Order Picking Performance in Warehouses With Multi-Item Orders

Order Picking Performance in Warehouses With Multi-Item Orders

Oliverio Cruz-Mejía (Universidad Autónoma del Estado de México, Mexico)
DOI: 10.4018/978-1-5225-8131-4.ch026

Abstract

Internet sales have increased exponentially in the last decade. Much of the internet sales are of physical products in urban areas that require product delivery transportation with a tight delivery lead time. With this challenge, a new type of transportation services has been developed aiming to cope with a strict control of transportation lead time. In this chapter, an internet product delivery service with customer orders that are multi-item as well as single item is simulated. The authors address specifically the mismatch between supply and demand when retailers for any reason are unable to estimate the configuration of multi-item orders. Three scenarios of demand patterns are simulated (demand as forecasted, lower than forecasted, and higher than forecasted) using discrete-event simulation to look at the effect on transportation lead time. Results show the positive effect on the mismatch between demand and resource capacity which is expressed in higher number delayed delivery orders. The excess of capacity in the product delivery supply chain has not had a positive impact on delivery time of orders.
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Introduction

Transportation services in urban areas have changed in the last years in terms of collaboration, configuration, operational practices and performance expectations. These challenges come from several factors that have changed the rules for providing competitive transportation services. Changes in product transportation management can be attributed to three main driving forces: Customers are raising their service expectations including the delivery of perishable products (Choi 2016). Customer demands for quick response and customized products are propagating along supply networks (Kibert 2016). Changes in life style of people require manufacturers and service providers to adjust to the new circumstances (Boyer and Frohlich 2015). Information technologies are providing more timely and detailed supply chain data that improves the performance of the transportation services (Waller and Fawcett 2013). Advances in information technologies in both connectivity and reach increase the potential for information sharing and enable tighter integration among supply chain partners (Zhang et al. 2011). Partnerships with transportation service providers allow manufacturers to focus on their core competences while taking advantage of the distribution efficiency and expertise of dedicated distributors (Yinan 2016). In turn, distributors are offering their services beyond the traditional warehousing and transportation functions to include value-added activities e.g., repackaging, labeling, light assembly, and non-inventory distribution services of which cross-docking and merge-in-transit distribution are examples.

Merge-in-transit distribution (MiT) is a logistics process introduced to cope with consolidation of orders in the same shipment (O´Leary 2000). Merge-in-transit is a distribution process that brings together at a consolidation center multi-product order components, coming from different origins, consolidates them into a single order, and then ships it for final delivery to the end customers. Some of the advantages obtained with MiT are: higher customer satisfaction is obtained by delivering multi-product orders in one event instead of making more than one delivery, one for each component or partial group of them. Savings are achieved by not keeping inventories in the distribution process, since merge-in-transit centers just hold order components for a short time (usually less than 24 hours) so the order is all the way in transit to its final delivery point. Holding costs associated with warehousing operations are avoided or at least minimized. Third, savings also arise by avoiding the risk of keeping obsolete inventories. MiT is normally applied to distribute orders where sometimes one component has been made-to-order. Those tailored components have been made for a specific need and are never kept in stock so there is no risk of keeping obsolete components (Ala-Risku et al. 2013).

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