Internet is needed for your forecasts
Do I really need an Internet connection to get your forecasts? is a question frequently asked by prospects having a look at our forecasting technology.
Well, the answer is YES. With Lokad, there is no work-around. Our forecasting engine does not come as an on-premises solution.
But why should we need an internet connection for an algorithmic processing such as forecasting?
The answer to this question is one of the core reason that have lead to the very existence of Lokad in the first place.
When we started working on the Lokad project - back in 2006 - we quickly realized that forecasting, despite appearances, was a total misfit for local processing.
1. Your can’t get your forecasts right without having the data at hand. Researchers have been looking for decades for a universal forecasting model, but the consensus among the community is that there is no free lunch; universal models do not exist, or rather, they tend to perform poorly. This is the primary reasons why forecasting toolkits feature so many models (don’t click this link, it’s 3000 pages manual for a popular toolkit). With Lokad, the process is much simpler because the data is made available to Lokad. Hence, it does not matter any more if thousands of parameters are needed, as parameters are handled by Lokad directly.
2. Advanced forecasting is quite resource intensive but the need to forecast is only intermittent. Even a small retailer with 10 point of sales and 10k product references represents already 100k time-series to be forecasted. If we consider a typical performance of 10k/series per hour for a single CPU (which is already quite optimistic for complex models), then computing sales forecasts for the 10 points of sales take a total 10h of CPU time. Obviously, retailers prefer not to wait for 10h to get their forecasts. Buying an amazingly powerful workstation is possible, but then does it make sense to have so much processing power staying idle 99% of the time when forecasts are made only once a week? Outsourcing the processing power is the obvious cost-effective approach here.
3. Forecasting is still under fast paced evolution. Since our launch about 3 years ago, Lokad has been upgraded every month or so. Our forecasting technology is not some indisputable achievement carved in stone, but on the contrary, is still undergoing a rapid evolution. Every month, the statistical learning research community moves forward with loads of fresh ideas. In such context, on-premise solutions undergo a rapid decay until the day the discrepancy between the performance of current version and the performance of the deployed version is so great that the company has no choice but to rush an upgrade. Aggressively developed SaaS ensure that customers benefit from the latest improvements without having to even worry about it.
In our opinion, going for an on-premise solution for your forecasts is like entering a golf competition with a large handicap. It might make the game more interesting, but it does not maximize your chances. Don’t expect your competitors to be fair enough to start with the same handicap just because you do.