Managing & Balancing Capacity to Increase Customer Satisfaction Prabhdeep Saimbhi Shyam Vijayaraghavan Jeremy S. Pomp Jamie Graziano Dawn Markovics
Georgetown University 12/16/2010
Capital Bikeshare (CBS) is a self‐service public bike rental program in Washington, DC and Arlington, VA funded by several government entities such as Federal Highways Administration and Crystal City Business Improvement District. Within 2 months of its existence it has grown to more than 5000 customers and finds itself in a tricky situation of managing capacity at its most frequented sites.
EXECUTIVE SUMMARY Our analysis of Capital Bikeshare’s (CBS) operations has led us to recommend that the firm implement “CaBi Points”, a reward points system for its members as a means to increase customer satisfaction by reducing docking demand at some of the most utilized stations during peak usage times throughout the network by incentivizing consumers to redistribute bicycles on their own accord by offering them points exchangeable for rewards and gift certificates from local Washington, DC metro area businesses. The point system initiative is intended to increase customer satisfaction by increasing the likelihood that member’s primary arrival docking location has capacity to dock their bike, while simultaneously engaging its active members to participate in the bike-balancing solution, easing the demand on Alta for bike redistribution, reducing operational CO2 emissions, and potentially adding an additional revenue stream through advertising. Based on the data provided by CBS we were able to determine which bike stations are likely to reach full capacity utilization and when they will do so. Given additional data over a longer time period it is likely we would be able to more accurately model consumer usage trends. Additionally, the CaBi Points system would allow CBS the flexibility to address capacity issues on a realtime basis when needed, while also using historical data analytics to capture predictive behavior of its members to address problem spots on an ongoing basis. Finally, we believe that the CaBi Points system is the most cost effective way of addressing this problem and has the potential to increase the firm’s profits through an innovative marketing agreement with local businesses. HISTORY AND BACKGROUND: CAPITAL BIKESHARE Capital Bikeshare is a self-service public bike rental program in Washington, DC and Arlington, VA funded by several government entities such as Federal Highways Administration and Crystal City Business Improvement District. It serves as an additional mode of transportation and utilizes the latest technologies to facilitate user access and is structured to enhance the city's public transportation system. Historically, the first self-service bike rental program started in 1998 in Rennes, France, and subsequently, rapidly spread across Scandinavia and Spain. Washington, DC adopted its first bike share program, SmartBikeDC, in 2008. The program had little reach with only about 100 bikes, 10 bike stations, and operating only in the Washington, DC area. Capital Bikeshare, on the other hand, offers some key features such as: higher quality bike, extensive fleet of 1,100 bikes, over 100 bike stations, and increased service area including Arlington and Crystal City.
HOW IT CAPITAL BIKESHARE WORKS A rider can purchase an annual, monthly or 24 hour membership. Annual and monthly memberships can be purchased online in advance, while 24 hour membership can be purchased on-sight at a CBS bike station. A member rider can go to any bike kiosk, 24 hours a day, 365 days a year, and check out a bike. Riders can also access the CBS website to locate bike kiosks, and receive real time data regarding the availability of bikes at each of the bike kiosks. Bike kiosks are located at various key locations throughout the city. The bikes are locked using a proprietary technology, which allows each bike to be safely stored when not in use. The borrowed bike is released using a key (for annual members) or a pin-pad code (for 24 hour membership users).
