Abstract
Warehouses play a significant role in the seamless distribution, integration, and storage of items as well as in supply chain operations. Automated identification (auto-ID) technologies that include barcode and RFID provide class- or item-level visibility to facilitate effective and efficient decisions in their respective environments. A warehouse environment benefits from auto-ID through improved cost savings, operational efficiency, and opportunities for higher revenues. It is therefore not surprising that both researchers and practitioners have considered the use of auto-ID in warehouses. We take stock of related literature to determine the state-of-the-art on auto-ID use in warehouse management, with specific focus on RFID, and identify potential directions of further research. Based on our review, we develop a conceptual framework that incorporates the primary factors that guide the decision to adopt auto-ID in warehouse management.
Introduction
A warehouse is a critical part of a supply chain that buffers variations in supply and demand. The operations and resulting productivity of warehouses directly affect the effectiveness and efficiency of supply chains (Reefke & Sundaram 2018). There is an increasing trend toward automation of business processes (e.g., Camargo et al. 2020) in a warehouse environment through automated identification technologies (Zhou et al. 2017, Micale & La Scalia 2018) for improved item-level visibility and data quality (e.g., Vial 2019) in these systems. The decision on whether to use automated identification (auto-ID) and the type (e.g., Tu et al. 2020) plays a significant role in warehouse operations as item visibility is of paramount importance in supply chains.
Warehouses vary in size and complexity and can be a small garage, a self-storage space, up to a massive and complex space that is used to support value-adding activities in addition to the storage of items (Karagiannaki et al. 2011). It is therefore clear that warehouses not only facilitate inventory protection and storage (Doss et al. 2020) but also may support several other operations that include after sales service, light assembly and inspection, item packaging, order picking, put away, and receiving. With such diverse set of operations and facilities, the variations in warehouses across and even within an industry is a given and therefore each warehouse requires customized design and development. Regardless of the design parameters, auto-ID use can help improve warehouse performance.
Automated identification is generally used to identify objects and store such identification information either locally (e.g., RFID tag memory) or in a database (Wu et al. 2020). Over the years, several automated identification technologies have been developed and used across various application domains. Some of the commonly used auto-ID technologies are barcode, biometrics, optical character recognition (OCR), Radio-Frequency IDentification (RFID) tags, smart cards, and voice recognition. While each of these is used in appropriate applications, warehouse environments are good matches for mature barcode and RFID (e.g., Bose et al. 2011) technologies that come with small footprint and low cost. RFID uses radio waves to identify, locate, and track objects while barcode consists of lines (bars) and spaces that together provide the information necessary to identify an object as well as to track and trace the same.
While both barcode and RFID can be used to facilitate most related requirements in a warehouse environment, RFID has an edge due to its ability to provide item-level visibility, the ability to easily track the location and status of objects in the warehouse in real-time, enhance productivity, and reduce operational costs (Konovalenko et al. 2020). Auto-ID technologies, specifically variants of RFID, are therefore attractive as is seen from the increasing number of its adoption among companies such as Gap, Gillette, Marks & Spencer, Tesco, and Wal-Mart for locating their warehouse resources (Collins 2006a, Collins 2006b).
Given the popularity of automated identification technologies in the business world, it is not surprising that there is a concomitant increased interest among academic researchers to study various facets of auto-ID technologies to improve their performance even further. The extent of interest toward auto-ID among academic researchers varies across disciplines. Among researchers who work either directly or tangentially in areas related to supply chain management, auto-ID is quite popular in inventory management, logistics, and transportation, while it is rather stagnant in research related to warehouse management (Lim et al. 2013). Regardless of researchers’ interest in the general area of auto-ID use in warehouse applications, the significance of warehousing or auto-ID technologies cannot be overstated for effective performance of supply chains. Therefore, we attempt to fill this void and provide a review of published research on auto-ID technologies in warehouse management over the past three decades to identify possible trends, observe the state-of-the-art, and identify potential directions for further research. Specifically, our objectives for this study include the following. analyze the distribution/trends of publications related to auto-ID and warehouse management identify the commonly used keywords in auto-ID and warehouse management publications to gauge their significance determine the type of auto-ID technologies used in auto-ID and warehouse management studies classify auto-ID and warehouse management publications as per the research methods employed utilize the state-of-the-art of auto-ID use in warehouses to propose a conceptual framework that embodies the essential features of auto-ID adoption decisions and to guide the practitioner in a warehouse environment.
