The order picking process is done item by item, and therefore it is a labour-intensive process. According to Dukic & Oluic (2005), 50% of the total order picking time is spent on unproductive traveling. The operations that need to be performed in warehouses are highly dependent on the customer demand. The customer demand shows seasonal patterns spread over the year caused by so-called seasonal influences. The objective of this research was to investigate and identify the opportunities and possibilities regarding the order picking process to react to the seasonal impact that influences the productivity of order picking. Order picking can be described as a warehouse process of retrieving products from storage in response to specific customer request and bringing them to an area dedicated for collecting the assembled customer orders. Order picking can be performed in a variety of Order Picking Systems (OPS). This study focusses on the lower automatization level of pick-and-sort batch picking using a picker-to-parts system. An insight of demand profiles in certain time series and order characteristics have been obtained by using a decomposition model with multiplicative seasonal-adjustment. Patterns in number of orders, items/order, weight/item and volume/item were determined for setting up design alternatives. The alternatives are based on some primary interdependent policies proposed in the study of Wascher (2004): storage policy, routing policy, zoning policy, and order consolidation policy. The three design alternatives in this study are: class-based randomized storage, volume-based randomized storage, and zone decomposition. These alternatives are tested in a model with discrete event simulation for both regular and peak periods, compared to a base alternative. The KPIs are cycle picking time and the variable order picking (labour) costs and both need to be minimized. All three design alternatives seem to be promising for future order picking operations compared to the base alternative. The results of this study show that major savings can be reached changing the order picking strategy in general. For companies, focusing on the reduction of travel distance of the operators can lead to more efficient processes in the fulfilment centre and in the end cause a decrease in labour costs. However, it is recommended for the company of the case study, to implement the class-based randomized storing policy. On yearly basis, the order picking costs decrease with 12%. Looking at cycle time and travel distances, even higher savings can be obtained in future operation by deploying the zone decomposition dynamically during peak periods. This means that during peak periods, zones are dynamically divided into two sub-zones causing a decrease in travel distance between picks, leading to lower cycle picking times.