insufficient orders	67.1	percent of plants	`CAP-027`
Insufficient supply of labor	22.2	percent of plants	`CAP-027`
Equipment limitations	11.5	percent of plants	`CAP-027`
Insufficient materials	11.3	percent of plants	`CAP-027`
Not profitable at full capacity	10.5	percent of plants	`CAP-027`
Seasonal operations	9.0	percent of plants	`CAP-027`
Sufficient finished-goods inventory	7.2	percent of plants	`CAP-027`
Storage limitations	6.3	percent of plants	`CAP-027`
Logistics or transportation	3.5	percent of plants	`CAP-027`
Environmental restrictions	1.6	percent of plants	`CAP-027`
Lack of fuel or energy	0.7	percent of plants	`CAP-027`
Other	10.7	percent of plants	`CAP-027`

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Note: Plants may cite more than one reason, so shares overlap and do not sum to 100. Standard errors 0.3 to 1.5 points. Strike or work stoppage withheld by Census. Ultramarine marks the two limits discussed in the text.

Source: U.S. Census Bureau, Quarterly Survey of Plant Capacity Utilization, 2026 Quarter 2, Table 3b (CAP-027).

From: The capacity you already own, Exhibit 4

03

Capital spending and productivity growth

2 exhibits · Read the chapter

Exhibit 5 · The capacity you already own

Manufacturers spent $314.3 billion on structures and equipment in 2022

Capital expenditures, US manufacturing companies with employees, 2021 (revised) and 2022, billions of current dollars

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Manufacturers spent $314.3 billion on structures and equipment in 2022 (Capital expenditures, US manufacturing companies with employees, 2021 (revised) and 2022, billions of current dollars)
Item	Value	Unit	Cite
2021 equipment	215.5	USD billion	`CAP-017`
2021 structures	68.6	USD billion	`CAP-017`
2021 total	284.2	USD billion	`CAP-017`
2022 equipment	237.8	USD billion	`CAP-017`
2022 structures	76.5	USD billion	`CAP-017`
2022 total	314.3	USD billion	`CAP-017`

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Note: Nominal dollars, not adjusted for inflation. Components may not sum to totals because of rounding. The survey has since been folded into the Annual Integrated Economic Survey; 2022 is its latest year.

Source: U.S. Census Bureau, 2022 Annual Capital Expenditures Survey, manufacturing table (CAP-017).

From: The capacity you already own, Exhibit 5

Exhibit 6 · The capacity you already own

Output per hour has grown 0.5% a year this cycle, against 2.1% long-term

Labor productivity (output per hour worked), US manufacturing sector, average annual growth, percent

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Output per hour has grown 0.5% a year this cycle, against 2.1% long-term (Labor productivity (output per hour worked), US manufacturing sector, average annual growth, percent)
Item	Value	Unit	Cite
Long-term rate	2.1	percent a year	`CAP-024`
Previous business cycle	0.1	percent a year	`CAP-024`
Current business cycle	0.5	percent a year	`CAP-024`

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Note: Business-cycle comparison as published by BLS; the current cycle is still open. Rates are averages for the whole sector, not for any plant.

Source: U.S. Bureau of Labor Statistics, Productivity and Costs, Second Quarter 2026, Revised, news release of September 3, 2026, manufacturing section (CAP-024).

From: The capacity you already own, Exhibit 6

04

Losses inside scheduled hours

3 exhibits · Read the chapter

Exhibit 7 · The capacity you already own

Downtime took 7.8% of planned production time in the NIST sample

Downtime as a share of planned production time (NIST survey question 9), US discrete manufacturers, annual values for 2016, percent

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Downtime took 7.8% of planned production time in the NIST sample (Downtime as a share of planned production time (NIST survey question 9), US discrete manufacturers, annual values for 2016, percent)
Item	Value	Unit	Cite
Lowest stratum	4.0	percent of planned production time	`CAP-012`
Average, all surveyed establishments	7.8	percent of planned production time	`CAP-012`
Highest stratum	12.8	percent of planned production time	`CAP-012`

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Note: Self-selected sample: 85 responses returned by maintenance managers, some dropped. The question asks for downtime as a share of planned time; NIST reads it as unplanned. Range shows the lowest and highest survey strata (NAICS 321-339, excluding 324 and 325).

Source: National Institute of Standards and Technology, Economics of Manufacturing Machinery Maintenance (NIST AMS 100-34), D. S. Thomas and B. A. Weiss, 2020, Table 5.1 (CAP-012).

