SENSEYE PREDICTIVE MAINTENANCE

The True Cost of
Downtime 2022
How much do leading manufacturers lose throughinefficient maintenance?

Find out more: siemens.com/senseye-predictive-maintenanceIntroduction
Unplanned downtime is costing manufacturers
significantly more than even two years ago. The costof every hour of downtime has soared so fast that,
despite there being fewer incidents, the overall cost ofdowntime to manufacturers is rising sharply.
These are the key findings of this report – our second
investigation into the true cost of downtime inmanufacturing, covering the period 2021-22, andupdating of our findings for 2019–20.
This new report suggests that unplanned downtime
now costs Fortune Global 500 companies 11% of
their yearly turnover – almost $1.5tn. This is up from$864bn (8% of turnover) two years ago. Among
Fortune Global 500 companies, the annual cost ofdowntime is now $129 million per facility, up 65% on
our last survey in 2019-20.
Breaking the costs down by sector, the cost of a lost
hour now ranges from an average of $39,000 for
factories producing Fast Moving Consumer Goods, tomore than $2m an hour in Automotive.
Companies must therefore minimize unplanned
downtime. But they also want to avoid the significant
costs of over-maintenance – shutting lines unnecessarily
and holding extensive inventories of costly andpotentially unnecessary spares. Predictive Maintenanceis the strategy firms are turning to – a solution provento cut downtime in half without the downsides ofover-maintenance.

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This report covers the period 2021-22 and updates ourlast survey, which covered 2019 and 2020. It answers
three critical questions in the field of manufacturing
maintenance:• What is the true cost of downtime for largemanu­facturing and industrial businesses?
• What savings could firms make by adopting
technology enabling Predictive Maintenance?
• How many firms have adopted Predictive
Maintenance strategies, and how many others areready to do so?
We answer these questions using the results of an
extensive survey of large manufacturing and industrialorganizations around the world. The results are striking.

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Executive Summary• The costs of unplanned downtime are soaring. In every sector surveyed,
an hour’s unplanned downtime now costs the manufacturer at least
50% more than it did two years ago.
• The cost of an hour’s downtime in an automotive plant is now morethan $2m – up from $1.3m in 2019–20.
• In Oil & Gas, the cost of an hour’s downtime has more than doubled injust two years, to almost $500,000.
• Inflation and stressed supply chains are key factors in increasing the costof an hour’s downtime.
• Total losses to downtime are also rising sharply. We estimate that the cost
for an average large plant in the sectors surveyed is now $129m a year –up 65% in just two years.
• We estimate that Fortune Global 500 industrial organizations lose almost$1.5tn a year through unplanned downtime. This is a 65% rise in two
years, and constitutes 11% of these firms’ turnover.
• This rise in total losses could easily have been worse. It has been minimized
by firms – with the help of Predictive Maintenance strategies and Industry
4.0 technologies – cutting the number of unplanned-downtime incidents
they suffer.
• Among respondents, the average manufacturing facility suffers 20
downtime incidents a month – six fewer than two years ago.
• An average large plant still loses 25 hours a month to unplanned downtime –more than a full day’s production.
• To counter these kinds of losses, Condition Monitoring and Predictive
Maintenance applications have now become indispensable mainstream
technologies.
• More than three-quarters of respondents are doing some form of condition
monitoring, and a similar proportion see predictive maintenance as a
strategic priority.
• The proportion of firms with an in-house Predictive Maintenance teamhas risen sharply to a third.

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The true impact of
unplanned downtime
on manufacturers
The cost of unplanned downtime to large manufacturing
and industrial organizations has rocketed since 2020.
This is the key conclusion of our 2022 True Cost Of
Downtime survey.

1 Hourof unplanned downtime costs manufacturers at least
50% more today than it did in the period 2019–20,
due to spiraling inflation and production lines runningat higher capacity.

$2 million

lost per hour in downtime within the automotive
industry last year.

These figures come from extrapolating the results of asurvey by Senseye Predictive Maintenance in 2021–22,
asking large manufacturing and industrial businesses
about unplanned downtime and its costs, as well as
about how they collect data, and their use of conditionbased maintenance and Predictive Maintenance.

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Hourly costs ofdowntime up at least
50% in just two years$2 million an hourcosts of unplanned downtime in certain sectors.

