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Logisticians in the worldwide industry are frequently faced with the problem of measuring the total cost of holding inventories with simple and easy-to-use methodologies. The purpose of this paper is to look at the problem, and in particular illustrate the inventory holding cost rate computation, when different kind of warehousing systems are applied.

A multiple case study analysis is here developed and supported by a methodological framework directly derived from the working group discussions and brainstorming activities. Two different field of application are considered: one related to five companies with manual warehousing systems operating with traditional fork lift trucks; the other is among five companies operating with automated storage/retrieval systems (AS/RS) to store inventories.

The multi-case study helps to understand how the holding cost parameter is currently computed by industrial managers and how much the difference between manual and automated/automatic warehousing systems impacts on the inventory cost structure definition. The insights from the ten case studies provide evidence that the kind of storage system adopted inside the factory can impact on the holding cost rate computation and permit to derive important considerations.

The final aim of this work is to help industrial engineers and logisticians in correctly understanding the inventory costs involved in their systems and their cost structure. In addition, the multi-case analysis leads to considerations, to be applied in different industrial contexts. As other industrial applications are identified, they may be analyzed by using the presented methodology, and with aid from the data from this paper.

The relevance of this work is to help industrial engineers and logisticians in understanding correctly the inventory costs involved in their logistics systems and their cost structure. In addition, the multi-case analysis lead to interesting final considerations, easily to be applied in different industrial contexts. As other industrial applications are identified, they may be analyzed by using the methodology and extrapolating the data from this paper.

The warehouse is the interface area for production lines, market, customers and suppliers, and the business environment in general. Goods inwards (GI), constituting part of the warehouse activities, is the department responsible for booking‐in the incoming parts from external suppliers. After the production process is improved, the resources in the warehouse are balanced to link the internal/ external transfer of physical items. With a broader view of the GI activities, and a look into its future, the actual problems may become clear, understandable, and hence easy to solve. The aim of this paper is to study the problem areas in GI and provide solutions to increase the performance of warehousing operations. First, a conceptual framework has been developed to improve the effectiveness of warehousing operations. Second, a case study has been conducted with the help of the model to minimize the through‐put time in warehousing operations. Finally, a summary of findings and recommendations are presented.

This paper seeks to illustrate the successful development of a warehousing decision support system (WDSS) that aided the Buckeye Cable Vision Incorporated (BCV) in making its strategic warehouse expansion and re‐warehousing decisions. The WDSS utilized by the BCV can be a blue‐print for other companies which would like to improve their warehousing productivity and strengthen the warehousing link to their supply chain.

Warehousing costs make up a significant portion of a company's business expenditures. Thus, many companies are pressured to control warehousing costs by improving their warehousing productivity. However, it is not an easy task to improve warehousing productivity given the increased complexity associated with today's warehousing activities such as value‐added services and cross‐docking operations. To cope with such warehousing challenges, a specific decision support system (DSS) that incorporates several computer‐based models into the warehouse decision‐making process is proposed. These models include: a simulation model based on computer‐aided design, an analytic hierarchy process, and a forecasting technique. To demonstrate the usefulness of the proposed DSS and provide practical guidance for other companies that seek the DSS as a powerful decision‐aid tool, this paper conducted an in‐depth case analysis of the BCV that successfully exploited the WDSS.

Through the BCV's case, it was shown that the WDSS could not only enhance warehousing productivity, but could also improve supply chain visibility. Also, it was found that the WDSS success lay in the company's ability to sustain high data quality by standardizing, cleaning, and updating relevant data on a real‐time basis.

Since the early 1970s, a DSS has attracted attention from both profit and non‐profit organizations. Despite the long history of DSS evolution, it has been rarely applied to strategic warehousing decisions. This paper is one of the first attempts to develop a specific DSS that can assist warehousing managers in documenting warehousing costs, identifying non‐value adding activities, evaluating strategic warehousing alternatives, and utilizing given warehousing resources.

To meet customers' expectations on shorter lead times, high product availability, flexibility, and variation in delivery and return options, retailers have turned their attention to warehousing and are making big investments in technology. Currently, technology providers are pushing for smart warehousing, a new and under-researched phenomenon. This study aims to conceptualize the term and examine pathways toward implementing smart warehousing.

An exploratory survey was administered to 50 leading Swedish retailers in varying segments. A two-tailed t-test for equality of means was used to detect significant differences between current and future states.

The study found that future smart warehouses will be automated, autonomous, digital, and connected, but that retailers will follow different paths along this journey, driven by contextual trends, e.g. sales growth, wider product assortment, shorter lead-time offerings, and integration of brick-and-mortar and online stores. Interestingly, the study revealed that many of the retailers that aim to create smart warehouses in five years are not the retailers with the most developed technology today.

The paper operationalizes smart warehousing in two dimensions: degree of automation and degree of digitalization and connectivity of information platforms. Based on the findings, 16 theoretical propositions are put forth that, based on contextual factors, explain different pathways for retailers to implement smart warehousing.

The empirical insights and theoretical discussions provide practically useful guidance, including outlined trends, for selecting and benchmarking automation and complementary technologies in warehouse operations.

This paper conceptualizes and operationalizes smart warehousing – an original approach. It is also one of the first to investigate the technological transformation in retail warehousing empirically, explaining how and why retailers choose different pathways toward smart warehousing.

In today’s dynamic and changing environment, companies have a strong need to create or sustain their competitive advantages. In order to be competitive, companies need to be responsive and closer to the customers, and deliver value‐added products and services as quickly as possible. Companies also need to be able to support organizational information needs faster and better than their competitors. These goals can be realized by applying two emerging information technologies: enterprise resource planning (ERP) supporting business process integration; and data warehousing supporting data integration. Companies with the further integration of ERP and data warehousing will have great advantages in the competitive environment. Two cases have been studied and presented to illustrate its values.