To this end, we conducted a systematic review of 128 published articles across several peer-reviewed journals during the period 1986–2020. In Section 2, we discuss the methods we used to analyze auto-ID and warehouse management research. We present and discuss our findings in Section 3. We then develop and present a conceptual framework that helps aid in the selection of automated identification technologies for warehouse management and discuss related implications and future research directions in Section 4. We conclude with discussion on the content and limitations of this study in Section 5.
Research methodology
Extant published studies have been used to create new knowledge through methods that include literature reviews (Chao et al. 2007). Ho et al. (2002) employ a similar method to analyze empirical studies related to the management of supply chains. Giachetti (2004) used critical literature review to analyze literature on enterprises for information integration. Later, Chao et al. (2007) evaluated RFID’s potential through bibliometric analysis. Other researchers have used meta-analysis through adoption of various data gathering techniques. An aim of meta-analysis is to summarize the findings based on specific constructs generated through existing empirical research (King & He 2006).
As we are interested in reviewing auto-ID and warehouse management research through extant publications, methods that include literature review and profiling meta-analysis are deemed appropriate research approaches. To begin this process, we collected data through a number of stages that we describe below.
Literature search
We used the Scopus Database of scholarly articles to search for relevant publications as this contains most journals of interest to this study on auto-ID technologies in warehouse management (Chao et al. 2007). In addition, search through a single data source helps remove unnecessary duplication in results generated from several different publication databases (Irani et al., 2010). We employed the ‘General Search’ (vs. ‘Advanced Search’) technique for consistent results across searches without any unnecessary confusion (Chao et al. 2007).
We used five specific terms to search for published research on the use of auto-ID in warehouse management: “Warehouse” OR “warehousing” AND “RFID” OR “Barcode” OR “Information Technology”. Our search of the database with these keywords netted 834 articles that included types such as chapters in books, conference publications, journal papers, reviews, and others. As research publications are our only interest for this study, we excluded the other publication types, with our list down to 289 publications. We then discarded those in languages other than English which brought down the list to 256 papers that appeared in 97 peer-reviewed journals as outlets between 1986 and 2020 (Table 3) spread across different subject categories (Table 1). To locate other relevant publications, we searched specific journals in related identified areas (decision support, information systems, operations research, production economics, and supply chain management). Specifically, the journals considered include Computers & Industrial Engineering, Decision Support Systems, Expert Systems with Applications, European Journal of Information Systems, European Journal of Operational Research, Industrial Management and Data Systems, Information Systems Frontiers, International Journal of Information Technology and Management (IJITM), International Journal of Logistics Research and Applications, International Journal of Logistics Systems and Management, International Journal of Manufacturing Technology and Management, International Journal of Physical Distribution & Logistics Management, International Journal of Production Economics, International Journal of Production Research, International Journal of RF Technologies: Research and Applications, International Journal of Value Chain Management, Journal of Cases on Information Technology, Management Research Review, Management Science, Material Handling Engineering, Operations Research, Packaging Technology and Science, Research Journal of Applied Sciences, Engineering and Technology, and Supply Chain Systems. This improved the number of hits to 269 articles as an additional 13 articles were recorded. We then manually examined all 269 articles to cross-verify and validate the search results in terms of their relevance (Irani et al. 2010). This resulted in us discarding 141 publications that seemed relevant at first through search-term match but were deemed irrelevant since these did not specifically consider auto-ID in warehouse management. For example, while warehouse is mentioned as an example for RFID/barcode application, research in the domain was not a part of the reported study. This left us with 128 articles for further consideration.
Relevant publications between 1986 and 2020
Relevant publications between 1986 and 2020
Relevant publications categorized by subject category
We carefully considered all relevant publications from 1986 through 2020 to generate a profile of research topics, research methodology, publication outlet, when the publication occurred, the type of publication, and type of auto-ID technologies used. We then carefully selected 128 research publications to generate relevant information of interest to our study. We extracted and recorded various items of information for each of the chosen publications such as research topics and the research methodology employed in these articles. We gathered data on the various characteristics of the identified publications and then generated count and percentage values corresponding to the dimensions we used to categorize the generated list of publications. The results of this exercise are presented in Tables 1–7. We adapted the categories presented in Irani et al. (2020) to categorize methodological variables.
