I want to forecast demand for the cement industry. I have data for 10 years- Monthly data. Is that desirable for forecasting?
You can forecast a time series based on a single historical observation, by simply extrapolating this observation into the indefinite future (the so-called naive no-change forecast). Or you could even forecast time series with no historical observations whatsoever, in your case e.g., by estimating the current size of the industry and assuming a certain growth rate.
Of course, forecasts based on very few data points will likely not be very good. Usually more historical data translates into better forecasts. So your question really isn't whether 10 years of data is enough for forecasting, but whether this is enough for your forecasts to be good enough. And that, in turn, depends heavily on what you want to do with your forecast.
Then again, 10 years is quite a lot of data. The question really is whether any more data will improve accuracy. I'd imagine that the cement industry may have changed a lot over the last decade, so 11 year old data may not be comparable to more recent data. Your forecast accuracy will likely be influenced much more by how well you can forecast the future economic growth and industrial policy in China.
It is good that you have 10 years of data, but you should consider what many ignore and check your data for changes in variance. You can use Tsay's test to do this and then use WLS to estimate, if necessary. See more here Outliers, Level Shifts, and Variance Changes in Time Series www.unc.edu/~jbhill/tsay.pdf
You should also check your model to see if the model is changing using the Chow test for structural breaks as patterns change over time within the data due to policy, competition or unknown factors. See more here http://en.wikipedia.org/wiki/Chow_test