Earlier this year I completed my Masters of Business Administration at the University of Redlands with a course that included participation in a business simulation from CAPSIM. What follows is an exert from our team’s final report regarding our strategy and lessons learnt. Hopefully the information below that allowed us to decimate our human competitors will also help you.
Animated chart showing how each team positioned their products with respect to the the “ideal market” colored with a large light purple circle. The red dots represent products from our team, whereas the black and blue dots are products from our two human competitors. Lastly, the greens dots represent products from our nemesis, the computer opponent.
Introduction
In the CAPSIM simulation, five generalized customer sectors are defined: low-end, traditional, size, performance and high-end. For each sector and for every year the simulation tells us the total number of customer, the ideal product specifications and customer preferences. For example, in 2018, the final year of the simulation, there will be 14,477 customers that can be associated with the low-end sector. The ideal product for low-end customers will be a unit with a performance of performance and size of 5.7 and 14.3 respectively. And lastly, low-end customers primarily use product age and price to differentiate between competing products.
Sector Growth
After a few rounds (aka years) it was evident that customer demand in each sector was grew constantly. We established that the five sectors had the following growth rates.
| Sector | Growth |
| Trad | 9.2% |
| Low | 11.7% |
| High | 16.2% |
| Perf | 19.8% |
| Size | 18.3% |
Even though the Size and Performance sectors had the strongest growth rates, it is the Low-end sector that balloons to almost 15,000 units in the final year of simulation.
| Sector | 2010 | 2018 |
| Trad | 4,925.00 | 9,958.35 |
| Low | 5,974.00 | 14,477.40 |
| High | 1,702.00 | 5,657.29 |
| Perf | 1,277.00 | 5,418.08 |
| Size | 1,322.00 | 5,071.18 |
During the last few rounds, the market was unable to satisfy customer demand due to insufficient plant capacity of all firms. This, in our opinion, was a lost opportunity for easy sales especially when Chester, our arch-rival, vacated this sector. In retrospect, bulking up on low-end capacity during early round would have been an advantageous strategy in anticipation of sector growth.
Sector Drift
Over time, technological advances allow chip manufacturers to offer smaller and faster units. Market competition and customer expectation result in a “drift” in the ideal specification for each generalized sector. The CAPSIM online documentation states the following drift rates for each sector.
| Segment | Performance | Size |
| Traditional | 0.7 | -0.7 |
| Low End | 0.5 | -0.5 |
| High End | 0.9 | -0.9 |
| Performance | 1.0 | -0.7 |
| Size | 0.7 | -1.0 |
When these drift rates are applied to the initial positions of each sector’s “ideal position” we see the following trend.
From the graph were able to ascertain the follow key factors:
- The size and performance sectors are diverging. This is significant because a product launched into these sectors will not be able to be repositioned into other sectors.
- The low-end, traditional and high-end sectors overlap. For example, an ideal high-end product in year 2010 is equivalent to an ideal traditional product in 5½. Andrews used this strategy to cease R&D on Adam in 2013 so that it could be a traditional product in 2016.
- The rate of drift in low-end sector is considerably less than the high-end sector.
The Automation Pitfall
In the simulation, automation refers to an investment to improve the efficiently of assembly line machinery. The investment results in a reduced labor cost and a substantial improvement in product contribution margins. Automation is not without adverse effects, as automation increases the cost of additional plant capacity becomes increasingly more expensive. However, in our opinion, the most damaging effect of high automation is the increased length of time to reposition sensors.
In a few instances we may have automated too much too early. This stifled the responsiveness of some products to annual drift corrections. This dampening effect on R&D resulted in less time in the “ideal position” for each sector, for the high-end sector this is critical for attracting customers.
During later years, the effects of high automation were mitigated when the introduction of the TQM module in the simulation. By funding TQM’s “concurrently engineering” we were able to reduce, to a small degree, the time to reposition products.
The Value of Leverage
Financial leverage takes the form of a loan or other borrowings, the proceeds of which are invested with the intent to earn a greater rate of return than the cost of interest. In the case of our firm, our primary form of capital resulted from issuing debt (both long and short term) and the sale of stock. Leverage allows greater potential returns for a firm that would not have been available. The potential for loss is greater because if the investment becomes worthless then the loan principal and accrued interest on the loan still need to be repaid. Our firm learned this firsthand because we had to take several emergency loans.
The chart below illustrates several important financial ratios: Return on Sales (ROS), Return on Assets (ROA), and Return on Equity (ROE). When you compare our firm’s performance over the course of the simulation relative to the percentage of borrowings as a function of our firm’s total liabilities and owner’s equity, there is no consistent correlation between these performance indicators as evidenced below:
However, what this data does show is the effect of leverage on our long-term success. Notice the significant disparity between the ROS, ROA, and ROE in 2013 versus 2016. Both years our firm borrowed significantly. However, in 2016 the money we borrowed was spent on plant improvements to the tune of over $15 Million, whereas in 2013 our firm actually sold plant capacity and turned a profit from the sales. In other words, early in the competition our firm made the mistake of not (re)investing the money we borrowed to finance our long-term capital goals. This is what resulted in the collapse from years 2014 – 2016 because the money we borrowed wasn’t properly invested in the firm. Consequently our firm’s performance in 2013 is an representation of pseudo-positive performance, when in reality our firm actually suffered from not reinvesting this money back into the firm’s manufacturing business.
Strategy of Competition
In the Capsim simulation we implemented a strategy of broad cost leader with focus on product lifecycle. We continuously updated our products so that they had competitive ages. In addition, we made sure our products fit into the ideal market. As we progressed through the years we changed our strategy to maintain our existing SIZE product and launch a new one, since two of our competitors exited this market. This did not result in immediate profits, as one of our products had to be re-engineered. In addition, we launched several high end products and soon after Chester copied our strategy of launching high end products. Again, we had to re-engineer some of our high end products to compete with two competitors. It is important to have a consistent strategy, but you must respond to competitor actions, otherwise you run the risk of losing out of market share or carrying inventory, both of which will hurt short and long term profit.
Impact of Inventory
Toyota is credited for the invention of Just in time inventory. Under ideal conditions, a company would purchase, produce, and ship product to meet the inventor for one day or one week. As the name states, “just in time” mean that raw materials are received just in time to go into production, manufactured, completed and shipped to customers. Just in time emphasizes producing exactly what is needed, as opposed to keeping everyone busy.
Carrying costs are definitely a lesson that the CAPSIM simulation demonstrated. Over production generally resulted in an emergency loan. When a company overproduces, they generally pay additional production costs, warehouse costs, and potentially product obsolescence. In addition, inventory that does not sell takes up valuable production space that could have been used for other purposes. While inventories act as buffers against unforeseen events, they have a cost. In addition to the money tied up in inventory, the presence of inventories encourages sloppy, inefficient work that may result in defects that dramatically increases total production time.
Good Luck!
