research paper topics about warehousing

Logistics 4.0 in warehousing: a conceptual framework of influencing factors, benefits and barriers

The International Journal of Logistics Management

ISSN : 0957-4093

Article publication date: 7 October 2022

Issue publication date: 19 December 2022

In the last decade, the Industry 4.0 paradigm had started to rapidly expand to the logistics domain. However, Logistics 4.0 is still in an early adoption stage: some areas such as warehousing are still exploring its applicability, and the technological implementation of this paradigm can become fuzzy. This paper addresses this gap by examining the relationship among influencing factors, barriers, and benefits of Logistics 4.0 technologies in warehousing contexts.

Design/methodology/approach

Starting from a Systematic Literature Review (SLR) approach with 56 examined documents published in scientific journals or conference proceedings, a conceptual framework for Logistics 4.0 in warehousing is proposed. The framework encompasses multiple aspects related to the potential adopter’s decision-making process.

Influencing factors toward adoption, achievable benefits, and possible hurdles or criticalities have been extensively analyzed and structured into a consistent picture. Company’s digital awareness and readiness result in a major influencing factor, whereas barriers and criticalities are mostly technological, safety and security, and economic in nature. Warehousing process optimization is the key benefit identified.

Originality/value

This paper addresses a major gap since most of the research has focused on specific facets, or adopted the technology providers’ perspective, whereas little has been explored in warehousing from the adopters’ view. The main novelty and value lie in providing both academics and practitioners with a thorough view of multiple facets to be considered when approaching Logistics 4.0 in logistics facilities.

• Logistics 4.0
• Warehousing
• Technology adopters

Perotti, S. , Bastidas Santacruz, R.F. , Bremer, P. and Beer, J.E. (2022), "Logistics 4.0 in warehousing: a conceptual framework of influencing factors, benefits and barriers", The International Journal of Logistics Management , Vol. 33 No. 5, pp. 193-220. https://doi.org/10.1108/IJLM-02-2022-0068

Emerald Publishing Limited

Copyright © 2022, Sara Perotti, Roman Felipe Bastidas Santacruz, Peik Bremer and Jakob Emanuel Beer

Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and no commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode

Introduction

Logistics is an ever-growing business that has gained increasing importance at a global level. Logistics market size was €5.6 trillion in 2018 and is projected to have a 4.6% compound annual growth rate (CAGR) until 2023 ( Transport Intelligence, 2019 ). In Europe, logistics market size was €0.9 trillion in 2019 with a 2.4% CAGR forecasted for the 2018–2023 timespan ( Transport Intelligence, 2019 ) and about 10.3 million citizens employed in 2018, thus making this industry highly relevant for the global economy ( Eurostat, 2018 ). Within the logistics market, in-house warehousing and Third-Party Logistics (3PL) represent key activities with 30% of the total market value, and 38% in Europe ( Transport Intelligence, 2019 ). Among logistics processes, warehousing is one of the most critical cost components ( Rodrigue, 2020 ; Perotti et al. , 2022 ), accounting for about 20% of logistics costs ( Dhooma and Baker, 2012 ). Logistics facilities have been challenged by a substantial evolution over time ( Baglio et al. , 2019 ), as they have transformed from simple repositories for inventory into multi-functional logistics hubs ( Baker, 2004 ; Onstein et al. , 2019 ). This brought along challenges with higher requirements in terms of efficiency and service level fulfillment ( Kembro et al. , 2018 ).

In the past decade, also the manufacturing sector has started experiencing substantial changes, driven by factors such as sustainability concerns ( Ghobakhloo, 2020 ). These changes have taken the manufacturing industry to experience a new transformation, for which Kagermann et al. (2011) have coined the term “Industry 4.0”, claiming to describe the fourth industrial revolution. In Industry 4.0, centralized control systems give way to decentralized decision-making. The aim of improving performances, and in some cases, the increase in complexity of business environments and more demanding requirements, are reshaping logistics and warehousing processes ( Dev et al. , 2021 ). To cope with this scenario, digitalization and the transition toward the Logistics 4.0 paradigm have become powerful means to compete in the market and help companies address the fragile trade-off between improved service levels and reasonable operating costs. Based on embedded sensors integrated with other technologies, objects such as machines, products, or orders, autonomously control themselves and are fully vertically integrated into the company’s information systems ( Kagermann et al. , 2011 ).

Since the term was coined in 2011, Industry 4.0 has become a dominant topic ( Phuyal et al. , 2020 ; Tang and Veelenturf, 2019 ). This is reflected by the growing number of publications, including an increasing number of logistics-related contributions since 2015 ( Grzybowska and Awasthi, 2020 ). In this context, the exploration of Industry 4.0 technologies such as Autonomous Mobile Robots ( Fragapane et al. , 2021 ), Machine Learning, Artificial Intelligence (AI), and the Internet of Things (IoT) has also increased ( Culot et al. , 2020 ; Phuyal et al. , 2020 ; Salamone et al. , 2018 ). These technologies modify how the manufacturing industry operates, leading to a higher complexity of the manufacturing processes ( Culot et al. , 2020 ). In this context, some papers center their attention on the investigation of drivers and barriers to Industry 4.0 technologies adoption by considering different industrial perspectives. For instance, Tortorella et al. (2021) and Frederico et al. (2021) investigate the effect of Industry 4.0 technologies on supply chain resilience, showing a positive relationship between disruptive technology adoption and supply chain performance. Chauhan et al. (2021) focusing on companies in an emerging economy, propose to further explore this topic by investigating barriers as well as effects on companies’ performance. Also, Raj et al. (2020) study the barriers to Industry 4.0 adoption, considering both developed and developing countries. They suggest analyzing enabling factors for Industry 4.0. Lastly, Horváth and Szabó (2019) explore the barriers and driving forces of Industry 4.0 adoption from a general industry perspective while Stentoft et al. (2020) investigate the same topic from an SME perspective.

Logistics, directly affecting company’s productivity and service level as well as customer satisfaction, must also be able to adapt to the characteristics of the new Industry 4.0 manufacturing environment. Hence, it is questionable whether the current logistics systems and structures will be able to handle the increased complexity generated by Industry 4.0, more specifically without increasing costs or decreasing quality ( Wang et al. , 2020 ; Winkelhaus and Grosse, 2020 ). Companies need to align their logistics performance and development with the new requirements to support the vital link between manufacturers and customers that depends on logistics and warehousing operations ( Winkelhaus and Grosse, 2020 ), resulting in the concept of “Logistics 4.0”. Logistics 4.0 is still a fuzzy term ( Bag et al. , 2020 ), and it is unclear which concepts it comprises ( Oleśków-Szłapka and Stachowiak, 2019 ). For instance, a recent definition of Logistics 4.0 by Winkelhaus and Grosse (2020) , refers to “the logistical system that enables the sustainable satisfaction of individualized customer demands without an increase in costs and supports this development in industry and trade using digital technologies”. Such definition, on the one hand, relates Logistics 4.0 to specific market factors (sustainability, individualized demand), while on the other hand is vague in the “digital technologies” required to implement them.

Warehouses play a key role in the Logistics 4.0 transition ( Valchkov and Valchkova, 2018 ). Kumar et al. (2021) highlight relevant gaps related to Logistics 4.0 in warehouses and, more specifically, the need for frameworks to identify and address the challenges of its technological adoption. Indeed, most of the extant research mainly addresses two streams: either general benefits related to Logistics 4.0 adoption or the description of innovative technologies and solutions.

The first stream analyses possible benefits related to Logistics 4.0 in the warehousing context ( Domański, 2019 ; Douaioui et al. , 2018 ; Issaoui et al. , 2021 ) and how operations could profit from Logistics 4.0 ( Feng and Ye, 2021 ). For instance, Loureiro et al. (2020) concentrate on how Logistics 4.0 solutions help improve transaction costs and business coordination. Other researchers focus on the implications of Industry 4.0 for the logistics sector, emphasizing concepts such as digitalization and automation ( Bag et al. , 2020 ; Barreto et al. , 2017 ; Schmidtke et al. , 2018 ). Finally, Winkelhaus and Grosse (2020) investigate the possible benefits and challenges of Logistics 4.0 and provide a framework combining external triggers, underlying technological innovations, and impacts on human interactions and logistic tasks. Looking at the second stream, Cano et al. (2021) identify technologies framed into the Industry 4.0 concept that can be implemented also in logistics. Golpîra et al. (2021) investigate the areas of application, current development stage, and gaps of IoT in Logistics 4.0 transformation. Other authors discuss IoT applications in logistics from the perspectives of both, advantages and challenges that limit their adoption ( Ding et al. , 2021 ; Song et al. , 2021 ; Tran-Dang et al. , 2020 ). Chung (2021) focuses on the applications which various Industry 4.0 technologies could have in logistics processes. Intralogistics is explored by Fottner et al. (2021) who investigate the level of automation in intralogistics and the technologies that can enable it. Winkelhaus et al. (2021) analyze the socio-technological effects of Industry 4.0 on order picking systems.

