ML Case-study Interview Question: Accurate Grocery Drop Time Prediction with MLP to Boost Delivery Efficiency.
Browse all the ML Case-Studies here.
Case-Study question
A fast-growing online grocery service wants to improve its delivery efficiency while maintaining high on-time performance. Customers choose a one-hour time slot, and the company commits to a shorter 20-minute window within that slot for the actual delivery. The business wants to optimize the drop time (the time it takes to hand over groceries at the door) for each order, because accurate drop-time estimates help them plan routes more efficiently and avoid late deliveries. The online grocer previously used a simplistic method to estimate drop times, but they suspect a machine learning approach could lead to better accuracy. They have a rich dataset with features related to customers, orders, local regions, and driver behaviors, but some key features, such as the customer’s floor or precise building details, are missing.
Propose a data science solution to predict each order’s drop time more accurately. Then explain how you would integrate this into the company’s route-planning system to reduce unnecessary buffers while keeping on-time deliveries high.
Proposed Solution
The solution starts by collecting and analyzing historical delivery data. Historical drop times for each order are stored with timestamps. This data captures possible correlations between factors like customer profiles, average basket weight, local address density, runner behaviors, and weather information. A machine learning model is then trained to predict drop times for upcoming deliveries.
Model Selection and Training
A good first step is a simple regression model such as a linear regression, but more flexible models (including neural networks) often capture nonlinearities in the data better. After basic models are tested, a multilayer perceptron (MLP) can be trained to account for interactions between features.
Handling Outliers and Appropriate Loss Function
Outliers arise from anomalies such as incorrect timestamps. Filtering them with business rules is crucial. A robust loss function can further reduce the impact of remaining outliers. One choice is the Huber loss function, which uses a quadratic penalty for small errors and a linear penalty for large errors. The linear component prevents a few large outliers from dominating the overall training.
$$ L_{delta}(r) =
\begin{cases} \frac{1}{2} r^2 & \text{if } |r| \le \delta \ \delta |r| - \frac{1}{2} \delta^2 & \text{if } |r| > \delta \end{cases} $$
Here, r is the difference between the predicted drop time and the actual drop time, and delta is a threshold that splits the error function into a quadratic and linear region. When |r| is below delta, the function acts like mean squared error. Otherwise, it becomes linear in the magnitude of the error. This makes it more robust to abnormal data points.
Model Deployment and Safety Buffer
Once the MLP is trained, predicted drop times replace the old estimates in the route-planning system. During a pilot phase, the system might include a safety buffer on top of the new predictions to offset external delays (traffic, roadblocks). Over time, that buffer is gradually reduced as the model’s performance proves reliable.
Impact
Better drop-time estimates let planners include just enough time for each delivery, allowing more stops in a single trip without sacrificing punctuality. If on-time performance drops noticeably, the buffer can be slightly increased. If everything remains on schedule, the buffer can be reduced further, boosting efficiency.
How would you approach feature engineering to handle missing factors like building floors?
Missing building-floor information can be approximated with proxies. A proxy is the historical average drop time for that address, or address density (e.g., whether the address is in a dense city zone). If data is only available at city or neighborhood level, approximate it with region-based averages. Weight and size of the order also help approximate the extra effort if the runner must climb stairs. Including these proxies helps reduce bias from not having explicit floor data.
How do you validate performance before full rollout?
A holdout set of historical data is used. A portion of deliveries is kept for final testing. The model’s predictions are compared with the actual durations. Metrics such as mean absolute error or root mean squared error measure accuracy. Robust metrics that penalize large deviations can also be monitored. Once offline evaluations confirm improvement, a limited pilot is launched in a single region. Performance is monitored in real time with actual on-time percentages, and the impact on route efficiency is measured. After a successful pilot, the model can be rolled out gradually to more locations.
How do you handle the trade-off between efficiency and on-time performance?
The trade-off is managed by carefully adjusting the buffer on top of predicted drop times. If the model is extremely accurate, the buffer can be low and more orders fit on a single route. If accuracy is slightly off or external delays occur, a modest buffer helps sustain punctuality. Constant monitoring of late deliveries is essential. If late deliveries rise above a threshold, the buffer is temporarily increased while the source of delays is investigated.
How would you ensure continuous improvement of the model?
Continual iteration depends on retraining with the latest data. Retrain the model periodically with feedback loops that capture new trends like changing shopping behavior or seasonal factors. Monitor performance metrics in a dashboard. If model drift is detected (for instance, an unusual spike in errors), investigate and retrain with up-to-date data or refine the feature set. Feature importance analysis can guide which new signals are most valuable to collect.
Why might you choose a multilayer perceptron instead of a more traditional approach?
A multilayer perceptron captures complex, nonlinear patterns. If order size interacts with address density or weather conditions in a non-linear way, a simple linear approach might miss those relationships. The MLP’s hidden layers extract interactions automatically, especially when data volume is large. Implementation overhead is manageable with modern frameworks, and performance gains can be significant.
How do you prevent overfitting in an MLP for regression?
Regularization methods such as dropout or weight decay guard against overfitting by forcing the network not to rely too heavily on any single neuron’s outputs. Early stopping monitors performance on a validation set and halts training once performance stops improving. Data augmentation (or expansion) can also help if the dataset is limited. Hyperparameter tuning, such as adjusting number of layers, hidden units, or learning rate, is done carefully to maintain generalization.
How can you communicate the results to business stakeholders?
Keep explanations concise and focus on metrics that matter: an improvement in daily deliveries per vehicle, and stable on-time percentages. Show how the new system can handle more orders without hurting punctuality. Present pilot results and highlight how the robust loss function mitigates outliers. Provide an iterative rollout plan with ongoing performance checks so stakeholders see the approach is controlled and data-driven.