User 2 - Adam received a notification from Ricepo - a shift swap from James. He decided to take over a portion of James's shifts.
Adam Peter
Background
Adam Peter, a recent master's graduate, aims to work extra shifts to repay student loans. Due to limited availability, he actively covers shifts for drivers with temporary issues.
Frustrations
Tools Used
performance
• Unable to select shifts promptly
• Not enough shifts at Ricepo
• Google Maps
• Ricepo
• Avg. 3.6 order/hr
• Avg. 0 no show/weekly
• Avg. 1 incomplete shift /wk
User 1 - James Parker has already selected his shifts for next week, but he wants to change a portion of his shift for next week.
James Parker
Background
James delivers food around Bloomington for Ricepo. He is very familiar with other drivers working at Ricepo. Sometimes, they chat while waiting for orders. He often no-shows or arrives late/leaves early for his shifts.
Frustrations
Tools Used
performance
• Traffic and road closures
• Delivery on snowy days is quite troublesome
• Poor performance causes the order delivery ratio to decrease (Less Earning)
• Google Maps
• Waze
• Ricepo
• Avg. 3 order/hr
• Avg. 2 no show/weekly
• Avg. 5 incomplete shift /wk
Driver Product
Driver Product
Driver Product
Driver Product
Driver Product
Driver Product
Driver Product
Driver Product
Date
Jun 2018- Aug 2022
Date
06/2018 - 08/2022
Date
06/2018 - 08/2022
Date
Jun 2018- Aug 2022
Date
06/2018 - 08/2022
Date
06/2018 - 08/2022
Team & Role
Aini - UX & UI
1x Business
2x Developer
2x Driver Ops
1x Data Science
1x Product Manager
Team & Role
Aini - UX & UI
1x Business
2x Developer
2x Driver Ops
1x Data Science
1x Product Manager
Team & Role
Aini - UX & UI
1x Business
2x Developer
2x Driver Ops
1x Data Science
1x Product Manager
Team & Role
Aini - UX & UI
1x Business
2x Developer
2x Driver Ops
1x Data Science
1x Product Manager
Team & Role
Aini - UX & UI
1x Business
2x Developer
2x Driver Ops
1x Data Science
1x Product Manager
Team & Role
Aini - UX & UI
1x Business
2x Developer
2x Driver Ops
1x Data Science
1x Product Manager
Tool
Figma
SQL
Asana
Kibana (Data Visualization)
Tool
Figma
SQL
Asana
Kibana (Data Visualization)
Tool
Figma
SQL
Asana
Kibana (Data Visualization)
Tool
Figma
SQL
Asana
Kibana (Data Visualization)
Tool
Figma
SQL
Asana
Kibana (Data Visualization)
Tool
Figma
SQL
Asana
Kibana (Data Visualization)
My Task - Delivery Product (Driver)
Scenario: At Ricepo, drivers benefit from a unique scheduling system tied to restaurant coverage. The fixed team of drivers ensures stability in their daily income, while experienced drivers play a key role in upholding order quality per hour. Despite our efforts, we cannot guarantee 100% attendance or prevent occasional driver turnover.
Problem: There is an issue of not meeting the order/hr standard. Drivers can face penalties and receive fewer orders due to no show, uncompleted shift, and late shift. leading to dissatisfaction with their earnings.
Solution: Introduce a Swap feature for drivers, allowing them to exchange shift times to address issues such as unavailability, lateness, and early departure.
My Task - Delivery Product (Driver)
Scenario: At Ricepo, drivers benefit from a unique scheduling system tied to restaurant coverage. The fixed team of drivers ensures stability in their daily income, while experienced drivers play a key role in upholding order quality per hour. Despite our efforts, we cannot guarantee 100% attendance or prevent occasional driver turnover.
Problem: There is an issue of not meeting the order/hr standard. Drivers can face penalties and receive fewer orders due to no show, uncompleted shift, and late shift. leading to dissatisfaction with their earnings.
Solution: Introduce a Swap feature for drivers, allowing them to exchange shift times to address issues such as unavailability, lateness, and early departure.
My Task - Delivery Product (Driver)
Scenario: At Ricepo, drivers benefit from a unique scheduling system tied to restaurant coverage. The fixed team of drivers ensures stability in their daily income, while experienced drivers play a key role in upholding order quality per hour. Despite our efforts, we cannot guarantee 100% attendance or prevent occasional driver turnover.
Problem: There is an issue of not meeting the order/hr standard. Drivers can face penalties and receive fewer orders due to no show, uncompleted shift, and late shift. leading to dissatisfaction with their earnings.
Solution: Introduce a Swap feature for drivers, allowing them to exchange shift times to address issues such as unavailability, lateness, and early departure.
My Task - Delivery Product (Driver)
Scenario: At Ricepo, drivers benefit from a unique scheduling system tied to restaurant coverage. The fixed team of drivers ensures stability in their daily income, while experienced drivers play a key role in upholding order quality per hour. Despite our efforts, we cannot guarantee 100% attendance or prevent occasional driver turnover.
Problem: There is an issue of not meeting the order/hr standard. Drivers can face penalties and receive fewer orders due to no show, uncompleted shift, and late shift. leading to dissatisfaction with their earnings.
Solution: Introduce a Swap feature for drivers, allowing them to exchange shift times to address issues such as unavailability, lateness, and early departure.
My Task - Delivery Product (Driver)
Scenario: At Ricepo, drivers benefit from a unique scheduling system tied to restaurant coverage. The fixed team of drivers ensures stability in their daily income, while experienced drivers play a key role in upholding order quality per hour. Despite our efforts, we cannot guarantee 100% attendance or prevent occasional driver turnover.
Problem: There is an issue of not meeting the order/hr standard. Drivers can face penalties and receive fewer orders due to no show, uncompleted shift, and late shift. leading to dissatisfaction with their earnings.
Solution: Introduce a Swap feature for drivers, allowing them to exchange shift times to address issues such as unavailability, lateness, and early departure.
My Task - Delivery Product (Driver)
Scenario: At Ricepo, drivers benefit from a unique scheduling system tied to restaurant coverage. The fixed team of drivers ensures stability in their daily income, while experienced drivers play a key role in upholding order quality per hour. Despite our efforts, we cannot guarantee 100% attendance or prevent occasional driver turnover.