Although the academic literature has started exploring how companies are approaching Logistics 4.0 adoption, a comprehensive conceptual framework addressing the adoption process of Logistics 4.0 in warehousing is missing. The aim of this paper is to offer a comprehensive conceptualization of Logistics 4.0 adoption in warehousing by embracing the adopters’ perspective and addressing the main influencing factors, achievable benefits as well as potential criticalities and barriers. This paper intends to address this research gap with a Systematic Literature Review (SLR) approach to provide robustness to the proposed conceptual framework. SLRs have been proved valuable as the initial step of defining a framework ( Oleśków-Szłapka and Stachowiak, 2019 ; Winkelhaus and Grosse, 2020 ; Zoubek and Simon, 2021 ). Starting from the available literature on this topic, we categorize the relevant elements into a conceptual framework that can be used as a guideline by academics and practitioners.

The novelty and value of this paper lie in providing both academics and practitioners with a thorough view of the different facets to be considered when approaching the adoption of Logistics 4.0 solutions in logistics facilities. Specifically, influencing factors towards adoption, achievable benefits, and possible hurdles or criticalities will be extensively analyzed and structured into a consistent picture.

The remainder of the paper is structured as follows. The next section motivates and describes the SLR methodology adopted to ground the conceptual framework. Then, we present and discuss the results of our analysis. Finally, we draw conclusions and suggest future research directions.

Methodology

Systematic literature review (slr) approach.

As Logistics 4.0 in warehousing is a cutting-edge topic, an SLR approach is ideal to gather the most relevant information ( Tranfield et al. , 2003 ). The final goal of the SLR is to perform a critical analysis of research papers on Logistics 4.0 in warehousing to better comprehend the existing trends and research gaps ( Carter and Rogers, 2008 ). Hence, the five-step methodology suggested by Denyer and Tranfield (2009) was adopted and hereinafter described.

Question formulation

1 Context: The specification of individuals, relationships, institutional settings, or wider systems that are studied. Higher service levels requested by the market and the increasing logistics complexity require companies to develop new solutions for their logistics activities and, more specifically, for their warehouses.

2 Intervention: The events, actions, and activities that are studied. In this paper, the intervention is the application of Logistics 4.0 technologies.

3 Mechanisms: The mechanisms that explain the relationship between interventions, outcomes, and the circumstances under which these mechanisms are active. This should help companies find the most suitable solutions that leverage the benefits of Logistics 4.0 while mitigating risks and controlling costs.

4 Outcome: The effects of intervention, both intended and unintended ones. The aims associated with Logistics 4.0 in warehouses include, on the one hand, cost and time reduction for decision-making and for operations while maintaining service levels; on the other hand, providing higher service levels (e.g. by better utilizing the data emanating from ubiquitous sensors, higher quality of decision-making) while maintaining or optimizing costs ( Winkelhaus and Grosse, 2020 ). The combination of these two objectives and their trade-offs is a constant challenge for managers and decision-makers.

What are the main factors influencing a company’s level of readiness for the adoption of Logistics 4.0 in their warehouses?

What are the benefits that companies could achieve by implementing Logistics 4.0 solutions in their warehouses?

What are the main barriers and criticalities faced by companies when implementing Logistics 4.0 solutions in their warehouses?

The focus is set on influencing factors, benefits, and barriers with the purpose of specifically investigating the adoption process of Logistics 4.0 in warehouses, in line with previous logistics literature dealing with adoption processes (e.g. Li et al. , 2020 ; Perotti et al. , 2015 ).

Locating documents

1 Group A comprehends keywords referring to Logistics 4.0, i.e.: “smart logistic*” OR “logistic* 4.0” OR “autonomous logistic*” OR “warehous* 4.0” OR “smart warehous*”.

2 Group B encompasses the specific aspects under investigation, i.e.: “adopt*” OR “demand*” OR “benefit*” OR “advantage*” OR “opportunit*” OR “barrier*” OR “criticalit*” OR “challeng*” OR “maturity” OR “readiness” OR “impact*” OR “factor*” OR “driver*”.

Paper selection and evaluation

328 documents were initially retrieved from Scopus and 201 from Web of Science, including duplicates. Merging and removing duplicates delivered 363 documents dated between October 2003 and April 2021. At this stage (Phase 3), a rigorous selection process, structured into screening, eligibility, and qualification, was applied using the inclusion and exclusion criteria reported in Table 1 .

In the screening stage, phase, criteria 1 to 4 ( Table 1 ) were considered to limit the results to those publications central to the purposes of this study. More specifically, criterion 1 evaluates the date of publication, due to the fact that the term Industry 4.0 has been first coined and used by Kagermann et al. (2011) . Criterion 2 considers the attribution of the research, while criterion 3 ensures the quality of the papers, as scientific journals have a more rigorous review process than other document types ( Colicchia et al. , 2018 ) and conference proceedings cover emerging trends and challenges. Criterion 4 evaluates the language of publication. English is the language of choice as it is the most adopted and formally approved language for publications in the field of supply chain management ( Colicchia et al. , 2018 ). The screening phase delivered 274 papers out of 363 for the long list of papers.

In the eligibility stage , criteria 5 and 6 were applied. Both criteria are directly related to the main topics of the research questions. In this phase, the abstract, introduction, and conclusions of the papers were analyzed. This led to the exclusion of 185 papers, with 89 papers remaining in the sample.

Finally, in the qualification stage, all 89 papers were entirely read by two reviewers and carefully examined. As a result of this process, 33 papers have been excluded, because they were not specifically centered on the topics of interest. This led to a shortlist of 56 papers for critical in-depth analysis.

Review results

1 Descriptive characteristics, i.e. general details such as article title, year of release, source title, and first author’s country.

2 Methodology adopted, namely literature reviews, conceptual works, analytical papers, empirical contributions (case studies/interviews and surveys), action research (implementation of a technology), and simulations. If a paper presented multiple methodologies, the prevailing one was considered for classification.

3 Research question addressed, by identifying the topics addressed i.e. (1) influencing factors regarding the company’s level of readiness for the adoption of Logistics 4.0 technologies assigned to RQ1 , (2) benefits of the implementation of Logistics 4.0 solutions assigned to RQ2 , and (3) barriers and criticalities that companies face when searching to implement Logistics 4.0 solutions assigned to RQ3 . The results led to the development of a conceptual framework integrating three main dimensions associated with Logistics 4.0 adoption, namely motivations to adoption, benefits achieved, and barriers that emerged.

The following sections illustrate the descriptive analysis of the papers and describe the proposed conceptual framework as a result of the SLR study.

Descriptive analysis

Figure 2 shows the number of publications over time and by source. Initially, researchers gave priority to the development of Industry 4.0 concepts rather than Logistics 4.0. However, the number of publications per year related to Logistics 4.0 has steadily increased over time, and recently accelerated the pace, with 73% of the shortlisted papers published after 2018. The peak is in 2019, while 2020 recorded a small drop, possibly because of the COVID-19 pandemic. It is interesting to notice that the number of papers published in the first quarter of 2021 is almost the same as the sum of the two previous years, highlighting the growing interest of academics in Logistics 4.0 in warehouses.

Looking at the sources of the documents, a balance was found between papers published in scientific journals (34 papers, 48.6% of the sample) and conference proceedings (36 papers, 51.4%). The journals chiefly belong to the engineering and production management area, while a few are centered in other disciplines, such as policy management. As expected, most of the earliest papers were published in conference proceedings, indicating their ability to catch emerging trends.

Focusing on the first author’s affiliation country, most contributions (30) were Europe-based, followed by Asia (17), indicating strong interest from these regions.

