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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.

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EE3: Weather, Weekdays, and Bike Rentals: A Quantitative Analysis

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

CIAM

BUS501

Dr. Edmund Khashadourian

July 12, 2025

Based on EE3 definitions, we did begin with data cleaning and organizing our data before it in the same manner by converting temperatures from Fahrenheit to Celsius for better handling. Data cleaning is important because it can lead to greater reliability and validity in making decisions and analyzing data. Hermanson et al. (2021) explain that validity, completeness, and accuracy in supporting data set the quality of insights. Furthermore, information is a strategic asset that has the potential to offer a valuable competitive advantage (Vasarhelyi et al., 2023).

Solving the Missing Data Problem

The data set did not have the maximum, minimum, and average temperature on February 1 to February 14, 2023. To complete the data set, one had to find out the geographic location where the data collection would be in a position to obtain the right weather details. The data set was not specific about the location. We employed AI-driven analysis to identify the location, which was Washington, D.C. The missing temperature figures in the data set were eventually obtained from places of pleasant climate to fill the gap.

Weather and Bike Usage Analysis

One can easily observe in the graph that the use of bikes drops significantly during rainy seasons. The evidence proves that people do not like to drive when it rains. This comes in handy for preliminary analysis when a company is deciding whether it should venture into a new market whose weather might affect total sales. Research shows that extreme weather events—such as snow and heavy rain—considerably hinder transportation infrastructures (Skevas et al., 2025). Since cycling is highly weather-dependent, weather conditions like rain and icy roads can drastically limit cycling use.

Additionally, weather condition data reveals that "partly cloudy" days are the most common type of weather in the two-year period under observation, and also the days on which maximum usage of bikes is noted. This again supports the hypothesis that weather has a considerable bearing on usage.

Casual vs. Member Bike Usage

It is clear that members utilize bicycles significantly more than non-members—nearly twice as much. This shows that members use the bike-sharing system more since they use bicycles for their daily transportation or as their day-to-day transport.

Weekday vs. Weekend Rentals

The weekday rentals totaled approximately 11,900,000 and weekend rentals totaled approximately 4,860,000. The weekday daily average rentals totaled approximately 21,046 and weekend daily average rentals were 23,307.

To ascertain whether this variation is significant, we used a t-test. Likewise, since the determination of e-cargo bike freight demand is made using quantitative models, we used a t-test to ascertain whether variation between bike use on weekend and weekday is significant. This enables fact-based decision-making and not assumption-based (Mantecchini, Nanni Costa, & Rizzello, 2025). The t-test result gave approximately a t-statistic of 3.32 and a p-value of ~0.0010, which shows there existed a statistically significant difference in bike usage on weekdays and weekends. The result shows people are likely to use bicycles more during weekends than weekdays.

Bike Type Preference

Statistics indicate that classic bikes are the most utilized. That implies that users will choose classic bikes more often, which is most likely due to the fact that most people don’t try or adopt the new trend of the eclectic bikes. Because the data geographically are centered in Washington, D.C., which is a relatively small city compared to other cities like Los Angeles, it would logically follow that normal bikes are utilized more with the relatively shorter distance.

References

Hermanson, D. R., Lawson, J. G., & Street, D. A. (2022). Detecting and Resolving 'Dirty' Data: Certified Public Accountant.  The CPA Journal, 92(7), 36-41. https://2q21eenab-mp02-y-https-www-proquest-com.proxy.lirn.net/scholarly-journals/detecting-resolving-dirty-data/docview/2708409756/se-2

Mantecchini, L., Nanni Costa, F. P., & Rizzello, V. (2025). Last Mile Urban Freight Distribution: A Modelling Framework to Estimate E-Cargo Bike Freight Attraction Demand Share.  Future Transportation, 5(1), 31. https://2q21eeas6-mp01-y-https-doi-org.proxy.lirn.net/10.3390/futuretransp5010031

Skevas, T., Thompson, W., Brown, B., Salin, D., Gastelle, J., & Edgar Marcillo-Yepez. (2025). Weather extremes and their impact on crop transportation networks: Evidence from U.S. Midwestern elevators.  PLoS One, 20(3) https://2q21eendo-mp02-y-https-doi-org.proxy.lirn.net/10.1371/journal.pone.0319815

Bike Usage Weekend vs. Weekday

Total Sunday Monday Tuesday Wednesday Thursday Friday Saturday 2272348 2194266 2384470 2485660 2484340 2436002 2576308

Sum of Total Bike Usage precipitation versus no precipitation

Total No Yes 10993934 5641866

Precipitation

Weather condition and Bike Usage

Total Clear Overcast Partially cloudy Rain Snow Snow, Partially cloudy 1158332 340876 9494726 5569526 64744 7596

