PROJECT FINAL PAPER

EE4: Cycle Right Helmet Strategic Decision Analysis

Huiling Liu, Jiaying Zhou, Min Jiang, Shreya Sawant, Yi Kiu Ho, Zuo Wang

California Institute of Advanced Management

BUS501

Dr. Edmund Khashadourian

July 18, 2025

Introduction

Capital Bikeshare is an established company and represents a major inflection point of urban micromobility in Washington D.C. They provide bikes to a diverse and expanding population of riders throughout the city. With our proposal to help them release the new Cycle Right helmets, we need to decide on the best manner to produce and distribute the products. In the EE4 part of the project, we will utilize decision analysis tools to carefully weigh the risk, potential for profit, and concept of more accurate market information. Our aim is to reach a wise, profitable, and informed final decision.

Our team's earlier work gave us a very strong foundation for making the final decision. In EE1, we explored and introduced the post-pandemic trends in micromobility and saw clear signs of market recovery, especially seen in increased ridership during the spring and summer seasons. In EE2, we discussed and focused on cleaning and preparing the data to support accurate modelling with emphasis on paying close attention to factors like weather, seasonality and commuter patterns. In EE3, the team worked on to create the statistical testing, which pointed out that usage of bikes was significantly different between weekdays and weekends. Another difference was that the ridership was highly affected by weather conditions. All these insights implied the importance of weather conditions, timings, days, seasons and ridership behaviour, which is a crucial part to build a successful launch of the Cycle Right helmet.

Calculations to determine profitable production

To figure out the best way to produce and distribute the Cycle Right helmet, we were given 5 possible partnerships. Each of these potential partners come with its own risks and rewards. It all depends on how the market performs. The market probabilities and the five partnerships are listed below:

The market could be:

1. Excellent (20%)

2. Good (40%)

3. Average (30%)

4. Poor (10%)

The five partnership options are:

1. Unique Products Inc. – Low risk, low return

2. Innovators LTD (ILTD) – Moderate risk, strong return

3. TechComm (TC) – Higher risk, moderate return

4. Star Cellular (SC) – High risk, potentially higher reward

5. Do-It-Yourself (DIY) (mix) – Maximum risk and reward, full internal assembly using external component suppliers

Expected Monetary Value

“Expected Monetary Value (EMV) method consists of maximizing the sum of payoff of each situation multiplied by the probability of that situation occurring.” (Theotista et al., 2023)

The formula used for EMV is:

EMV=∑(Probability×Payoff)

EMV=(0.2×Excellent)+(0.4×Good)+(0.3×Average)+(0.1×Poor)

We calculated the EMV for each potential partners by multiplying the probability of each market scenario by its associated net profit or loss:

Partnerships	Excellent (20%)	Good (40%)	Average(30%)	Poor(10%)	EMV ($)
Unique Products Inc.	$5000	$2000	$-2000	$-5000	700
Innovators LTD (ILTD)	$12,000	$6000	$-4000	$-10,000	2,600
TechComm (TC)	$13,000	$7000	$-10,000	$-15,000	900
Star Cellular (SC)	$30,000	$10,000	$-20,000	$-30,000	1000
Mix (DIY)	$55,000	$20,000	$-35,000	$-60,000	2500

Expected Value of Perfect Information (EVPI)

“Decision making under uncertainty is an extensive research field concerned with aiding the decision maker through uncertain problem spaces such as financial markets, product analysis, or medical treatment options. It is often helpful in this type of problem space to obtain additional data before making a risky or costly decision.” (Sessions & Perrine, 2013)

First, we find out the best result in each market condition:

Excellent(20%): Mix ($55,000)

Good(40%): Mix ($20,000)

Average(30%): Unique (-$2,000)

Poor(10%): Unique (-$5,000)

Now, we multiply each by probability and sum:

EVwPI = (0.2 x 55,000) + (0.4 x 20,000) + (0.3 x -2000) + (0.1 x -5000)

11,000 + 8000 + (-600) + (-500) = 17,900

The last step is to subtract the EVwPI with the best EMV:

17,900 - 2600 (ILTD) = 15,300

The expected value of perfect information is: 15,300

Therefore, it is not required to spend more than $15,300 on market research to predict the helmet market perfectly.

Final Recommendation

The team suggests moving forward with the Innovators LTD for the development and launch of the Cycle Right helmet. The major reason being that, it offers the highest expected profit i.e. $2,600/month at a moderate risk. Although the Mix has more upside potential in the case of a strong market, the loss from average or weak markets is a much sharper risk than what can be accepted at this stage.

Innovators LTD offers a good mix of potential profits, experience and a reasonable risk profile and therefore is the most logical and smart decision.

REFERENCES

Sessions, V., & Perrine, S. (2013, November). Methods for Adjusting Expected Value of Information (EVPI) Under Situations of Data Missing Not at Random (MNAR) https://www.researchgate.net/publication/323557174_Methods_for_Adjusting_Expected_Value_of_Information_EVPI_Under_Situations_of_Data_Missing_Not_at_Random_MNAR_Research-in-progress

Theotista, G., Febe, M., & Marshelly, Y. (2023). Development Of Expected Monetary Value Using Binomial State Price In Determining Stock Investment Decisions. BAREKENG: Jurnal Ilmu Matematika Dan Terapan, 17(3), 1703–1712. https://doi.org/10.30598/barekengvol17iss3pp1703-1712

Xiong, A. (2021). Capital Bikeshare: Analyzing Bike Rental Demand (Case No. 9B21E008). Ivey Publishing.