Figure 3 illustrates the main research methodology used. Most of the early papers belong to the theoretical and conceptual domain whereas more recently the number of empirical contributions has increased substantially. Action research only started to appear in the last years. This shows that Logistics 4.0 in warehousing is attracting rising attention and it is likely going to become a well-developed research topic. Following a similar methodology as some documents found in the literature ( Golpîra et al. , 2021 ; Kumar et al. , 2021 ; Winkelhaus et al. , 2021 ), in our study, all the research methodologies (theoretical, conceptual, and empirical or action research) are considered relevant. Since the results of some methodologies can complement others, this helps to get a clearer idea of current Logistics 4.0 adoption as well as of future trends.

Finally, as far as the research question(s) being addressed, topics connected to RQ2 (35 related papers) and RQ3 (25 results) are prevailing, thus indicating that benefits from adoption as well as related barriers and criticalities have already started to be analyzed. Conversely, it seems that so far very little has been explored regarding the influencing factors on the company level for the readiness for adopting Logistics 4.0 in their warehouses.

Conceptual framework of logistics 4.0 adoption in warehousing

1 Influencing factors, referring to the elements that might influence the company’s decision to adopt Logistics 4.0 solutions in their warehouses. Companies are chiefly affected by their warehouse management and operation, their digital awareness and readiness, their employees’ educational level, and governmental support and policies.

2 Benefits, indicating the advantages that Logistics 4.0 solutions applied in warehouses might offer. In terms of operations, these benefits are process optimization, transaction cost reduction, flexibility increase, traceability and visibility enhancement, human error reduction, human resource management and safety enhancement, and sustainability improvement. Additionally, from the customer perspective, the main benefits are increased customer loyalty and satisfaction.

3 Barriers and criticalities, dealing with all the challenges that companies might face when embracing Logistics 4.0 in warehousing. Several types of hurdles can be identified: strategic (e.g. no standardized implementations exist), economic (e.g. high implementation costs), technological (e.g. obsolete infrastructures), cultural (e.g. companies are not ready for advanced technologies), and safety and security related (e.g. risk of cyber-attacks).

In the framework, the elements that compose each of the three dimensions are organized by their relative importance in the examined literature i.e. the frequency with which each aspect was a relevant point of discussion. This gives a clear view of the most and least relevant factors from the academic perspective. Additionally, the framework shows how each of the influencing factors is related to specific barriers and criticalities, giving an insight into how these two dimensions are interrelated and affected by one another. Finally, the benefits that Logistics 4.0 adopters could obtain are shown and organized from most to least investigated in the literature, which is relevant as Logistics 4.0 adopters can relate the specific requirements in their warehouses with the benefits identified by academics.

Our approach is in line with typical technology acceptance models (TAMs). In its basic form ( Figure 4 ) it is similar to the original TAM developed by Davis et al. (1989) : The influencing factors resemble the external variables while benefits correspond to perceived usefulness, and barriers and criticalities indicate the obstacles to the ease of use. We did not follow TAM2 ( Venkatesh and Davis, 2000 ), as we consider its main extensions compared to the original TAM, namely a more differentiated approach to external factors like social influence and cognitive processes, not relevant for our study. For the same reason, we have not used the unified theory of acceptance and use of technology (UTAUT) model suggested by Venkatesh et al. (2003) , as we think that factors like gender and age do not affect Logistics 4.0 adoption or moderate key influencing factors substantially. Our approach is in accordance with general concerns that the more elaborated models suggest additional moderators without explaining the reasons behind the proposed interaction effects ( Bagozzi, 2008 ). Following Bagozzi (2008) , we believe that the parsimony of the framework, its simple set-up, is strength rather than weakness and fits well into the managerial decision-making context.

Table 2 reports a detailed analysis of the framework elements and related references. In the subsequent paragraphs, each element is carefully described, as well as its related factors.

Influencing factors ( RQ1 )

Warehouse management and operation, the company’s digital awareness and readiness, employees’ educational level, and governmental support and policies have emerged as the main influencing aspects, thus addressing RQ1 .

First, the warehouse management and operations currently in place represent a major influencing factor. From this viewpoint, companies need to carefully consider their as-is configuration first – e.g. financial as well as operational factors, product characteristics as well as supply chain structure – together with the related performance and criticalities before deciding whether and how to embrace the digital transition that Logistics 4.0 implies ( Boonsothonsatit et al. , 2020 ). For example, Zoubek et al. (2021) propose a methodology to address the rationalization of a warehouse system by offering a range of 4.0 scenarios with different digital solutions that can be evaluated and selected based on the specific warehouse setting and requirements.

The second key influencing factor refers to the company’s digital awareness and readiness ( Zouari et al. , 2020 ). The lack of technological culture is one of the biggest hurdles the logistics industry is facing, and the company’s maturity and attitude toward the digital landscape affect the implementation of Logistics 4.0 in warehouses. As companies are not always fully aware of the digital options and how such solutions might impact their business, their perception might be biased and, consequently, implementation of Logistics 4.0 technologies in warehouses might be perceived as risky ( Barczak et al. , 2019 ). Some researchers have started analyzing the company’s technological maturity level, e.g. by means of frameworks such as the one proposed by Mahroof (2019) with technology, organization, and environment as the main pillars or five levels ( Stachowiak et al. , 2019 ) ranging from “ignoring” (i.e. full unawareness of Logistics 4.0) to “integrated” (i.e. companies that have effectively implemented fully integrated Logistics 4.0 solutions). Also, more general characteristics such as automation level or capability to manage data are included ( Zoubek and Simon, 2021 ). Finally, Modrak et al. (2019) propose a self-assessment model for smart logistics maturity, in which one of the five clusters is entirely focused on warehouses.

As far as employees’ educational level is concerned, Logistics 4.0 requires at its base a certain level of digital education. The development of human skills is one of the main requirements to maintain competitiveness ( Krishnan and Wahab, 2019 ; Wrobel-Lachowska et al. , 2018 ), and employees must be educated in a way that permits them to stay in line with cutting-edge trends. When approaching the 4.0 paradigm, training in technological knowledge and software/hardware usage is required ( Woschank and Pacher, 2020a ) and a combination of scientific, industry-specific, and firm-related capabilities should be promoted ( Wrobel-Lachowska et al. , 2018 ). Some scholars have investigated the learning process and suggested specific methods in the context of logistics engineering education, seeking to guarantee comprehensive training, characterized by both a theoretical and practical approach ( Nazir et al. , 2019 ; Woschank and Pacher, 2020b ). Anecdotal evidence from a large number of planning and consulting projects in the warehousing industry conducted by the authors indicates that, traditionally, warehouses have not been considered work environments that require any significant level of technological education on the operational level, suggesting that a high employee’s educational level, if present, would likely rather be qualified as an influencing factor (e.g. higher technology awareness and understanding of the benefits potentially achievable) than a barrier to implementation.

Finally, policies used by different countries to promote the transition to the 4.0 paradigm and their governments’ intervention can significantly affect the implementation of Logistics 4.0 in warehouses. For instance, actions such as (1) cost reductions in the import of external technology or (2) the promotion of international exchange of knowledge can support the local development of technologies and competence ( Krishnan and Wahab, 2019 ). Moreover, the government could financially support companies through incentives and strategic programs. Also, the collaboration among companies, academia, and the public sector might be fundamental for accelerated Logistics 4.0 implementation by increasing the adopters’ readiness level ( Stachowiak et al. , 2019 ).

Benefits ( RQ2 )

The main advantages emerging from Logistics 4.0 implementation refer to warehousing process optimization, transaction costs reduction, flexibility increase, traceability and visibility enhancement, human error reduction, human resource management, safety enhancement, sustainability improvement, and increased customer loyalty and satisfaction.

The possibility to improve process performance through the implementation of Logistics 4.0 technologies in warehouses is a widely addressed topic, especially from a conceptual perspective ( Barreto et al. , 2017 ; Correa et al. , 2020 ; Issaoui et al. , 2021 ; Kuczyńska-Chałada et al. , 2018 ; Nantee and Sureeyatanapas, 2021 ; Oleśków-Szłapka and Stachowiak, 2019 ; Song et al. , 2021 ; Wen et al. , 2018 ; Winkelhaus and Grosse, 2020 ; Woschank and Zsifkovits, 2021 ).

For instance, Wang (2016) suggests potential cost savings and a reduction in inventory costs. Some other scholars offer empirical studies to corroborate their views ( Affia and Aamer, 2021 ; Domański, 2019 ; Gialos and Zeimpekis, 2020 ; Hamdy et al. , 2018 ; Kekana et al. , 2020 ; Krishnan and Wahab, 2019 ; Lee et al. , 2018 ; Plakas et al. , 2020 ; Zhang et al. , 2021 ). However, it is necessary to critically assess the benefits directly associated with the technologies mentioned in the Logistics 4.0 literature to clearly point out whether and how they add something new to the technologies already adopted in warehouses, i.e. it is necessary to carve out what Logistics 4.0 adds to standard automation in warehouses.

