This research focuses on optimizing the performance of a hybrid warehouse system that combines automation with manual picking processes. The study utilizes a case study of an online grocer, Picnic, and investigates the allocation of orders within a buffer and the configuration of the manual picking process.

The research identifies the key performance metrics for the system, including the number of late totes, average sojourn time, average stack throughput time, and picker productivity. Several factors impacting the system's performance are examined, such as buffer lane selection and group formation strategies, minimum and maximum group sizes, number of workers, pick times, and input data.

Through comprehensive experimentation and simulation modelling, the study reveals that prioritizing the deadline of totes for buffer lane selection and the IPB time for group formation results in the best system performance. The research also suggests that the configuration of the manual process should consider the group size should be adjusted based on the expected workload.

The study identifies the consolidation stations as a significant bottleneck in the system, and repairing them promptly on busy days is essential to maintain performance. The research provides practical implications for Picnic, emphasizing efficient buffer allocation and group formation strategies.

In conclusion, this research offers valuable insights into enhancing the performance of hybrid warehouse systems. Companies like Picnic can improve operational efficiency and customer satisfaction by optimizing buffer allocation, buffer lane selection, and group formation. The findings from this research can serve as a basis for further optimization and decision-making in similar warehouse setups.