Fixes That Fail in Product Management
Why Quick Solutions Can Lead to Long-Term Problems. A detailed case study on Fixes That Fail in Food Industry.
Food delivery businesses are always looking for ways to deliver food faster and optimize operations. In the rush to improve efficiency, companies often introduce quick fixes to solve immediate problems. But here’s the catch—these short-term solutions can end up causing bigger issues down the road. This is what the systems thinking archetype called "Fixes That Fail" is all about.
Let's dive into how this pattern plays out in food delivery, what the data reveals, and what you as product managers can do to create long-term solutions.
What is "Fixes That Fail"?
"Fixes That Fail" happens when a quick solution seems to work at first but creates unintended side effects that make the original problem worse over time. In food delivery, this might look like increasing driver incentives for speed or favoring top-rated drivers with more orders. While it may boost short-term performance, it often leads to issues like driver burnout, declining customer satisfaction, and inefficiencies.
Here are some common quick fixes in food delivery:
Incentives for Faster Delivery:
Bonuses for drivers who meet tight deadlines.
The downside? Fatigue, unsafe driving, and declining service quality.
More Orders for Top-Rated Drivers:
Rewarding high-performing drivers with more deliveries.
The problem? Burnout for top drivers and fewer opportunities for others.
Ignoring Traffic and Weather Conditions:
Setting fixed delivery targets without considering real-world challenges.
Result? Delays, frustrated customers, and stressed drivers.
Dataset: We will analyse the following dataset from the lens of Fixes that fail. This is a sample dataset and to be used for the purpose of the case study.
Here are some details about the dataset:
ID: Unique identifier for the order.
Delivery_person_ID: Identifier for the delivery person.
Delivery_person_Age: Age of the delivery person.
Delivery_person_Ratings: Ratings of the delivery person.
Restaurant_latitude, Restaurant_longitude: Coordinates of the restaurant.
Delivery_location_latitude, Delivery_location_longitude: Coordinates of the delivery location.
Type_of_order: Type of food order (e.g., Snack, Drinks, Buffet).
Type_of_vehicle: Type of vehicle used for delivery.
temperature, humidity, precipitation: Weather conditions during delivery.
weather_description: Description of weather (e.g., haze, mist).
Traffic_Level: Traffic condition during delivery (e.g., Low, High, Very High).
Distance (km): Distance between restaurant and delivery location (some missing values).
TARGET: Delivery time in minutes.
Key Metrics to Analyze Fixes That Fail
To analyze whether these quick fixes have led to unintended consequences, we can investigate the following relationships in the dataset:
Delivery Time (TARGET) vs. Driver Ratings (Delivery_person_Ratings):
Are drivers who consistently meet shorter delivery times experiencing lower ratings over time?Delivery Distance (Distance (km)) vs. Ratings:
Do longer distances coupled with quick delivery expectations lead to fatigue and lower ratings?Traffic Levels (Traffic_Level) vs. Delivery Time:
Are high-traffic conditions leading to unrealistic delivery times and declining driver ratings?Weather Conditions (temperature, humidity, precipitation) vs. Delivery Time: Are drivers struggling to meet fast delivery expectations under unfavorable weather conditions?
Data Analysis
Let's conduct some exploratory data analysis to identify patterns related to the "Fixes That Fail" archetype.
Impact of Faster Deliveries on Driver Ratings: We'll check if there's a negative correlation between delivery times and driver ratings.
Effect of Increasing Order Allocation on Performance: We'll analyze whether drivers with more deliveries tend to have a decline in ratings or increased delivery times.
Influence of External Factors (Traffic & Weather): We'll assess if ignoring these factors has resulted in worsening delivery times and satisfaction.
Findings from the Data Analysis
Negative Correlation Between Delivery Time and Driver Ratings:
The correlation coefficient between delivery time (TARGET) and driver ratings is approximately -0.10, indicating a weak negative correlation. This suggests that faster deliveries may have a slight tendency to lower ratings, potentially due to rushed deliveries leading to poor service quality.
Impact of Traffic on Delivery Time:
The boxplot shows that higher traffic levels correlate with longer delivery times, which is expected. However, if businesses pressure drivers to meet deadlines despite heavy traffic, it could lead to frustration and declining ratings.
Distance vs. Delivery Time (Colored by Ratings):
The scatter plot reveals that longer distances generally lead to higher delivery times, but ratings tend to be lower for longer trips. This indicates that drivers may struggle to maintain service quality for long-distance orders, especially if they are pushed to deliver quickly.
What the Data Tells Us?
To see if these quick fixes are causing problems, we analyzed data on delivery times, driver ratings, distances, traffic levels, and weather conditions. Here’s what we found:
1. Faster Deliveries, Lower Ratings
The data showed a weak negative correlation between delivery time and driver ratings. In other words, when drivers rush to meet tight deadlines, service quality can drop, leading to lower ratings.
Takeaway: Speed isn't everything—quality matters just as much.
2. Traffic’s Impact on Delivery Times
Higher traffic levels were linked to longer delivery times. When companies ignore traffic conditions and push for unrealistic deadlines, it creates stress for drivers and frustration for customers.
Takeaway: Traffic conditions should always be factored into planning.
3. Longer Distances, Lower Ratings
Longer deliveries naturally take more time, but our analysis showed that customer ratings tend to drop for these orders. This indicates that drivers may struggle to maintain service quality over longer routes.
Takeaway: Managing expectations for long-distance orders is key.
What Product Managers Can Do?
If you're a product manager, avoiding the "Fixes That Fail" trap means thinking long-term. Here’s how you can do that:
1. Shift Focus from Speed to Quality
Instead of only rewarding fast deliveries, focus on overall service quality. Metrics like customer feedback, order accuracy, and driver safety should be part of the equation.
2. Use Data to Drive Decisions
Analyze past delivery data to set realistic expectations. Predictive analytics can help you factor in variables like weather and traffic to avoid over-promising.
3. Distribute Orders Fairly
Don’t overburden top-rated drivers. A fair distribution system helps maintain a balance, giving all drivers opportunities to improve.
4. Optimize Routes with Technology
Leverage route optimization tools that factor in real-time traffic data to improve efficiency without overburdening drivers.
5. Invest in Driver Training
Prepare drivers to handle different situations like traffic congestion and difficult weather conditions, ensuring they deliver high-quality service consistently.
How to Build Long-Term Success?
Want to avoid falling into the "Fixes That Fail" trap? Here are some practical steps:
Set Realistic Delivery Expectations: Base goals on actual data rather than arbitrary targets.
Keep Customers Informed: Transparency about delays due to traffic or weather helps manage expectations.
Monitor Trends Regularly: Stay on top of performance data to spot and fix recurring issues early.
Adopt AI and Automation: Use AI to predict demand, optimize routes, and streamline operations.
Quick fixes might give you a short-term boost, but they rarely lead to lasting success. The key is to focus on sustainable solutions that balance efficiency, service quality, and driver well-being.
Key Takeaways:
Short-term fixes often create long-term problems.
Data-driven decisions lead to better operations.
Balancing workload improves service consistency.
By thinking long-term and implementing smart solutions, product managers can help businesses thrive without falling into the trap of quick fixes.
Have you faced similar challenges in your industry? Share your thoughts and strategies in the comments below!