Conceptualizing Warehouse 4.0 Technologies in the Third-Party Logistics Industry: An Empirical Study

Abstract

Background: Industry 4.0 (I4.0) has gained significant attention in recent years, with the term Logistics 4.0 (L4.0) emerging in the logistics industry. However, L4.0 remains vague and lacks a unified definition or classification of related technologies. Existing studies defining L4.0 are mainly conceptual and speculative, rather than grounded in empirical research. To address this gap, this study contributes to defining L4.0 through the sub-area of Warehouse 4.0 (W4.0), focusing on the challenges of adopting I4.0 technologies in warehouses. Methods: Through the I4.0 and L4.0 literature, an initial classification of W4.0 technologies in third-party logistics (3PL) was developed. This was refined using a case study of a global logistics service provider (LSP) in the 3PL industry, through semi-structured interviews with stakeholders. Results: The empirical findings identify new application areas for I4.0 technology in 3PL warehouses, including horizontal and vertical system integration, big data, and cybersecurity, technologies that can enhance 3PL competitiveness. Conclusions: This study offers a structured classification of W4.0 technologies and insights into the application areas of W4.0 in 3PLs. It contributes practical insights into which I4.0 technologies are relevant for the 3PL warehouse industry and their potential application areas.

1. Introduction

Industry 4.0 (I4.0) has gained significant attention in recent years [1,2,3]. While I4.0 generally refers to the Fourth Industrial Revolution in manufacturing and service systems [2], Logistics 4.0 (L4.0) can be summarized as the optimization of inbound and outbound activities, supported by intelligent systems and data-enabled technology systems (e.g., blockchain and Internet of Things (IoT)) that provide and share relevant information to achieve a high degree of automation [4]. More recently, Warehouse 4.0 (W4.0) or Warehousing 4.0 has emerged as a subfield of L4.0. While L4.0 encompasses technologies related to transportation, distribution, and warehousing [5], W4.0 is only concerned with inbound and outbound activities in warehouses [6,7].

With the advent of e-commerce, warehouse operations have become increasingly critical to companies’ success [6]. However, e-commerce poses several challenges to logistics service providers (LSPs), including the need to store large quantities of unique items and to handle large and daily variable order volumes [8] with e-tailers who are willing to switch providers if they are not satisfied with their services [9]. This positions warehousing as a prime candidate for automation solutions that can significantly improve warehouse operations [10,11,12]. However, warehousing is behind other segments in the adoption of new technologies [7], which can be attributed to the large initial investments in automation solutions [13,14,15].

Generally, L4.0 is still a relatively unspecified concept with no unified definition. Specifically, the few studies classifying L4.0 technologies are not rooted in empirical research [5,16]. Thus, the technologies described in the L4.0 literature may not necessarily reflect the technologies that are emerging and relevant in the logistics industry today. Even fewer studies have focused on specifying W4.0 technologies. As this segment could significantly benefit from the adoption of these technologies, Perotti et al. [13] argued that there is a need to conceptualize and develop taxonomies that clarify L4.0 technologies for warehousing. They emphasized that this issue is particularly critical for warehousing, which often receives less attention than other logistics areas and therefore lacks standardized taxonomies or classifications. This lack of clarity complicates the assessment of benefits and costs, leads to inconsistent technology adoption, and hinders the comparability of empirical research findings, ultimately hindering effective technological evaluation and implementation. Thus, instead of focusing on L4.0, this study limits its focus to warehousing because of the highly diverse technologies relevant to different areas of logistics. In this vein, this study aims to identify and classify W4.0 technologies, contributing to the literature by enabling more consistent comparisons of empirical studies and best practices, and thereby supporting improved technology assessment and adoption.