Problem: There is an issue of not meeting the order/hr standard. Drivers can face penalties and receive fewer orders due to no show, uncompleted shift, and late shift. leading to dissatisfaction with their earnings.
Solution: Introduce a Swap feature for drivers, allowing them to exchange shift times to address issues such as unavailability, lateness, and early departure.
Design
Design
Design
Research
Research
Research
Information Architecture
Information Architecture
Information Architecture
Product Design Impacts - Driver's App
After introducing the Swap feature, what impact has it had on the performance of drivers and Ops?
The Swap feature helped the Ricepo drivers earn an additional $87 per week by avoiding system penalties due to driver's no-shows and incomplete shifts.
The Swap feature helped the Ricepo Ops team reduce the average monthly operational costs by $127,000.00 due to driver tickets, calls, and messages.
Product Design Impacts - Driver's App
After introducing the Swap feature, what impact has it had on the performance of drivers and Ops?
The Swap feature helped the Ricepo drivers earn an additional $87 per week by avoiding system penalties due to driver's no-shows and incomplete shifts.
The Swap feature helped the Ricepo Ops team reduce the average monthly operational costs by $127,000.00 due to driver tickets, calls, and messages.
Product Design Impacts - Driver's App
After introducing the Swap feature, what impact has it had on the performance of drivers and Ops?
The Swap feature helped the Ricepo drivers earn an additional $87 per week by avoiding system penalties due to driver's no-shows and incomplete shifts.
The Swap feature helped the Ricepo Ops team reduce the average monthly operational costs by $127,000.00 due to driver tickets, calls, and messages.
Product Design Impacts - Driver's App
After introducing the Swap feature, what impact has it had on the performance of drivers and Ops?
The Swap feature helped the Ricepo drivers earn an additional $87 per week by avoiding system penalties due to driver's no-shows and incomplete shifts.
The Swap feature helped the Ricepo Ops team reduce the average monthly operational costs by $127,000.00 due to driver tickets, calls, and messages.
Product Design Impacts - Driver's App
After introducing the Swap feature, what impact has it had on the performance of drivers and Ops?
The Swap feature helped the Ricepo drivers earn an additional $87 per week by avoiding system penalties due to driver's no-shows and incomplete shifts.
The Swap feature helped the Ricepo Ops team reduce the average monthly operational costs by $127,000.00 due to driver tickets, calls, and messages.
Product Design Impacts - Driver's App
After introducing the Swap feature, what impact has it had on the performance of drivers and Ops?
The Swap feature helped the Ricepo drivers earn an additional $87 per week by avoiding system penalties due to driver's no-shows and incomplete shifts.
The Swap feature helped the Ricepo Ops team reduce the average monthly operational costs by $127,000.00 due to driver tickets, calls, and messages.
User 1 - James Parker has already selected his shifts for next week, but he wants to change a portion of his shift for next week.
User 1 - James Parker has already selected his shifts for next week, but he wants to change a portion of his shift for next week.
James Parker
James Parker
Background
James delivers food around Bloomington for Ricepo. He is very familiar with other drivers working at Ricepo. Sometimes, they chat while waiting for orders. He often no-shows or arrives late/leaves early for his shifts.
Background
James delivers food around Bloomington for Ricepo. He is very familiar with other drivers working at Ricepo. Sometimes, they chat while waiting for orders. He often no-shows or arrives late/leaves early for his shifts.
Frustrations
Frustrations
Tools Used
Tools Used
performance
performance
• Traffic and road closures
• Traffic and road closures
• Delivery on snowy days is quite troublesome
• Delivery on snowy days is quite troublesome
• Poor performance causes the order delivery ratio to decrease (Less Earning)
• Poor performance causes the order delivery ratio to decrease (Less Earning)
• Google Maps
• Google Maps
• Waze
• Waze
• Ricepo
• Ricepo
• Avg. 3 order/hr
• Avg. 3 order/hr
• Avg. 2 no show/weekly
• Avg. 2 no show/weekly
• Avg. 5 incomplete shift /wk
• Avg. 5 incomplete shift /wk
Ambiguous Visualization Design
When drivers check performance data, font, color, and presentation need improvement.
Ambiguous Visualization Design
When drivers check performance data, font, color, and presentation need improvement.
Ambiguous Visualization Design
When drivers check performance data, font, color, and presentation need improvement.
Clear Information Visualization
The information is clearer, color aids categorization, and the shift progress bar is improved.
Clear Information Visualization
The information is clearer, color aids categorization, and the shift progress bar is improved.
Clear Information Visualization
The information is clearer, color aids categorization, and the shift progress bar is improved.