One of the key factors that must be addressed in order to optimize logistics and warehousing processes is increasing their efficiency ( Domański, 2019 ; Krishnan and Wahab, 2019 ; Zhang et al. , 2021 ). For instance, this can be obtained with the implementation of technologies such as IoT-based solutions which offer real-time data visibility ( Hofmann et al. , 2019 ; Lee et al. , 2018 ), Augmented Reality, and Smart Glasses which improve operations performance ( Plakas et al. , 2020 ), or AI tools to automate the recognition of objects and, through Machine Learning, to infer insights valuable for decision-making ( Wen et al. , 2018 ).

Transaction cost reduction has been also highlighted as a benefit of Logistics 4.0 implementation. Transaction costs are defined as “the consumption of economic resources resulting from adapting, structuring, and monitoring the interactions between the different agents, ensuring compliance with contracts” ( Loureiro et al. , 2020 ). According to these authors, the implementation of Logistics 4.0 solutions can reduce transaction costs in warehousing by providing timely information supporting the decision-making process and improving the relationship with other stakeholders. One example is the implementation of smart sensors to locate items inside the warehouse. Transmitting the information to other partners of the supply chain, optimizing resources assignment, and reducing the costs associated with the process have emerged as the foremost achievements.

The implementation of Logistics 4.0 in warehouses might increase flexibility and/or responsiveness ( Barreto et al. , 2017 ; Karunarathna et al. , 2019 ; Oleśków-Szłapka and Stachowiak, 2019 ; Song et al. , 2021 ). Several authors suggest equipping existing automation technology such as automated guided vehicles (AGVs) with smart features to increase flexibility. For instance, Mehami et al. (2018) combine AGVs with RFID technology to allow RFID-tagged items to determine the path of the AGV at runtime. The implementation of robots in the warehousing context has been a topic of discussion for its possibilities to increase efficiency and reduce repetitive tasks for humans ( Raji et al. , 2021 ). To this end, Lourenco et al. (2017) prototyped an autonomous mobile robot that can handle transportation from manufacturing supermarkets to assembly stations while avoiding obstacles, as it is intended to operate in a dynamic environment together with other autonomous robots and human operators. The approach of adding autonomous features to existing technologies is also in line with the maturity model proposed by Zoubek and Simon (2021) related to Logistics 4.0 in internal processes.

However, although many scholars support the view that Logistics 4.0 might offer ample opportunities for flexibility increase, this is not endorsed by the entire academic community ( Nantee and Sureeyatanapas, 2021 ). For instance, Cimini et al. (2021) found that the introduction of Logistics 4.0 in the picking process did not prove to be the best option in terms of flexibility, thus preferring humans to robots.

A major benefit refers to traceability and visibility enhancement, intended as the availability of data, the visibility of logistics objects and actors, and the transparency of processes within the value chain. Thanks to the implementation of Logistics 4.0, information flows can be synchronized with product flows ( Barreto et al. , 2017 ; Douaioui et al. , 2018 ; Oleśków-Szłapka and Stachowiak, 2019 ; Wang, 2016 ). For instance, as the IoT enables device connectivity, the visibility of logistics activities and sharing capabilities in warehouses can be considerably improved ( Winkelhaus and Grosse, 2020 ; Nantee and Sureeyatanapas, 2021 ).

To guarantee the visibility and traceability of logistics objects, it is necessary to be able to precisely localize them inside and outside warehouses. Liu et al. (2018) discuss the state-of-the-art technologies available to perform this task. The most common technologies are GPS, Bluetooth, and RFID. For several years, RFID has been considered to have a possible positive effect on visibility and efficiency in warehousing ( Vijayaraman and Osyk, 2006 ). Nevertheless, the specific drawbacks of each technology must be considered. While GPS has high accuracy for outdoor localization, it cannot be used indoors. RFID help localize objects indoors with a high degree of accuracy, while it requires an extensive infrastructure that can have limitations in large-scale outdoor applications. In addition, in some cases, the calculation of its ROI can be fuzzy ( Vijayaraman and Osyk, 2006 ). Therefore, each warehouse case must be assessed based on its specific needs. From a more practical perspective, Affia and Aamer (2021) propose a roadmap to design and apply an IoT-based smart warehouse infrastructure allowing data recording, tracking, reporting, and immediate distribution to all authorized stakeholders. Despite the increase in visibility and traceability, it is noteworthy to say that these shared data could represent a challenge for digital security.

The reduction in error rates and associated risks are two of the main benefits related to the implementation of Logistics 4.0 in warehouses. Numerous studies have tackled this issue, either theoretically ( Karunarathna et al. , 2019 ; Nantee and Sureeyatanapas, 2021 ; Oleśków-Szłapka and Stachowiak, 2019 ; Plakas et al. , 2020 ; Wang, 2016 ; Zoubek et al. , 2021 ; Zoubek and Simon, 2021 ) or empirically ( Lee et al. , 2018 ). For instance, the implementation of cyber-physical system (CPS) which combines virtual and physical worlds through smart objects can reduce errors during the process ( Zoubek et al. , 2021 ). In this context, AR picking, and RFID solutions could mitigate the risk of human error ( Karunarathna et al. , 2019 ; Nantee and Sureeyatanapas, 2021 ; Plakas et al. , 2020 ; Winkelhaus and Grosse, 2020 ).

Another key benefit refers to human resource management and safety enhancement. Employees are expected to work in a safe environment, allowing them to perform their tasks and improve their skills while feeling safe and aligned with the company’s mission. Logistics 4.0 technologies can help minimize stressful and repetitive human tasks and reduce the risk of injuries, fatigue, and mental stress. For instance, Nantee and Sureeyatanapas (2021) highlighted that employees perceived increased ease in their daily operations and the development enhancement of their analytical and computing skills. A general improvement in operational efficiency in the warehouse has been also highlighted ( Cimini et al. , 2019 , 2021 ; Halawa et al. , 2020 ).

Sustainability improvements have also been identified ( Calza et al. , 2020 ), e.g. poor energy management ( Buntak et al. , 2019 ). The reduction of costs generated by inefficiencies would make available additional resources for environmental and social improvements. Some studies suggest that Logistics 4.0 technologies in long-term and high-scale operations have the potential to bring sustainable advantages in terms of increased efficiency and reduced waste and emissions ( Krishnan and Wahab, 2019 ; Nantee and Sureeyatanapas, 2021 ).

Additional advantages are increased customer satisfaction and the possibility of improved customer loyalty, thus reducing the churn rate ( Kekana et al. , 2020 ). In this sense, four dimensions have appeared highly significant: (1) reliability of the delivery, (2) process visibility, (3) empathy for the customer, and (4) tangibility of the company. Logistics 4.0 can leverage these domains to build a long-term relationship between a company and its customers. From this perspective, Kekana et al. (2020) assessed the relationship between the warehousing style of an organization and both customer satisfaction and loyalty. It was found that IoT and RFID were the main levers enhancing logistics performance in the warehouse. In other cases, it was pointed out that Logistics 4.0-automated warehouses can increase customer satisfaction by improving shipping and information accuracy, product customization, and reducing lead time ( Nantee and Sureeyatanapas, 2021 ). These results are also supported by other sources which highlight that improved visibility, achieved by means of technologies such as IoT, blockchain, and cloud platforms, is another key dimension that leads to higher customer satisfaction ( Markov and Vitliemov, 2020 ).

Barriers and criticalities ( RQ3 )

Different types of hurdles have been identified for Logistics 4.0 adoption in warehouses i.e. strategic, economic, technological, cultural, and safety- and security-related obstacles.

The first obstacle to Logistics 4.0 implementation involves strategic considerations. Implementation of 4.0 technologies in warehouses cannot be standardized but needs to be tailored to the specific case ( Jung and Kim, 2015 ). The design of a Logistics 4.0 warehouse needs to be adapted to the specific company’s operating environment ( Affia and Aamer, 2021 ), while the company’s targets and priorities must be carefully taken into account ( Wen  et al. , 2018 ).