The purpose of this study is to gain a better understanding of the impacts of Logistics 4.0 initiatives (focusing on automated warehousing systems) on the economic, environmental and social dimensions of firms' sustainability performance. To achieve this objective, a new framework for the assessment of sustainable warehousing in the 4.0 era is developed.

The framework, developed via the item-objective congruence index, Q-sort method and interviews with experts, is employed to assess performance changes through management interviews in two warehousing companies after the implementation of automation technologies.

Most aspects of both companies' sustainability performance are considerably improved (e.g. productivity, accuracy, air emission, worker safety and supply chain visibility); however, the outcome for some criteria might be worsened or improved depending on each company's solutions and strategies (e.g. increasing electricity bills, maintenance costs and job losses).

The findings provide insight into the effective implementation of warehousing technologies. The proposed framework is also a valid and reliable instrument for sustainability assessment for warehousing operators, which companies can utilise for self-assessment.

This paper contributes to establishing a body of literature that explores the previously unclarified effects of Logistics 4.0 on firms' sustainability performance. The proposed framework, which captures critical concerns of corporate sustainability and technological adaptation, is also the first of its kind for warehouse performance assessment.

While several warehouses are now technologically equipped and smart, the implementation of real-time analytics in warehouse operations is scarcely reported in the literature. This study aims to develop a practical system for real-time analytics of process monitoring in an internet-of-things (IoT)-enabled smart warehouse environment.

A modified system development research process was used to carry out this research. A prototype system was developed that mimicked a case company’s actual warehouse operations in Indonesia’s manufacturing companies. The proposed system relied heavily on the utilization of IoT technologies, wireless internet connection and web services to keep track of the product movement to provide real-time access to critical warehousing activities, helping make better, faster and more informed decisions.

The proposed system in the presented case company increased real-time warehousing processes visibility for stakeholders at different management levels in their most convenient ways by developing visual representation to display crucial information. The numerical or textual data were converted into graphics for ease of understanding for stakeholders, including field operators. The key elements for the feasible implementation of the proposed model in an industrial area were discussed. They are strategic-level components, IoT-enabled warehouse environments, customized middleware settings, real-time processing software and visual dashboard configuration.

While this study shows a prototype-based implementation of actual warehouse operations in one of Indonesia’s manufacturing companies, the architectural requirements are applicable and extensible by other companies. In this sense, the research offers significant economic advantages by using customized middleware to avoid unnecessary waste brought by the off-the-shelves generic middleware, which is not entirely suitable for system development.

This research’s finding contributes to filling the gap in the limited body of knowledge of real-time analytics implementation in warehousing operations. This should encourage other researchers to enhance and develop the devised elements to enrich smart warehousing’s theoretical knowledge. Besides, the successful proof-of-concept implementation reported in this research would allow other companies to gain valuable insights and experiences.

In this paper, the authors describe a geographical information system (GIS)‐based software system for managing and integrating multi‐facility warehousing and production systems that are distributed within a relatively large geographical area. The development of the software system is motivated by a unique warehousing environment at the Pine Bluff Arsenal in Pine Bluff, Arkansas. The arsenal scenario is characterized by a novel set of highly limiting warehouse constraints. Although motivated by this unique problem, the software system has been designed to maximize technology transfer capability into diverse general warehouse settings. The paper presents motivation, describes features, and demonstrates the efficacy of operations using the software system. The system is verified and validated in a case study setting. It is demonstrated that the GIS platform offers unique capabilities that enhance problem solutions. In conclusion, the paper offers a contribution to the literature by presenting the use of GIS as an integration strategy in an exciting new area of application.

World‐class warehousing is examined and 10 dimensions for measuring world‐class performance are identified. For each measure, criteria for assigning world‐class ‘scores’ are provided. For a firm to be judged to be world‐class, it will have to perform well across all 10 dimensions.

Warehousing is becoming more and more a critical activity in the supply chain to outperform competitors on customer service, lead times, and costs. However, if warehousing is to be a source of competitive advantage, then the implementation of a warehouse management information system (WMS) is a necessary condition to achieve efficiently the high performance of warehousing operations required in today’s marketplace. A major practical question is then whether a given warehouse should implement a standard or a tailor‐made WMS. A standard WMS offers many advantages; it is a proven solution, it is less costly, the implementation lead time is shorter, and the after‐sales service is better. On the other hand, a standard WMS remains largely making compromises between the way a warehouse wants to work and the way the system allows the warehouse to work. In certain environments, such compromises might seriously degrade warehouse performance, in which case it then seems better to implement a tailor‐made WMS. To answer the above question, we conducted an exploratory field study of warehouses with recently implemented WMSs to first understand the empirical reality and then build up a theory linking the constructs warehouse complexity and warehouse planning and control structure. Warehouse complexity refers to the number and variety of items to be handled, the degree of their interaction, and the number, nature, i.e. technologies used, and variety of processes, determined among others by the warehouse’s position in the logistic chain and the nature of its market. Warehouse planning and control structure refers to the management functions that plan, direct, coordinate and control the flow of goods through the warehouse, from the time of receiving to the time of shipping. It is strongly related to the WMS in use. We found that the number of orderlines to be processed per day and the number of stock‐keeping units are the two main observable aspects of warehouse complexity; that the more complex the warehouse is, the more tailor‐made the planning and control structure should be; that for simple warehouses a standardized planning and control structure suffices; and that the design of a new‐to‐build warehouse should be carried out in close concert with the design of the warehouse planning and control structure.