Shift Data Visualization
The shift dashboard is clear, color aids categorization, and the shift progress bar is improved
Shift Data Visualization
The shift dashboard is clear, color aids categorization, and the shift progress bar is improved
Shift Data Visualization
The shift dashboard is clear, color aids categorization, and the shift progress bar is improved
Quick Action
Default settings for the current week's shifts and quick access to next week's shift
Quick Action
Default settings for the current week's shifts and quick access to next week's shift
Quick Action
Default settings for the current week's shifts and quick access to next week's shift
Flexibility
Drivers don’t want to work
Options: 1. Drop a shift
2. Swap a shift
3. No show
Flexibility
Drivers don’t want to work
Options: 1. Drop a shift
2. Swap a shift
3. No show
Flexibility
Drivers don’t want to work
Options: 1. Drop a shift
2. Swap a shift
3. No show
The Optimal Choice
Drivers can choose to swap their entire shift or just specific parts of it, addressing the issue of incomplete shifts
The Optimal Choice
Drivers can choose to swap their entire shift or just specific parts of it, addressing the issue of incomplete shifts
The Optimal Choice
Drivers can choose to swap their entire shift or just specific parts of it, addressing the issue of incomplete shifts
Making A Quick Decision
We partnered with Driver Ops to automate shift scheduling based on operational factors, facilitating faster driver decisions
Making A Quick Decision
We partnered with Driver Ops to automate shift scheduling based on operational factors, facilitating faster driver decisions
Making A Quick Decision
We partnered with Driver Ops to automate shift scheduling based on operational factors, facilitating faster driver decisions
Reconfirming Actions
Drivers have the flexibility to double-check and confirm their decisions regarding swapping or dropping shifts
Reconfirming Actions
Drivers have the flexibility to double-check and confirm their decisions regarding swapping or dropping shifts
Reconfirming Actions
Drivers have the flexibility to double-check and confirm their decisions regarding swapping or dropping shifts
Choose Driver
The system displays only conflict-free, same-zone drivers, enhancing swap success and speed
Choose Driver
The system displays only conflict-free, same-zone drivers, enhancing swap success and speed
Choose Driver
The system displays only conflict-free, same-zone drivers, enhancing swap success and speed
Final Confirmation
The system summarizes shift swap details, allowing corrections by clicking blue text if needed
Final Confirmation
The system summarizes shift swap details, allowing corrections by clicking blue text if needed
Final Confirmation
The system summarizes shift swap details, allowing corrections by clicking blue text if needed
Custom Loading Animations
Specially crafted handshake animation creates a cooperative and friendly atmosphere for drivers
Custom Loading Animations
Specially crafted handshake animation creates a cooperative and friendly atmosphere for drivers
Custom Loading Animations
Specially crafted handshake animation creates a cooperative and friendly atmosphere for drivers
System Confirmation
After confirmation, drivers receive a status message and can check their updated schedule by clicking 'My Shift'
System Confirmation
After confirmation, drivers receive a status message and can check their updated schedule by clicking 'My Shift'
System Confirmation
After confirmation, drivers receive a status message and can check their updated schedule by clicking 'My Shift'
Swap - Partial Shift
Swap - Partial Shift
Swap - Partial Shift
Swap - Partial Shift
Swap - Partial Shift
Swap - Partial Shift
Guiding James to make better choices
Guiding James to make better choices
Swap - Partial Shift
Swap - Partial Shift
Swap - Partial Shift
Swap - Partial Shift
Swap - Partial Shift
Swap - Partial Shift
James swaps a partial shift with another driver
James swaps a partial shift with another driver
User 2 - Adam received a notification from Ricepo about a shift swap from James. He decided to take over a portion of James's shifts.
User 2 - Adam received a notification from Ricepo about a shift swap from James. He decided to take over a portion of James's shifts.
Adam Peter
Adam Peter
Background
Adam Peter, a recent master's graduate, aims to work extra shifts to repay student loans. Due to limited availability, he actively covers shifts for drivers with temporary issues.
Background
Adam Peter, a recent master's graduate, aims to work extra shifts to repay student loans. Due to limited availability, he actively covers shifts for drivers with temporary issues.
Frustrations
Tools Used
performance
• Unable to select shifts promptly
• Not enough shifts at Ricepo
• Google Maps
• Ricepo
• Avg. 3.6 order/hr
• Avg. 0 no show/weekly
• Avg. 1 incomplete shift /wk
Frustrations
Tools Used
performance
• Not enough shifts at Ricepo
• Unable to select shifts promptly
• Google Maps
• Ricepo
• Avg. 3.6 order/hr
• Avg. 0 no show/weekly
• Avg. 1 incomplete shift /wk
Timely Notification
Ricepo will send a push notification to inform Adam that James wants to swap shifts with him
Timely Notification
Ricepo will send a push notification to inform Adam that James wants to swap shifts with him
Timely Notification
Ricepo will send a push notification to inform Adam that James wants to swap shifts with him
Differentiation Of Shift Status
After Adam opens the notification, he is directed to the shift page, where he can click "Take" to proceed with the swap
Differentiation Of Shift Status
After opening the notification, Adam can click 'Take' on the shift page to proceed with the swap
Differentiation Of Shift Status
After opening the notification, Adam can click 'Take' on the shift page to proceed with the swap
Differentiation Of Shift Status
After Adam opens the notification, he is directed to the shift page, where he can click "Take" to proceed with the swap
Differentiation Of Shift Status
After Adam opens the notification, he is directed to the shift page, where he can click "Take" to proceed with the swap
Differentiation Of Shift Status
After opening the notification, Adam can click 'Take' on the shift page to proceed with the swap
Making The Decision
Adam can either Take the shift directly, Reject it, or Call James for further discussion
Making The Decision
Adam can either Take the shift directly, Reject it, or Call James for further discussion
Making The Decision
Adam can either Take the shift directly, Reject it, or Call James for further discussion
New Shift Created
Adam accepted the new shift, and the shift page automatically updated the schedule
New Shift Created
Adam accepted the new shift, and the shift page automatically updated the schedule
New Shift Created
Adam accepted the new shift, and the shift page automatically updated the schedule
My Research
Mechanism Research
Quantitative research
Qualitative research
System Research
Operational Research
My Research
Mechanism Research
Quantitative research
Qualitative research
System Research
Operational Research
My Research
Mechanism Research
Quantitative research
Qualitative research
System Research
Operational Research
My Research
Mechanism Research
Quantitative research
Qualitative research
System Research
Operational Research
My Research
Mechanism Research
Quantitative research
Qualitative research
System Research
Operational Research
My Research
Mechanism Research
Quantitative research
Qualitative research
System Research
Operational Research
Mechanism Factors Affecting Driver's Performance
After collaboration and discussions with various departments, I have concluded that a driver's performance can be categorized into passive and active mechanisms.
Mechanism Factors Affecting Driver's Performance
After collaboration and discussions with various departments, I have concluded that a driver's performance can be categorized into passive and active mechanisms.
Mechanism Factors Affecting Driver's Performance
After collaboration and discussions with various departments, I have concluded that a driver's performance can be categorized into passive and active mechanisms.
Mechanism Factors Affecting Driver's Performance
After collaboration and discussions with various departments, I have concluded that a driver's performance can be categorized into passive and active mechanisms.
Mechanism Factors Affecting Driver's Performance
After collaboration and discussions with various departments, I have concluded that a driver's performance can be categorized into passive and active mechanisms.
Mechanism Factors Affecting Driver's Performance
After collaboration and discussions with various departments, I have concluded that a driver's performance can be categorized into passive and active mechanisms.
How to determine if the issue primarily stems from the driver themselves, the driver onboarding process, or the driver's app backend system issues?
How to determine if the issue primarily stems from the driver themselves, the driver onboarding process, or the driver's app backend system issues?
How to determine if the issue primarily stems from the driver themselves, the driver onboarding process, or the driver's app backend system issues?
How to determine if the issue primarily stems from the driver themselves, the driver onboarding process, or the driver's app backend system issues?
