Nine sick days per head per year: the cost line that appears on no robot quote

The BKK Dachverband is the federation of Germany's Betriebskrankenkassen, the company health insurance funds. Its annual Gesundheitsreport evaluates the incapacity-for-work notifications of employed BKK members; for reporting year 2024 that is 4.78 million member-years, roughly 13.7 percent of everyone in Germany in employment subject to social insurance. The report itself is a text volume. The numbers this article is about sit in the freely downloadable table appendix: table A.9 by economic division of the WZ 2008 classification and table A.10 by occupational group of the 2010 classification of occupations, each broken down by main diagnosis group.

The metric is simple: days of incapacity for work per 100 members per year. Across all occupations it is 2,233 days, or 22.3 days per head. That is the reference value against which every sector row is read. Three caveats belong with it before any of these figures travels into a calculation. First, the statistics count the days of incapacity notified to the insurer, not the shifts actually lost on a roster; the two are related but not the same. Second, this is an insurer's statistic: it describes BKK members, whose share of the workforce varies by sector, and in cleaning occupations that share is around 8 percent. Third, it groups by the occupation on record or the employer's sector, not by what a person was doing on a particular day.

Occupational main group 54, cleaning occupations, records 3,138 days of absence per 100 members in reporting year 2024: 31.4 days per head, at a sickness rate of 8.6 percent. That is nine days more than the average across all employees. The average age in this group is 49.3, and three quarters are women.

The diagnoses show where the gap comes from. Diseases of the musculoskeletal system and connective tissue, ICD chapter M00 to M99, cause 933 days of absence per 100 members in cleaning occupations. That is 9.3 days per head per year, 29.7 percent of all the group's absence. Across all employees the figure is 453 days per 100, 4.5 per head and 20.3 percent. On this one diagnosis group, cleaning sits at double the average; of the nine days' total gap, 4.8 come from the musculoskeletal system. The second-largest difference comes from injuries: 316 days per 100 against 218 on average.

The structure matters more than the sum. Per 100 employees in cleaning occupations there are 43 musculoskeletal cases a year, and a case lasts 21.7 days on average. So these are not many short absences but comparatively few long ones. For a roster that is the worse case: a three-week absence cannot be absorbed with an hour of overtime from colleagues. It needs cover, which either comes out of the existing team and is missing there, or is bought in from a contractor.

For residential care the sector row says more than the occupation row, because a home does not only employ nursing staff. Economic division 87, residential homes, records 3,365 days of absence per 100 members in table A.9: 33.7 days per head, a sickness rate of 9.2 percent, and with 121,186 member-years one of the larger groups in the appendix. That is eleven days more than the average across all employees and the highest value among the sectors considered here.

The musculoskeletal system contributes 708 days per 100, 7.1 per head, 21 percent of absence; a case lasts 22.4 days on average. The largest single item in homes, though, lies elsewhere: mental and behavioural disorders cause 740 days per 100, and at 41.6 days per case they are the longest absences of all. A transport or cleaning robot removes no psychological strain, and whoever justifies one with that row is confusing two things. It removes walking, pushing, lifting and the night floor programme. The row it can be priced against is the one with the 7.1 days.

Looking at the occupation rather than the sector gives similar values: occupational main group 82, which contains elderly care, sits at 31.6 days of absence per head and 6.8 days from the musculoskeletal system. The medical health occupations, which contain hospital nursing, sit at 23.6 days in total and 4.0 from the musculoskeletal system, close to the average. In these statistics the gap between elderly care and hospital nursing is larger than the gap between hospital nursing and office work.

Contract cleaning as a service sector does not appear on its own in table A.9 but within economic division 81, building services together with landscaping. This group records 2,688 days of absence per 100 members, 26.9 per head, a sickness rate of 7.3 percent. That is below the occupational group of cleaners because the division also contains caretakers, facility management and horticulture.

