{"id":45293,"date":"2026-06-24T16:45:38","date_gmt":"2026-06-24T11:15:38","guid":{"rendered":"https:\/\/www.verdantis.com\/?p=45293"},"modified":"2026-06-24T19:08:42","modified_gmt":"2026-06-24T13:38:42","slug":"ausfallkosten","status":"publish","type":"post","link":"https:\/\/www.verdantis.com\/de\/ausfallkosten\/","title":{"rendered":"Ungeplante Ausfallzeiten: Einblicke in Kosten und Wiederherstellung"},"content":{"rendered":"
\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t

Organizations across manufacturing, oil and gas, energy, utilities, mining and chemicals face mounting pressure to raise asset availability while holding down cost. Unplanned downtime sits at the centre of that pressure, and it has become the single largest hidden cost in industrial operations.<\/p>

Siemens, in its Wahre Kosten der Ausfallzeit<\/a>\u00a0report, sized the problem at about $1.4 trillion a year<\/strong> across the world’s 500 largest companies. The striking part is the trend: the cost has risen 62 percent since 2019 even as the number of incidents has fallen. Stoppages are rarer, yet each one is more expensive, because plants run leaner and the hidden costs around each event have grown.<\/p>

This report picks up where the macro number leaves off. It maps where the loss actually accumulates and, more usefully, how much of it is recoverable. The short answer from Verdantis analysis: most of the loss is not the mechanical failure itself but the response, the search for the right part and the data to use it, and a large share of that is recoverable without adding headcount. For the practical side of cutting it, see our guide to downtime reduction strategies<\/a>.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t

Key Findings<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t
$1.4T<\/span>
The macro cost.<\/span> Unplanned downtime costs the world's 500 largest companies about $1.4 trillion a year, around 11 percent of revenue, up 62 percent since 2019.
Siemens, Wahre Kosten der Ausfallzeit 2024<\/div><\/div><\/div>
83%<\/span>
Failures are mostly random.<\/span> Verdantis attributes roughly 83 percent of failures to random patterns, so calendar-based maintenance can only catch the minority.
Verdantis analysis<\/div><\/div><\/div>
#1<\/span>
Parts drive repair time.<\/span> Parts unavailability is the single largest driver of how long a stopped asset stays down, because the line waits for the whole search, not just the fix.
Verdantis analysis<\/div><\/div><\/div>
25-35%<\/span>
The wrench-time gap.<\/span> Technicians spend only a quarter to a third of a shift on hands-on work; much of the rest is search and logistics.
Industry wrench-time studies<\/div><\/div><\/div>
~$17M<\/span>
The recoverable prize.<\/span> Of an estimated $56 million lost per facility each year, roughly $17 million is recoverable through predictive maintenance alone.
Verdantis estimate<\/div><\/div><\/div><\/div>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t

Report purpose<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t

MRO and asset data are often dispersed across ERP, CMMS and spreadsheets, which makes downtime a source of recurring loss rather than a managed risk. This report quantifies the impact of connected parts and asset data on downtime cost, recovery, and working capital, and gives operations leaders evidence-based benchmarks to act on.<\/p>\n

The report's primary objectives are to establish defensible benchmarks for the cost of unplanned downtime anchored to published research, to show where inside the macro number the recoverable money sits, to rank the levers that return the most downtime per unit of effort, and to support decisions on EAM, predictive maintenance, and Datenverwaltung im Bereich MRO<\/a> with data-backed estimates.<\/p><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t

Methodology and scope<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t

The figures in this report are not drawn from a fielded survey of respondents. They are modeled estimates, built by combining published research with Verdantis scenario modeling, and they are written throughout as "Verdantis analysis" or "Verdantis estimate" so the basis is never in doubt. The approach combines four methods.<\/p>\n

Benchmark anchoring.<\/strong> The macro cost is taken directly from Siemens, True Cost of Downtime 2024. Wrench-time, MRO inventory and predictive-maintenance figures are drawn from published industry studies (including Deloitte and McKinsey) and reliability literature (Nowlan and Heap).<\/p>\n

Scenario-based modeling.<\/strong> Per-industry downtime-cost rates are applied to a representative asset-intensive facility to estimate annual loss and the share recoverable by each lever, using input ranges rather than single points.<\/p>\n

Data triangulation.<\/strong> Modeled outputs are cross-checked against the published benchmarks above to keep them within defensible bounds.<\/p>\n

Forward validation.<\/strong> A structured survey instrument has been designed to field these estimates against operator-reported data, and results will update this report when available.<\/p><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t

Scope: industries and downtime-cost rates modeled<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t

The model uses representative downtime-cost rates per hour for six asset-intensive sectors. These are inputs, not survey responses, and can be tuned to a specific operation.<\/p><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t
Industrie<\/th>Modeled downtime cost per hour<\/th><\/tr>
Discrete manufacturing<\/td>$260,000<\/td><\/tr>
Oil and gas<\/td>$250,000<\/td><\/tr>
Chemikalien<\/td>$190,000<\/td><\/tr>
Mining and metals<\/td>$187,500<\/td><\/tr>
Power and utilities<\/td>$160,000<\/td><\/tr>
Essen und Trinken<\/td>$120,000<\/td><\/tr><\/table>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t