How to determine if the issue primarily stems from the driver themselves, the driver onboarding process, or the driver's app backend system issues?
How to determine if the issue primarily stems from the driver themselves, the driver onboarding process, or the driver's app backend system issues?
[Quantitative Research] Low-performance data.
Firstly, I reviewed the profiles and historical data of drivers performing poorly in different cities. I found that the lower the attendance rate, the worse the performance.
100% of drivers have had incomplete shifts.
82% of drivers have experienced shift no-shows.
60% of drivers recognize that their poor performance poses a risk of lower income.
[Quantitative Research] Low-performance data.
Firstly, I reviewed the profiles and historical data of drivers performing poorly in different cities. I found that the lower the attendance rate, the worse the performance.
100% of drivers have had incomplete shifts.
82% of drivers have experienced shift no-shows.
60% of drivers recognize that their poor performance poses a risk of lower income.
[Quantitative Research] Low-performance data.
Firstly, I reviewed the profiles and historical data of drivers performing poorly in different cities. I found that the lower the attendance rate, the worse the performance.
100% of drivers have had incomplete shifts.
82% of drivers have experienced shift no-shows.
60% of drivers recognize that their poor performance poses a risk of lower income.
[Qualitative Research] Complaints from drivers.
Secondly, I collected the reasons for no-shows or incomplete shifts among underperforming drivers. The distribution is as follows:
[Qualitative Research] Complaints from drivers.
Secondly, I collected the reasons for no-shows or incomplete shifts among underperforming drivers. The distribution is as follows:
[Qualitative Research] Complaints from drivers.
Secondly, I collected the reasons for no-shows or incomplete shifts among underperforming drivers. The distribution is as follows:
[System Research] Backend System: Driver Location (Reference Value).
First, I collected feedback from the driver-side deputy and investigated some system problems that drivers had previously complained about. For instance, there were instances where drivers were not actually late, but the system incorrectly identified them as such. By examining the location data with the Dev team and BD team, when drivers come online, we discovered a discrepancy between the location where the drivers come online and the delivery zoom. Our delivery zones are planned based on the restaurant's range, and as the number of restaurants expands, the footprint of the delivery zoom also expands. If the data for the new delivery zone is not updated, the system cannot recognize the driver's location when they come online in the new delivery zoom.
[System Research] Backend System: Driver Location (Reference Value).
First, I collected feedback from the driver-side deputy and investigated some system problems that drivers had previously complained about. For instance, there were instances where drivers were not actually late, but the system incorrectly identified them as such. By examining the location data with the Dev team and BD team, when drivers come online, we discovered a discrepancy between the location where the drivers come online and the delivery zoom. Our delivery zones are planned based on the restaurant's range, and as the number of restaurants expands, the footprint of the delivery zoom also expands. If the data for the new delivery zone is not updated, the system cannot recognize the driver's location when they come online in the new delivery zoom.
[System Research] Backend System: Driver Location (Reference Value).
First, I collected feedback from the driver-side deputy and investigated some system problems that drivers had previously complained about. For instance, there were instances where drivers were not actually late, but the system incorrectly identified them as such. By examining the location data with the Dev team and BD team, when drivers come online, we discovered a discrepancy between the location where the drivers come online and the delivery zoom. Our delivery zones are planned based on the restaurant's range, and as the number of restaurants expands, the footprint of the delivery zoom also expands. If the data for the new delivery zone is not updated, the system cannot recognize the driver's location when they come online in the new delivery zoom.
System: Delivery Zoom
Actual: Delivery Zoom
System: Delivery Zoom
Actual: Delivery Zoom
System: Delivery Zoom
Actual: Delivery Zoom
System: Delivery Zoom
Actual: Delivery Zoom
System: Delivery Zoom
Actual: Delivery Zoom
System: Delivery Zoom
Actual: Delivery Zoom
[Operational Research] Data Science: Accuracy In Scheduling (Reference Value).
A backend algorithm governs the driver's scheduling. Various conditions can influence the algorithm, such as the number of orders, average order per hour values, and the restaurant's average serving speed, among others. All of these factors impact the number of shifts available for drivers at different times. Understanding the mechanism of the algorithm is crucial for better assisting driver performance.
D>D′; Inew<Iold
The # of driver shifts > The actual demand
Cause: Average driver income↓
Driver's action: Drop shifts/ Incomplete shifts
D<D′; Onew>Oold; Pnew>Pold
The # of driver shifts < The actual demand
Cause: The total dropped orders↑
Cause: The total negative feedback to the drive↑
Driver's action: Working overtime / Speeding delivery
[Operational Research] Data Science: Accuracy In Scheduling (Reference Value).
A backend algorithm governs the driver's scheduling. Various conditions can influence the algorithm, such as the number of orders, average order per hour values, and the restaurant's average serving speed, among others. All of these factors impact the number of shifts available for drivers at different times. Understanding the mechanism of the algorithm is crucial for better assisting driver performance.
D>D′; Inew<Iold
The # of driver shifts > The actual demand
Cause: Average driver income↓
Driver's action: Drop shifts/ Incomplete shifts
D<D′; Onew>Oold; Pnew>Pold
The # of driver shifts < The actual demand
Cause: The total dropped orders↑
Cause: The total negative feedback to the drive↑
Driver's action: Working overtime / Speeding delivery
Data Science: Accuracy In Scheduling (Reference Value).
A backend algorithm governs the driver's scheduling. Various conditions can influence the algorithm, such as the number of orders, average order per hour values, and the restaurant's average serving speed, among others. All of these factors impact the number of shifts available for drivers at different times. Understanding the mechanism of the algorithm is crucial for better assisting driver performance.
D>D′; Inew<Iold
The # of driver shifts > The actual demand
Cause: Average driver income↓
Driver's action: Drop shifts/ Incomplete shifts
D<D′; Onew>Oold; Pnew>Pold;
The # of driver shifts < The actual demand
Cause: The total dropped orders↑
Cause: The total negative feedback to the drive↑
Driver's action: Working overtime / Speeding delivery
Ideation
How do I come up with the best-fit design solution to help drivers improve performance?
1. How can I filter all the issues to identify the most impactful ones to solve for?
2. What are the possible solutions and how to prioritize?
3. How can I collaborate with others to build the solution?
Ideation
How do I come up with the best-fit design solution to help drivers improve performance?
