The literature review shows that the most commonly employed research method (Table 4) is mathematical modeling where no real-case data are used (51.9 % of reviewed papers). Research based on empirical case studies (13.6 %) or using real data as input to modeling (21 %) is less frequent. Most of the case study papers fit into the category of warehousing operation strategy and cover high-level decision- and policy-making activities. Real-case data are mostly used in research related to the effects of material handling activities on personnel health in the human resource management category. Appendix B of ESM presents how the papers and their outlets (major journals) are classified due to methods employed.
Table 4 Classification of the reviewed papers based on research category and the method employed
This observed imbalance between methods employed, and the gap between desk studies and the practitioners’ world, raise questions like (a) how could research be more directly connected to business, (b) why are different methods not applied to more categories, and (c) how useful can the mathematical models developed without “real data” be for practitioners?
The reviewed literature will now be discussed in greater detail based on content, in the order following the suggested framework (Fig. 1).
Operation strategy
By operation strategy, we mean both strategic planning and general operational policies. Detailed operational planning would fit to the scope of research related to each operation, namely receiving, storage, picking and shipping. Examples of operation strategies and policies are decision making on managerial philosophies [12], outsourcing [13], material flow pattern and FIFO versus LIFO [14].
In addition to the strategic decisions, appropriate operation policies should be devised. Different researchers have worked on the proper utilization of technologies to support the overall strategy: for example, employment of a WMS in cooperation with RFID technology [15] and decision support systems [16].
The distribution of the papers between the two categories of strategic planning and operational policy making is similar. Most of the studies focus on unit-load warehouse, while research on less than unit load is scarce and adds more complications to the analysis. Most of the published works are under deterministic conditions, although the business environment contains a lot of uncertainties and risks.
Infrastructure design
Many papers cover different aspects of infrastructure design including estimations of required space [17], layout design and determination of departments’ location relative to each other [18], department design [19] and equipment configuration [20]. Most of the papers reviewed in this category follow a mathematical modeling research method, and more specifically the majority of the solution techniques are meta-heuristic algorithms.
The other group of papers in this field is focusing on determining location and size of warehouse departments. Most of the published papers in this field try to capture other aspects in their models as well, such as designing the operation [21] and product allocation to different departments [22]. Multiple-level layout design is one of the most complicated problems in the literature [23]. The more detailed studies are those concentrating on design of the warehouse area, e.g., configuration of lanes in picking/storage areas [24]. Apart from the layout of the departments, configuration of warehouse equipment is another area of the literature [20]. Finally, very few papers investigate warehouse construction and design from energy efficiency perspective [25–27].
Infrastructure design is one of the richest categories in the literature. Several aspects including sizing, dimensioning, department design and configuration of equipment are covered. Research on warehouses with special needs, such as temperature-controlled departments and high seasonality, is relatively scarce. The majority of the literature approach warehouse design problem from economic efficiency perspective, while research with special consideration to environmental performance is scarce. Most of the design papers focus on the storage departments, while research on the layout of receiving and shipping is rare; these departments are more important for a cross-dock warehouse, another field of research which seems to be overlooked in the literature.
Human resource management
Human resource management can be investigated from different angles. Scheduling of the staff [28], psychological considerations [29], and ergonomics issues and safety [30] are the main areas explored in the literature. Chakravorty [31] posits that academic research in warehousing and material handling is mainly focused on technical factors to improve performance, but usually neglects human factors.
Ergonomics of warehousing jobs is the most investigated topic in human resource management. Such research can be useful when designing material handling equipment. Even the way that male and female operators [32] and expert and non-expert workers [33] are performing can be different. Awareness regarding such differences would be helpful not only in design of the equipment but also in job assignment in the warehouses.
The reviewed literature shows that human resource management has been considered mainly from an ergonomics point of view, i.e., how handling of different items can have negative impact on the body. Apart from that, researchers have also explored impact of exposure to carbon monoxide from equipment, job rotation and psychological aspects of warehousing work, even though their proportions have been significantly less. Scheduling and rotation of tasks in warehouses are among the everyday work in most of the warehouses and distribution centers, but have not attracted much research.