As the literature on W4.0 is sparse, this study begins by developing an initial classification of warehousing technologies using the I4.0 and L4.0 literature. This is then refined through a case study of a global world-leading LSP. The case company is representative of the sector due to its global presence, which enables a broader, international perspective and ensures that this study is not limited to a specific region. Additionally, the company offers a comprehensive service portfolio covering the full scope of logistics operations, allowing for a thorough examination of warehousing technologies across service types. By leveraging the case study insights, the research identifies current and future application areas for individual technologies, as well as the challenges associated with their adoption in warehousing. This approach contributes to a more robust understanding of W4.0 classification and implementation.

2. Literature Review

To identify the relevant literature on the use of I4.0 in the 3PL warehousing industry, two searches were conducted in the Scopus database (see

Table 1. Search in Scopus database. Asterisk (*) is used for right-hand truncation.

Search	Keywords	Results
1	title-abs-key (“logistics service provider *” or “lsp *” or “third party logistics *” or “3pl”) and title-abs-key (“emerging technolog *” or “industry 4.0” or “logistic * 4.0”) and (limit-to (language, “english”))	93
2	title-abs-key ((“intelligent warehous *” or “smart warehous *” or “warehous * 4.0”)) and (limit-to (language, “english”)) and (limit-to (doctype, “ar”) or limit-to (doctype, “ch”) or limit-to (doctype, “re”))	252

). The results are subsequently discussed.

The first search focused on I4.0 or L4.0 in the 3PL industry, a generally narrow field of research with 93 results. The objective of the first search was to identify sources that describe L4.0 and to provide classifications of related technologies in the 3PL industry. This is essential, as warehousing is a core subfield of logistics, particularly within 3PL operations, where integrated warehousing solutions are essential to delivering L4.0 capabilities. Consequently, many L4.0 technologies are directly applicable to warehousing environments. The second search focused on W4.0, but it was not limited to 3PL due to the few results yielded by this limitation. This search aimed to identify classifications of W4.0 technologies, specifically through literature studies, case studies, and surveys. Most of the sources concerned “smart” and “intelligent” warehousing and did not mention LSPs or 3PL. Nevertheless, these papers were still considered. On the other hand, experimental studies and simulations were excluded because they primarily focused on test environments. Furthermore, the snowballing technique was used to identify additional relevant sources in both searches. This resulted in 22 papers that were analyzed to identify W4.0 technologies.

2.1. Third-Party Logistics Warehousing

LSPs in the 3PL warehousing industry offer inbound and outbound warehousing solutions to clients [7], that is, receiving, putting away, picking, consolidating, packing, and shipping goods [11], without taking ownership of the clients’ goods. Wallenburg and Knemeyer [7] highlighted four benefits of using 3PLs: (1) efficiency gains from outsourcing logistics activities to LSPs, (2) provide dynamic (i.e., short-term operational) and structural (i.e., mid- to long-term strategic) flexibility through access to outside resources to their clients, (3) access to solutions and technologies that would not be economically viable for the single client or smaller client, and (4) profit from a portfolio effect because LSPs can reduce volatility and uncertainty by pooling the demand of different clients. While this enables economies of scale, it is challenging for LSPs to adopt emerging technologies because of short-term contracts with clients [15], high costs of technologies [14], and complexity of the logistics network [17], among others [14,15,17].

2.2. Studies Focusing on Industry 4.0 in a Warehouse Context

Emerging technologies refer to innovations that have the potential to create a new industry or transform an existing one [18]. Generally, these technologies are synonymous with I4.0 and often refer to technologies such as IoT, data analytics, artificial intelligence (AI), machine learning (ML), robotics, and automation solutions [2,15].

I4.0 in a warehouse context is known by various terms, such as “Warehouse 4.0” or “warehousing 4.0” [6], “smart warehouse” [19,20], and “intelligent warehouse” [12]. W4.0 is used to describe the Fourth Industrial Revolution within warehousing [6,12], but it is not a common term in the literature [12]. This term is used interchangeably with “intelligent warehouse” or “smart warehouse” [12], which are more common in the examined literature.

