OLAP modeling: one cube versus many cubes?

I am still puzzled about the correct way to design a cube vs. several cubes so that reports and queries that compare several different measurements can be made as easily as possible. The problem is that when there are different measurements, these measurements may only share some dimensions or some levels of a hierarchical dimension.

For example (a very artificial example just to illustrate the question) a measurement about working hours may be available on a daily basis, involve the dimension male/female, job category, and also the dimension overtime. Another measurement e.g. production may be available only on a monthly basis, and have the dimension product type. Both measurements share the hierarchies year and month of the time dimension.

These measurements are very different and only share a subset of dimensions, and dimension values, yet we want to create reports and queries where one measurement is compared with the other. For example we want to compare the total female working hours of job category A with the total production of product type X on a monthly basis. Or we want to compare the average daily working hours of all males with total production on a monthly basis.

The usual advice given is not to make the cube to big, avoid "unrelated" measures and avoid too many dimensions, but for some applications, reporting over many measures that are related only by e.g. the time dimension is important. There seems to be no easy, "standard" way to create reports over many cubes in an ad-hoc fashion.

So - how is this kind of problem usually dealt with? Is there some good reading stuff for this?