Caual vs. Member

member casual 5204474 3212223

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EE2: Weather-Calibrated Data Cleaning for Urban Mobility Modeling

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

CIAM

BUS501

Dr. Edmund Khashadourian

June 28, 2025

EE1 Summary: Market Trends and Behavioral Insights

In the EE1 phase, we performed a systematic analysis of Capital Bikeshare’s operating environment as well as post-pandemic market trends by combining Ivey case materials with market data from Statista. Our analysis shows that the U.S. bike-sharing sector is recovering robustly. Some estimates predict that the industry will grow from $800 million in 2023 to $1.1 billion by 2029. And the user base, according to these projections, will pass 60 million by 2028.

The demand for bikesharing shows a dual pattern. One part of these trends includes seasonal peaks occurring in spring and summer, as you can see that 67% of all annual rides happen between April and September. Another trend is seen within each day: there is a clear ride peak from midday up until early evening. Moreover, the demand is very influenced by weather conditions. For example, ridership can drop by 30% to 40% when temperatures go above 35°C or when rainfall is more than 5mm.

User behavior is showing more diversity each day. Around 62% of rides seem to be for commuting purposes and 32% are done for leisure or social use. These points guide us toward a focused product strategy for Cycle Right helmets. That is, the helmets should be designed to work well for both commuters—keeping them light and safe—and for casual users who also care about style. In addition, the spring-summer period must be taken advantage of to help increase market share.

The Importance of Data Cleaning in EE2

Our preliminary analysis revealed that Capital Bikeshare demand is not only seasonal but also highly responsive to weather conditions. Ride volume peaks during warm, dry months (especially April to September) and drops sharply in adverse weather. Similarly, usage spikes midday and in the afternoon. These patterns imply that the market demand for Cycle Right helmets will align closely with high-usage periods and favorable weather—making precise demand modeling essential. This begins with rigorous data cleaning.

In EE2, we focus on building a high-quality data foundation. Patterns uncovered in EE1—such as the optimal temperature range (18–28°C) and sensitivity thresholds for rainfall—serve as benchmarks for identifying anomalies. Cleaned, reliable data is critical for pinpointing ideal product launch windows—especially for dynamic inventory planning during peak seasons and for tailoring distribution strategies across commuting and leisure use cases.

Our Targeted Data Cleaning Framework

We propose a data-cleaning methodology grounded in statistical theory and domain-specific best practices. This approach aims to preserve key variable relationships while improving overall data quality to support robust modeling.

1. Diagnosing the Missingness Mechanism

The first step is to assess why data is missing. Missing values can be:

· MCAR: Missing Completely At Random

· MAR: Missing At Random (conditional on observed data)

· MNAR: Missing Not At Random (dependent on unobserved data)

For example, missing weather data may result from sensor outages (likely MAR), while gaps in rental counts might indicate system downtime or logging issues. As Rubin (1976) emphasized, identifying the missingness mechanism is crucial—it determines whether deletion, imputation, or model-based methods are appropriate.

In our case, we found 14 missing entries for temperature fields (Max, Min, and Avg Temp), mostly in early February 2023 (snow days) and late April 2023 (rainy days). This suggests possible sensor downtime or data upload failures.

2. Contextual Imputation of Numerical Variables

For numeric variables like temperature, humidity, and ride counts, imputation strategy should match the pattern and duration of missingness:

Short gaps (1–3 hours): Linear interpolation maintains continuity and local trends.

Longer or irregular gaps: Use Multivariate Imputation by Chained Equations (MICE), which leverages related variables (e.g., season, hour, humidity) to iteratively estimate missing values.

Given the time-series nature of the dataset and strong inter-variable correlations, we recommend MICE. As shown by van Buuren and Groothuis-Oudshoorn (2011), this method preserves inter-variable relationships better than mean or forward-fill imputation.

3. Unit Consistency and Standardization

Although the original temperature fields are recorded in Fahrenheit (℉), we converted them to Celsius (°C) for three reasons:

· Alignment with EE1: Our analysis defined optimal ride demand at 18–28°C.

· Consistency with external sources: The Ivey case and most global references (e.g., WMO, Statista) use °C.

· Better model readability and interoperability: Using °C simplifies interpretation and avoids future unit mismatches with other datasets.

The standard conversion formula used was:

°C = (°F – 32) × 5/9

4. Outlier Detection and Contextual Validation

Outliers must be assessed in context—not blindly removed. For instance, 500 rentals at 3 AM is likely an error, while 500 rentals at 1 PM on a holiday is likely valid.