Upon completion of bike usage, the member can
return the bicycle to any CBS bike station. COMPETITIVE LANDSCAPE The only true bike share competitor to Capital Bikeshare is SmartBikeDC, which will soon be completely defunct. It is currently unwinding its operations, and no longer issuing any new memberships. Other indirect competitors to the Capital Bikeshare are the metro rail and bus systems (also a part of DDOT), taxis, walking, and driving. While technically Metro and bus are competitors to CBS, CBS is intended to enhance the traditional transportation options, not compete with them. Further, no other commuting option offers the environmental and personal health benefits as bike riding. CURRENT BUSINESS AND OPERATING STRATEGIES Capital Bikeshare is a partnership between the District Department of Transportation (DDOT) and the Arlington County, VA local government. Alta won the RPF to handle the operations and management of the bicycles. The membership and usage fee schedule in [Table 1] shows the revenue source for Capital Bikeshare. As noted in an interview with Chris Eatough, the BikeArlington Program Manager, in the system’s second month of operations, there are currently over 5000 members (4,800 annual and 400 monthly) that provide revenues to cover about 50% of the expenses associated with daily operations. The shortfall of cash flow to operate the system is offset by its funders’ help. The system is expected to reach a break-even status with 15,000 annual members. For day to day operations, Alta’s team of bike specialists works tirelessly to upkeep and maintain the bike fleet, as well as to balance availability at each of the bike kiosks. Balancing presents a complex operational dilemma, especially during peak hours, since during this time, the demand for bikes and docking stations can often exceed the system’s capabilities, especially in prime locations, such as
Washington, DC’s downtown business district. While Alta does have a van that it uses to rebalance the bikes, the efficiency of the van’s operations can be hindered by factors such as capacity (can only move about 10 bikes at a time), and the van’s inability to move through the city at peak hours, due to traffic. CHALLENGES AND ISSUES CBS’s most difficult challenge is ensuring an all-around excellent customer experience at peak time and as well as at other times. This includes users’ ability to locate a nearest bicycle, unlock it, use the bicycle without a malfunction, and ultimately return the bicycle to a bike station that is close to the final destination. It is the return of the bicycle that can have a severely negative impact on a customer’s experience, and is the problem that we have been asked to address by Capital Bikeshare. Chris Eatough, Program Manager, Capital Bikeshare, found that the most customer complaints arise when the consumer attempts to return the bicycle to the rack, and finds that rack to be full. The customer then has to relocate to the nearest location with an empty receptacle. Although this task of finding an empty receptacle is made easier with the use of a button that the user can press on the signage at the station, a mobile phone app, or via the company’s web page, it is nonetheless an inconvenience especially when users have time sensitive schedules. Currently, customers are given an additional 15 minutes of time if they press the button because the station is full. During the first two months of operation, a pattern seems to have developed among Bikeshare users. It seems that many annual users are taking the bikes from near their residences in the outlying neighborhoods of Washington, DC and Virginia, and then commuting to work downtown via the bicycles. Customers often find the downtown station locations full during the peak morning commute time. As a result, Alta has been attempting to relocate the bikes to the busy stations throughout periods of the day in order to meet customer demand and then return then downtown for commuters to get back home after work. All Capital Bikeshare rentals are recorded electronically; however, the organization has just begun recording this data and has provided our team with the first three days’ worth of data to confirm this assumption. CBS acknowledges the utility of specialized software designed to aid it in adjusting capacity at the bike stations; however, enough funding to pursue such advancements is currently unavailable. Therefore, CBS is seeking another alternative that keeps in mind the “green” aspect of bike sharing as well as having the ability to solve the bike balancing problem.