Relevant publications categorized by publication outlets
Relevant publications categorized by publication outlets
Keywords/topics utilized frequently in auto-ID and warehouse management studies
Relevant publications categorized based on publication type
Relevant publications categorized based on auto-ID type
Employed research methods (adapted from Irani et al. 2010)
For analyzing the research topics investigated in the considered publications, the keywords from the search outputs were manually collated. We used a variant of the classification scheme used in Barki et al. (1993) to meaningfully organize keywords into nine themes that include external environment, information technology, IS development and operations, IS education and research, IS management, IS usage, information systems types, information technology, organizational environment, and reference discipline. Moreover, from existing warehouse management research (Rouwenhorst et al. 2000, Lim et al. 2013), we narrowed the nine categories into seven categories: Reference discipline, external environment, organizational environment, auto-ID management, auto-ID technological and development issues, auto-ID applications types, and auto-ID education and research. The rationale for our choice is better clarity in the classification scheme and the absence of competing schemes that are appropriate for our purpose. From our observation of published literature on auto-ID use in warehouses, the number of publications that focused on development and operations was rather few and most of these publications were on issues associated with auto-ID technologies. Therefore, we merged auto-ID development and operations and auto-ID technology into ‘auto-ID technological and development issues.’ For a similar reason, we merged ‘usage’ and auto-ID management into a single category. We renamed the categories with IS replaced with auto-ID. The categories of classification terms we used narrowed down to auto-ID application types, auto-ID management, auto-ID research and education, auto-ID technological and development issues, external environment, organizational environment, and reference discipline. We present our research findings and related discussion in Section 3.
Subject category
Our findings suggest that auto-ID and warehouse management publications span a total of 31 Scopus subject categories, with 26 (20.63%) publications on various auto-ID facets of Industrial Engineering. The next highest is that of Computer Science & Information Engineering with 21 (16.67%) publications, followed by Engineering & Materials Science with 9 (7.14%) publications, Information & Communication Engineering with 7 (5.56%) publications, and Information Engineering with 6 (4.76%) publications. There are relatively few articles in the other categories. These include Management, Business, and Computer Science with 5 (3.97%) articles each; Industrial Engineering & Engineering Management, Information Technology, and Information Systems & Technology with 4 (3.17%) articles each; Computer & Communication Sciences, Systems Engineering & Technology, Electrical Engineering, and Chemical Engineering with 3 (2.38 %) articles each (Table 1). Only two (1.59%) articles are associated with each of the four categories such as Mechanical & Manufacturing Engineering, Computer Science & Automation, Communication Science Management, Information Management, and Electrical & Electronic Engineering. However, the largest number of categories (10) contains the fewest number of articles (1, 0.79%) on auto-ID use in warehouse management. These categories include Engineering Systems & Management, Computer Science & Technology, Technology & Innovation, Science & Technology, Computer & Mechanical Engineering, Logistics & Manufacturing Technology, Financial Management & Engineering, Construction Management, Computer Science & Math, and Electronic Engineering & Physics.
Publication outlet
Our search for auto-ID use in warehouse management resulted in 128 research publications that spanned 97 academic journals. Our results based on the publication outlet is presented in Table 2. Please note that due to space considerations we have included only the top 16 outlets that had at least two relevant publications. As can be seen in Table 2, the most number of publications (13) were in the International Journal of RF Technologies: Research and Applications. Expert Systems with Applications journal published 7 of our relevant papers. Then comes the International journal of Production Economics with 4 publications. This is closely followed by IEEE Transactions on Antennas and Propagation, Industrial Management and Data Systems, and Supply Chain Systems each with 3 relevant publications. The other ten journals had only two of the relevant papers in each. These outlets include International Arab Journal of Information Technology, International Journal of Electronics, International Journal of Logistics Research and Applications, International Journal of Production Research, Journal of Manufacturing Systems, Journal of Theoretical and Applied Electronic Commerce Research, Packaging Technology and Science, Research Journal of Applied Sciences, Engineering and Technology, Wireless Personal Communications, and World Academy of Science, Engineering and Technology. The remaining publications appeared in 81 other journals that each had one relevant paper. We do not list these in Table 2.
Results presented in Table 2 also show that very few of these listed journals are related to business and management areas that include information systems and operations management. The remaining publication outlets are in technical disciplines that include engineering. This observation could be because most of the papers that were published earlier had primary focus on technology development with an engineering-oriented approach and engineering publication outlets are perhaps relatively more open to publication of such studies.