1. How can I filter all the issues to identify the most impactful ones to solve for?
2. What are the possible solutions and how to prioritize?
3. How can I collaborate with others to build the solution?
Ideation
How do I come up with the best-fit design solution to help drivers improve performance?
1. How can I filter all the issues to identify the most impactful ones to solve for?
2. What are the possible solutions and how to prioritize?
3. How can I collaborate with others to build the solution?
How can I filter all the issues to identify the most impactful ones to solve for?
Step 1: Clarify the corresponding departments and responsibility mechanisms to eliminate infeasibility.
Providing information on passive and active mechanisms. I can extend this to assess the feasibility of the design plan and identify corresponding departments.
How can I filter all the issues to identify the most impactful ones to solve for?
Step 1: Clarify the corresponding departments and responsibility mechanisms to eliminate infeasibility.
Providing information on passive and active mechanisms. I can extend this to assess the feasibility of the design plan and identify corresponding departments.
How can I filter all the issues to identify the most impactful ones to solve for?
Step 1: Clarify the corresponding departments and responsibility mechanisms to eliminate infeasibility.
Providing information on passive and active mechanisms. I can extend this to assess the feasibility of the design plan and identify corresponding departments.
Step 2: The key performance indicator for drivers are the Orders/Hr (OPH), Attendence Rate, & Late Drop Off Rate.
Ricepo classifies drivers into three levels based on several factors, including their order/hr rate, attendance rate, frequency of late-drop shifts, customer reviews, and accuracy in delivering orders.
After understanding the weightage of different indicators, I selected the indicators with the highest weight for my research. I focused primarily on the driver attendance rate, the number of shifts dropped by drivers, and the order/hr.
To determine the most impactful among these three metrics, I took further steps in my investigation.
Step 2: The key performance indicator for drivers are the Orders/Hr (OPH), Attendence Rate, & Late Drop Off Rate.
Ricepo classifies drivers into three levels based on several factors, including their order/hr rate, attendance rate, frequency of late-drop shifts, customer reviews, and accuracy in delivering orders.
After understanding the weightage of different indicators, I selected the indicators with the highest weight for my research. I focused primarily on the driver attendance rate, the number of shifts dropped by drivers, and the order/hr.
To determine the most impactful among these three metrics, I took further steps in my investigation.
Step 2: The key performance indicator for drivers are the Orders/Hr (OPH), Attendence Rate, & Late Drop Off Rate.
Ricepo classifies drivers into three levels based on several factors, including their order/hr rate, attendance rate, frequency of late-drop shifts, customer reviews, and accuracy in delivering orders.
After understanding the weightage of different indicators, I selected the indicators with the highest weight for my research. I focused primarily on the driver attendance rate, the number of shifts dropped by drivers, and the order/hr.
To determine the most impactful among these three metrics, I took further steps in my investigation.
Step 3: Verify the correlation between the data and the low performance.
I collaborated with the DS Team to conduct a comprehensive analysis of drivers with low performance and the corresponding low OPH (Orders Per Hour) time. We also examined records of past system routes.
Our findings indicate a direct proportionality between OPH and driver performance. On days when driver performance is lower, OPH is also lower.
The primary reasons for drivers' low performance are identified as NS (No Show) and INC (Incomplete shifts). These behaviors cannot be predicted before the start of the shift, narrowing the scope of the issue to real-time delivery operations.
Step 3: Verify the correlation between the data and the low performance.
I collaborated with the DS Team to conduct a comprehensive analysis of drivers with low performance and the corresponding low OPH (Orders Per Hour) time. We also examined records of past system routes.
Our findings indicate a direct proportionality between OPH and driver performance. On days when driver performance is lower, OPH is also lower.
The primary reasons for drivers' low performance are identified as NS (No Show) and INC (Incomplete shifts). These behaviors cannot be predicted before the start of the shift, narrowing the scope of the issue to real-time delivery operations.
Step 3: Verify the correlation between the data and the low performance.
I collaborated with the DS Team to conduct a comprehensive analysis of drivers with low performance and the corresponding low OPH (Orders Per Hour) time. We also examined records of past system routes.
Our findings indicate a direct proportionality between OPH and driver performance. On days when driver performance is lower, OPH is also lower.
The primary reasons for drivers' low performance are identified as NS (No Show) and INC (Incomplete shifts). These behaviors cannot be predicted before the start of the shift, narrowing the scope of the issue to real-time delivery operations.
2. What are the possible solutions and how to prioritize?
Velocity of decision-making
Center of excellence
Flaten of reporting line
Impacts towards OPH
Cost behind features
After discussions with Driver Ops, DS, and Dev departments, the Swap functionality has been identified as the most feasible solution.
2. What are the possible solutions and how to prioritize?
Velocity of decision-making
Center of excellence
Flaten of reporting line
Impacts towards OPH
Cost behind features
After discussions with Driver Ops, DS, and Dev departments, the Swap functionality has been identified as the most feasible solution.
2. What are the possible solutions and how to prioritize?
Velocity of decision-making
Center of excellence
Flaten of reporting line
Impacts towards OPH
Cost behind features
After discussions with Driver Ops, DS, and Dev departments, the Swap functionality has been identified as the most feasible solution.
2. What are the possible solutions and how to prioritize?
Velocity of decision-making
Center of excellence
Flaten of reporting line
Impacts towards OPH
Cost behind features
After discussions with Driver Ops, DS, and Dev departments, the Swap functionality has been identified as the most feasible solution.
2. What are the possible solutions and how to prioritize?
Velocity of decision-making
Center of excellence
Flaten of reporting line
Impacts towards OPH
Cost behind features
After discussions with Driver Ops, DS, and Dev departments, the Swap functionality has been identified as the most feasible solution.
2. What are the possible solutions and how to prioritize?
Velocity of decision-making
Center of excellence
Flaten of reporting line
Impacts towards OPH
Cost behind features
After discussions with Driver Ops, DS, and Dev departments, the Swap functionality has been identified as the most feasible solution.
3. How can I collaborate with others to build the solution?
Since the project's inception, I've collaborated with multiple departments to discuss metrics affecting driver performance. Considering time costs and task priorities across departments, I've confirmed the best-fit solution and provided data to the product manager. Short story: (Initially, some of my ideas weren't accepted by the PM, and communication with the dev team was a bit challenging. However, through more effective communication and effort, we eventually reached consensus.) Contact me if you want to know more.
