# Forecasts

My previous job was doing operations research in a big logistic company. The problem of the day was making accurate forecasts. For example, the amount of work fluctuated significantly from day to day and we needed an accurate gauge of the manpower requirements for the next day to give us time to make adjustments.

While the people on the ground have been doing the forecast manually based on their years of experience, the management wanted us the techies to make the process scientific. Honestly, it was a lost cause to begin with. And the biggest reason for failure was that there was no accurate benchmarking of our forecasts compared to the manual prediction.

In real world situations, I don’t really think it is possible to make any reliable forecast. Yet, you see the economists and financial analysts telling you the dow jones will hit 12000, google’s stock price will hit \$450. Mostly, we the silly people laps up their predictions but nobody bothers to check how good they were.

As an experiment, I tabled the GDP forecast by the Singapore Government Statistics Board. These were the official figures, not those dime a dozen ones given by analysts, as reported in the newspapers. So the combined wisdom of the Singapore Chief Statistician and his minions told us:

Jan 2005: 3% to 5%
Apr 2005: Low end of 3% to 5%
May 2005: 2.5% to 4.5%
Aug 2005: 3.5% to 4.5%
Nov 2005: 5%
Feb 2006: Accurate figure 6.4%

So despite the fact that they were continuously revising their figures as more data came in, they were still completely way off. One might argue that they chose to err on the side of caution but I honestly think that I might have done better by picking a figure at random. Random walk theory I guess.

This entry was posted in Statistics. Bookmark the permalink.

### 2 Responses to Forecasts

1. pAt84 says:

Hello,

I am quite interested in the methods you used (or at least the ones you tried to use) for your forecasts during your job. Do you mind telling a little bit more about it?

Besides I am quite puzzled by the GDP of Singapore. On the one hand I am used to my good old german one to two percent, on the other hand I am getting quite used to the nine percent here in China.

Take care,
Pat

2. tpc says:

Hi,

Back then we did not really use any scientific methods. It was like identifying all the different processes and from which location/system we could extract data, then writing a simple code to combine the data. It doesn’t make sense unless one knows that operational processes very well.

Well, Singapore is a developing country. Our main industry is electronics, for example Sea-gate and Maxtor (before being gobbled up) are here. That’s from the 70s till now. Since the 90s, lots of these manufacturing have fled to other countries, notably China, due to low labour costs. So it makes sense that our GDP is midway between Germany and China.