50% rise in costsof an hour’s downtime in every sector surveyed,compared with 2019–20. Downtime is getting morecostly – much more costly and very fast.

In every sector
surveyed

an hour’s downtime now costs a firm at least 50% morethan it did two years ago. Unplanned downtime is a dragon firms’ profits that businesses can no longer ignore.

Cost of one hour's unplanned plant downtime
Automotive

Oil & Gas
2021 / 22

Heavy Industry

2019 / 20

FMCG / CPG$0© SIEMENS 2023

$ 500,000$ 1,000,000$ 1,500,000$ 2,000,000$ 2,500,000

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The cost to firms
reflects the following:• Loss of revenue (the loss of goods that would
have been produced for sale in that period)• The cost of wages (paying staff who can’t work)• The cost of wages of those involved in rectifying
the problem• The cost of emergency spare parts• Any penalties incurred, such as contractual
compensation© Siemens©
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Senseye Predictive Maintenance estimates thatthe full adoption of condition monitoring andpredictive maintenance practices at Fortune
Global 500 industrial organizations could enable:

1.6 million hoursof downtime saved annually$734 billion savings
through a 6% increase in productivity$236 billion savings
through a 40% reduction in maintenance costs© Siemens©
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Why is the cost of anhour’s downtime rising?
Firstly, pent-up post-lockdown demand means many factories have been
operating at full capacity and are producing more products. Any interruption
to production because of unplanned downtime means significant delays inmeeting customer demand, and this costs money.
The exception to lines running at full capacity is the automotive sector.
Here, despite strong demand, a shortage of components has been capping
production levels.
The demand challenge is especially acute for electronic manufacturers,
businesses providing components for both consumer and industrial applications.
The global microchip shortage has led to even more pent up demand in this
sector, and lines are running 24/7 to keep up. Any time lost is therefore notrecoverable later on. Besides consumer demand, the needs of businessdigitization and Industry 4.0 strategies are exacerbating the problem. This
surge in demand is likely to last at least another 18 months.
Inflation is also a key factor. Manufactured and processed goods are getting
more expensive, with higher sales prices. Naturally, that raises the cost ofdowntime. For instance, losing an hour’s oil production that you can sell for$115 a barrel (June 2022) is much more costly than losing an hour when oil
was trading at $30 a barrel (March 2020).

$115 a barrel
There are also sector-specific factors at play.
In automotive, the average number of unplanned downtime incidents ismarkedly down (16 a month, compared to 30 two years ago), largely becauselines are not running at full capacity. However, those incidents that do occur
remain cripplingly costly. The growing complexity and interdependence of
auto-production systems mean downtime in one process can halt production
across a big part of a plant. Vehicle prices are also increasing, and plants are
getting bigger, deepening the losses incurred when production lines stop.
These factors help explain why the cost of an hour’s downtime has also risen
sharply in automotive and now tops $2m per hour. Despite this, the key issue
in the sector today is less downtime than the global silicon shortage, which isresulting in lines lying silent much of the time, despite demand for vehicles.
Without silicon chips, producers cannot make vehicles.

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In FMCG, the cost of an hour’s downtime is rising because of a different set ofchallenges. As retailers protect their own positions with bigger non-delivery
penalties, businesses in this sector are facing a higher penalty when downtime
means they cannot supply those retailers in time. A second issue is FMCG
firms’ onward supply chain stopping working with them if they repeatedly
can’t deliver on time, leading to higher costs. They can also easily lose shelf
space at supermarkets if downtime means products are regularly unavailable– again this is a serious business cost. And the end customer (the shopper)
can move to rival brands because of delays in availability of product. This too
is making downtime ever more costly.