3. How can I collaborate with others to build the solution?
Since the project's inception, I've collaborated with multiple departments to discuss metrics affecting driver performance. Considering time costs and task priorities across departments, I've confirmed the best-fit solution and provided data to the product manager.
Short story: (Initially, some of my ideas weren't accepted by the PM, and communication with the dev team was a bit challenging. However, through more effective communication and effort, we eventually reached consensus.) Contact me if you want to know more.
3. How can I collaborate with others to build the solution?
Since the project's inception, I've collaborated with multiple departments to discuss metrics affecting driver performance. Considering time costs and task priorities across departments, I've confirmed the best-fit solution and provided data to the product manager.
Short story: (Initially, some of my ideas weren't accepted by the PM, and communication with the dev team was a bit challenging. However, through more effective communication and effort, we eventually reached consensus.) Contact me if you want to know more.
3. How can I collaborate with others to build the solution?
Since the project's inception, I've collaborated with multiple departments to discuss metrics affecting driver performance. Considering time costs and task priorities across departments, I've confirmed the best-fit solution and provided data to the product manager. Short story: (Initially, some of my ideas weren't accepted by the PM, and communication with the dev team was a bit challenging. However, through more effective communication and effort, we eventually reached consensus.) Contact me if you want to know more.
3. How can I collaborate with others to build the solution?
Since the project's inception, I've collaborated with multiple departments to discuss metrics affecting driver performance. Considering time costs and task priorities across departments, I've confirmed the best-fit solution and provided data to the product manager.
Short story: (Initially, some of my ideas weren't accepted by the PM, and communication with the dev team was a bit challenging. However, through more effective communication and effort, we eventually reached consensus.) Contact me if you want to know more.
3. How can I collaborate with others to build the solution?
Since the project's inception, I've collaborated with multiple departments to discuss metrics affecting driver performance. Considering time costs and task priorities across departments, I've confirmed the best-fit solution and provided data to the product manager.
Short story: (Initially, some of my ideas weren't accepted by the PM, and communication with the dev team was a bit challenging. However, through more effective communication and effort, we eventually reached consensus.) Contact me if you want to know more.
Why Swap?
“
The drivers control their own time instead of being controlled by work hours.
Pro
Flexibility: Enables drivers to efficiently manage schedules.
Adaptability: Adapts to unforeseen circumstances or personal commitments.
User-Friendly Design: Enhances the overall user experience for all drivers in different places.
Employee-Focused: Improves driver satisfaction and engagement.
Operational Resilience: Contributes to a dynamic and resilient operating structure.
Improved Coverage: Ensures better coverage and responsiveness to changing needs in delivery services.
Cost Reduction: Drivers can independently address issues, reducing the number of tickets received by the operations team and lowering operating costs.
Con
Consistency Concerns: Frequent shift swaps might lead to inconsistent service quality if not monitored closely. This could impact overall operational reliability.
System Abuse: There's a risk that some drivers may abuse the system, frequently swapping shifts without genuine reasons, which can impact overall scheduling stability.
Training and Familiarity: Introducing a new feature requires training for drivers to use it effectively. Ensuring that all drivers are familiar with and properly use the swap shifts feature is crucial.
Why Swap?
“
The drivers control their own time instead of being controlled by work hours.
Pro
Flexibility: Enables drivers to efficiently manage schedules.
Adaptability: Adapts to unforeseen circumstances or personal commitments.
User-Friendly Design: Enhances the overall user experience for all drivers in different places.
Employee-Focused: Improves driver satisfaction and engagement.
Operational Resilience: Contributes to a dynamic and resilient operating structure.
Improved Coverage: Ensures better coverage and responsiveness to changing needs in delivery services.
Cost Reduction: Drivers can independently address issues, reducing the number of tickets received by the operations team and lowering operating costs.
Con
Consistency Concerns: Frequent shift swaps might lead to inconsistent service quality if not monitored closely. This could impact overall operational reliability.
System Abuse: There's a risk that some drivers may abuse the system, frequently swapping shifts without genuine reasons, which can impact overall scheduling stability.
Training and Familiarity: Introducing a new feature requires training for drivers to use it effectively. Ensuring that all drivers are familiar with and properly use the swap shifts feature is crucial.
Why Swap?
“
The drivers control their own time instead of being controlled by work hours.
Pro
Flexibility: Enables drivers to efficiently manage schedules.
Adaptability: Adapts to unforeseen circumstances or personal commitments.
User-Friendly Design: Enhances the overall user experience for all drivers in different places.
Employee-Focused: Improves driver satisfaction and engagement.
Operational Resilience: Contributes to a dynamic and resilient operating structure.
Improved Coverage: Ensures better coverage and responsiveness to changing needs in delivery services.
Cost Reduction: Drivers can independently address issues, reducing the number of tickets received by the operations team and lowering operating costs.
Con
Consistency Concerns: Frequent shift swaps might lead to inconsistent service quality if not monitored closely. This could impact overall operational reliability.
System Abuse: There's a risk that some drivers may abuse the system, frequently swapping shifts without genuine reasons, which can impact overall scheduling stability.
Training and Familiarity: Introducing a new feature requires training for drivers to use it effectively. Ensuring that all drivers are familiar with and properly use the swap shifts feature is crucial.
Why Swap?
“
The drivers control their own time instead of being controlled by work hours.
Pro
Flexibility: Enables drivers to efficiently manage schedules.
Adaptability: Adapts to unforeseen circumstances or personal commitments.
User-Friendly Design: Enhances the overall user experience for all drivers in different places.
Employee-Focused: Improves driver satisfaction and engagement.
Operational Resilience: Contributes to a dynamic and resilient operating structure.
Improved Coverage: Ensures better coverage and responsiveness to changing needs in delivery services.
Cost Reduction: Drivers can independently address issues, reducing the number of tickets received by the operations team and lowering operating costs.
Con
Consistency Concerns: Frequent shift swaps might lead to inconsistent service quality if not monitored closely. This could impact overall operational reliability.
System Abuse: There's a risk that some drivers may abuse the system, frequently swapping shifts without genuine reasons, which can impact overall scheduling stability.