DATA ANALYSIS To begin tackling the problem of bicycle and empty slot load-balancing throughout the Washington DC metro area, our team obtained raw data from Chris Eatough, Program Manager at BikeArlington. The raw data contained over 65,000 data entries and tracked the location of each station, the number of bicycles at each station, and the number of empty bicycle docks at each station over a 3day period from October 19, 2010 through October 21, 2010. Data samples were taken every 5 to 15 minutes at each station. This sampling of data was used to carry out the remainder of the data-specific analysis1. Before we carry out our data analysis, it is important to point out that having no empty slots at a dock is equally if not further harmful to consumer experience as having no bikes at all. Though it varies a lot from region to region; there are some regions where lack of bikes during morning peak hours should be avoided, especially in the outskirts where customers use bikes in heavy quantity to travel to downtown in the morning for work and other reasons. On the other hand and perhaps more importantly, having no empty slots at locations closer to downtown and market places in the morning translate into customers waiting for a slot, which can be a potentially time sensitive situation. Special events and festivals also present issues that require handling dock capacity in ways other than traditionally employed. Our data synthesis began by determining the stations at which bicycle racks were either full or empty and how often such occurrences happened. Through this methodology, we isolated key “problem stations” that had frequent service failures (See Exhibit 1). In order to look at the relationship between stations we then created a grid map (See Exhibit 2) and began performing a block-by-block analysis. Comparing the problem stations to our grid map, we determined that blocks E3, E4, and F3 were having particular problems meeting customer demand cycles. Isolating block F3 for the purposes of starting analysis we took a closer look at the three stations within our newly created zone; 20th and E Street NW Station, 19th and E Street NW Station, and Virginia Ave. and 21st Street Station (See Exhibit 3). These three stations are each within 3 blocks from one another. Extracting the bicycle count data for each station individually, we were then able to plot the number of bicycles at each station over the 3-day period (See Exhibits 4a, 4b, and 4c). Comparing the three stations, we noticed that all of the stations within the same block experienced a similar cyclical
1
It should be noted that only 3 days of data were available to be analyzed by our team. The remainder of the analysis section utilizes these points to develop a framework that can be expanded to analyze the system as a whole. Therefore, with the limited data used, this analysis draws only general conclusions about the inter‐station relationships and should be taken more as an analysis procedure recommendation than an in depth study.
nature of bike usage, but that the Virginia Avenue station was the only station that was being filled up entirely. The data also points out to locations that require lesser number of bikes as the docks are close to always full throughout the day. Managing this appropriately can help keeping lesser bikes in the day to day operations and in turn minimize costs by reducing maintenance and upkeep costs. Not surprisingly, most of these areas tended to be away from the city center. The most filled up docks were located at locations such as: 12 & Newton St NE, 8th & H St NW, and 4th & M St SW. [Exhibit 5] shows the list of locations with highest number of average bikes. Some locations always remained relatively full while others in the list were full on an average during the day; the ones that remained full forever or in the morning are marked red in the exhibit. For example, 12 & Newton St NE docks were always full while 5th St & K St NW docks were relatively empty during the working hours while quite full after 6 pm on an average. Also note that some stations such as Georgetown Harbor / 30th St NW remained quite full in the morning but number of bikes dropped as the day went by. Finally, through our analysis, we believe that the following will hold true: 1. General cycles will remain similar between stations within the same block. 2. In most cases, not every station in every problematic block will reach holding capacity during peak periods. 3. The cyclicality suggests that users are using the bicycles on their way to and from work. The current method used of utilizing trucks to remove bicycles at full station and redistribute them to more underutilized stations is therefore problematic, as patrons who ride into work may be left without a bicycle to ride home. 4. As ridership increases and new bicycle stations are installed along the outskirts of the served area, congestion will worsen in the Central Business District and East of the White House. 5. As previously mentioned, a more in depth study over several weeks of data (and probably through various seasons) is required to concretely identify under and over utilized stations and how to handle demand fluctuations. SOLUTION ANALYSIS Several options were considered for encouraging a more optimal distribution of bicycles for Capital Bikeshare. The solution strategies included adding bike storage capacity, shortening the supply chain, subcontracting the bike balancing, and providing customer incentives for rebalancing the bikes.
 
Although all of the proposed solutions can effectively rebalance the bikes and thereby improve customer service, the optimal solution must adhere to certain constraints in order to successfully implement a viable short-term solution as membership in Capital Bikeshare continues to grow: Major Constraints •
Time – Capital Bikeshare has grown quickly since their September launch. And in order to support continued success, customer service should be improved quickly.
•
Capital – As a public works project, there is little short-term additional capital available for new and unplanned projects. Furthermore, the current capital is being devoted to expanding the network of bicycles as opposed to focusing on profit-maximization.
•
Space – There is limited space to install bike-share stations on the DC sidewalks and parking lots. The chance of adding more capacity at each station is thus reduced.
