Life’s been very hectic. In between a recent promotion, growing interest in body reconstruction and failing attempts to jumpstart my writing again, I haven’t been putting much time for other activities.
In terms of writing, my output felled to almost nil. I produced no usable work, and often restart when I’m already one third of my way in. It’s very inefficient and waster of time. I also lost my keyboard to malfunction and sent it out for warranty repairs. So far, it’s been a month of wait and unproductivity.
The body project is something I’d interest in that popped up from time to time, but only recently did I began to look into it more seriously. Judgment still to come whether I can sustain it. Recent attempts left me with a very sore back, but I was hoping with some sort of physical bildungsroman out of this.
On top of everything else, I’d also recently changed role. It’s a promotion so to say. My workload remained almost the same, but only because I’d been carrying the load for quite some time now, being interim team lead and everything. Only this time, it’s more official and have a better title to go with it. Hopefully, my salary will adjust to reflect this promotion. The company’s still being cheap that way.
On to more interesting stuff, I’d been involved with a certain Business Analytics project, with hopes of revamping the organization into embracing more of the analytics movement, in hope of reducing workload and deliver higher value work.
Recently, there’s an education organized to showcase the power of such analytics to the managers line. I got in at the last minute because my boss’s boss’s boss wanted to swap. He wants me to take his place. Turns out, the education is a simulation of sorts, making decisions based on data available.
It’s very similar to the competitive strategy game I previously played with during university time, only this is supposed to be more real life. How, as a CEO and CFO makes business decisions based on data.
So, that’s what we did. 4 hours of understanding how the software works, and then discussion on what should we do – relative pricing, how to gain market share. Our decisions are then transmitted to a server in South Africa that computes and simulates outcomes based on our decisions. We were separated into groups and compete among each other, with the main goal here of getting the highest profits (a goal to provide objective measurement that decided by consensus before we start things off).
We have to sort through a lot of information and decide which ones are good and others to ignore – too much info for too little time. That’s where the consultant model structure comes in. It really helps provide segmentation and highlight area that we can focus on by Pareto.
Here’s the basic scenario. The company sells three different items – Alpha, Charger and Nova to different regions (NA, Europe, AP) that have different split of customer preferences. Alpha is the low cost, low margin product. Charger is the largest piece of the pie product with higher costs and better margin. Nova is the recently introduced line that commands a high margin and appeal to those seeking quality. Also, the regions have different split of customers too. In general, NA customers are more willing to pay for the higher price, whereas Asia customers are cheapskate.
|T1 Rev||T1 GP||T2 Rev||T2 GP||T3 Rev||T3 GP||T4 Rev||T4 GP|
The table above is the same as the picture. What happened was after we study past trend data, we set out targets – where should we fight for market share and how many should each region contribute to the overall performance. A formula calculate things out and spit out the full year target to us.
Then, we start making our decisions, and the quarter actuals reflect the results of the course that we took. Everyone start off with the same data and initial starting point, and at the end of Q4, we compared our FY actuals to that of the original target.
The B/(W) measures the actual better or worse compared to budget, whereas Atmt measures the percentage attainment over the target.
I’m in team 4, and so naturally, we have the best results by far, outpacing our initial target and the nearest competitors. Our FY GP is at $39.6M, almost $20M better than the target set out, and $9M higher than our nearest competitor. That provided me with a good enough excuse to gloat over the next few days.
Revenue wise, our $165.1M FY tops any other competitors by far, better $16.4M against the nearest team, also team 1.
In the picture, there’s some red lines. That’s actually the last quarter strategy used by each team. When we finally reveal our strategy, I was quite surprised that team 1 – 3 have almost similar strategy based on their data, while team 4 employed a slight different approach.
All the team unsurprisingly focused on Nova, and whereas team 1 – 3 placed a premium on it, high above the competitors price, we took a different approach, rising our price to have better margins but at the same time still priced it lower than competitors. Their justification was that Nova was bought predominantly in NA, who has less price elasticity. We thought the same thing too, but tested things out and found out that the high price won’t make up to our drop in volume. That insight allow us to change our approach and consistently get better results each quarter.
Aside from maximizing our revenue and GP, team 4 also has the best GP margin there. The margin actually improved from 9% last year (based on the data) to a 24% this year for us. The administrator of this simulation, Kyle, mentioned that this is the first time this was administrated outside of the US, and the highest achievement he saw so far. The previous revenue range had always been in the $140M – $150M. He was intrigued by our result that he asked for our by quarter strategy and see if he could replicate it.
All in all, it’s a fun simulation. I guess, aside from the usual grunt work, things like this are part of the days work. I should seek to move to the strategy department. I’m good at this.