Training and Familiarity: Introducing a new feature requires training for drivers to use it effectively. Ensuring that all drivers are familiar with and properly use the swap shifts feature is crucial.
Why Swap?
“
The drivers control their own time instead of being controlled by work hours.
Pro
Flexibility: Enables drivers to efficiently manage schedules.
Adaptability: Adapts to unforeseen circumstances or personal commitments.
User-Friendly Design: Enhances the overall user experience for all drivers in different places.
Employee-Focused: Improves driver satisfaction and engagement.
Operational Resilience: Contributes to a dynamic and resilient operating structure.
Improved Coverage: Ensures better coverage and responsiveness to changing needs in delivery services.
Cost Reduction: Drivers can independently address issues, reducing the number of tickets received by the operations team and lowering operating costs.
Con
Consistency Concerns: Frequent shift swaps might lead to inconsistent service quality if not monitored closely. This could impact overall operational reliability.
System Abuse: There's a risk that some drivers may abuse the system, frequently swapping shifts without genuine reasons, which can impact overall scheduling stability.
Training and Familiarity: Introducing a new feature requires training for drivers to use it effectively. Ensuring that all drivers are familiar with and properly use the swap shifts feature is crucial.
Why Swap?
“
The drivers control their own time instead of being controlled by work hours.
Pro
Flexibility: Enables drivers to efficiently manage schedules.
Adaptability: Adapts to unforeseen circumstances or personal commitments.
User-Friendly Design: Enhances the overall user experience for all drivers in different places.
Employee-Focused: Improves driver satisfaction and engagement.
Operational Resilience: Contributes to a dynamic and resilient operating structure.
Improved Coverage: Ensures better coverage and responsiveness to changing needs in delivery services.
Cost Reduction: Drivers can independently address issues, reducing the number of tickets received by the operations team and lowering operating costs.
Con
Consistency Concerns: Frequent shift swaps might lead to inconsistent service quality if not monitored closely. This could impact overall operational reliability.
System Abuse: There's a risk that some drivers may abuse the system, frequently swapping shifts without genuine reasons, which can impact overall scheduling stability.
Training and Familiarity: Introducing a new feature requires training for drivers to use it effectively. Ensuring that all drivers are familiar with and properly use the swap shifts feature is crucial.
Information Architecture
Ricepo drivers have diverse backgrounds. To ensure that all Ricepo drivers can effortlessly learn the Swap feature and improve their performance, instead of designing a learning mechanism, it's better to provide them with a self-adjusting solution. Although Swap is a highly ideal design solution, I still encounter with three design challenges.
1. How might we reduce the time it takes for drivers to learn the Swap feature?
2. How might we ensure that the Swap feature enables substitute drivers to promptly receive information and react quickly?
3. How might we lower the potential for drivers to abuse the Swap feature?
Information Architecture
Ricepo drivers have diverse backgrounds. To ensure that all Ricepo drivers can effortlessly learn the Swap feature and improve their performance, instead of designing a learning mechanism, it's better to provide them with a self-adjusting solution. Although Swap is a highly ideal design solution, I still encounter with three design challenges.
1. How might we reduce the time it takes for drivers to learn the Swap feature?
2. How might we ensure that the Swap feature enables substitute drivers to promptly receive information and react quickly?
3. How might we lower the potential for drivers to abuse the Swap feature?
Information Architecture
Ricepo drivers have diverse backgrounds. To ensure that all Ricepo drivers can effortlessly learn the Swap feature and improve their performance, instead of designing a learning mechanism, it's better to provide them with a self-adjusting solution. Although Swap is a highly ideal design solution, I still encounter with three design challenges.
1. How might we reduce the time it takes for drivers to learn the Swap feature?
2. How might we ensure that the Swap feature enables substitute drivers to promptly receive information and react quickly?
3. How might we lower the potential for drivers to abuse the Swap feature?
Information Architecture
Ricepo drivers have diverse backgrounds. To ensure that all Ricepo drivers can effortlessly learn the Swap feature and improve their performance, instead of designing a learning mechanism, it's better to provide them with a self-adjusting solution. Although Swap is a highly ideal design solution, I still encounter with three design challenges.
1. How might we reduce the time it takes for drivers to learn the Swap feature?
2. How might we ensure that the Swap feature enables substitute drivers to promptly receive information and react quickly?
3. How might we lower the potential for drivers to abuse the Swap feature?
Information Architecture
Ricepo drivers have diverse backgrounds. To ensure that all Ricepo drivers can effortlessly learn the Swap feature and improve their performance, instead of designing a learning mechanism, it's better to provide them with a self-adjusting solution. Although Swap is a highly ideal design solution, I still encounter with three design challenges.
1. How might we reduce the time it takes for drivers to learn the Swap feature?
2. How might we ensure that the Swap feature enables substitute drivers to promptly receive information and react quickly?
3. How might we lower the potential for drivers to abuse the Swap feature?
Information Architecture
Ricepo drivers have diverse backgrounds. To ensure that all Ricepo drivers can effortlessly learn the Swap feature and improve their performance, instead of designing a learning mechanism, it's better to provide them with a self-adjusting solution. Although Swap is a highly ideal design solution, I still encounter with three design challenges.
1. How might we reduce the time it takes for drivers to learn the Swap feature?
2. How might we ensure that the Swap feature enables substitute drivers to promptly receive information and react quickly?
3. How might we lower the potential for drivers to abuse the Swap feature?
Testing & iteration & Feedback
To facilitate rapid feedback and iterative refinement of the new design, we employ agile testing for swift design and program responsiveness. We conducted tests in various delivery zones, including the San Francisco Bay Area, Boston Allston, and New York City. Our findings are as follows:
46% of active drivers, on average, use the swap feature weekly.
The average attendance rate of drivers who use the swap feature reaches 89% per week.
Cities with higher attendance rates experience a greater increase in Order/hr (varied based on zoom size and transportation options).
Tickets sent by drivers to the driver operations department reduced by 36%.
Testing & iteration & Feedback
To facilitate rapid feedback and iterative refinement of the new design, we employ agile testing for swift design and program responsiveness. We conducted tests in various delivery zones, including the San Francisco Bay Area, Boston Allston, and New York City. Our findings are as follows:
46% of active drivers, on average, use the swap feature weekly.
The average attendance rate of drivers who use the swap feature reaches 89% per week.