Adding Capacity Adding capacity can solve the problem of bike balancing by effectively increasing the number of “bike slots” so a customer is much less likely to arrive at a full station. Three potential tactics for increasing capacity were investigated: 1) Increase the existing infrastructure by adding bike slots to the “full” stations: This tactic increases the number of bikes that can arrive at each stop. There is an ancillary cost of removing a greater number of bicycles from circulation as more bicycles are docked all-day in the business district while waiting for the commuters to return on their evening commute. This tactic should be pursued in the long-term as greater numbers of member subscribe to Capital Bikeshare and capacity is added to the system. Unfortunately, the space and capital constraints are violated, implying that this is not an appropriate short-term solution. 2) Manned Stations Introducing manned stations brings temporary increased capacity to business-district area bike stations. A temporary manned station can operate during peak hours in order to store a greater number of bikes. Unfortunately, it is subject to the same constraints as the above, and therefore not an ideal solution – however it is an effective solution for temporary events such as festivals or rallies where additional capacity is necessary.
3) Mobile Stations A mobile station (possibly one built into a truck) can be posted downtown to dock bikes during the morning rush hour, driven to tourist areas during business hours, then re-deposited in the downtown areas for the return commute. This would be the ideal rebalancing solution if not for the major time and capital constraints to develop the technology, and the capital constraints required to purchase the infrastructure. Although this is a promising long-term solution for a mature bike-sharing program, it is not an effective short-term solution for the current Capital Bikeshare. Shortening the Supply chain Shortening the supply chain can result in less time in transit for bicycle rebalancing and maintenance, thereby improving the existing balancing efficiency. 1) Institute maintenance / corral stations in downtown DC near the primary business districts A nearby station allows bikes to be maintained while they are docked downtown while waiting for the evening commuters. It also allows excess bike capacity to be removed during the morning commute and added again in the evening without having to drive a van full of bikes across town and through heavy traffic. Unfortunately, space and capital constraints are a big concern due to land rental rates in downtown DC. Again, this is a potential long-term solution for a mature market, but not a viable short-term solution for the current Capital Bikeshare. Subcontracting active bicycle rebalancing 1) Subcontracting with formal agreements with the city bus system Bikes can be placed in a trailer behind specific bus routes, or with independent contractors will not necessarily solve the rebalancing problems, because they are subject to the same traffic constraints that currently affect the four bicycle rebalancing vans. However, it can allow the rebalancing of bikes to grow as Capital Bikeshare itself expands and adds to its network of bicycles. Encouraging desired customer behaviors through incentives Encouraging customer behavior through incentive programs does not violate any of the above constraints and has been successful for other bike-sharing programs. 1) “Paying� customers with credits to encourage certain routes The city of Paris uses this method to encourage users to bike uphill routes. Although the costs and time for implementation would be small, there is an ancillary cost for this method. Specifically,
 
the marginal revenue for each usage would decrease. This method would be effective, but the same results could be achieved through a points system that would not cause revenue reductions for each “balancing” ride. 2) Live Points system to encourage specific routes, “CaBi Points” The points system is an evolution of the above. Specifically, it allows Capital Bikeshare to encourage and reward customers for choosing specific beginning and endpoints without losing the revenue from each bike ride. Furthermore, it has an ancillary benefit – it provides opportunities for corporate sponsorships and donations. Furthermore, although there would be some costs to implement the system, they are relatively minimal compared to the other options presented. This is therefore the optimal short-term solution to balancing the bikes. IMPLEMENTATION PROCESS We suggest the following steps to ensure successful implementation of the “CaBi Points” system. 