Looking at the economic perspective, the costs associated with the investment for warehousing 4.0 represent another barrier. These costs, of course, depend on the technologies being implemented. When a complete warehouse re-design is required, the investment tends to be high ( Cyplik et al. , 2019 ; Markov and Vitliemov, 2020 ; Oleśków-Szłapka and Stachowiak, 2019 ; Zoubek et al. , 2021 ), thus preventing companies from easily embracing the Logistics 4.0 paradigm. In some cases, a step-by-step implementation strategy is preferred ( Phuyal et al. , 2020 ; Schmidtke et al. , 2018 ). The investment costs to be considered include numerous factors, such as equipment, deployment, and training costs ( Tran-Dang et al. , 2020 ). To cope with these factors, a detailed cost and Return on Investment analysis should be performed by companies before deciding on implementation of Logistics 4.0 technologies ( Verma et al. , 2020 ). Companies are sometimes reluctant since they find it difficult to quantify the beneficial effect of Logistics 4.0 implementation in advance. This involves not only direct but also indirect effects that are hardly measurable ( Poenicke et al. , 2019 ).

Technological barriers exist, too ( Cyplik et al. , 2019 ; Verma et al. , 2020 ; Zoubek et al. , 2021 ). They include the lack of reliable infrastructures or difficulties of integration with the legacy systems running within the warehouse. For instance, the use of cutting-edge engineering applications such as multi-robot collaboration requires companies to develop algorithms that must be supported by robust middleware systems and programming models ( Liu et al. , 2018 ). In general, as Logistics 4.0 is still in its infancy, immature technologies together with unstandardized function modules are also identified as key barriers to Logistics 4.0 adoption ( Feng and Ye, 2021 ). Overall, suitable digital infrastructure has been identified as a basic requirement for implementing Logistics 4.0 applications ( Schmidtke et al. , 2018 ).

Furthermore, cultural hurdles have been highlighted. Logistics 4.0 implementation requires the integration of a broad range of technologies, and companies require additional knowledge and skills that can be achieved through investments and training ( Correa et al. , 2020 ). However, many companies tend to act as routine-blinded adopters as their digital maturity level is still low, and also resistance to change might be another hurdle to adoption ( Correa et al. , 2020 ).

Also, the lack of specific skills to operate the components of a Logistics 4.0 warehouse is considered an obstacle ( Affia and Aamer, 2021 ; Zoubek et al. , 2021 ). Since collaboration with smart equipment and technologies will be increasingly common in future warehouses, the education of specialized employees will become a key requirement ( Schmidtke et al. , 2018 ; Verma et al. , 2020 ). Such a shift in terms of technical skills must be accompanied by a change of mentality in the companies themselves ( Mahroof, 2019 ).

Finally, safety and security issues represent another important barrier. Making logistics and warehousing systems secure is vital for technology adopters. This involves several concerns related to cyber-attacks ( Hamdy et al. , 2018 ; Jamai et al. , 2020 ; Markov and Vitliemov, 2020 ). The higher the number of devices connected to the IoT network, the higher the possibility of security and privacy issues ( Song et al. , 2021 ). As an example, privacy violations related to tracking the locations of certain items could compromise a company’s competitive advantages ( Ding et al. , 2021 ). For this reason, companies must consider security and privacy urgent requirements ( Verma et al. , 2020 ; Zhu et al. , 2020 ). In this context, blockchain-based systems are often proposed. However, blockchains are not able to avoid and defuse cyber-attacks ( Liu et al. , 2018 ) but are centered on ensuring that information cannot be modified ex-post ( Tan and Ngan, 2020 ). Besides, additional physical safety challenges have been raised for automated devices, such as robots, drones, or AGVs, that can cause harm for operators ( Trab et al. , 2017 ).

Discussion and conclusions

Warehouses are crucial components of logistics networks, and their strategic role has been increasingly recognized by both researchers and practitioners. Logistics 4.0 in warehousing involves the introduction of Industry 4.0 technologies and practices within warehouses with the intention to enhance operations and service levels. In recent years, this field has gained growing interest among academics and a rising number of studies emphasize the relevance of this topic in the logistics domain.

Looking at RQ1 (What are the main factors influencing a company’s level of readiness for the adoption of Logistics 4.0 in their warehouses?), four main clusters of factors have been identified, namely warehouse setting and management, company’s digital awareness and readiness, employees’ educational level, and governmental support and policies. Specifically, warehouse setting and requirements (e.g. goods flows to be managed, products to be stored, service level, expected lead times) as well as the company’s digital awareness ( Zouari et al. , 2020 ) are critical elements impacting Logistics 4.0 adoption in warehousing.

As for RQ2 (What are the benefits that companies could achieve by implementing Logistics 4.0 solutions in their warehouses?), the literature reviewed mentions a variety of possible benefits that Logistics 4.0 technologies in warehousing can bring about. However, the lack of empirically validated data does not allow one to state with certainty which (or even if ) benefits can be achieved in practice. In some cases, benefits claimed by suppliers of technology associated with Logistics 4.0 for warehouses were uncritically repeated (e.g. Mahroof, 2019 ). In other cases, it is impossible to tell apart whether proclaimed improvements can be attributed to the introduction of technology or simply to the review and reorganization of warehouse processes that typically accompany the introduction of technology. This challenge is further exacerbated by the finding that the technologies associated with the label Logistics 4.0 are highly inconsistent among the authors of the literature reviewed. Indeed, some authors point out that technologies that have existed in warehouses for decades, preceding the concept of Industry 4.0 and Logistics 4.0, e.g. Automated Storage and Retrieval Systems ( Domański, 2019 ) RFID, and AGV, are placed under the 4.0 umbrella.

With respect to RQ3 (What are the main barriers and criticalities faced by companies when implementing Logistics 4.0 solutions in their warehouses?), strategic, economic, technological, cultural, and safety and security-related barriers and criticalities have been identified. Particularly, the coverage of economic aspects, arguably the most important decision-making criterion for technology adoption, has been weak. Generally speaking, economics suggest technology adoption when the capital invested will lead to overall cost savings within a defined period of time. Since tangible benefits from the adoption of Logistics 4.0 technology in warehouse applications were found to be only vaguely defined, and with little reliable quantitative underpinning, it is not surprising that the discussion of economic barriers has remained equally vague. Also, the organizational structure has received little attention in the context of economic considerations, though it can be speculated that (for example in the case of third-party logistics providers) the interplay between independently managed warehouses (as profit centers) and headquarters (which include marketing and sales functions) would influence the adoption of Logistics 4.0 technologies.

Both academic and practical implications can be identified. From an academic perspective, this paper, by means of an SLR approach, offers a conceptual framework for Logistics 4.0 adoption in warehousing from the technology adopter’s perspective. It provides a clear outlook on the motivations, benefits, and challenges the implementation of Logistics 4.0 in warehousing could entail. From a practical viewpoint, the framework intends to ease the understanding of the technological possibilities that Logistics 4.0 could bring, with the final objective to better understand the specific technology adoption process. It also highlights the importance of analyzing the individual requirements for each specific company and application. The overall aim is to promote knowledge on the topic of Logistics 4.0 in the warehousing domain, stimulating a higher awareness of the topic, and fostering the adoption process of such applications. More practically, it helps organizations understand the breadth of technologies associated with Logistics 4.0, as well as both, challenges and benefits that can reasonably be expected, albeit predominantly qualitatively rather than quantitatively.

A more sober implication for academia results from the finding that the use of the term Logistics 4.0 in the warehousing concepts with its synonyms (e.g. “smart”) and related concepts (e.g. “IoT”) in the literature reviewed seems sometimes ambiguous, ranging from pure automation to decades-old identification technology to picking support devices (e.g. pick-by-voice) to more recent digital technologies such as artificial intelligence. Considering the breadth of its use, it can be questioned whether the term Logistics 4.0 is useful at all. Since academics should strive for conceptual and terminological clarity, the ambiguity of the term and its related concepts is creating serious concerns for use outside of corporate marketing departments. Should researchers decide to continue using the term, it is strongly recommended to focus efforts on some of the research lines pointed out in the section “Research gaps and suggested future research directions”.

Lastly, the study’s limitations must be acknowledged. In particular, the main limitation lies in the potential omission of relevant contributions from the review as the process of selection considered only journal and conference papers. Although the keyword structure was designed through several trials to ensure the most effective and feasible research space, it cannot be excluded that other papers dealing with this subject exist under different labels. Several papers discussed the same terms with a different understanding or definition of them. Further research is, therefore, recommended to encourage a higher degree of standardization. Moreover, it can be assumed that the more generic term “Industry 4.0” is sometimes used when Logistics 4.0 would apply as a more specific label. Nevertheless, because of the methodology adopted, it is believed that this analysis provides an adequate representation of the state of the art of literature related to influencing factors, benefits, and barriers dealing with Logistics 4.0 in warehousing. The study should be further supplemented with empirical research, including challenging the proposed framework.