Technology and equipment
Within this category, two groups of soft and hard technology/equipment are considered. Selection of automation level [34], appropriate equipment [35], suitable warehousing management system [36], decision support systems [37], planning and application of those technologies and equipment [38] are included in this group of research. Among the technologies introduced in warehousing is tracking equipment such as RFID-based devices or barcodes. Some researchers have shown cost and time efficiency of employing such technologies [39], although some skepticism exists on the potential cost-saving capabilities and positive ROI in the near future for RFID [40]. Designing a proper WMS for a warehouse is also an important aspect to consider. Even though such works are not very common in academic research, Shiau and Lee [36] design a WMS to integrate picking and packing operations. Employment of automatic guided vehicles (AGV) in warehousing has become very popular during last decade. This trend can also be witnessed in the literature [41–43], but most of the relevant papers can be found in robotic and mechatronic literature with more emphasis on engineering aspects.
The automation and equipment category of research addresses the problems of choosing the level of required automation and suitable equipment/technology. Among the most recent research are those that consider emission level as one of the factors influencing the choice of appropriate equipment [44]. This is a strategic decision that can significantly affect the investment level, financial outcome and operational performance of the warehouse. This category of research has been studied more extensively after 2000 than before (see Table 5). Most of the scholarly research focuses on the soft technologies and how their employment can influence warehouse performance.
Table 5 Number of the reviewed papers based on category and year
Performance evaluation
Performance evaluation is a critical activity in every business. It provides useful feedback on performance and potential impact of infrastructure design, operational policy and improvement methods. This category includes all publications with the main objective of performance evaluation, measurement and improvement. This evaluation can be considered on three different levels:
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Overall performance of a warehouse: e.g., development of an AHP-DEA model to compare performances of different warehouses [45], or a structural equations model to evaluate the logistics capability [46].
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Performance of a department or specific process: e.g., level of responsiveness based on order batching policy, capacity of the picking and sorting operations, and picking policy [47], and connection of warehouse design and order picking efficiency [48].
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Performance of specific technology or equipment: e.g., evaluation of dual command mini-load throughput time [49], carousel throughput [50], AS/RS performance measurement by using queuing theory [51], travel time of tower crane S/R machine under single and double cycle command [52].
This category was the most researched field after 2000, and it also had a quite high number of published papers in previous decades (Table 5). Most of the publications focus on the performance evaluation of a particular operation and equipment, and especially for automated warehouses. Studies with consideration to performance management and benchmarking of the successful cases can make significant contributions in future.
Receiving and shipping
The warehouse operations initiate in goods receiving and finish in the shipping area. Products are received into the warehouse, assigned to different locations, and after picking, they are packed and shipped out to the customers. The planning of receiving and shipping operations is among the least investigated topics in the literature. Some examples of potential problems to investigate are preparation of goods to be stored [53] and resource allocations, e.g., truck-dock assignment for receiving and delivery and order-truck assignment if orders of different customers are aggregated into one truck [54]. Receiving and shipping are the least investigated categories in the warehousing literature, with preparation operation and resource assignment as the main aspects explored.
Storage
Apart from inventory management, different decisions should be made to form the warehousing function, e.g., where SKUs should be stored in the warehouse [7]; how much space is required for each product [55]; what storage policy should be followed [56]; how products should be classified [57]; and how the scheduling and sequencing of operations should be planned [58]. Warehouse managers usually try to design storage processes in association with picking activities, because what is done in the storage stage directly influences the picking performance.
The operation of storage needs to be done based on a plan, and sometimes in combination with the picking process when the warehouse is working under double command operation. The estimation of average traveling time for multi-aisle AS/RS [59], sequencing the storage and picking in AS/RS system [60], scheduling the equipment in AS/RS system [61] and sequencing of storage and re-storage process [58] is among the published works in this field.