Tutam [6] described the four stages of the warehousing revolution. W4.0 incorporates autonomous systems and redesigns existing technologies and is distinguishable from the adoption of autonomous handling systems, such as autonomous storage and retrieval systems (AS/RS) and collaborative robots. Some of the benefits of using autonomous handling systems include increased space efficiency, the possibility of operating 24/7, and less manual handling, thus increasing the effectiveness of warehouses.

Winkelhaus and Grosse [10] defined a smart warehouse as “A highly integrated warehouse that uses advanced digital technologies and automation for efficient and effective operations to adapt to the dynamic business environment of today’s economy.” They classified four types of order-picking systems (OPSs) based on supportive (digital) and substitutive (automation) technologies: (1) traditional OPSs, for example, conveyor belts and stacker-crane-based AS/RS; (2) smart operator OPSs, that is, application of automation technologies to a similar (low) degree compared to traditional OPSs, but the operator is equipped with more advanced technologies, such as wearables; (3) autonomous OPSs, for example, robotic mobile fulfillment systems (RMFSs); and (4) collaborative OPSs, for example, autonomous robots and humans in shared space.

Azadeh et al. [8] classified automated picking systems into four main categories: (1) cranes or automated forklifts, for example, AS/RS; (2) carousels and dispensers; (3) shuttles; and (4) automated guided vehicles (AGVs). At the time, cranes or automated forklifts and carousels and dispensers were more conventional systems, while shuttles and AGVs are recent automated picking systems. The advantages of robotic solutions are scalability and throughput flexibility, which are relevant in an e-commerce environment. On the other hand, investment in the automation of storage and order picking is medium- to long-term because it requires considerable scale and long-term vision [8].

Warehousing 4.0 has a significant impact on the synergy between humans and robots. Automation solutions can alleviate warehouse workers of tedious labor and reduce injuries [6]. Jacob et al. [19] classified these robots into four types: (1) autonomous mobile robots (AMRs), (2) RMFSs, (3) AGVs, and (4) drones. Both AMRs and AGVs can transport goods and people, for example, for transport in and between picking and packing areas. While AMRs can navigate to any accessible point, AGVs either lead or follow humans to transport picked goods [19]. RMFSs can lift and transport moving racks to a work area, either for picking or replenishment [6,19]. Unlike AMRs and AGVs, they do not share workspaces with humans. Drones can be used for transporting goods or for inventory checking in hard-to-reach areas [19]. This interaction between robots and humans is also called a collaborative robot system, with collaborative robots also referred to as cobots [6].

Zhen and Li [20] examined four characteristics of smart warehouse operations: (1) information interconnection, (2) equipment automation, (3) process integration, and (4) environmental sustainability. Information interconnection technologies, such as IoT and CPS, enhance information interaction within warehouse systems and the logistics chain. Zhen and Li [20] identified prevailing interconnection technologies, such as radio-frequency identification (RFID), warehouse management systems (WMSs), augmented reality, and reinforced learning, with RFID being the primary IoT technology used in warehouse operations. IoT and CPS technologies, including radio frequency, pick-to-light, and pick-by-voice, are commonly used in the picking process. Interconnection technology can enable warehouse information collection and exchange, which could be a leading factor in future warehouse development. Zhen and Li [20] also identified equipment automation solutions, such as AS/RS and other variants, for example, AVS/RS, AGVs, RMFSs, and warehouse robots.

Tubis and Rohman [12] reviewed studies on the design and operation of warehouses using concepts from I4.0. They found that studies primarily focused on the implementation of I4.0 technologies, such as IoT, augmented reality, RFID, visual technology, and other emerging technologies, as well as autonomous and automated vehicles in warehouse processes.

Chayutthanabun et al. [21] examined the adoption of smart warehouse technology in Thailand through interviews with users of smart warehouse technology and service providers, although it has not been clarified whether these interviews included LSPs. They identified 61 technologies from literature reviews and company reports and divided them into seven groups: (1) automation systems, (2) digital software and technology, (3) robots, (4) IoT, (5) smart warehouse and logistics facilities, (6) controllers, and (7) security and safety technology.

