Big Data Simulation

Big Data Result

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 RevT1 GPT2 RevT2 GPT3 RevT3 GPT4 RevT4 GP
FY Tgt159.120.5153.319.8155.119.9153.619.7

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.

Competitive Strategy Game: Week 8, 9 & 10


(Washington DC) Home to American policies’ decision making

Almost a month had passed by since this game ended. My memory of the thought process that went into making the decisions is a little hazy by now. After Week 10 results are announced, the cost for each tram was finally revealed to others. We looked back at how the costs affected the entry decision of each team: which cost that truly matters and how some teams managed to bluff their way to immense profit despite having higher costs structure than the other teams.

I will summarized the collective action of our team into the following thoughts:

  • By this time, our team is quite profitable and (in hindsight, we grew complacent). We do not know the profits that other teams are having or the range that participants in previous games had achieved. Steps were taken to make sure that such information was not freely transferred between the participants. Hence, this show the benefit of market research, it gave further insight into the market and competitors to allow better decision making. We stick to our initial markets, not foraying into any new markets considering the short remaining periods left.
  • As our capacities are expiring, we decided not to renew them because the depreciation for earlier periods are not worth the initial investment gains. We saw our market share in each market dropping as another firm increased its capacity and began undercutting the rest. That impudent fool! I was a bit slighted because the new entry disturbed the market power and reduced the dividends that we had been enjoying. In retaliation, we did not pick a fight with the new entry. Instead we conceded our market share and raise prices continuously to keep our revenue steady. Now, I understood how those big firms felt when uprising new startups began digging into their own market share.
  • Because we are flushed with cash by now, I made suggestions to enter another market, but perhaps because of my weak argument and my own belief that investing this late who do us not much better, we decided to sit on our cash and wait for the remaining period to expire.


  • The results was announced and turns out, there a twist to this game. Turns out that in order to achieve the highest profit, the winners had been consistently invested in one market. That was the key behind gaining immense profit in this game. I learnt that this is comparable to the real world, why some companies grew to multinational level while others remained mom and pop stores. The answer was in the different markets that each company invested in. To rub salt into the wounds, this aforementioned market was not originally that appealing to us but it was also the market that I had recommended to enter earlier.
  • My team was in the third place out of the rest of the teams. As consolation, the professor mentioned that my team was the most profitable team who has never invested in the aforementioned market. The first place team gained roughly almost 8 million, second place had 5 million while we earned 4.6 million over the initial investment of 1 million. The teams after us barely break beyond 1.5 million threshold. Our team has the smarts to extract the most surplus out of our markets but not enough to identify the most promising growth market. In addition, the professor even put up quotes that we had written in our reports: “Those who don’t take risk don’t drink champagne!”
  • Ironically, the first place team had rather high cost in the profitable market and managed to overcame this by producing beyond their capacity. They are willing to incurred additional cost just to sell more goods. I had been considering this tactic before and practiced in our markets but their was done at a much grandeur scale.  In the end, they revealed that they had done the calculations and concluded that they would be profitable enough to justify for the additional expenses. Indeed, they took a calculated gamble and it paid off. I even joked that the team deserved the first place because they had two mathematicians in their team while we only had one: me. I observed that the teams that had great returns all had a few members with mathematical background like me. This reinforced my belief that good decisions should be made with good data support. The top teams are able to made it this far because they could calculate the returns from their investment in aiding them to make investment decisions.

Still, this was an interesting competition and I learnt a lot about making decisions here. Making decisions appears easy on book but it is different in real life. There is often much more at stake with the decisions made in real life. A key reminder for myself is to always prepare beforehand and speak up.

Competitive Strategy Game: Week 6 & 7


(Boston, Mass.) Chapel within Boston University.

Basically, there are not a lot of changes within this two period. Each firms are playing cat and mouse with each other. My group has been trying to undercut each other in the effort to drive other firm out of the competition. Considering the short period of time left, this is a futile act. I am wondering whether the other firms who take the low production high price route might fare better within the time period. Considering Market A, I finally gave the white flag and resigned that I could hold much of the market share, allowing the price to rise higher so that we could gain more revenue. In Market B, our presence was so strong that the market leader gave up and cut their production, pushing us into the leading position. This market differentiated taste is hurting us significantly though because it is hard to convert the rest of the market share despite the low cost allowing me to reconsider whether my expansionary plan was a good one. Nevertheless, Market B is a good revenue generator which has provided enough ROI.

Competitive Strategy Game: Week 5


(Providence, Rhode Island) Fear not the ambiguity that lies in front of you.

Market update for the previous period produced a lot of headache, for me especially.  We were too greedy in Market A and did not expected another new entrant to undercut us so much as the was an equilibrium with other firms in thee previous periods. There was also a huge capacity investment by that entrant. Our reasoning behind why the low price and huge capacity investment was to kick us “old timer” out of the market. Was it predatory pricing, I’m not sure but the possibility of them recouping their losses is slim if they continued on like this. As a result, we sold less than half of what we produced. Market A now looks utterly too competitive and not desirable to continue on. Yet, we believed that we should teach the new entrant a lesson, and cut our price significantly for two reasons: sell our inventory at a loss to cut down on inventory costs and to give the new entrant a taste of what it meant to have your market share taken away. Our profit margin is almost nonexistent with this pricing scheme but we believed we could progress with it for now.