16 per monthaverage number of unplanned downtime
incidents in automotive sector instead of 30
two years ago$2 million
the cost of an hour’s downtime that has risen
sharply in automotive© Siemens©
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Total losses to unplanned downtime
also up steeply in two years
With hourly costs of downtime up so sharply, total losses suffered bymanufacturers have also risen very steeply in two years.
The total losses suffered by manufacturers were already vast two years ago,
and these costs are spiraling.
The average annual cost of unplanned downtime to each plant operated bya Fortune Global 500 firm is $129m a year – up 65% on two years ago.
At large automotive plants, average yearly losses to unplanned downtime
now top $600m. And in this sector, losses could have been worse. Big
automotive manufacturers have limited them by reducing the amount of lost
production time by 45% in the last two years, more than any other sector (seenext section). This is partly a reflection of lines not running at full capacity
because of the global microchip shortage. It also reflects Predictive Maintenancestrategies becoming more mainstream in the sector: adoption of predictive
maintenance (Predictive Maintenance) has grown, with the number ofmanufacturers with Predictive Maintenance teams increasing from 11% to
38% in the last two years.
In Heavy Industry costs of unplanned downtime per plant have more thandoubled in two years, to $128m a year. In Oil & Gas, they have risen 76% to$149m. A key factor in these rises is that sectors producing these goods sit
early in the supply chain – they produce steel, aluminium and other metals
for a huge variety of purposes, pulp for paper, and oil for all sorts of uses,
including plastics. This can mean they face the stiffest penalties for failure todeliver on time: every subsequent user of the raw material faces their own
penalties for missing deadlines set by customers, and these penalties tend tobe passed down the supply chain, leading to the severest penalties for thosenear the start. In short, the costs of failure in stressed supply chains flow
downwards to those nearest the start.

Total annual losses to unplanned downtime per plant ($US millions)
Automotive
Oil & Gas
2021 / 22

Heavy Industry

2019 / 20

FMCG / CPG$0© SIEMENS 2023

$ 100

$ 200

$ 300

$ 400

$ 500

$ 400

$ 500

THE TRUE COST OF DOWNTIME 202211

A second factor in the rising cost of downtime to producers in both Heavy
Industry and Oil and Gas are price rises. For instance, in the period covered(2021–22) the oil price peaked at 300% higher than in the period covered by
the last report (2019–20). This helps explain why producers in this sector
now lose an average of $149 million a year for each facility they run. And thisspike in the total cost of downtime has come despite businesses in this sector
cutting the number of failures by 25% and the total amount of downtime by
16%. Digitization and Predictive Maintenance are playing a crucial role inlimiting losses here.
The total cost of downtime in FMCG has also grown, but less sharply than in
any other sector. Here the average facility run by a Fortune Global 500 firm islosing just under $10m a year to downtime, up 36% in two years.
Across the Fortune Global 500, firms are now losing an estimated $1.5tn eachyear to unplanned downtime – a rise of 70%. Unplanned downtime losses
equate to 11% of these firms’ annual revenues (up from 8% two years ago). The
loss of trust and contractual penalties that come from being the point of failure
in a stressed supply chain are getting ever-more problematic for manufacturers.

The Figures$129 million

annual cost to a typical large plant through unplanned
downtime (up 65% in two years)$1.5 trillion

annual downtime losses for Fortune Global 500 firms

70% risein losses for Fortune Global 500 firms compared totwo
years ago

11% of annual revenues
amount lost to unplanned downtime over the Fortune
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Fewer downtime
incidents but longer
recovery times
The total losses incurred by manufacturers across all sectors would have been
far worse, but with the help of Predictive Maintenance strategies and Industry
4.0 technology, they have in the past two years cut the amount of downtime
they are facing.
Compared with two years ago, the number of unplanned downtime incidents
is down. But the average plant is taking longer to get running again.
The average plant now has 20 unplanned monthly downtime incidents – six
fewer incidents than two years ago. This is a key metric for business success. With
the costs of downtime soaring, even with fewer incidents total costs of downtime
are rising. It is very hard for manufacturers to reduce the cost of an hour’s
downtime. The only way for businesses to keep costs under control is to keep
driving the number of incidents down. And only predictive maintenance strategies and the technology to implement them enable businesses to do this reliably.
Although there were fewer incidents, the production time lost to those incidents fell by just two hours, to 25 hours a month. The average facility still
loses more than a full day’s production to unplanned downtime every month.
Why might the number of incidents have fallen? Largely because digitizationand predictive maintenance efforts are starting to have a significant impact. And
in automotive, with lines working below capacity, engineers have had morechances to nip issues in the bud before they lead to unplanned downtime.