Research gaps and suggested future research directions

Develop strong conceptualization and taxonomies clarifying 4.0 technologies for warehousing.

Foster empirical research in the field of Logistics 4.0 adoption in warehousing.

Improve the examination of the relationship between Logistics 4.0 application and specific warehousing activities.

Promote further investigation on the role of governmental support in influencing Logistics 4.0 investments at logistics sites.

Encourage further cost-benefit trade-off analyses of Logistics 4.0 in warehouses.

Develop quantitative assessment research of the sustainability implications of Logistics 4.0 in warehousing.

As a final remark, quantitative assessment of sustainability-related impacts of Logistics 4.0 in warehousing has emerged as a promising research arena. According to the SLR, one contribution has been specifically found that assesses the impact of 4.0 in warehousing through the lenses of the Triple Bottom Line (TBL) framework ( Nantee and Sureeyatanapas, 2021 ). However, in their assessment, only a qualitative approach centered on a single case was included, leaving ample room for further contributions in this field; additional quantitative-based studies, models, or simulations are recommended.

Methodological framework of the study

Examined publications over time

Publications by methodology over time

Conceptual framework for logistics 4.0 adoption in warehousing: influencing factors, benefits, and barriers

Inclusion and exclusion criteria

Detailed analysis of framework elements and related references

Documents resulting from the SLR

Note(s): * The term “empirical” refers to case studies, interviews, and surveys, while the term “action research” refers to the implementation of a Logistics 4.0 technology

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TinyLFU-Based Semi-Stream Cache Join for Near-Real-Time Data Warehousing

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Large Scale System for Social Media Data Warehousing

Social media data become an integral part in the business data and should be integrated into the decisional process for better decision making based on information which reflects better the true situation of business in any field. However, social media data are unstructured and generated in very high frequency which exceeds the capacity of the data warehouse. In this work, we propose to extend the data warehousing process with a staging area which heart is a large scale system implementing an information extraction process using Storm and Hadoop frameworks to better manage their volume and frequency. Concerning structured information extraction, mainly events, we combine a set of techniques from NLP, linguistic rules and machine learning to succeed the task. Finally, we propose the adequate data warehouse conceptual model for events modeling and integration with enterprise data warehouse using an intermediate table called Bridge table. For application and experiments, we focus on drug abuse events extraction from Twitter data and their modeling into the Event Data Warehouse.

Understanding the Concept of Data Warehousing and Challenges in Its Implementation

The aim of this paper is to understand the concept of Data ware housing and how it is implemented. It is related to the data analysis of the data in an organisation. It facilitates and makes the analysis process easy for the workers of the organisation. The paper will also explain two approaches that are followed in data ware housing. The process of implementation of data ware house will also discussed further in this paper. There are certain challenges to create data ware house.

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ETL (Extract, Transform, and Load) is an essential process required to perform data extraction in knowledge discovery in databases and in data warehousing environments. The ETL process aims to gather data that is available from operational sources, process and store them into an integrated data repository. Also, the ETL process can be performed in a real-time data warehousing environment and store data into a data warehouse. This paper presents a new and innovative method named Data Extraction Magnet (DEM) to perform the extraction phase of ETL process in a real-time data warehousing environment based on non-intrusive, tag and parallelism concepts. DEM has been validated on a dairy farming domain using synthetic data. The results showed a great performance gain in comparison to the traditional trigger technique and the attendance of real-time requirements.

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A framework for developing an enterprise data warehousing solution, developing a corporate data warehousing strategy, mapping the road to elimination: a 5-year evaluation of implementation strategies associated with hepatitis c treatment in the veterans health administration.

Abstract Background While few countries and healthcare systems are on track to meet the World Health Organization’s hepatitis C virus (HCV) elimination goals, the US Veterans Health Administration (VHA) has been a leader in these efforts. We aimed to determine which implementation strategies were associated with successful national viral elimination implementation within the VHA. Methods We conducted a five-year, longitudinal cohort study of the VHA Hepatic Innovation Team (HIT) Collaborative between October 2015 and September 2019. Participants from 130 VHA medical centers treating HCV were sent annual electronic surveys about their use of 73 implementation strategies, organized into nine clusters as described by the Expert Recommendations for Implementing Change taxonomy. Descriptive and nonparametric analyses assessed strategy use over time, strategy attribution to the HIT, and strategy associations with site HCV treatment volume and rate of adoption, following the Theory of Diffusion of Innovations. Results Between 58 and 109 medical centers provided responses in each year, including 127 (98%) responding at least once, and 54 (42%) responding in all four implementation years. A median of 13–27 strategies were endorsed per year, and 8–36 individual strategies were significantly associated with treatment volume per year. Data warehousing, tailoring, and patient-facing strategies were most commonly endorsed. One strategy—“identify early adopters to learn from their experiences”—was significantly associated with HCV treatment volume in each year. Peak implementation year was associated with revising professional roles, providing local technical assistance, using data warehousing (i.e., dashboard population management), and identifying and preparing champions. Many of the strategies were driven by a national learning collaborative, which was instrumental in successful HCV elimination. Conclusions VHA’s tremendous success in rapidly treating nearly all Veterans with HCV can provide a roadmap for other HCV elimination initiatives.

Explicitly Disclosing Clients Illness Catalogue Using Data Science Techniques

Abstract: Across the world in our day-to-day life, we come across various medical inaccuracies caused due to unreliable patient’s reminiscence. Statistically, communication problems are the most significant aspect that hampers the diagnosis of patient’s diseases. So, this paper represents the best theoretical solution to achieve patient care in the most adequate way. In these pandemic days, the communication gap between the patient and the physician has begun to decline to a nominal level. This paper demonstrates a vital solution and a steppingstone to the complete digitalization of the client’s illness catalogue. To attain the solution in a specified manner we are using adverse pre-existential technologies like data warehousing, database management system, cloud computing, big data, etc. We also persistently maintain the most secure, impenetrable infrastructure enabling the client’s data privacy. Keywords: Illness catalogue, cloud computing, data warehousing, database management systems, big data.

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Recent Advances and Research Problems in Data Warehousing

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• Sunil Samtani 7 ,
• Mukesh Mohania 8 ,
• Vijay Kumar 7 &
• Yahiko Kambayashi 9  

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In the recent years, the database community has witnessed the emergence of a new technology, namely data warehousing . A data warehouse is a global repository that stores pre-processed queries on data which resides in multiple, possibly heterogeneous, operational or legacy sources. The information stored in the data warehouse can be easily and efficiently accessed for making effective decisions. The On-Line Analytical Processing (OLAP) tools access data from the data warehouse for complex data analysis, such as multidimensional data analysis, and decision support activities. Current research has lead to new developments in all aspects of data warehousing, however, there are still a number of problems that need to be solved for making data warehousing effective. In this paper, we discuss recent developments in data warehouse modelling, view maintenance, and parallel query processing. A number of technical issues for exploratory research are presented and possible solutions are discussed.

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Samtani, S., Mohania, M., Kumar, V., Kambayashi, Y. (1999). Recent Advances and Research Problems in Data Warehousing. In: Kambayashi, Y., Lee, D.L., Lim, EP., Mohania, M.K., Masunaga, Y. (eds) Advances in Database Technologies. ER 1998. Lecture Notes in Computer Science, vol 1552. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-49121-7_7

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Research: What Companies Don’t Know About How Workers Use AI

• Jeremie Brecheisen

Three Gallup studies shed light on when and why AI is being used at work — and how employees and customers really feel about it.

Leaders who are exploring how AI might fit into their business operations must not only navigate a vast and ever-changing landscape of tools, but they must also facilitate a significant cultural shift within their organizations. But research shows that leaders do not fully understand their employees’ use of, and readiness for, AI. In addition, a significant number of Americans do not trust business’ use of AI. This article offers three recommendations for leaders to find the right balance of control and trust around AI, including measuring how their employees currently use AI, cultivating trust by empowering managers, and adopting a purpose-led AI strategy that is driven by the company’s purpose instead of a rules-heavy strategy that is driven by fear.