Cities with higher attendance rates experience a greater increase in Order/hr (varied based on zoom size and transportation options).
Tickets sent by drivers to the driver operations department reduced by 36%.
Testing & iteration & Feedback
To facilitate rapid feedback and iterative refinement of the new design, we employ agile testing for swift design and program responsiveness. We conducted tests in various delivery zones, including the San Francisco Bay Area, Boston Allston, and New York City. Our findings are as follows:
46% of active drivers, on average, use the swap feature weekly.
The average attendance rate of drivers who use the swap feature reaches 89% per week.
Cities with higher attendance rates experience a greater increase in Order/hr (varied based on zoom size and transportation options).
Tickets sent by drivers to the driver operations department reduced by 36%.
Testing & iteration & Feedback
To facilitate rapid feedback and iterative refinement of the new design, we employ agile testing for swift design and program responsiveness. We conducted tests in various delivery zones, including the San Francisco Bay Area, Boston Allston, and New York City. Our findings are as follows:
46% of active drivers, on average, use the swap feature weekly.
The average attendance rate of drivers who use the swap feature reaches 89% per week.
Cities with higher attendance rates experience a greater increase in Order/hr (varied based on zoom size and transportation options).
Tickets sent by drivers to the driver operations department reduced by 36%.
Testing & iteration & Feedback
To facilitate rapid feedback and iterative refinement of the new design, we employ agile testing for swift design and program responsiveness. We conducted tests in various delivery zones, including the San Francisco Bay Area, Boston Allston, and New York City. Our findings are as follows:
46% of active drivers, on average, use the swap feature weekly.
The average attendance rate of drivers who use the swap feature reaches 89% per week.
Cities with higher attendance rates experience a greater increase in Order/hr (varied based on zoom size and transportation options).
Tickets sent by drivers to the driver operations department reduced by 36%.
Testing & iteration & Feedback
To facilitate rapid feedback and iterative refinement of the new design, we employ agile testing for swift design and program responsiveness. We conducted tests in various delivery zones, including the San Francisco Bay Area, Boston Allston, and New York City. Our findings are as follows:
46% of active drivers, on average, use the swap feature weekly.
The average attendance rate of drivers who use the swap feature reaches 89% per week.
Cities with higher attendance rates experience a greater increase in Order/hr (varied based on zoom size and transportation options).
Tickets sent by drivers to the driver operations department reduced by 36%.
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Product Design
The Roasters Pack - Coffee Plan, 2023
UX | UI
UX | UI
UX | UI
UX | UI
UX | UI
The Roasters Pack - Coffee Plan, 2023
The Roasters Pack - Coffee Plan, 2023
The Roasters Pack - Coffee Plan, 2023
The Roasters Pack - Coffee Plan, 2023
The Roasters Pack - Coffee Plan, 2023
User 1 - James Parker has already selected his shifts for next week, but he wants to change a portion of his shift for next week.
James Parker
Background
James delivers food around Bloomington for Ricepo. He is very familiar with other drivers working at Ricepo. Sometimes, they chat while waiting for orders. He often no-shows or arrives late/leaves early for his shifts.
Frustrations
Tools Used
performance
• Traffic and road closures
• Delivery on snowy days is quite troublesome
• Poor performance causes the order delivery ratio to decrease (Less Earning)
• Google Maps
• Waze
• Ricepo
• Avg. 3 order/hr
• Avg. 2 no show/weekly
• Avg. 5 incomplete shift /wk
User 2 - Adam received a notification from Ricepo about a shift swap from James. He decided to take over a portion of James's shifts.
Adam Peter
Background
Adam Peter, a recent master's graduate, aims to work extra shifts to repay student loans. Due to limited availability, he actively covers shifts for drivers with temporary issues.
Frustrations
Tools Used
performance
• Unable to select shifts promptly
• Not enough shifts at Ricepo
• Google Maps
• Ricepo
• Avg. 3.6 order/hr
• Avg. 0 no show/weekly
• Avg. 1 incomplete shift /wk
[Quantitative Research] Low-performance data.
Firstly, I reviewed the profiles and historical data of drivers performing poorly in different cities. I found that the lower the attendance rate, the worse the performance.
100% of drivers have had incomplete shifts.
82% of drivers have experienced shift no-shows.
60% of drivers recognize that their poor performance poses a risk of lower income.
[Qualitative Research] Complaints from drivers.
Secondly, I collected the reasons for no-shows or incomplete shifts among underperforming drivers. The distribution is as follows:
[System Research] Backend System: Driver Location (Reference Value).
First, I collected feedback from the driver-side deputy and investigated some system problems that drivers had previously complained about. For instance, there were instances where drivers were not actually late, but the system incorrectly identified them as such. By examining the location data with the Dev team and BD team, when drivers come online, we discovered a discrepancy between the location where the drivers come online and the delivery zoom. Our delivery zones are planned based on the restaurant's range, and as the number of restaurants expands, the footprint of the delivery zoom also expands. If the data for the new delivery zone is not updated, the system cannot recognize the driver's location when they come online in the new delivery zoom.
[Operational Research] Data Science: Accuracy In Scheduling (Reference Value).
A backend algorithm governs the driver's scheduling. Various conditions can influence the algorithm, such as the number of orders, average order per hour values, and the restaurant's average serving speed, among others. All of these factors impact the number of shifts available for drivers at different times. Understanding the mechanism of the algorithm is crucial for better assisting driver performance.
D>D′; Inew<Iold
The # of driver shifts > The actual demand
Cause: Average driver income↓
Driver's action: Drop shifts/ Incomplete shifts
D<D′; Onew>Oold; Pnew>Pold
The # of driver shifts < The actual demand
Cause: The total dropped orders↑
Cause: The total negative feedback to the drive↑
Driver's action: Working overtime / Speeding delivery
Ideation
How do I come up with the best-fit design solution to help drivers improve performance?
1. How can I filter all the issues to identify the most impactful ones to solve for?
2. What are the possible solutions and how to prioritize?
3. How can I collaborate with others to build the solution?
How do I filter issues to identify the most impactful ones to solve?
Step 1: Clarify the corresponding departments and responsibility mechanisms to eliminate infeasibility.
Providing information on passive and active mechanisms. I can extend this to assess the feasibility of the design plan and identify corresponding departments.