Common methods for outlier detection include:

· Z-score or modified Z-score

· Domain checks (e.g., temperature above 45°C or negative humidity = physically impossible)

· Temporal benchmarks (e.g., moving median or seasonal baselines)

Importantly, we avoid discarding extreme yet valid data points (e.g., spikes during festivals or heatwaves), as they may reveal critical commercial opportunities for helmet marketing.

5. Final Validation & Sensitivity Testing

Once imputation and anomaly correction are complete, we perform integrity checks:

· Compare pre- and post-cleaning descriptive statistics

· Re-fit baseline models (e.g., random forest) to identify residual issues

· Run sensitivity analysis to assess how different imputation strategies affect predictions

This process helps ensure that the cleaning process itself doesn’t introduce unintended modeling bias—an issue highlighted in many empirical modeling studies (Little & Rubin, 2019).

References

1. Rubin, D. B. (1976). Inference and missing data. Biometrika, 63(3), 581–592. https://doi.org/10.1093/biomet/63.3.581

2. van Buuren, S., & Groothuis-Oudshoorn, K. (2011). MICE: Multivariate Imputation by Chained Equations in R. Journal of Statistical Software, 45(3), 1–67. https://doi.org/10.18637/jss.v045.i03

3. Schafer, J. L., & Graham, J. W. (2002). Missing data: Our view of the state of the art. Psychological Methods, 7(2), 147–177. https://doi.org/10.1037/1082-989X.7.2.147

4. Little, R. J. A., & Rubin, D. B. (2019). Statistical Analysis with Missing Data (3rd ed.). https://doi.org/10.1002/9781119482260

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EE1 Summary: Capital Bikeshare and the Post-COVID Market Environment

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

CIAM

BUS501

Dr. Edmund Khashadourian

June 15, 2025

EE1 Summary: Capital Bikeshare and the Post-COVID Market Environment

The U.S bike-sharing market has grown significantly since the COVID-19 pandemic. Companies should therefore evaluate the potential opportunities to support the market’s infrastructure. As the Capital Bikeshare team, we are developing the Cycle Right helmet for our customers. The helmet gives our customers stylish, safety-oriented features that improve their riding experience. This analysis will inform the optimal timing and design strategy for introducing our product in alignment with micromobility trends and usage patterns. This report combines contextual information from the Ivey case study and recent Statista market data to create a well-rounded view of current conditions.

Capital Bikeshare Overview (from Ivey case)

Capital Bikeshare was launched in 2010 as a public-private partnership that the District Department of Transportation aimed to provide mobility to Americans. The firm worked hand in hand with Motivate, a New York-based bike-sharing company, to improve people’s driving experience. The Capital Bikeshare system has provided customers with over 4,300 bicycles in 500 stations across Washington (Xiong, 2021). Capital Bikeshare provides its customers with membership options; it allows customers to rent their bikes frequently. The bikeshare system generated large volumes of trip data, which was instrumental in understanding rider behavior and planning system expansions. As noted in the case, usage patterns were affected by seasonality, temperature, and weather conditions, which made demand forecasting and expansion planning essential.

Market Trends and Post-COVID Outlook

The study collected data from Statista that highlighted the current micromobility trends in the bike-sharing market in the U.S. The results indicate that the market has rebounded significantly since COVID-19. Statista (2024, B) forecasts that the bike-sharing market will increase from $0.8 billion in 2023 to more than $1.1 billion by 2029. The U.S bike-sharing market is experiencing an increase in the number of riders. Statista (2023, B) also forecast that the U.S shared ride market will have 60 million new customers by 2028. Many Americans are using station-based bike sharing services for various trip purposes. These purposes include commuting, recreation, errands, and social activities (Statista, 2023, A). There is also an increase in the number of shared rides in the U.S. Statista (2023, C) indicates that the industry had 186 and 188 shared ride customers in 2023 and 2024, respectively. The market will have 195 million shared rides by 2028. Figure 1 and Figure 2 indicate the shared micromobility trips in 2022 and the number of riders the market has gained in the last few years:

Figure 1. U.S. bike-sharing market revenue from 2017 to 2029, in billions of U.S. dollars (Statista, 2024).

Figure 2. Number of shared micromobility trips in the United States in 2022, by trip purpose (Statista, 2023).

The demand for dockless bike-share systems has declined in the last few years. Statista (2024, A) highlights that there were 348 stations in 2022, which later declined to 306 in 2023. The market share later declined to 254 stations in 2022. The emergence of the e-scooter bike system has triggered the decline of the dockless bike-share system. The following chart highlights the trends in dockle

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