Publication year
We now consider the publication year of relevant articles. We summarize our results to this regard in Table 3, which shows an increasing trend from 1986 with one publication to 2011 with 19 publications. So far, 2011 remains as the year with the most number of relevant publications per year. This is closely followed by 2012 with 18 publications.
Our search returned no publication before 1986. In general, there is a lack of publications on the use of barcode technology in warehouse environments. Moreover, widespread adoption of RFID tags began in the early 2000s, with mandates from Wal-Mart, the US Department of Defense, among others. Therefore, it is not surprising to see a general increasing trend from 2003. It should be noted that the number of articles per year has fallen precipitously since 2013.
Topics/keyword analysis
To assess the frequently utilized keywords, our adaption of Barki et al. (1993) scheme included 1150 keywords based on our literature search. Specifically, from Barki et al. (1993), we used auto-ID application types, auto-ID education and research, auto-ID management, auto-ID technological and development issues, external environment, organizational environment, and reference discipline. Many keywords were used several times, with 1150 used at least twice. Therefore, we considered the placement of these 1150 keywords in the seven categories. After removal of duplicates, we had 672 keywords with the distribution among the respective categories as listed above –60, 4, 70, 360, 34, 60, and 80. Keywords also included Greece, United States of America, China, and Hong Kong as well as case study and case-based reasoning. We list the more frequently observed keywords and their frequencies in Table 4.
Publication Type
Our findings on publication type are presented in Table 5. As is seen in Table 5, most of the relevant publications based on our selected list are categorized as research paper (52, 40.63%). Case study (41, 32.03%) is next, followed by general review (21, 16.41%), technical paper (9, 7.03 %), conceptual paper (1, 0.78 %) and literature review (4, 3.13 %).
Auto-ID technology type
We now categorize the publications based on the auto-ID technology of focus. Our results presented in Table 6 show that a majority of the publications (C = 121, 96.03%) in our considered list are on RFID use to supplant competing technology, specifically barcode. The number of publications that consider barcode use in warehouse management is rather low (here, C = 5, 3.97%). It is worth noting that none of the studies considered the use of a hybrid RFID/barcode system although such a setup is used in other applications where both barcode and RFID are placed on an object but are made use of in different situations.
Research methods
We adapted the categories of research methods used from Irani et al. (2010). Our results based on these categories are presented in Table 7. We considered seven research method categories. Among these, most of the publications (59, 46.09 %) in our considered set of articles utilized multi-method research in which multiple methods such as design, experimentation, and simulation are incorporated together in one study.
The next highest number of publications fall into the analysis/frameworks/conceptual model/design research category with 25 papers. This is followed by experimental test with 15 publications, case study with 12 publications, mathematical modeling/simulation modeling/algorithms with 11 publications, and both survey and interview with 3 publications each.
Conceptual framework for auto-ID selection decision
We now present and discuss a conceptual framework that supports auto-ID selection decision based on our literature review and resulting combinatorial synthesis. During our review of published literature, we observed several factors and sub-factors with the potential to influence the adoption of auto-ID technologies in warehouses. We chose to use the technology-organization-environment (TOE) framework (Tornatzky & Fleischer 1990) as the theoretical basis to categorize identified factors through literature review organized as external environmental, operational, organizational, resources, structural, and technological (Fig. 1).
Proposed conceptual framework [adapted from Tornatzky and Fleischer (1990)].
Among the technology adoption IS theories, we consider only those that are at the firm level such as Diffusion of Innovation (DoI) and the TOE frameworks (Oliveira & Martins 2011). Both TOE and DoI are consistent when antecedents for adoption decisions include organizational external and external characteristics, individual characteristics, and technological characteristics (e.g., Zhu et al. 2006). This is similar to the technology and organization part of TOE which includes environmental context with associated constraints and opportunities related to IT innovations (Oliveira & Martins 2011). Given this, the TOE framework affords the DoI theory more explainability in terms of intra-firm adoption of innovation (Hsu, Kraemer, & Dunkle 2006). Based on this perspective, TOE is better to understand technology adoption (Wang et al. 2010). We discuss the conceptual framework presented in Fig. 1 in detail below.