Hours of unplanned downtimes a month (average)
Oil & Gas
Automotive
2021 / 22

Heavy Industry

2019 / 20

FMCG / CPG
0

5

10

15

20

25

30

35

And why might those incidents be taking longer to recover from? Two key
factors are at play here. Global supply-chain issues mean emergency
replacement components have become harder to source in the past two years.
And as part of the so-called great resignation, many experienced engineers
have left the industry, creating a skills and knowledge gap. Inevitably, thesefactors affect recovery times.
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20 downtime
incidents a month
per facility on average(down six on two years ago)

25 hours
lost per plant each month(down by an hour compared to two years ago)

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Appetite forPredictive Maintenance
How widespread is Predictive Maintenance?
Predictive Maintenance is a strateguc priority
2021 / 22

We have a dedicated Predictive Maintenance team2019 / 20
We perform some condition-based maintenanceWe collect some data that indicates asset health
0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Seven out of 10 businesses surveyed see Predictive Maintenance as a strategic
priority. Two-thirds of firms are now doing some form of condition-based
maintenance. And a third now have their own dedicated Predictive
Maintenance team.
These figures paint a clear picture. In 2022, Predictive Maintenance is not a
future or niche technology. It is well-established and well-proven.
Our data may slightly inflate the prevalence of the technology, because it isdrawn from large multinational firms that engage with Senseye Predictive
Maintenance, and who are therefore likely to have some interest in Predictive
Maintenance.
However, it is now so clear that companies save money with these technologies
that Predictive Maintenance has moved from nice-to-have to must-have.
For instance, in our survey, the Overall Operating Efficiency shown by firms
where Predictive Maintenance is a strategic priority was almost double (60)than that offered by firms where it was not (29).
Predictive Maintenance is also about predicting upcoming machinery issues
to avoid failures. That means lower costs in the spares that have to be held,
which is especially important given the current uncertainty in supply.
It enables prognostics – particularly, the ability to predict Remaining UsefulLife. And it is getting less and less expensive. On average, Senseye Predictive
Maintenance customers who deploy the technology for Predictive Maintenance atscale recoup their Predictive Maintenance investment costs in three to six
months.
It is, therefore, no surprise that Predictive Maintenance is now a mainstream
technology for large multinational manufacturers and industrial companies,
as this survey shows.
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Seven out of ten
businesses see Predictive Maintenanceas a strategic priority

One in threebusinesses have their own dedicatedPredictive Maintenance teamTwo out of threefirms are now doing some form of condition
based maintenance© Siemens©
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Are firms collecting the kindof data that makes Predictive
Maintenance possible?
More than three-quarters of businesses collect some basic data from theirmachines. They measure OEE, record downtime, and use a factory historian.
This is now mainstream practice.
However, the data collected via a factory historian is not always as helpful
as businesses imagine, and increasingly this is seen as legacy technology.
A richer and more useful data set is needed to get the benefits of a Predictive
Maintenance strategy and less unplanned downtime.
The businesses reaping the greatest rewards are those who have gone
beyond using factory historians to implement IoT platforms that let them
integrate and use data from multiple sources. These include maintenancerecords (CMMS), operational systems, Manufacturing Execution Systems,
service data and human insights.
The leading businesses in the field are integrating information from many
more sources to create the most powerful machine learning and AI
predictions about the health of their equipment.
The key is finding the data points that have the most predictive power andthen monitoring them. In Senseye Predictive Maintenance’s experience thesmartest factories are now adopting a “small data” model; they identify andmonitor the most important data points amongst the swathes of data thatturns out to be irrelevant.

Manufacturers' progress on data collection
We us a factory historian to collate data
We measure OEE
We record downtime
We record downtime to the minuteWe record current drawn by machines2021 / 22

We record vibration in machines

2019 / 20

We record temperature in machines
0%© SIEMENS 2023

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

THE TRUE COST OF DOWNTIME 202217

With the focus very firmly on value over volume, this approach involves
setting out precisely the types of data needed to ascertain and analyze aparticular pattern or outcome.
Furthermore, data doesn’t necessarily need to be collected in real-time, but atpreset intervals pertinent to the function of the machine. For some this could
be hourly, for others daily, but by switching off the constant data flow, data
volumes are substantially reduced.
It is much more effective to focus on what is relevant, in a pragmatic way,with a steer from those who know the equipment best. Otherwise, insightcan be drowned out by the sheer noise of a huge volume of data.

Businesses measure:• OEE• Downtime• Factory HistorianOver three quartersof businesses collect some basic datafrom their machines.