From: The capacity you already own, Exhibit 7

Exhibit 8 · The capacity you already own

NIST's lost-sales estimate is large but uncertain

Maintenance costs and revenue at risk, US discrete manufacturing, NIST estimates, annual values for 2016, billions of dollars

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NIST's lost-sales estimate is large but uncertain (Maintenance costs and revenue at risk, US discrete manufacturing, NIST estimates, annual values for 2016, billions of dollars)
Item	Value	Unit	Cite
Direct maintenance spending	57.3	USD billion (2016)	`CAP-008`
Total maintenance-related costs	74.5	USD billion (2016)	`CAP-008`
Downtime cost	18.1	USD billion (2016)	`CAP-007`
Defect cost	0.8	USD billion (2016)	`CAP-007`
Sales lost to delays and defects	100.2	USD billion (2016)	`CAP-007`
Lost sales, 90% CI low	33.5	USD billion (2016)	`TC-V-001`
Lost sales, 90% CI high	166.8	USD billion (2016)	`TC-V-001`
Lost sales, stratified estimate	88.3	USD billion (2016)	`TC-V-001`

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Note: Whisker: NIST 90% confidence interval for lost sales, $33.5B to $166.8B; ink mark: NIST's estimate by size and industry strata, $88.3B. Lost sales are revenue, not profit, some of it moved to other US plants; they are not added to the cost rows. Self-selected sample of 85 returned responses.

Source: National Institute of Standards and Technology, Economics of Manufacturing Machinery Maintenance (NIST AMS 100-34), D. S. Thomas and B. A. Weiss, 2020, Executive Summary, Section 5.3 and Table 5.3 (CAP-007, CAP-008, TC-V-001).

From: The capacity you already own, Exhibit 8

Exhibit 9 · The capacity you already own

The most reactive plants had 3.3 times the downtime and 16 times the defects

Downtime (share of planned production time) and defect rate, establishments most and least reliant on reactive maintenance (top and bottom quartile), US discrete manufacturing, NIST self-selected sample, annual values for 2016, percent

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The most reactive plants had 3.3 times the downtime and 16 times the defects (Downtime (share of planned production time) and defect rate, establishments most and least reliant on reactive maintenance (top and bottom quartile), US discrete manufacturing, NIST self-selected sample, annual values for 2016, percent)
Item	Value	Unit	Cite
Downtime, least reactive quartile	4.0	percent of planned time	`TC-V-002`
Downtime, most reactive quartile	13.0	percent of planned time	`TC-V-002`
Defect rate, least reactive quartile	0.2	percent	`TC-V-002`
Defect rate, most reactive quartile	3.3	percent	`TC-V-002`
Downtime ratio	3.3	ratio	`CAP-010`
Defect ratio	16.0	ratio	`CAP-010`

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Note: Associations from 85 returned responses, not causal effects. Small bases: a 16x ratio on defect rates of 0.2% and 3.3%. Each panel has its own scale.

Source: National Institute of Standards and Technology, Economics of Manufacturing Machinery Maintenance (NIST AMS 100-34), D. S. Thomas and B. A. Weiss, 2020, Executive Summary and Table 7.1 (CAP-010, TC-V-002).

From: The capacity you already own, Exhibit 9

05

Putting a price on lost hours

4 exhibits · Read the chapter

Exhibit 10 · The capacity you already own

In this illustrative line, four losses take 1,312 of 6,000 planned hours

Illustrative data

Planned production time and losses, one constraint line, one year, hours

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In this illustrative line, four losses take 1,312 of 6,000 planned hours (Planned production time and losses, one constraint line, one year, hours)
Item	Value	Unit	Cite
Planned time	6000	hours per year	`Illustrative`
Changeovers	-500.0	hours per year	`Illustrative`
Unplanned stops	-300.0	hours per year	`Illustrative`
Slow running	-416.0	hours per year	`Illustrative`
Scrap	-95.7	hours per year	`Illustrative`
Fully productive time	4688.3	hours per year	`Illustrative`

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Note: Not a description of any real plant. Replace the assumptions with your own.

Source: Illustrative data. Trapped Capacity illustrative line model; assumptions are listed in Chapter 5.