Unit-load and single-deep rack are the main setups for the publications in the storage category. Most papers belong to the subcategory of operational aspects, which includes more common problems of warehousing. Planning for the storage operation is one of the well-researched areas, but most publications neglect the dynamism of warehouse requirements and information. Employing robust planning can significantly contribute to this field.
Picking
Order picking is the most labor-intense operation in the warehouse [7]. Different picking strategies consist of some or all of the following activities: batching, routing, activities sequencing, sorting and packing. The picking activity can be done manually or with automated systems; both of these approaches require different models and planning. The reviewed literature can be categorized into the following areas.
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Order picking policy: Evaluating the performance of different policies including single order picking, batch picking, zone picking and wave picking (e.g., [62]).
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Order batching: Most of the published works in this area develop heuristic algorithms to solve batching problems. But Chen et al. [63] employ a data mining technique to develop a clustering procedure for orders of a warehouse with parallel-aisle layout.
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Picker routing: For example, application of intelligent agent-based models to find picking routes in near real time [64], developing the picking route in a cross-aisle warehouse [65], in a multi-parallel-aisle warehouse [66] and picking sequence from different zones [56].
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Automated picking operation: For example, routing and operations sequencing in automated warehouses [67] and positioning the dwell point (location of S/R shuttle during idle time) in a unit-load AS/RS [68].
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Combination of picking plan with other activities: For example, integrated problem of order batching and picking [69], and combination of order picking and packing operations [36].
Different picking methods are suggested in previous research, but we find that the literature lacks comprehensive analysis and comparison of these methods for different warehouse designs and operational requirements. Most of the papers consider travel/picking time as the main indicator to optimize when evaluating the picking operation. Finally, similar to the papers in the storage category, static information is the main mode of input for the problems addressed, while uncertainty of the business environment is usually neglected.
Connection to other departments and companies
Warehouses are connected to other departments and companies through both incoming and outgoing goods, and as a potentially outsourced function. This interface can be captured in different ways: e.g., by integration of planning and processes with purchasing, production, transportation and contracting with external companies.
Poon et al. [70] focus on the interface between production and raw material warehouse. Chou et al. [71] address a warehousing problem when products can be ordered from a production site and then after use are returned to the warehouses for future reuse. The connection of transportation and cross-dock warehouse operations is considered by Hill and Galbreth [72]. Contracting 3PL warehouses is becoming very common. As an example of analytical modeling of 3PL contracts, Chen et al. [73] formulate three different settings of warehousing contract with capacity commitment from buyer perspective and show which types can be cost-efficient to choose.
The relation of the warehouse to other departments and companies is among the least explored fields in the literature. Most of the papers in this category focus on the connection of warehouse and production to have a smooth flow of material between these two functions, especially when the material flow is from both sides. Two other investigated areas are contracting aspects and the connection with transportation, although not very extensively. As a summary of the literature discussed in this section, Appendix C of ESM exhibits the classification of each primary and supporting component and lists the related papers.
The status of warehousing research published before 2000 was extracted from [2, 3]. They have a similar classification and definition for each category of research as in this paper, but the categories of human resource management and the connection to other companies/departments were not included. Comparing recent studies with those before 2000, most of older literature was devoted to operational aspects (storage and picking). Also performance evaluation attracted many publications as well as infrastructure design, in almost one-fifth of the total papers (Table 5). The three categories of technology and equipment, receiving/shipping and operations strategy are far behind with just 14 papers in total. Publications after 2000 are more evenly divided between different categories, although operations strategy, connection to other departments/companies and receiving/shipping are still significantly fewer. When comparing the number of papers in each category before and after 2000, one observation is the increase of publications in the technology and equipment category. The trend of automation, rapid change and emergence of new soft/hard technologies, together with the e-commerce evolution during the last decade, can be some of the motivations for this increase. Another observation is the lower share of papers focusing on operational aspects. Even though the proportion of publications in these categories is still relatively high, it has decreased and become more similar to the supportive aspects.