The simplest and often best method to forecast monthly data (that may be seasonal) is seasonal Exponential Smoothing - with or without a trend component. Here are details. (Incidentally, I'll never tire of recommending the entire free online forecasting textbook.)
I already used Double Exponential Smoothing for the previous months but the deviation is quite big!
The problem is that often series are simply not very forecastable. There may be a lot of residual variation. Or there may be unmodeled drivers, such as promotions. If you know about such external drivers, then include them in a causal model, e.g., a regression model with ARIMA errors.
Conversely, if you don't have any information about factors that may influence your time series, there may simply be a lot of variation that you simply cannot explain. A different model won't help you there. The only thing you can do is to understand the residual variation and cope with it - e.g., by setting appropriate safety stocks, or by working to reduce the variation, possibly by working with your clients and trying to get orders earlier in advance or some such.