Identifying an optimal picking method that minimizes travel time, distance, and picking cost has become an important decision in an omnichannel warehouse. This study investigates the picking operation in an omnichannel warehouse and focuses on how automation techniques can improve the efficiency of picking operations. The research develops several models to simulate omnichannel warehouse picking operations. It begins by analyzing existing non-automated picking operations and then incorporates automation techniques into the same models for comparative analysis. The research then measures the efficiency improvements achieved by implementing three automation techniques based on simulation outcomes. The study first identifies the optimal approach for non-automated picking operations. It then evaluates and compares automated models to determine the most effective technique for omnichannel warehouses, considering factors such as picking time, picking route distance, and cost. The research is confined to single and batch picking integrated with wave picking methods, with fixed warehouse parameters, including picking zones, storage locations, and pick lines. Additionally, the picking approach simultaneously handles a dual flow of stock and online orders. The findings highlight the potential of automation in optimizing omnichannel warehouse picking processes and provide actionable insights for practitioners. Future research is encouraged to explore additional automation tools and integrate AI-driven approaches for a more comprehensive understanding of omnichannel warehouse automation.