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The purpose of this paper is to investigate the costs/benefits of implementing the cross-docking strategy in a retail supply chain context using a cost model. In particular, the effects of using different typologies of cross-docking compared to traditional warehousing are investigated, taking into consideration an actual case study of a fast-moving consumer goods (FMCG) company and a major French retailer.

The research is based on a case study of an FMCG company and a major French retailer. The case study is used to develop a cost model and to identify the main cost parameters impacted by implementing the cross-docking strategy. Based on the cost model, a comparison of the main cost factors characterizing four different configurations is made. The configurations studied are, the traditional warehousing strategy (AS-IS configuration, the reference configuration for comparison), where both retailers and suppliers keep inventory in their warehouses; the cross-docking pick-by-line strategy, where inventory is removed from the retailer warehouse and the allocation and sorting are performed at the retailer distribution centre (DC) level (TO-BE1 configuration); the cross-docking pick-by-store strategy, where the allocation and sorting are done at the supplier DC level (TO-BE2 configuration); and finally a combination of cross-docking pick-by-line strategy and traditional warehousing strategy (TO-BE3 configuration).

The case study provides three main observations. First, compared to traditional warehousing, cross-docking with sorting and allocation done at the supplier level increases the entire supply chain cost by 5.3 per cent. Second, cross-docking with allocation and sorting of the products done at the retailer level is more economical than traditional warehousing: a 1 per cent reduction of the cost. Third, combining cross-docking and traditional warehousing reduces the supply chain cost by 6.4 per cent.

A quantitative case study may not be highly generalisable; however, the findings form a foundation for further understanding of the reconfiguration of a retail supply chain.

This paper fills a gap by proposing a cost analysis based on a real case study and by investigating the costs and benefits of implementing different configurations in the retail supply chain context. Furthermore, the cost model may be used to help managers choose the right distribution strategy for their supply chain.

Cross-docking is a supply chain distribution and logistics strategy for which less-than-truckload shipments are consolidated into full-truckload shipments. Goods are stored up to a maximum of 24 hours in a cross-docking terminal. In this chapter, we build on the literature review by Ladier and Alpan (2016), who reviewed cross-docking research and conducted interviews with cross-docking managers to find research gaps and provide recommendations for future research. We conduct a systematic literature review, following the framework by Ladier and Alpan (2016), on cross-docking literature from 2015 up to 2020. We focus on papers that consider the intersection of research and industry, e.g., case studies or studies presenting real-world data. We investigate whether the research has changed according to the recommendations of Ladier and Alpan (2016). Additionally, we examine the adoption of Industry 4.0 practices in cross-docking research, e.g., related to features of the physical internet, the Internet of Things and cyber-physical systems in cross-docking methodologies or case studies. We conclude that only small adaptations have been done based on the recommendations of Ladier and Alpan (2016), but we see growing attention for Industry 4.0 concepts in cross-docking, especially for physical internet hubs.

The technique of cross-docking is attractive to organisations because of the lower warehousing and transportation (consolidated shipments) costs. This concept is based on the fast movement of products. Accordingly, cross-docking operations should be monitored carefully and accurately. Several factors in cross-docking operations can be impacted by uncertain sources that can lead to inaccuracy and inefficiency of this process. Although many papers have been published on different aspects of cross-docking, there is a need for a comprehensive review to investigate the sources of uncertainties in cross-docking. Therefore, the purpose of this paper is to analyse and categorise sources of uncertainty in cross-docking operations. A systematic review has been undertaken to analyse methods and techniques used in cross-docking research.

A systematic review has been undertaken to analyse methods and techniques used in cross-docking research.

The findings show that existing research has limitations on the applicability of the models developed to solve problems due to unrealistic or impractical assumption. Further research directions have been discussed to fill the gaps identified in the literature review.

There has been an increasing number of papers about cross-docking since 2010, among which three are literature reviews on cross-docking from 2013 to 2016. There is an absence of study in the current literature to critically review and identify the sources of uncertainty related to cross-docking operations. Without the proper identification and discussion of these uncertainties, the optimisation models developed to improve cross-docking operations may be inherently impractical and unrealistic.

This paper investigates a problem in a reverse logistics (RLs) network to decide whether to dispose of unsold goods in primary stores or re-commercialize them in outlet centers. By deducting the costs associated with each policy from its revenue, this study aims to maximize the profit from managing unsold goods.

