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If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services.Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation.*Related content and download information correct at time of download. Purpose -The development of e-grocery allows people to purchase food online and benefit from home delivery service. Nevertheless, a high rate of failed deliveries due to the customer's absence causes significant loss of logistics efficiency, especially for perishable food. The purpose of this paper is to propose an innovative approach to use customer-related data to optimize e-grocery home delivery. The approach estimates the absence probability of a customer by mining electricity consumption data, in order to improve the success rate of delivery and optimize transportation. Design/methodology/approach -The methodological approach consists of two stages: a data mining stage that estimates absence probabilities, and an optimization stage to optimize transportation. Findings -Computational experiments reveal that the proposed approach could reduce the total travel distance by 3-20 percent, and theoretically increase the success rate of first-round delivery approximately by18-26 percent.Research limitations/implications -The proposed approach combines two attractive research streams on data mining and transportation planning to provide a solution for e-commerce logistics. Practical implications -This study gives an insight to e-grocery retailers and carriers on how to use customer-related data to improve home delivery effectiveness and efficiency. Social implications -The proposed approach can be used to reduce environmental footprint generated by freight distribution in a city, and to improve customers' experience on online shopping. Originality/value -Being an experimental study, this work demonstrates the effectiveness of data-driven innovative solutions to e-grocery home delivery problem. The paper also provides a methodological approach to this line of research. The current issue and full text archive of this journal is available on Emerald Insight at: www.emeraldinsight.com/0263-5577.htm IntroductionRecent developments of e-commerce have had a significant impact on food supply chains. Today, many traditional grocery retailers offer their customers the opportunity to purchase food items online and have them deliv...

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Warehousing has been traditionally viewed as a non value-adding activity but in recent years a number of new developments have meant that supply chain logistics have become critical to profitability. This paper focuses specifically on order-picking which is a key factor affecting warehouse performance. Order picking is the operation of retrieving goods from specified storage locations based on customer orders. Today's warehouses face challenges for greater responsiveness to customer orders that require more flexibility than conventional strategies can offer. Hence, dynamic order-picking strategies that allow for changes of picklists during a pick cycle have attracted attention recently. In this paper we introduce an interventionist routing algorithm for optimising the dynamic order-picking routes. The algorithm is tested using a set of simulations based on an industrial case example. The results indicate that under a range of conditions, the proposed interventionist routing algorithm can outperform both static and heuristic dynamic order-picking routing algorithms. Across the various operations in a warehouse, order-picking is the most time consuming operation in general and accounts for around 55-75% of total warehousing costs (Chiang et al., 2011). Therefore, order-picking has the highest priority for productivity improvement (De Koster et al., 2007). The order-picking operation is particularly important in manual picker-to-parts picking systems 1 , which are the most common ones (Gong and De Koster, 2008) and account for over 80% of all order-picking systems in Western Europe (De Koster et al., 2007). In a picker-to-parts system, orders are firstly batched to form a pick-list. The list then guides the order-picker to travel along the aisles with a picking device (e.g. picking cart or fork-lifter) and collect requested items from designated storage locations (storage racks or bins) (De Koster et al., 2007).Although many studies have been conducted on improving the order-picking operation, managing it efficiently remains complex (Gong and De Koster, 2008). On the demand side, the complexity arises from the introduction of new sales channels such as on-line shopping; and on the supply side, it arises from new operating programs, e.g. JIT, cycle-time reduction (Tompkins, 2010;Davarzani and Norrman, 2015). In such novel business models, customers can place an order by a click of the mouse in their computer, expecting inexpensive, rapid and accurate delivery (De Koster, 2003), i.e. they tend to order more frequently but in smaller quantities asking for more customised service. In response, more companies are inclined to accept late orders which leads to tighter windows for timely deliveries (Gong and De Koster, 2008). Moreover, many logistics companies are replacing small warehouses by fewer but larger warehouses to realise the economies of scale (De Koster et al., 2007). Consequently, the time available for order picking is increasingly shorter. Hence, a fast response is critical for warehouses to operate in such ...

The role of logistics in effective supply chain management is increasingly critical, and researchers and practitioners have recently focused their attention in designing more intelligent systems to address today's challenges. In this paper, we focus on one such challenge concerning improving the role of the customer in logistics operations. In particular, we identify specific developments in the systems governing core logistics operations, which will enhance the customer experience. This paper proposes a conceptual model for customer orientation in intelligent logistics and describes a number of specific developments the authors are involved in.

a b s t r a c tOrganizations and individuals can use open source software (OSS) for free, they can study its internal workings, and they can even fix it or modify it to make it suit their particular needs. These attributes make OSS an enticing technological choice for a company. Unfortunately, because most enterprises view technology as a proprietary differentiating element of their operation, little is known about the extent of OSS adoption in industry and the key drivers behind adoption decisions. In this article we examine factors and behaviors associated with the adoption of OSS and provide empirical findings through data gathered from the US Fortune-1000 companies. The data come from each company's web browsing and serving activities, gathered by sifting through more than 278 million web server log records and analyzing the results of thousands of network probes. We show that the adoption of OSS in large US companies is significant and is increasing over time through a low-churn transition, advancing from applications to platforms. Its adoption is a pragmatic decision influenced by network effects. It is likelier in larger organizations and those with many less productive employees, and is associated with IT and knowledge-intensive work and operating efficiencies.

As the role of the customer becomes more important in modern logistics, warehouses are required to improve their response to customer orders. To meet the responsiveness expected by customers, warehouses need to shorten completion times. In this paper, we introduce an interventionist order picking strategy that aims to improve the responsiveness of order picking systems. Unlike existing dynamic strategies, the proposed strategy allows a picker to be intervened during a pick cycle to consider new orders and operational disruptions. An interventionist strategy is compared against an existing dynamic picking strategy via a case study. We report benefits both in terms of order completion time and travel distance. This paper also introduces a set of system requirements for deploying an interventionist strategy based on a second case study.

Sustainability has become an important objective of city logistics management. Smart city, being a technology and data driven paradigm for a city's sustainable development, has entailed new research opportunities from different perspectives. It is foreseeable that smart city will keep evolving in the domain of city logistics, which plays a key role in this game changing evolution. Recent research in this field is characterised by interdisciplinary approaches and disruptive innovations. We review the state-of-the-art of this general area and conduct a bibliometric analysis. We conclude with a new conceptual framework of smart city for sustainable urban freight logistics and the relevant key perspectives.