The anatomy of the $1.4 trillion<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t

Downtime is usually attacked one cause at a time. Verdantis maps it across five connected domains, and because the domains are linked, a parts or data gap turns a contained failure into a long, expensive one. No single function owns the majority.<\/p><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t
Share of lost hours by failure domain · Verdantis analysis<\/div>
Asset integrity<\/span><\/span><\/span>39%<\/span><\/div>
Maintenance & data<\/span><\/span><\/span>20%<\/span><\/div>
Arbeitskr\u00e4fte<\/span><\/span><\/span>18%<\/span><\/div>
Spare-parts supply chain<\/span><\/span><\/span>12%<\/span><\/div>
External & digital<\/span><\/span><\/span>10%<\/span><\/div><\/div>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t

The spare-parts domain is only an eighth of failures, yet it is the largest driver of how long an asset stays down once one occurs. That is the recurring theme of this report, and the reason Verwaltung kritischer Ersatzteile<\/a> earns more attention than its share of incidents alone would suggest.<\/p><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t

Key research findings<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t

1. The cost of unplanned downtime<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t
$1.4T<\/div>
Annual cost, world's 500 largest companies (Siemens)<\/div><\/div>
~$56M<\/div>
Estimated loss per asset-intensive facility, per year (Verdantis)<\/div><\/div>
~$206K<\/div>
Estimated cost per idle hour at a critical line (Verdantis)<\/div><\/div>
+62%<\/div>
Rise in downtime cost since 2019, even as incidents fell (Siemens)<\/div><\/div><\/div>
Insight.<\/span> The cost is increasingly in the response, not the failure. A leaner plant loses more per idle hour, which is why shortening each outage matters more than ever. <\/div><\/div>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t

2. Most failures are random<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t
~83%<\/div>
Of failures follow random, not age-related, patterns (Verdantis)<\/div><\/div>
~30%<\/div>
Of recoverable downtime addressable by predictive maintenance (Verdantis)<\/div><\/div>
up to 70%<\/div>
Fewer breakdowns with predictive maintenance (Deloitte)<\/div><\/div>
~25%<\/div>
Lower maintenance cost with predictive maintenance (Deloitte, McKinsey)<\/div><\/div><\/div>
Insight.<\/span> Calendar-based maintenance can only ever address the age-related minority. Predictive, condition-based maintenance is the single largest recoverable lever, more than twice what time-based preventive maintenance reaches. <\/div><\/div>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t

This is why condition-based work matters more than the calendar. The full picture sits in our companion data set on predictive maintenance statistics<\/a>.<\/p><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t

3. Parts and data drive repair time<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t
#1<\/div>
Parts unavailability is the largest single driver of repair time (Verdantis)<\/div><\/div>
25-35%<\/div>
Of a shift technicians spend turning wrenches (industry studies)<\/div><\/div>
~70%<\/div>
Of machine data industrial operations collect goes unused (industry)<\/div><\/div>
+20-30pts<\/div>
Wrench-time lift from planned, kitted, data-complete work (industry)<\/div><\/div><\/div>
Insight.<\/span> The asset stays down for the whole search, not just the fix. Fragmented and duplicated parts and asset records send skilled technicians on a scavenger hunt, and that search is where the controllable hours hide. <\/div><\/div>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t

4. What is recoverable<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t
~$17M<\/div>
Recoverable per facility via predictive maintenance alone (Verdantis)<\/div><\/div>
$22-34M<\/div>
Total anticipated recovery per facility across levers (Verdantis)<\/div><\/div><\/div>
Insight.<\/span> Recovery does not require more people or new sensors first. It requires connecting the parts, asset and work-order data the business already owns, so the right part and plan are ready before a technician walks up to the asset. <\/div><\/div>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t

5. The inventory and procurement drag<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t
15-25%<\/div>
Of MRO inventory is obsolete or surplus stock (industry)<\/div><\/div>
18-30%<\/div>
Annual carrying cost of held MRO inventory (industry)<\/div><\/div>
30-60%<\/div>
Premium on emergency orders versus planned procurement (industry)<\/div><\/div>
40-50%<\/div>
Share of the maintenance budget MRO represents (industry)<\/div><\/div><\/div>
Insight.<\/span> The same data gap that extends downtime also locks up capital: parts no one can find are reordered, while surplus stock sits on a shelf and another site runs short of the same item. <\/div><\/div>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t

Right-sizing this stock is its own discipline. See how demand-driven MRO-Bestandsverwaltung<\/a> cuts both stock-outs and surplus at the same time.<\/p><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

\n\t\t
\n\t\t
\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\tFind your recoverable downtime<\/span>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t

Verdantis maps your parts, asset and work-order data to show where downtime and working capital are recoverable. Request your facility estimate.<\/p><\/span>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t
\n\t\t\t\n\t\t\t\n\t\t\t\n\n\t\t\t\t\t\t\t\n\t\t\t\n\t\t\t
\n\t\t\t\t\t\t\t\t
\n\t\t\t\t\t\t\t\t\t\t\t\t