A few of the identified studies described augmented reality experiments in logistics operations at the provider DHL, which observed improved efficiency and productivity of warehouse operations [22,23].

While there is a substantial body of research within smart and intelligent warehouses, it is worth mentioning that most of these sources do not focus on LSPs or 3PL. This study argues that due to the challenges outlined in the 3PL literature of adopting I4.0 technologies in the warehousing industry, there is a need to examine and classify these technologies from an empirical standpoint to examine their applicability. Other noteworthy sources that have examined I4.0 technologies in a warehousing context include MacCarthy and Ivanov [24] and van Geest et al. [25,26]. MacCarthy and Ivanov [24] provided an overview of technologies and systems that enable digital supply chains, including smart warehouses, and discussed blockchain, digital twins, IoT, 5G, and edge and fog computing. van Geest et al. [25] presented a reference architecture for developing smart warehouses that showed common and variant features. Finally, van Geest et al. [26] reviewed studies discussing the design of smart warehouses and the transition to these warehouses. They found that a complex and diverse market drives this transition. Furthermore, they identified key subdomains that have adopted smart warehouse technology with current features, such as RFID and IoT, that characterize smart warehouses.

2.3. Logistics 4.0

L4.0 is a paradigm derived from I4.0 [5], which has gained significant attention in the past decade [16]. While there is no unified definition of L4.0, Winkelhaus and Grosse [16] defined it as “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.” This definition is based on three aspects: (1) the implications for logistics from shifting to mass customization in production, (2) the changes to logistics processes by using new digital technologies, and (3) the importance of people along with environmental changes [16]. Moldabekova et al. [4] summarized L4.0 as the optimization of inbound and outbound activities supported by intelligent systems and data-enabled technology systems (e.g., blockchain and IoT) that provide and share relevant information to achieve a high degree of automation.

L4.0 technologies have the potential to positively impact the logistics industry with benefits such as increasing flexibility, proactivity, and visibility; reducing process design costs; and decreasing operational imprecision and costs. Consequently, this can improve the level of customer service; optimize logistics activities; increase productivity, safety, and performance; and reduce storage and picking costs [5,13]. These advantages are shared with customers and positively affect loyalty [13]. Thus, it is essential to understand the adoption of L4.0 technologies in the 3PL industry and their advantages [5]. In particular, the warehousing segment can benefit. This is a segment pressured by the scarcity of warehouse space and the increasing operating costs [8]. The rise in online shopping is putting additional pressure on LSPs’ performance [6,15]; LSPs must store large quantities of unique items and handle large and daily variable order volumes [8]. The process of picking is especially suited for automation because of its repetitive and manual nature [8]. However, while this segment can benefit significantly from the adoption of L4.0, it is also a segment that lags behind in the adoption of new technologies [7]. This can be attributed to LSPs’ reluctance to invest in these technologies [7]. This is in part because of the large initial investments needed [13,14,15] and the lack of confidence in the promised gains of L4.0 technologies [14]. On the other hand, technological advancements in e-commerce, such as chatbots, AI assistants, and social media platforms, are also boosting online shopping and driving increased demand for logistics services [6].

Despite several studies of L4.0, as described above, its meaning remains unclear. Specifically, only a few studies have identified and classified L4.0 technologies, and these do not fully agree on what L4.0 involves. Such studies include the systematic literature review of L4.0 by Winkelhaus and Grosse [16], who developed a framework of L4.0 that combines external triggers, main technological innovations, human factors, and logistics tasks. Technologies can be divided into three subcategories, all with information at the center: (1) information generation, (2) information handling, and (3) information usage. Winkelhaus and Grosse [16] found that IoT was the most predominant in research. Thus, further research into other technologies is required. Baglio et al. [5] reviewed the literature on L4.0 and created a classification of L4.0 technologies. This is divided into two main categories: (1) the physical world, that is, sensors, robots, and so on; and (2) the digital world, that is, data analytics tools, simulations, and so on. The technologies were classified according to the main areas of application. Although this classification is the most extensive, it is not exhaustive. Da Silva et al. [14] also identified 3D printing as a technology related to L4.0. Nand et al. [15] identified emerging technologies in the logistics industry through interviews with stakeholders in the Australian logistics industry. They identified additional technologies, such as data warehousing, smartphone apps, and WMSs. Thus, there is still a need for an exhaustive classification that reflects emerging technologies in the logistics industry.