In Market B, we sold off everything and learn something new about producing beyond our capacity. Our main revenue generator seems to come from Market B instead of A like initially predicted. At this point, I thought it would be rational for other firms to cut their losses and move into other markets, which are essentially still a duopoly. We are engaging in a price war with two different firms with capacity advantage in each respective market and I’m afraid that we are stretching ourselves a bit too thin. I wanted to expand our capacity in Market B much higher despite the differentiated market and plentiful competitors. It’s a gamble that will raise our profit if it goes well. For now, I’m maintaining the price as previous period because there’s an equilibrium over here as well.

I’m envious of the firms who initially ventured into the other two market because they seemed to be doing quite well with few competitors.

Competitive Strategy Game: Week 4


(Orlando, Florida) Mickey, cast a spell and dissolve my worries away.

Week 4

Updates from the previous week, we sold off all of the productions and netted some profits. But considering that we sold out, there’s the unfortunate hindsight that we are charging too low and do not command enough market share. So, the challenge this week is to tweak our parameters a little to adjust the profit margin. Also, another team made a huge investment into Market A, effectively displacing us as the major competitor in the market with double our production capacity. This sent a lot of mixed signals to us. It’s already a fairly competitive market and then someone else invested to account for half of the market capacity. There’s going to be price competition going on.

Bringing in the next decision, I wanted to reduce our price in Market A, we’ll gain less profit but we’ll send a signal to the other team that we are not to mess with and they should cut off their sunk cost and move out of the market. But, the rest of the team wanted to increase the price to fight with our profit margins instead. Also, I wanted to increase capacity in Market B but they opted to maintain everything. Looks like things are not going my way but it’s a team decision and I’d voiced out my opinions. There’s still some time left to make amendments. So, we will see.

Competitive Strategy Game: Week 3

(Miami, Florida) Too many competitors fighting for a piece of the pie.

Week 3

The result of Week 2 was out, the price we charged in Market A was about 25% high than our closest competitor. They undercut us and produce more beyond their capacity, resulting in us losing some sales and storing some in the inventory. It is still a pretty good week because we are taking advantage of the capacity constraint in the market to sell at high price to recoup some of the initial investment.

The duopolistic approach taken was no longer applicable in Week 3 because there are now more participants in the market A. However, due to the previous capacity investment, my team is still the dominant power around in market A. However, the new players really shook us, and we decided to price much lower than anyone would expect to scare them off, where we cut off almost half of our initial price to wage a price war with the other firms. We still decided to invest more in our capacity to take in more market share.

In Market B, we opted to enter in small capacity because of resource constraint but also to see the economic environment in Market B. We price it low, slightly lower than the lowest competitor in the previous period. For this period, it’s not about recouping our investment, but more to the point of “We’re here, and you guys better watched out for us!” We made another investment, almost doubling what we invested last time to show our commitment to stay in this market.

Market C and D are not in our concern yet. Each market has two firms in it, making it very enticing to enter, but we want to established our dominance in Market A and B before entering others. It would be even better if the other competitors would forgo their Market A and B, and just enter the other markets.

Competitive Strategy Game: Week 1 & 2


(Orlando, Florida) Fiery explosion of spectacular performance

I am currently enrolled in a Competitive Strategy Game by Haas Business School. It was created by Severin Borenstein, my professor’s thesis advisor. He was very enthusiastic  about this game, and I can’t see why not. The game mimics real situation where firms only know about their production cost but not of other firms. This really test the judgment because depending on the decisions made, the initial investment could turn into profits or losses.

I’m recording the team’s thought plan over here. So that, I could look back here in retrospective and figure out what had gone wrong. Of course, the main motive of the game is to maximize profit and that is just what I intend to achieve.

Week 1

In the first week, we could only invest in one market and decide on the production capacity. Because the game is all about uncertainty and analysis, my team spent roughly 12 hours creating spreadsheets and the basic intuition to succeed in the game. We derived rough demand curves and estimated future calculations from these curves. We arrived towards two markets, both almost equally appealing. Let’s call thee first market, Market A. For our firm, we have a good lower marginal cost but somewhat higher capacity cost and entry cost. You can think of this as Mexico. The other market, let’s call it Market B, we have very low marginal cost. Think of the production facility akin to China. We are very sure that we have the lowest marginal cost among the competitors, but this is a differentiated goods market, so other firms would still control some market share despite the price difference. The net present value was also the highest in this market, if we could command a monopoly in the market. Here’s where the team divided, some wanted to invest in Market A because it’s easier to fight on cost in that market. But I wanted in go to Market B because of the high profit prospect. We came to the conclusion of investing in Market A, but just with half of our investment capital. That way, we still have some cash to go to other market if needed.

Week 2

The result of all firms’ decisions came in. There’s a duopoly in Market A, including us but both teams are playing it safe because the production capacity is somewhat lower than the optimum capacity. Surprisingly, Market B was crowded with firms, probably coming to the same conclusion that Market B is a good investment. There are little activities in other markets, one with no firms entering it and the other with a monopoly. Because of the duopoly, I recommended that we charge a very high price for our goods because there’s bound to be a shortage in the market. We charge at almost 10x our marginal cost for the largest recoupment we can get. Our calculations was that we could fulfill almost every demand in the market despite even if the competitor undercut us. We also foray in the hotly crowded Market B, for despite the competition, we still have the comparative advantage on our side. But still, the investment is small just to test the water.