The structural factors as they relate to warehouse decisions are generally considered at the design and development state of a warehouse or during the addition of extensions to an existing warehouse (Karagiannaki et al. 2011). Bhuptani and Moradpour (2005) indicate that warehouse designers need to determine the warehouse’s characteristics and structure - a set of physical and internal environmental dimensions that include dust and dirt, humidity, material type, noise, number of aisles, number of racks, temperature, and warehouse size –and thoroughly consider their entire environment to make auto-ID decisions. In addition, product type (liquid/metallic objects) and E-Plane (electric field) influence the auto-ID read rate accuracy (Mercer et al. 2011). Moreover, higher warehouse automation level is generally associated with higher need for automated identification technologies such as RFID when compared against a warehouse in which most tasks are manually done. Therefore, the level (manual, semi-automatic, completely automated) of mechanization should be considered when deciding to use auto-ID (Karagiannaki et al. 2011). To this end, we consider structural factors as main influential factors for auto-ID adoption and related decisions in warehouse settings.
Operational factors
These factors generally play a role at the operating state (Karagiannaki et al. 2011). Each product that enters a warehouse go through several operational or process steps (Rouwenhorst et al. 2000). Some common operational factors in a warehouse include batching, forward reserve allocation, order accumulation and sorting, picking, put away, receiving, shipping, storage assignment policy, and zoning (Karagiannaki et al. 2011). In addition to its direct influence on the productivity and associated warehouse cost, warehouse operations affect the performance of the entire supply chain. Moreover, the perception at a warehouse with complex operations may be disparate when compared to one with relatively simple operations. Therefore, it is important to investigate the warehouse operations when deciding to use auto-ID technology (Karagiannaki et al. 2011). To this end, we consider operational dimensions as key factors that affect warehouse auto-ID adoption decisions.
Resource-related factors
The resources associated with warehouses are all equipment, means, and staff necessary for its smooth operation (Karagiannaki et al. 2011). The resource-related dimensions in such an environment include material handling equipment, space capacity, storage systems, storage units, warehouse management system (WMS), and labor. Moreover, WMS plays a significant role in the adoption of RFID technology (Vijayaraman & Osyk 2006). An important factor that goes into auto-ID adoption decisions in a warehouse is space capacity utilization, which can be improved through RFID technology (Wang et al. 2010, Karagiannaki et al. 2011). Therefore, auto-ID adoption decisions must include warehouse resources in terms of their availability, general characteristics, and limits. Accordingly, resource-related dimensions are other important influencers of auto-ID adoption decisions in warehouse environments.
Organizational factors
For successful adoption of auto-ID technology in warehouse environments, the importance of organizational factors like support from top management, IT knowledge, and the specific requirements of the warehouse cannot be overstated. According to Chan and Chan (2011), the warehouse decision makers should identify all needs and potential for possible issues using appropriate key performance indicators during adoption consideration of auto-ID technologies. Support from top management is important for their commitment, support, and vision (Lee & Kim 2007). Such support is especially important for RFID adoption decisions as the adoption process involves various resources and reengineering (Hwang et al. 2004). Management support is essential for the success of RFID adoption and implementation projects (Irani et al. 2010). RFID technology also requires necessary and appropriate skills for its implementation (Wang et al. 2010). We therefore consider organizational factors as significant input for auto-ID adoption decisions in warehouses.
Technological factors
From TOE theory (Tornatzky & Fleischer 1990), both external and internal relevant technologies belong to the technological context. From our combinatorial analysis of existing research, we identified a set of technological factors. These factors are categorized along 28 sub-factors that include accuracy, communication range, deployment cost, ease of use, environmental sensitivity, established standards, information properties, interference, item-level tracking and tracing, labor, line-of-sight, memory, multi-tag collection, ongoing innovations, operational life, performance, privacy, product recalls, quality control, reliability, return on investment, security, tag data storage, tag read/write capabilities, tag weight, technology cost, traceable warranty, and visibility.
Technology analysis is important for auto-ID adoption since the different auto-ID technologies (e.g., barcode, RFID) have disparate features that differently affect cost, labor, and essentially the entire supply chain (Llie-Zudor et al. 2011). Based on this perspective, the effects of technological factors on RFID system performance and in turn that of the warehouse are bound to be significant. It is therefore important to carefully consider the performance characteristics of various RFID technologies under varying set of warehouse conditions to identify and develop the best RFID-based solution. Therefore, we consider the technological factors to play a significant role in warehouse auto-ID adoption decisions.
