Anticipatory order picking

Order picking describes the process of retrieving a set of products from a warehouse in response to newly incoming customer orders. Deterministic order picking models assume all customer orders to be known at the beginning of the planning horizon. As a result, the application of such models in a dynamic environment is limited to a reactive strategy and ad-hoc decision-making each time new information becomes available. To mitigate demand uncertainty and improve the efficiency of the picking operations, we explore the idea of anticipatory order picking (AOP). In AOP, expected (but uncertain) customer orders are considered when planning and executing order picking activities. In contrast to the confirmed orders, these expected orders - if picked - are stored in a small buffer zone close to the packing and/or labeling station from which they can be retrieved with very limited travel time. Extensive simulation experiments revealed the following advantages. First, AOP can provide a better workload balance by relocating peak hour orders during preceding, off-peak time intervals. Second, by picking orders at a time when their marginal travel cost is low, the total travel time and distance are reduced as well as the overall amount of traffic in the warehouse. Third, by better utilizing the pickers’ working time, the picking of all confirmed orders can complete earlier

Warehouse optimization through anticipatory order picking

The growing importance of e-commerce puts warehouses under high pressure. First, small order sizes typically result in efficiency loss of the process of retrieving the requested products from storage. Second, the extreme short lead times require warehouses to respond to incoming customer orders within hours or even minutes, while keeping operational costs at their minimum. This high degree of dynamism, however, leads easily to ad hoc decisions and missed opportunities for optimization. In this research, we focus on the development of decision-support systems for anticipatory order picking, i.e., the retrieval of products from storage before the customer actually placed the order. Next to already confirmed customer orders, a dynamic list of expected (uncertain) orders is generated based on developed forecasting techniques that use historical and real-time data. Next, each time a picker becomes idle, a new pick tour is constructed based on known and expected orders. The advantage is twofold. First, increased opportunities for warehouse optimization appear through better batching procedures as the pool of potential orders that can be picked together is larger. Second, once a customer eventually orders such an anticipated product, the required time to prepare the order and ship the products to the customer will belower as the product has already been retrieved from storage

Anticipatory order picking

Order picking describes the process of retrieving a set of products from a warehouse in response to newly incoming customer orders. Deterministic order picking models assume all customer orders to be known at the beginning of the planning horizon. As a result, the application of such models in a dynamic environment is limited to a reactive strategy and ad-hoc decision-making each time new information becomes available. To mitigate demand uncertainty and improve the efficiency of the picking operations, we explore the idea of anticipatory order picking (AOP). In AOP, expected (but uncertain) customer orders are considered when planning and executing order picking activities. In contrast to the confirmed orders, these expected orders - if picked - are stored in a small buffer zone close to the packing and/or labeling station from which they can be retrieved with very limited travel time. Extensive simulation experiments revealed the following advantages. First, AOP can provide a better workload balance by relocating peak hour orders during preceding, off-peak time intervals. Second, by picking orders at a time when their marginal travel cost is low, the total travel time and distance are reduced as well as the overall amount of traffic in the warehouse. Third, by better utilizing the pickers’ working time, the picking of all confirmed orders can complete earlier

Warehouse optimization through anticipatory order picking

The growing importance of e-commerce puts warehouses under high pressure. First, small order sizes typically result in efficiency loss of the process of retrieving the requested products from storage. Second, the extreme short lead times require warehouses to respond to incoming customer orders within hours or even minutes, while keeping operational costs at their minimum. This high degree of dynamism, however, leads easily to ad hoc decisions and missed opportunities for optimization. In this research, we focus on the development of decision-support systems for anticipatory order picking, i.e., the retrieval of products from storage before the customer actually placed the order. Next to already confirmed customer orders, a dynamic list of expected (uncertain) orders is generated based on developed forecasting techniques that use historical and real-time data. Next, each time a picker becomes idle, a new pick tour is constructed based on known and expected orders. The advantage is twofold. First, increased opportunities for warehouse optimization appear through better batching procedures as the pool of potential orders that can be picked together is larger. Second, once a customer eventually orders such an anticipated product, the required time to prepare the order and ship the products to the customer will belower as the product has already been retrieved from storage