If you’re a leader who wants to shift your workforce toward using AI, you need to do more than manage the implementation of new technologies. You need to initiate a profound cultural shift. At the heart of this cultural shift is trust. Whether the use case for AI is brief and experimental or sweeping and significant, a level of trust must exist between leaders and employees for the initiative to have any hope of success.

• Jeremie Brecheisen is a partner and managing director of The Gallup CHRO Roundtable.

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Study explains why the brain can robustly recognize images, even without color

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Even though the human visual system has sophisticated machinery for processing color, the brain has no problem recognizing objects in black-and-white images. A new study from MIT offers a possible explanation for how the brain comes to be so adept at identifying both color and color-degraded images.

Using experimental data and computational modeling, the researchers found evidence suggesting the roots of this ability may lie in development. Early in life, when newborns receive strongly limited color information, the brain is forced to learn to distinguish objects based on their luminance, or intensity of light they emit, rather than their color. Later in life, when the retina and cortex are better equipped to process colors, the brain incorporates color information as well but also maintains its previously acquired ability to recognize images without critical reliance on color cues.

The findings are consistent with previous work showing that initially degraded visual and auditory input can actually be beneficial to the early development of perceptual systems.

“This general idea, that there is something important about the initial limitations that we have in our perceptual system, transcends color vision and visual acuity. Some of the work that our lab has done in the context of audition also suggests that there’s something important about placing limits on the richness of information that the neonatal system is initially exposed to,” says Pawan Sinha, a professor of brain and cognitive sciences at MIT and the senior author of the study.

The findings also help to explain why children who are born blind but have their vision restored later in life, through the removal of congenital cataracts, have much more difficulty identifying objects presented in black and white. Those children, who receive rich color input as soon as their sight is restored, may develop an overreliance on color that makes them much less resilient to changes or removal of color information.

MIT postdocs Marin Vogelsang and Lukas Vogelsang, and Project Prakash research scientist Priti Gupta, are the lead authors of the study, which appears today in Science . Sidney Diamond, a retired neurologist who is now an MIT research affiliate, and additional members of the Project Prakash team are also authors of the paper.

Seeing in black and white

The researchers’ exploration of how early experience with color affects later object recognition grew out of a simple observation from a study of children who had their sight restored after being born with congenital cataracts. In 2005, Sinha launched Project Prakash (the Sanskrit word for “light”), an effort in India to identify and treat children with reversible forms of vision loss.

Many of those children suffer from blindness due to dense bilateral cataracts. This condition often goes untreated in India, which has the world’s largest population of blind children, estimated between 200,000 and 700,000.

Children who receive treatment through Project Prakash may also participate in studies of their visual development, many of which have helped scientists learn more about how the brain's organization changes following restoration of sight, how the brain estimates brightness, and other phenomena related to vision.

In this study, Sinha and his colleagues gave children a simple test of object recognition, presenting both color and black-and-white images. For children born with normal sight, converting color images to grayscale had no effect at all on their ability to recognize the depicted object. However, when children who underwent cataract removal were presented with black-and-white images, their performance dropped significantly.

This led the researchers to hypothesize that the nature of visual inputs children are exposed to early in life may play a crucial role in shaping resilience to color changes and the ability to identify objects presented in black-and-white images. In normally sighted newborns, retinal cone cells are not well-developed at birth, resulting in babies having poor visual acuity and poor color vision. Over the first years of life, their vision improves markedly as the cone system develops.

Because the immature visual system receives significantly reduced color information, the researchers hypothesized that during this time, the baby brain is forced to gain proficiency at recognizing images with reduced color cues. Additionally, they proposed, children who are born with cataracts and have them removed later may learn to rely too much on color cues when identifying objects, because, as they experimentally demonstrated in the paper, with mature retinas, they commence their post-operative journeys with good color vision.

To rigorously test that hypothesis, the researchers used a standard convolutional neural network, AlexNet, as a computational model of vision. They trained the network to recognize objects, giving it different types of input during training. As part of one training regimen, they initially showed the model grayscale images only, then introduced color images later on. This roughly mimics the developmental progression of chromatic enrichment as babies’ eyesight matures over the first years of life.

Another training regimen comprised only color images. This approximates the experience of the Project Prakash children, because they can process full color information as soon as their cataracts are removed.

The researchers found that the developmentally inspired model could accurately recognize objects in either type of image and was also resilient to other color manipulations. However, the Prakash-proxy model trained only on color images did not show good generalization to grayscale or hue-manipulated images.

“What happens is that this Prakash-like model is very good with colored images, but it’s very poor with anything else. When not starting out with initially color-degraded training, these models just don’t generalize, perhaps because of their over-reliance on specific color cues,” Lukas Vogelsang says.

The robust generalization of the developmentally inspired model is not merely a consequence of it having been trained on both color and grayscale images; the temporal ordering of these images makes a big difference. Another object-recognition model that was trained on color images first, followed by grayscale images, did not do as well at identifying black-and-white objects.

“It’s not just the steps of the developmental choreography that are important, but also the order in which they are played out,” Sinha says.

The advantages of limited sensory input

By analyzing the internal organization of the models, the researchers found that those that begin with grayscale inputs learn to rely on luminance to identify objects. Once they begin receiving color input, they don’t change their approach very much, since they’ve already learned a strategy that works well. Models that began with color images did shift their approach once grayscale images were introduced, but could not shift enough to make them as accurate as the models that were given grayscale images first.

A similar phenomenon may occur in the human brain, which has more plasticity early in life, and can easily learn to identify objects based on their luminance alone. Early in life, the paucity of color information may in fact be beneficial to the developing brain, as it learns to identify objects based on sparse information.

“As a newborn, the normally sighted child is deprived, in a certain sense, of color vision. And that turns out to be an advantage,” Diamond says.

Researchers in Sinha’s lab have observed that limitations in early sensory input can also benefit other aspects of vision, as well as the auditory system. In 2022, they used computational models to show that early exposure to only low-frequency sounds, similar to those that babies hear in the womb, improves performance on auditory tasks that require analyzing sounds over a longer period of time, such as recognizing emotions. They now plan to explore whether this phenomenon extends to other aspects of development, such as language acquisition.

The research was funded by the National Eye Institute of NIH and the Intelligence Advanced Research Projects Activity.

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Dynamic Collective Action and the Power of Large Numbers

Collective action is a dynamic process where individuals in a group assess over time the benefits and costs of participating toward the success of a collective goal. Early participation improves the expectation of success and thus stimulates the subsequent participation of other individuals who might otherwise be unwilling to engage. On the other hand, a slow start can depress expectations and lead to failure for the group. Individuals have an incentive to procrastinate, not only in the hope of free riding, but also in order to observe the flow of participation by others, which allows them to better gauge whether their own participation will be useful or simply wasted. How do these phenomena affect the probability of success for a group? As the size of the group increases, will a “power of large numbers” prevail producing successful outcomes, or will a “curse of large numbers” lead to failure? In this paper, we address these questions by studying a dynamic collective action problem in which n individuals can achieve a collective goal if a share of them takes a costly action (e.g., participate in a protest, join a picket line, or sign an environmental agreement). Individuals have privately known participation costs and decide over time if and when to participate. We characterize the equilibria of this game and show that under general conditions the eventual success of collective action is necessarily probabilistic. The process starts for sure, and hence there is always a positive probability of success; however, the process “gets stuck” with positive probability, in the sense that participation stops short of the goal. Equilibrium outcomes have a simple characterization in large populations: welfare converges to either full efficiency or zero as n→∞ depending on a precise condition on the rate at which the share required for success converges to zero. Whether success is achievable or not, delays are always irrelevant: in the limit, success is achieved either instantly or never.

The paper has benefited from comments and suggestions by seminar audiences at Brown University, New York University, Northwestern University, Princeton University, Stanford University, and The University of Chicago. We also wish to thank Sandeep Baliga, Gary Cox, John Ferejohn, Emir Kamenica, Roger Myerson, and Andrea Prat for discussions and comments. We are responsible for any remaining shortcomings. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.

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How Much Research Is Being Written by Large Language Models?

New studies show a marked spike in LLM usage in academia, especially in computer science. What does this mean for researchers and reviewers?

In March of this year, a  tweet about an academic paper went viral for all the wrong reasons. The introduction section of the paper, published in  Elsevier’s  Surfaces and Interfaces , began with this line:  Certainly, here is a possible introduction for your topic. 

Look familiar? 