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Improving productivity with
Predictive Maintenance
Senseye Predictive Maintenance Asset Intelligence automates the analysis ofmachine health, enabling Predictive Maintenance. It can be introduced costeffectively and at scale across large manufacturing and industrial organizations.
It uses artificial intelligence to analyze data about all aspects of machine
health, such as current drawn down by machines and vibration. That allows
Senseye Predictive Maintenance to give you early warning of deterioration in
performance and reliability. With that information, engineers can direct theirattention where needed to prevent failure.
Predictive Maintenance means firms can service machines before they break
down, not after. And it eliminates the need for exhaustive, costly preventative
maintenance schedules.
By bringing in Predictive Maintenance, Senseye Predictive Maintenance’s
clients have shown the following:• An 85% improvement in downtime forecasting accuracy• A 50% reduction in unplanned machine downtime• A 55% increase in maintenance staff productivity• A 40% reduction in maintenance costs
These gains mean large manufacturers have recouped the cost of theirinvestment within three months.

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What could manu­facturers
be saving with Predictive
Maintenance?
Extrapolating from these figures, Senseye Predictive Maintenance estimatesthat the use of AI-driven machinehealth monitoring across the Fortune Global
500 could:• Save 1.6 million hours of downtime annually• Realize a 6% productivity boost worth $734 billion• Achieve a 40% reduction in maintenance costs worth $236 billion anddeliver a 4% boost to the bottom line1• Recoup 171,000 lost hours and deliver a 12% boost in productivity worth
up to $354 billion a year for automotive manufacturers in the Fortune
Global 500• Save Fortune Global 500 FMCG manufacturers 607,000 production hours
a year, and deliver a 2% productivity boost worth $23.5 billion• Save Fortune Global 500 organizations in Heavy Industry over 740,000 losthours a year, and provide a $323 billion annual productivity boost• Save Fortune Global 500 Oil & Gas producers 72,000 hours a year in theirrefineries alone, and deliver a $33 billion productivity boost
These figures are enormous. But AI-driven machine-health monitoring delivers
more. It improves things for people, production and the planet. Fixing before
failure reduces the need for replacement parts by up to 40%, which cuts
wastage and carbon usage. Condition monitoring also reduces energy usage.
And Predictive Maintenance strategies allow organizations to cope betterwith the post-Covid retirement of many experienced engineers, by focussing
engineers’ time where it is most needed.

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Methodology
The results in this report come from an ongoing survey bySenseye Predictive Maintenance, asking large manufacturing
and industrial businesses about unplanned downtime, datacollection, condition-based maintenance and Predictive­Maintenance.
The results used here cover the period from January 2021
to August 2022. They are based on 56 completed online
interviews with maintenance, engineering and IT professionals
at large industrial organizations in the following sectors:• Automotive• FMCG (fast-moving consumer goods)• Heavy Industry• Oil & Gas.
These organizations were across the world.
Senseye Predictive Maintenance used extrapolation to estimate
unplanned downtime in Fortune Global 500 companies.
We extrapolated findings from our research using publicly
available information on the number of plants operated bythese organizations and the number of employees.
Data on Predictive Maintenance’s benefits come from live
deployments of Senseye Predictive Maintenance’s softwareat large manufacturing and industrial organizations.

Footnotes:
1

 revious research from Senseye Predictive Maintenance in the metals and mining sector show that large industrial organizations typically spend
Papproximately 3% of annual turnover on maintenance. This estimate assumes that 3% figure is consistent across all sectors analyzed for this report.

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Published bySiemens AG
Digital Industries
Customer Services
P.O. Box 31 80
91050 Erlangen, Germany
For the U.S. published bySiemens Industry Inc.
100 Technology Drive
Alpharetta, GA 30005, United States
Article No. DICS-B10153-00-7600 PDF DÖ© Siemens AG 2023

The information provided in this brochure contains me­re­ly general descriptions or characteristics of per­for­ma­ nce which in case of ac­tual use do notalways ap­ply as described or which may change as a result of fur­ther de­ve­lopment of the products. An obligation to provide the respective characteristics
shall only exist if ex­pressly agreed in the terms of contract.
All product designations may be trademarks or product na­mes of Siemens AG or supplier companies whose use by third parties for their own purposes
could violate the rights of the owners.

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