From: The capacity you already own, Exhibit 10

Exhibit 11 · The capacity you already own

In this illustrative line, the four losses cost $1.65 million a year

Illustrative data

Annual cost of losses, one constraint line, thousands of dollars of contribution, plus variable cost of scrapped units

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In this illustrative line, the four losses cost $1.65 million a year (Annual cost of losses, one constraint line, thousands of dollars of contribution, plus variable cost of scrapped units)
Item	Value	Unit	Cite
Changeovers, contribution	600.0	USD thousand per year	`Illustrative`
Slow running, contribution	499.2	USD thousand per year	`Illustrative`
Unplanned stops, contribution	360.0	USD thousand per year	`Illustrative`
Scrap, contribution	114.8	USD thousand per year	`Illustrative`
Scrap, variable cost of scrapped units	76.5	USD thousand per year	`Illustrative`
Total	1650.6	USD thousand per year	`Illustrative`

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Note: Prices apply only on a constraint line with demand above output. A scrapped unit forfeits the contribution of its line time and its variable cost. Not a description of any real plant.

Source: Illustrative data. Trapped Capacity illustrative line model: price $2.00 and variable cost $0.80 a unit ($1.20 contribution), 1,000 units an hour ($1,200 a constraint hour).

From: The capacity you already own, Exhibit 11

Exhibit 12 · The capacity you already own

Value added per production-worker hour ranged from $62 to $1,022 in 2021

Value added per production-worker hour, US manufacturing industries (three-digit NAICS), 2021, dollars

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Value added per production-worker hour ranged from $62 to $1,022 in 2021 (Value added per production-worker hour, US manufacturing industries (three-digit NAICS), 2021, dollars)
Item	Value	Unit	Cite
Petroleum and coal	1022	USD value added per production-worker hour, 2021	`TC-D-006-01`
Chemicals	489	USD value added per production-worker hour, 2021	`TC-D-006-02`
Beverage and tobacco	436	USD value added per production-worker hour, 2021	`TC-D-006-03`
Computers and electronics	273	USD value added per production-worker hour, 2021	`TC-D-006-04`
Primary metals	247	USD value added per production-worker hour, 2021	`TC-D-006-05`
All manufacturing	180	USD value added per production-worker hour, 2021	`TC-D-006-06`
Paper	174	USD value added per production-worker hour, 2021	`TC-D-006-07`
Electrical equipment	167	USD value added per production-worker hour, 2021	`TC-D-006-08`
Machinery	160	USD value added per production-worker hour, 2021	`TC-D-006-09`
Miscellaneous	160	USD value added per production-worker hour, 2021	`TC-D-006-10`
Transportation equipment	151	USD value added per production-worker hour, 2021	`TC-D-006-11`
Food	147	USD value added per production-worker hour, 2021	`TC-D-006-12`
Nonmetallic minerals	134	USD value added per production-worker hour, 2021	`TC-D-006-13`
Wood	122	USD value added per production-worker hour, 2021	`TC-D-006-14`
Plastics and rubber	118	USD value added per production-worker hour, 2021	`TC-D-006-15`
Fabricated metals	114	USD value added per production-worker hour, 2021	`TC-D-006-16`
Printing	97	USD value added per production-worker hour, 2021	`TC-D-006-17`
Textile mills	93	USD value added per production-worker hour, 2021	`TC-D-006-18`
Furniture	86	USD value added per production-worker hour, 2021	`TC-D-006-19`
Textile products	84	USD value added per production-worker hour, 2021	`TC-D-006-20`
Leather	71	USD value added per production-worker hour, 2021	`TC-D-006-21`
Apparel	62	USD value added per production-worker hour, 2021	`TC-D-006-22`

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Note: Current (2021) dollars: value added reflects product prices as well as productivity. Value added includes labor, overhead and profit, so it is not contribution; a line hour uses several production-worker hours. 2021 is the last ASM year.

Source: U.S. Census Bureau, 2018–2021 Annual Survey of Manufactures, Statistics for Industry Groups and Industries (AM1831BASIC01), fields VALADD and HOURS (CAP-029). Ratios: Trapped Capacity derived figures TC-D-006.

From: The capacity you already own, Exhibit 12

Exhibit 13 · The capacity you already own

In the illustrative case, the cheapest hour depends on how many the plant needs

Illustrative data

Annual cost per productive constraint hour actually needed, by option and by hours needed a year, illustrative line, dollars per hour