It should, if you are a user of ChatGPT and have applied its talents for the purpose of content generation. LLMs are being increasingly used to assist with writing tasks, but examples like this in academia are largely anecdotal and had not been quantified before now. 

“While this is an egregious example,” says  James Zou , associate professor of biomedical data science and, by courtesy, of computer science and of electrical engineering at Stanford, “in many cases, it’s less obvious, and that’s why we need to develop more granular and robust statistical methods to estimate the frequency and magnitude of LLM usage. At this particular moment, people want to know what content around us is written by AI. This is especially important in the context of research, for the papers we author and read and the reviews we get on our papers. That’s why we wanted to study how much of those have been written with the help of AI.”

In two papers looking at LLM use in scientific publishings, Zou and his team* found that 17.5% of computer science papers and 16.9% of peer review text had at least some content drafted by AI. The paper on LLM usage in peer reviews will be presented at the International Conference on Machine Learning.

Read  Mapping the Increasing Use of LLMs in Scientific Papers and  Monitoring AI-Modified Content at Scale: A Case Study on the Impact of ChatGPT on AI Conference Peer Reviews  

Here Zou discusses the findings and implications of this work, which was supported through a Stanford HAI Hoffman Yee Research Grant . 

How did you determine whether AI wrote sections of a paper or a review?

We first saw that there are these specific worlds – like commendable, innovative, meticulous, pivotal, intricate, realm, and showcasing – whose frequency in reviews sharply spiked, coinciding with the release of ChatGPT. Additionally, we know that these words are much more likely to be used by LLMs than by humans. The reason we know this is that we actually did an experiment where we took many papers, used LLMs to write reviews of them, and compared those reviews to reviews written by human reviewers on the same papers. Then we quantified which words are more likely to be used by LLMs vs. humans, and those are exactly the words listed. The fact that they are more likely to be used by an LLM and that they have also seen a sharp spike coinciding with the release of LLMs is strong evidence.

Some journals permit the use of LLMs in academic writing, as long as it’s noted, while others, including  Science and the ICML conference, prohibit it. How are the ethics perceived in academia?

This is an important and timely topic because the policies of various journals are changing very quickly. For example,  Science said in the beginning that they would not allow authors to use language models in their submissions, but they later changed their policy and said that people could use language models, but authors have to explicitly note where the language model is being used. All the journals are struggling with how to define this and what’s the right way going forward.

You observed an increase in usage of LLMs in academic writing, particularly in computer science papers (up to 17.5%). Math and  Nature family papers, meanwhile, used AI text about 6.3% of the time. What do you think accounts for the discrepancy between these disciplines? 

Artificial intelligence and computer science disciplines have seen an explosion in the number of papers submitted to conferences like ICLR and NeurIPS. And I think that’s really caused a strong burden, in many ways, to reviewers and to authors. So now it’s increasingly difficult to find qualified reviewers who have time to review all these papers. And some authors may feel more competition that they need to keep up and keep writing more and faster. 

You analyzed close to a million papers on arXiv, bioRxiv, and  Nature from January 2020 to February 2024. Do any of these journals include humanities papers or anything in the social sciences?  

We mostly wanted to focus more on CS and engineering and biomedical areas and interdisciplinary areas, like  Nature family journals, which also publish some social science papers. Availability mattered in this case. So, it’s relatively easy for us to get data from arXiv, bioRxiv, and  Nature . A lot of AI conferences also make reviews publicly available. That’s not the case for humanities journals.

Did any results surprise you?

A few months after ChatGPT’s launch, we started to see a rapid, linear increase in the usage pattern in academic writing. This tells us how quickly these LLM technologies diffuse into the community and become adopted by researchers. The most surprising finding is the magnitude and speed of the increase in language model usage. Nearly a fifth of papers and peer review text use LLM modification. We also found that peer reviews submitted closer to the deadline and those less likely to engage with author rebuttal were more likely to use LLMs. 

This suggests a couple of things. Perhaps some of these reviewers are not as engaged with reviewing these papers, and that’s why they are offloading some of the work to AI to help. This could be problematic if reviewers are not fully involved. As one of the pillars of the scientific process, it is still necessary to have human experts providing objective and rigorous evaluations. If this is being diluted, that’s not great for the scientific community.

What do your findings mean for the broader research community?

LLMs are transforming how we do research. It’s clear from our work that many papers we read are written with the help of LLMs. There needs to be more transparency, and people should state explicitly how LLMs are used and if they are used substantially. I don’t think it’s always a bad thing for people to use LLMs. In many areas, this can be very useful. For someone who is not a native English speaker, having the model polish their writing can be helpful. There are constructive ways for people to use LLMs in the research process; for example, in earlier stages of their draft. You could get useful feedback from a LLM in real time instead of waiting weeks or months to get external feedback. 

But I think it’s still very important for the human researchers to be accountable for everything that is submitted and presented. They should be able to say, “Yes, I will stand behind the statements that are written in this paper.”

*Collaborators include:  Weixin Liang ,  Yaohui Zhang ,  Zhengxuan Wu ,  Haley Lepp ,  Wenlong Ji ,  Xuandong Zhao ,  Hancheng Cao ,  Sheng Liu ,  Siyu He ,  Zhi Huang ,  Diyi Yang ,  Christopher Potts ,  Christopher D. Manning ,  Zachary Izzo ,  Yaohui Zhang ,  Lingjiao Chen ,  Haotian Ye , and Daniel A. McFarland .

Stanford HAI’s mission is to advance AI research, education, policy and practice to improve the human condition.  Learn more . 

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Conservation of nature's strongholds needed to halt biodiversity loss

Researchers argue for scaling-up area-based conservation to maintain ecological integrity.

To achieve global biodiversity targets, conservationists and governments must prioritize the establishment and effective management of large, interconnected protected areas with high ecological integrity, John G. Robinson from the Wildlife Conservation Society, US, and colleagues argue in an essay publishing May 21 in the open-access journal PLOS Biology .

The Kunming-Montreal Global Biodiversity Framework (GBF), signed at the 2022 Conference of Parties to the UN Convention on Biological Diversity in Montreal, recognized the importance of protecting large areas of natural habitat to maintain the resilience and integrity of ecosystems. To halt biodiversity loss, these protected and conserved areas need to be in the right places, connected to one another, and well managed. One of the GBF targets is to protect at least 30% of the global land and ocean by 2030, known as the 30x30 target.

To achieve GBF targets, the authors propose prioritizing large, interconnected protected areas with high ecological integrity, that are effectively managed and equitably governed. They emphasize the importance of conserving landscapes at scales large enough to encompass functioning ecosystems and the biodiversity they contain. In many cases, this will require interconnected groups of protected areas that are managed together. Effective governance means that the diversity of stakeholders and rights holders are recognized and that the costs and benefits are shared equitably between them. The authors argue that protected and conservation areas that meet all four criteria -- which they name "Nature's Strongholds" -- will be disproportionately important for biodiversity conservation. They identify examples of Nature's Strongholds in the high-biodiversity tropical forest regions of Central Africa and the Amazon.

By applying the four criteria presented in this essay to identify Nature's Strongholds around the world, governments and conservationists can coordinate their efforts to best address threats to biodiversity, the authors say.

The authors add, "'Nature's Strongholds' -- large, interconnected, ecologically intact areas that are well managed and equitably governed -- are identified in Amazonia and Central Africa. The approach offers an effective way to conserve biodiversity at a global scale."

• Ecology Research
• Endangered Plants
• Biodiversity
• Rainforests
• Land Management
• Urbanization
• Environmental Policies
• Biodiversity hotspot
• Organic farming
• Agroecology
• Conservation biology
• Sustainable land management
• Deforestation
• Unified neutral theory of biodiversity

Story Source:

Materials provided by PLOS . Note: Content may be edited for style and length.

Journal Reference :

• John G. Robinson, Danielle LaBruna, Tim O’Brien, Peter J. Clyne, Nigel Dudley, Sandy J. Andelman, Elizabeth L. Bennett, Avecita Chicchon, Carlos Durigan, Hedley Grantham, Margaret Kinnaird, Sue Lieberman, Fiona Maisels, Adriana Moreira, Madhu Rao, Emma Stokes, Joe Walston, James EM Watson. Scaling up area-based conservation to implement the Global Biodiversity Framework’s 30x30 target: The role of Nature’s Strongholds . PLOS Biology , 2024; 22 (5): e3002613 DOI: 10.1371/journal.pbio.3002613

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