External environmental factors
The external environment comprises the business environment that includes competitors, government, and the entire industry (Tornatzky & Fleischer 1990). The factors include pressures related to competition, customer, government, and technology provider support as these have been identified as being significant in several studies on auto-ID and such environment (e.g., Brown & Russell 2007, Hwang et al. 2004, Lin & Ho 2009, Spekman & Sweeney 2006, Wang et al. 2010, et al. 2008). Therefore, we consider external environmental factors as important factors that affect warehouse auto-ID adoption decisions.
To our knowledge, previous studies have not considered the important factors that affect auto-ID adoption process in its entirety. However, all important factors that have the potential to influence the decision to select an auto-ID technology are necessary to understand and appreciate the process. A series of decisions that are based on several issues that affect auto-ID adoption is seen as a sequence of choices that result in the selection of an auto-ID technology such as barcode or RFID (Ilie-Zudor et al. 2011). In other words, the decision makers essentially go through multiple stages to select an auto-ID technology to implement (Pero & Rossi 2014). It is worth noting that the significance of the factors that affect auto-ID adoption decisions may appreciably vary across time among different contexts (Adhiarna et al. 2011). This therefore necessitates further empirical studies on the selection processes that lead to warehouse auto-ID adoption decisions. Such studies could help develop a theoretical basis for auto-ID adoption decisions. Moreover, this will also provide warehouse managers with necessary help and guidance when they make auto-ID adoption decisions.
Discussion and conclusions
We considered the current state of auto-ID technology in warehouse management through a systematic review of 128 articles spread across 97 peer-reviewed journals between 1986 and 2020. Employing bibliometric analysis, literature review, and profiling meta-analysis, our results comprise a set of dimensions that include publication outlet, type of publication, when it was published, subject group, most commonly used keywords, type of auto-ID technologies, and research methods.
We observe the following from our literature survey and analysis of auto-ID and warehouse management research. In terms of subject category, published research exists in a total of 31 Scopus subject categories on auto-ID and warehouse management, with the most number of publications across Industrial Engineering, Computer Science & Information Engineering, Engineering & Materials Science, Information & Communication Engineering, Information Engineering, Management, Business, and Computer Science categories. In terms of source titles/journals, a majority of papers (11) were published in the International Journal of RF Technologies: Research and Applications. Expert Systems with Applications was next with 7 papers. This is followed by the International Journal of Production Economics with 4 papers, and with 3 papers each in IEEE Transactions on Antennas and Propagation, Industrial Management and Data Systems, International Journal of Production Economics, and Supply Chain Systems. These observations show that a large number of publications relate to engineering and other technical disciplines while only a few relate to business or management. This could be because a significant number of earlier papers were technology and/or engineering related and the outlets that published these are relatively more open to such studies. The types of published papers on the use of auto-ID in warehouse management, in decreasing number of publications, are research papers, case study, general review, technical paper, conceptual paper, and literature review. The year 2011 witnessed the most (i.e., 19) number of publications. The year 2012 had the next highest number (i.e., 18) of publications. It should be noted that there were no publications on the use of auto-ID in warehousing before 1986 based on our Scopus search. It is possible that the increase in the number of publications between 2003 and 2011 is due to the increased interest toward RFID adoption. This level has since then declined to a very small number in recent years. Our keywords analysis indicate that the most frequently used keywords after adapting Barki et al.’s (1993) classification scheme to categorize all the keywords are, in decreasing order of frequency counts, Auto-ID technological and development issues, reference discipline, auto-ID management, organizational environment, auto-ID application types, external environment, and auto-ID education and research. A large proportion of studies were on the replacement of barcode by RFID. However, no study supported the notion of a hybrid barcode/RFID system. Such a system could make use of the appropriate technology at the right context in a warehouse (White et al. 2007). A multi-method design that included design, experimental test, and simulation was the dominant research approach utilized by auto-ID and warehouse management studies. This is followed by reviews of the literature, case study, mathematical modeling, surveys, and interviews. We observed several dimensions that influence warehouse auto-ID adoption decisions. We used the Technology–Organization–Environment (TOE) framework for our conceptual framework to examine and understand the primary influencers and their significance in warehouse auto-ID adoption decisions. The framework also clarifies the individual roles and responsibilities of all actors and stakeholders in auto-ID selection decision. Based on the proposed framework, it is clear that there is a need for more empirical studies that guide warehouse management researchers as well as practitioners.
We hope that this paper serves as a starting point for readers interested auto-ID and warehouse management research.