During our first simulation in the course, I was a product manager at a medical equipment company. The new product, GlucoGauge, that helped diabetic patients self-monitor their blood-glucose levels had performance issues stemming from various problems not detected before release. These included the younger and smarter demographic tests leaving certain issues unchecked, as well as the problems with using the devices based on both instruction comprehension and microprocessor compatibility. When faced with the decision to invest in either instruction clarity or better communication with doctors, I opted to continue funding the instructions and ignored the doctor communication. I also chose to lay off 400 workers and provide the consumers with 3 months worth of compatible strips for the GlucoGauge device, instead of sending out new devices and possibly firing all 600 workers or none at all. There was also an issue with data syncing from the device onto apps providing accurate readings, and I estimated that 45% of consumers were syncing too much data, while the doctor recommended that only 10% of consumers were syncing the month’s worth of data at once.

With cognitive bias, I learned about confirmation bias, anchoring, sunk cost, and framing. I had confirmation bias when I kept my survey results the same when first trying to identify the problem with the devices, and used all new information to fuel my theory that the issue was based on consumer interpretation of instructions. I demonstrated sunk cost by continuing to invest in the instruction clarity instead of the doctor communication, which I was not invested in and rarely attempted to address. I saw the effects of anchoring when observing the % of people estimated to delay syncing data compared to the doctor’s number, since we were reluctant to go too far away from the initial number. Framing was shown in the different situational introductions when asking to fire 400 employees or save 200 jobs. Laying-off people was risk-averse framing, while saving was gain-related framing. I felt like I made a decision outside of the framing, though I definitely had confirmation bias, anchoring, and sunk cost responses within the simulation.

In the future, I’ll definitely be aware of these cognitive biases and attempt to put myself in different perspectives as I’m leading. Whether this be from someone unrelated to the issue, or the group as a whole, I believe that the more perspectives there are, the better. I’ll also attempt to look at the complete opposite to my personal position and work through the reasoning behind that as well.

The part of the simulation I felt the most challenged by was the decision to either take a chance and gamble all-or-nothing with the 600 employees, or fire a set 400 and keep 200 jobs secure. The 1/3 chance given of complete success depending on the separate microprocessor company paying for damages was too risky for me, and I did not want to trust the process outside of my control. I spent a good amount of time on contemplating my decision, and the 200 secure jobs tipped the balance, though the 400 fired were still a big loss.

Throughout the simulation, I was not as distressed about the ambiguity as much as I was attempting to work with what I had. Because of this, I often would simply follow what others sent via email and voicemail and did not think of the situation outside of those suggestions, especially when identifying the instruction clarity issue. I wasn’t extremely stressed at any point during the simulation, though I would sometimes squeeze a stress toy when making the “Big Decisions”.

A company crisis I connected to this simulation was resignation of Nike’s VP and General Manager Ann Hebert after it was found out that her son used her credit card in order to aid his shoe reselling business. This was very fishy, considering the conflict of interest, and led consumers to believe that the Nike SNKRS app was “rigged” so that people with connections were more likely to get limited edition sneakers. Though this interpretation has been refuted, it was nevertheless the conclusion reached by consumers and how I ended up hearing about it. Nike handled this by putting out a statement announcing Hebert’s resignation a week afterwards, and a plan to appoint a new person to the position. They did not touch on any controversy. I believe that they responded well by not mentioning the claim and having a speedy response to the issue, since addressing the claims would be seen as an admittance to some that there is a link between the app and people with connections. Here’s a video explaining the situation as well!