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In the illustrative case, the cheapest hour depends on how many the plant needs (Annual cost per productive constraint hour actually needed, by option and by hours needed a year, illustrative line, dollars per hour)
Item	Value	Unit	Cite
need 300 h: recover	1000	USD per needed hour	`Illustrative`
need 300 h: schedule	1024	USD per needed hour	`Illustrative`
need 300 h: build	10510	USD per needed hour	`Illustrative`
need 300 h: stack	1000	USD per needed hour	`Illustrative`
need 1000 h: recover	cannot cover	USD per needed hour	`Illustrative`
need 1000 h: schedule	1024	USD per needed hour	`Illustrative`
need 1000 h: build	3153	USD per needed hour	`Illustrative`
need 1000 h: stack	988	USD per needed hour	`Illustrative`
need 2500 h: recover	cannot cover	USD per needed hour	`Illustrative`
need 2500 h: schedule	cannot cover	USD per needed hour	`Illustrative`
need 2500 h: build	1261	USD per needed hour	`Illustrative`
need 2500 h: stack	cannot cover	USD per needed hour	`Illustrative`
need 4688 h: recover	cannot cover	USD per needed hour	`Illustrative`
need 4688 h: schedule	cannot cover	USD per needed hour	`Illustrative`
need 4688 h: build	673	USD per needed hour	`Illustrative`
need 4688 h: stack	cannot cover	USD per needed hour	`Illustrative`

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Note: Recover: $300k a year program, up to 328 h (25% of losses). Schedule: weekend hours at $800 per scheduled hour, $1,024 per productive hour, up to 1,875 h. Build: $12M line, 10 years, 10% cost of capital, $1.2M a year to run; adds 4,688 h.

Source: Illustrative data. Trapped Capacity illustrative model; assumptions in Chapter 5 and formulas in the method notes.

From: The capacity you already own, Exhibit 13

06

The Hidden Line Test

1 exhibit · Read the chapter

Exhibit 14 · The capacity you already own

A capital request should show seven figures, demand first

A gate for any request to add capacity; the sponsor shows each figure for the constraint asset

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A capital request should show seven figures, demand first (A gate for any request to add capacity; the sponsor shows each figure for the constraint asset)
Item	Value	Unit	Cite
Demand	Orders or forecast that exceed the constraint's output	Pass: Backlog or lost orders, documented \| Borderline: Forecast only \| Fail: Not shown	`TC method`
The constraint	The asset that limits output, shown with output and queue data	Pass: Named and measured for 4 weeks or more \| Borderline: Named from estimates \| Fail: Not named	`TC method`
Unscheduled hours	Hours the constraint is not scheduled, and the full cost of scheduling them	Pass: Hours and full cost of added time \| Borderline: Hours only \| Fail: Not considered	`TC method`
Losses in hours	Hours lost in scheduled time last quarter, by cause	Pass: Recorded by cause, in hours \| Borderline: Sampled or partly recorded \| Fail: Percentages only	`TC method`
Price of an hour	Contribution per constraint hour	Pass: Agreed in writing with the controller \| Borderline: Estimated by operations \| Fail: Revenue per hour, or none	`TC method`
Recoverable hours	Hours recoverable within 12 months	Pass: A range, an owner and a plan per loss \| Borderline: A single estimate, no owner \| Fail: Not stated	`TC method`
Cost per needed hour	Capital cost per needed hour against cost per recovered hour	Pass: Both on one page \| Borderline: One of the two \| Fail: Neither	`TC method`

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Note: Pass rules are editorial judgment, not industry benchmarks; adjust them to your own hurdle rates. Formula for figure 7 in the method notes.

Source: Trapped Capacity method.

From: The capacity you already own, Exhibit 14

+

From insights

Exhibit 1 · Changeovers are a capital question

In this illustrative plant, the same changeover runs longest on the night shift

Illustrative data

Average changeover duration by shift, one product family, minutes

Show data table

In this illustrative plant, the same changeover runs longest on the night shift (Average changeover duration by shift, one product family, minutes)
Category	Value
Day	42 min
Swing	48 min
Night	66 min
Standard	40 min

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Note: Values are placeholders chosen to demonstrate the exhibit format. Standard is the documented changeover time.

Source: Illustrative data; placeholder pending research

From: Changeovers are a capital question

Exhibit 1 · Read OEE like a finance partner

In this illustrative line, availability losses outweigh performance and quality losses combined

Illustrative data

OEE losses by category, one line, one year, percentage points of planned time

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In this illustrative line, availability losses outweigh performance and quality losses combined (OEE losses by category, one line, one year, percentage points of planned time)
Category	Value
Availability	18 pp
Performance (speed)	9 pp
Quality	4 pp

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Note: Values are placeholders chosen to demonstrate the exhibit format. OEE is overall equipment effectiveness; performance loss is time lost to running below rated speed.

Source: Illustrative data; placeholder pending research

From: Read OEE like a finance partner