How worrying is it that I was exchanging emails and spreadsheets with a complete stranger called Shane at 5.50 on Saturday morning? Hopefully he was emailing from Hong Kong or Sydney. He has harvested the first two rounds of results from the Times website. (Only registered players can log in – everyone else is limited to the table of averages).
A couple of unexpected things appear to come out the data.
Firstly there were only about 470 participants in Round 2, but not everyone who dropped out of Round 1 did badly: the drop-outs are spread remarkably evenly throughout. The chap who had over $5m profit in the first round has left us – maybe he decided to take the virtual dollars and run. I’m curious why people who were doing well dropped out – didn’t they realise how well they were doing, or did they decide their time was better spent being good at their real businesses? Or maybe their partners had had enough and unplugged their PCs.
Also, scores can go down as well as up: the people in the top 10 places at the end of Round 1 who are still in the game scored as follows in Round 2:
Round 1 Place Round 2 Place Dropped out 1st 191 – 2nd 39 – 3rd 1 – 4th 458 (dropped out) 5th 463 (dropped out) 6th 55 – 7th 75 – 8th 466 (dropped out) 9th 3 – 10th 16 – 11th 20 – 12th 51 – 13th 468 (dropped out) 14th 467 (dropped out) 15th 44 –
From 1st to 191st is a pretty dramatic drop, and you see what I mean about the drop-outs being evenly spread? The top 10 scorers in Round 2 came from all over the field and their scores in Round 1 were:
Round 1 Place Round 2 Place 3 1st 47 2nd 9 3rd 63 4th 152 5th 18 6th 132 7th 290 8th 190 9th 100 10th
The ranking in the early rounds may be volatile but it’ll stabilise pretty soon I’m sure. If it was actually raining outside I’d be tempted to analyse the data to find out what the average change from Round 1 to Round 2 was, but there are weeds in the garden which need pulling up by the roots.
I’m not saying where I am in this whole thing, but I went into Round 3 feeling rather pleased because my ranking in the league had improved even though I’d fallen behind one of the five robot players I’m competing with in the simulation.
The Times tell us the final league table will be based on the Balanced Scorecard, which is how Shane and I have been ranking the players.
A brief aside here: the Balanced Scorecard was devised by Robert Kaplan and David Norton to help companies tune and track their progress in ways that aren’t just dollars and cents. For example, if being green is important to a company and it uses recycled paper even though it is more expensive, then its “sustainability” score goes up balancing out the fact that the “profitability” score goes down. The Balanced Scorecard is there to stop the lunatics, sorry, to stop the accountants taking over the asylum. In practice, I think it may just give them a wider range of beans to count.
So I spent most of Friday plonking my way through the tutorial to discover how to predict which way my customers will jump. In the end it still seemed to come down to licking my finger and holding it up in the wind so I am not convinced they were hours well spent, though I may have been using better informed lick.
Then on Saturday I checked my decision-making against the Balanced Scorecard. This forced me to undo various decisions I made last week – for example I’d made the production lines too big and I’m clearly not going to sell all the product I can currently make. I’m glad I’d doing this on my own, I’m not fond of recriminations and acrimony. I reached the point a couple of times where my decisions all seemed to balance out – if I spent more on marketing my “awareness” score went up but my “profitability” score went down. After bringing it up from about 34, I stalled with a predicted score of 43 until I realised that borrowing is good so long as your profits outweigh the interest charges, (think “buy-to-let”) and after that my predicted score climbed to the mid 50s where it stalled again.
One thing this has reinforced is the danger of relying on assumptions and not planning for things to go wrong. If you assume you will sell 1000 widgets and leave no room for selling fewer or more, then you’ll end up in deep doo-doo if your customers only want 500 or if they want 1500. It’s intuitive enough that you’ll be stuffed if you make 1000 widgets and only sell 500 because your fixed costs like staff, rent and insurance stay the same and your income’s gone down. However it’s not as intuitive that only having 1000 widgets if your customers want 1500 will stuff you too, in this case over-time will eat up your profits and if you run out of stock completely as I did in Round 1, you’ll just hand your competitors over to your customers on a plate – in real life you’d never get them all back.
So how did Aphra Inc do in Round 3? AT first sight it looks as if I’ve been over-cautious again; I am certainly solvent, but I could have sold more widgets than I made. This is often described as “a nice problem to have” but in reality it’s still a problem and it means I’m not accurate enough with my forecasting.
My investment in marketing and sales activities is paying off, and the decisions I make are doing a good job of making my original strategy happen. I am pleased to know that I can have a vision of what sort of company Aphra Inc should be, and make the decisions that makes that happen in our little simulated on-line world.
The differences between my predicted results and my actual results this Round show that my financial decisions and marketing decisions were better than predicted, but running out of stock damaged my overall score. The Times have not yet updated the scores published on their results page, so it’s impossible to tell where I rank among the remaining players.
The phrase “could do better” is ringing in my head.
Onwards and onwards.
PS – I’m not the only one focussing on the Balanced Scorecard, The Times today have written up the Balanced Scorecard up in today, with a mention of this very blog!