A new mixed-integer linear programming model has been developed to address the problem, which considers the selling prices of products in primary and secondary stores and the costs of transportation, cross-docking and returning unwanted items. As a result of uncertain nature of the cost and time parameters, gray numbers are used to deal with it. In addition, an innovative uncertain solution approach for gray programming problems is presented that considers objective function satisfaction level as an indicator of optimism.

According to the results, higher costs, including transportation, cross-docking and return costs, make sending goods to outlet centers unprofitable and more goods are disposed of in primary stores. Prices in primary and secondary stores heavily influence the number of discarded goods. Higher prices in primary stores result in more disposed of goods, while higher prices in secondary stores result in fewer. As a result of the proposed method, the objective function satisfaction level can be viewed as a measure of optimism.

An integral contribution of this study is developing a new mixed-integer linear programming model for selecting the appropriate goods for re-commercialization and choosing the best outlet center based on the products' price and total profit. Another novelty of the proposed model is considering the matching percentage of boxes with secondary stores’ desired product lists and the probability of returning goods due to non-compliance with delivery dates. Moreover, a new uncertain solution approach is developed to solve mathematical programming problems with gray parameters.

The purpose of this study is truck scheduling and assignment of trucks to the doors simultaneously since these issues were considered mainly separately in the previous research. Also, the door service time and its impact on truck scheduling were not taken into account, so this research endeavors to cover this gap.

In this research, a novel model has been presented for simultaneous truck scheduling and assignment problem with time window constraints for the arrival and departure of trucks, mixed service mode dock doors and truck queuing. To resolve the developed model, two meta-heuristic algorithms, namely, genetic and imperialist competitive algorithms, are presented.

The computational results indicate that the proposed framework leads to increased total costs, although it has a more accurate planning; moreover, these indicate that the proposed algorithms have different performances based on the criteria considered for the comparison.

There are some limitations in this research, which can be considered by other researchers to expand the current study, among them the specifications of uncertainty about arrival times of inbound and outbound trucks, number of merchandises which has been loaded on inbound trucks are the main factors. If so, by considering this situation, a realistic scheme about planning of cross docking system would be acquired. Moreover, the capacity of temporary storage has been considered unlimited, so relaxing this limitation can prepare a real and suitable situation for further study. Examining the capacity in the front of each type of doors of cross-dock and executive servers are the other aspects, which could be expanded in the future.

In this study, a mathematical programing model proposed for truck scheduling to minimize total costs including holding, truck tardiness and waiting time for queue of trucks caused by the interference of each carrier’s movement. At the operational levels, this research considered a multi-door cross-docking problem with mixed service mode dock doors and time window constraints for arrival and departure time of trucks. Moreover, M/G/C queue system was developed for truck arrival and servicing of carriers to trucks.

The cross-docking strategy has a significant influence on supply chain and logistics efficiency. This paper aims to investigate the most suitable and efficient way to schedule the transfer of logistics activities and present a meta-heuristic method of the truck scheduling problem in cross-docking logistics. A truck scheduling problem with products time window is investigated with objectives of minimizing the total product transshipment time and earliness and tardiness cost of outbound trucks.

This research proposed a meta-heuristic method for the truck scheduling problem with products time window. To solve the problem, a lower bound of the problem is built through a novel two-stage Lagrangian relaxation problem and on account of the NP-hard nature of the truck scheduling problem, the novel red deer algorithm with the mechanism of the heuristic oscillating local search algorithm, as well as adaptive memory programming was proposed to overcome the inferior capability of the original red deer algorithm in the aspect of local search and run time.

Theory analysis and simulation experiments on an industrial case of a cross-docking center with a product’s time window are conducted in this paper. Satisfactory results show that the performance of the red deer algorithm is enhanced due to the mechanism of heuristic oscillating local search algorithm and adaptive memory programming and the proposed method efficiently solves the real-world size case of truck scheduling problems in cross-docking with product time window.

The consideration of products time window has very realistic significance in different logistics applications such as cold-chain logistics and pharmaceutical supply chain. Furthermore, the novel adaptive memory red deer algorithm could be modified and applied to other complex optimization scheduling problems such as scheduling problems considering energy-efficiency or other logistics strategies.

For the first time in the truck scheduling problem with the cross-docking strategy, the product’s time window is considered. Furthermore, a mathematical model with objectives of minimizing the total product transshipment time and earliness and tardiness cost of outbound trucks is developed. To solve the proposed problem, a novel adaptive memory red deer algorithm with the mechanism of heuristic oscillating local search algorithm was proposed to overcome the inferior capability of genetic algorithm in the aspect of local search and run time.