The musculoskeletal share is nevertheless almost identical: 711 days per 100, 7.1 per head, 26.5 percent of all absence, 36.6 cases per 100 at 19.4 days per case. For a building services contractor deploying robots on client sites, this is the row that counts: it employs the cleaners, its sickness rate sits in this division, and its client pays for the floor area, not for the cover.

A vendor cannot write this gap into a quote, for a simple reason: it knows neither the operator's sickness rate nor the operator's cost per day of absence. Both are specific to the business. The sickness rate of a cost centre with twelve cleaners can be 4 percent one year and 12 the next, depending on whether two long cases fall into it. And the price of a day of absence depends on how cover is arranged: from the existing team, through overtime, through a contractor, or not at all, by leaving the area uncleaned that day.

That is why quotes work with coverage and with the staff hours the robot replaces. This is not wrong, but it is incomplete. According to these statistics, a full-time cleaning post in reporting year 2024 was unfilled on 31.4 calendar days, one in a home on 33.7. Whoever prices a robot against 1,600 working hours is pricing it against a post that does not exist in that form. The post that does exist delivers fewer hours and generates cover costs on top, and both rise with the age of the workforce: according to table A.5 of the same appendix, musculoskeletal days of absence per 100 members stand at 219 for 30- to 34-year-olds, 569 for 50- to 54-year-olds and 1,003 for 60- to 64-year-olds. At an average age of 49.3, cleaning occupations are the oldest group considered here.

The route does not run through the insurer's statistics but through your own. Any business above a certain size can obtain from its health insurer, or through its occupational physician, an evaluation of incapacity for work by diagnosis group; the BKK tables are the benchmark for it, not the substitute. Three numbers are enough: the absence days of the affected cost centre in the last year, the share of them from the musculoskeletal system, and the actual cost of cover per day, meaning the overtime premium, the contractor's hour or the output forgone.

The robot is then priced not against a post but against a task. A scrubber-dryer robot takes over the wet cleaning of the circulation areas, not the sanitary rooms and not the furniture. A transport robot takes over the runs between store and ward, not the handover at the bedside. The share of that task in the shift is the figure the absence days may be multiplied by. Whoever calculates this way arrives at a smaller amount than with the whole post, but at one that survives scrutiny.

And the order matters: first your own sickness rate, then the quote. An operator who knows its absence days by diagnosis group can tell a vendor which task it wants to hand over and why. An operator who does not gets sold a coverage figure.

The BKK tables show that cleaners and employees in residential homes have markedly more days of absence than the average, and that a large part of the gap comes from the musculoskeletal system. They do not show that a particular task causes those days, and they do not show that a robot reduces them. A breakdown by diagnosis is not a causal analysis. A cleaner's back complaints can come from scrubbing, from lifting buckets, from climbing stairs between floors, or from a second job.

For the business case that means: the absence days are a documented quantity, the share of them a robot removes is an assumption. Both belong in the calculation, but separately and by name. An operator who writes that the robot cuts the sickness rate by a third is asserting something that appears in no source. An operator who writes that the cost centre had nine days of absence per head from the musculoskeletal system in 2024 and that the robot will take over 40 percent of the shift time that loads the back has two numbers it can document and one expectation it can hold against the next evaluation after a year.

All values in this article come from the table appendix to the BKK Gesundheitsreport 2025 of the BKK Dachverband, reporting year 2024, tables A.9, A.10 and A.5; the accompanying fact sheet is dated 29 October 2025. The metrics apply to employed BKK members, not to all employees, and coverage varies by sector. Occupational main group 82 contains personal-care and wellness occupations alongside elderly care; economic division 81 contains caretaking and horticulture alongside building cleaning; economic division 87 covers all residential homes, not only care homes. Whoever needs a narrower row will find it in their own insurer's evaluation, not in this appendix. Other insurers' reports, from DAK, TK or BARMER for instance, arrive at similar rankings for the same groups but at different absolute values, because their insured populations differ; figures from different insurers must not be netted against one another.