1. Beta Test 1.0 with 10-20 members a. Contact current members and offer the option for them to test the new system b. Allocate $400 for rewards c. Track the change in behavior from the participating members through data analysis, survey, and phone interview, if possible. d. Beta test will also help determine the appropriate level of gift/reward that would change the member’s behavior e. Did “CaBi Points” work? 2. Determine whether or not to proceed with Beta Test 2.0 3. Contact local businesses to establish marketing and advertising partnerships and secure rewards for members 4. Beta Test 2.0 with 100 members attempting to gain further insight 5. Determine whether or not to proceed with full scale implementation or additional testing EXAMPLE Joe, a member of CBS, lives in Arlington, VA and works at the State Department. He rides a CaBi bike to work Monday through Friday. He usually docks his bike at the CBS station at Virginia Ave. and 21st Street NW, one of the most heavily utilized stations in the CBS network. However, today he noticed on the CaBi website that he would get 10 “CaBi Points” if he docked the bike at the 19th & E St. station
instead. Joe enjoys Starbucks coffee and knows that this month’s reward is a $20 gift certificate at Starbucks if he is able to accrue 100 points in total. Joe decides that walking an extra block or two is worth if for $20 worth of coffee, which he can get doing this just 10 times. So he decides to change his behavior for the reward thereby increasing the available capacity at the Virginia Ave & 21st Street station. Julie a new member to CBS, who also works at the State Department, was recently frustrated when she arrived at Virginia Ave & 21st Street in the morning before work and there weren’t any docks available at the station. However, since the CaBi Points system was implemented she has noticed that she rarely runs into this problem anymore and her level of anxiety about finding a parking spot has decreased. Both Joe and Julie have increased levels of customer satisfaction as a result of the CaBi points system. Behind the scenes Starbucks is benefitting from being associated with CBS’s green and healthy image. CBS enjoys a servicing its members better while it increases its membership numbers based on positive word of mouth recommendations from its members.
 
a. Table 1: Capital Bikeshare Membership and Usage Fee Structures MEMBERSHIP FEE 24 Hours
$5
30 Days
$25
Annual
$75 USAGE FEE
0-30 Minutes
FREE
31-60 Minutes
+ $1.50
61-90 Minutes
+ $3.00
Each Additional 30 Minutes
+ $6.00
Exhibit 1: Top 10 Stations with Service Failures Station Name
Number of Documented Service Failures in 3 day period
1
19th St & Pennsylvania Ave NW
331
2
19th & L St NW
289
3
US Dept of State / Virginia Ave & 21st St
223
NW 4
12 & Newton St NE
220
5
18th & M St NW
185
6
14th & D St SE
182
7
1800 Martin Luther King
179
8
17th & L Street NW
171
9
Florida Ave & R St NW
169
10
7th & Water St SW / SW Waterfront
148
Exhibit 2: Grid Creation
1
A
2
3
4
6
5
7
8
B
C
D
E F G H I J
K
Exhibit 3: Focused Analysis on Sector F3
3
F
3 Stations
th
• 20 & E St. NW
st
• Virginia Ave. & 21 th
• NW19 & E St. NW
Exhibit 4a: Data Analysis of 20th and E Street NW Station
Number of Bicycles
20th & E Street NW Bicycle Usage (Capacity = 15) 14 12 10 8 6 4 2 0
Number of Bikes
Raw Bikeshare Data Provided by Lance Schine, Chief Information Officer of DDOT Exhibit 4b: Data Analysis of Virginia Ave and 21th Street NW Station
Number of Bicycles
Virginia Ave & 21st Street NW Bicycle Usage (Capacity = 11) 10 8 6 4 2 0
Number of Bikes
Raw
Bikeshare
Data
Provided
by
Lance
Schine,
Chief
Information
Officer
of
DDOT
Exhibit 4c: Data Analysis of 19th and E Street NW Station
19th and E Street Northwest Bycicle Usage (Capacity = 11) Number of Bicycles
12 10 8 6 4 2 0
Number of Bikes
Raw Bikeshare Data Provided by Lance Schine, Chief Information Officer of DDOT Exhibit 5: Locations with most number of bikes on an average
Locations 12 & Newton St NE 8th & H St NW 4th & M St SW 5th St & K St NW Eastern Market - 7th & North Carolina Ave SE 14th & Harvard NW 14th & V St NW Georgetown Harbor / 30th St NW 1st & M Street NE USDA / 12th & Independence Ave SW
Avg # of Bikes
Avg # of Empty Docks
14.3 13.2 11.9 11.3 11.1 11.0 10.9 10.7 10.6 10.4
0.7 11.8 3.1 7.7 3.9 8.0 8.1 8.3 4.4 12.6