Logistics real estate has been experiencing a recent rebirth led by the growth of retailing and e-commerce. Although these sectors are looking for facilities matching their logistics needs, the identification of the most suitable building becomes a challenging task. To date, from both the practitioner’s and academic perspectives there is a lack of models for assessing the quality of logistics facilities together with functionality (i.e. whether a warehouse is suitable for hosting a given logistics activity). The purpose of this paper is to fill this gap by developing a rating model for assessing the quality and functionality of logistics facilities.

A three-pronged methodology was adopted. First, a Systematic Literature Network Analysis (SLNA) was carried out to identify the relevant features that must be taken into consideration when assessing logistics real estate. Second, a Delphi method involving experts in the field was used to fine-tune the list of features that emerged from the SLNA process and to evaluate the importance of each feature from a company perspective. The rating model was developed and validated through pilot tests on 27 logistics facilities.

The rating model is divided into four sections: location, technical specifications, external spaces and internal areas. As an output, the model determines the building quality and main functionality, together with a gap analysis to detect the weakest emerging elements.

This research fills an identified research gap in the logistics real estate literature. Specifically, it offers a quantitative and shared evaluation method, which can be used to estimate building quality and functionality, thus extending the scope of the previous assessment methods available.

The study aims to explore and develop a smart route planning system for the cross-docking delivery operations of a large supermarket chain using an action research (AR) approach and assessing through a design science research (DSR) lens.

This study took a problem-solving AR (PAR) approach toward the delivery operational issue of the case firm. The research process has accorded with the solution incubation and the refinement phases defined by a DSR framework. An intervention-based research framework for DSR is developed to assess the validity of this study as a DSR research and derive mid-range theories.

Dramatic operational and financial improvements were achieved for the case firm. Significant and unintended environmental and social benefits were also found. A design proposition (DP) and several mid-range theories are proposed as an extension of AR research to DSR research.

A problem-solving DSR research can be better assessed by the intervention-based DSR framework developed in this study. DSR studies should be encouraged for both practical and theoretical advancement purposes.

A challenging business problem-solving study can be tackled effectively through an industry/academic collaboration taking a PAR approach to deliver substantial values and organization transformational results.

Drivers and store associates are safer with smart delivery operations in the case firm.

There are still limited PAR design science case studies in the supply chain/logistics research literature. The research experience and findings gained from this study provide more insights toward how this type of research can be conducted and assessed.

Aims to highlights the changes ongoing in the distribution and logistics property sector across Europe and to show that wide differences remain between the sophisticated warehouse properties available in mature property markets and those available in traditional parochial markets found prevalently across the continent and especially in central and eastern Europe.

The briefing is based on empirical and commercial research in all the national industrial property markets within the EU. It addresses changes in the drivers affecting logistics property and the industry responses to them. The changes include outsourcing; a desire for more flexible, responsive supply chains; globalisation of manufacturing; legislative change; and growing IT capability. Industry responses include the development of complex, highly specified warehousing; increased demand for sophisticated telecommunications; and optimization of location.

Sub‐optimal locations and poor specification have a direct impact on the cost of the operations using them. As a consequence logistics providers gravitate to newer, more highly specified buildings in locations optimised for their use. In an environment where, traditionally, distribution has a national focus, optimising locations for pan‐European distribution will inevitably render some locations sub‐optimal.

As Europe moves towards a genuine single market, traditional, nationally‐based distribution chains become ever less efficient in the delivery of goods. Concurrently, the complexity and configuration of the warehouse product are changing to accommodate more efficient identification, storage and retrieval technologies along with the need to service new types of market.

During the 1990s, globalization and time‐based competition have emerged as important business strategies leading to renewed emphasis on the logistics function. This has opened up opportunities for strategic alliances between manufacturing firms and specialized logistics services providers, with each partner focusing on its core area of competence. Although such alliances are common in North America and Europe, the determinants of success of such partnerships in the global context are not well researched and understood. Moreover this concept is relatively new in Asia. This paper presents an overview of the issues that impact the success of a strategic partnership between manufacturing firms and global logistics services providers (GLSPs). A case study featuring Motorola’s Semiconductor Products Sector and United Parcel Service (UPS) is described, for bringing out the important tradeoffs in such an alliance.