3. Initial Classification of Warehouse 4.0 Technologies

3.1. Evaluation of Existing L4.0 Classifications

As the literature does not provide detailed definitions of W4.0 or accounts of the technologies involved, the conceptualizations of the broader concept of L4.0 were subsequently taken as a point of departure. However, some problems rendered them less suitable for this purpose, including (1) incomplete accounts of relevant technologies, (2) questionable inclusions of technologies, (3) a lack of details about technology applications, and (4) unclear categorizations (i.e., overlaps).

First, the classifications of L4.0 technologies discard some technologies that appear to have potential relevance for LSPs. One example is “additive manufacturing” (or 3D printing), which is omitted from the existing L4.0 classification [5]. However, this technology could potentially be relevant for producing spare parts for warehouse equipment in distribution centers [27] or offered as a value-added service [10]. Da Silva et al. [14] and Nand et al. [15] mentioned other examples, such as Internet services, wireless sensor networks, data warehousing, smartphone apps, and WMSs.

Second, existing L4.0 classifications comprise technologies whose inclusion is debatable, for example, social media [5,16], which is rarely found in the general I4.0 literature [1,2].

Third, existing L4.0 classifications lack details and examples from the logistics industry. For example, few or no technologies related to blockchain, cloud, and cybersecurity are mentioned [5], which is problematic for understanding the adoption of these technologies in the logistics industry.

Finally, the categories in the existing L4.0 classifications are not clearly distinguished but seem to involve some overlap. For example, technologies for smart pallets closely relate to tracking [5], and augmented reality also falls within IoT [20].

3.2. Initial W4.0 Classification

Given the problems described above, the focus was turned toward some of the most referenced I4.0 classifications. The list of potentially relevant L4.0 technologies was compiled using Rüßmann et al. [28], Ustundag and Cevikcan [2], Tsaramirsis et al. [29], and Zheng et al. [3]. This list of technologies is based on the nine pillars of technological advancement described by Rüßmann et al. [28]. In addition, mobile technologies [2], real-time location systems (RTLS) and RFID technologies [2], and blockchain [5,14,29] were added to the set of technologies mentioned by Rüßmann et al. [28] to ensure thorough technological consideration. In this context, potential application areas were derived from the L4.0 and W4.0 literature. The initial list of potential W4.0 technologies derived from the existing literature is shown in

Table 2. List of potentially relevant L4.0 technologies in warehousing derived from the literature.

Main Technologies	Definition of Technology	Potential Warehouse Applications	Sources
IoT	Systems of physical objects collect and exchange data, allowing interaction and cooperation of these objects along value chain activities [2,3].	Tracking, flow analysis, pallet management, information/data collection and exchange.	[5,11,12,14,15,20,21]
Automation and robotics	Machinery and equipment that automates manual processes or allows humans and machines to operate in a shared environment [3].	Automation of warehouse activities from inbound to outbound.	[5,6,7,8,10,11,12,14,15,19,20,21]
Augmented reality	Techniques that embed virtual objects to coexist and interact in the real environment [3].	Picking (pick-by-vision), quality check, driving assistant, stock monitoring, remote maintenance and repairs of warehouse equipment.	[5,10,12,14,16,22,23,30]
Horizontal and vertical system integration	Cross-company, universal data-integration networks that enable automated value chains [2,28].	Enterprise integration.	[5,7,11,21]
Additive manufacturing	Technologies that produce three dimensional objects from digital models through an additive process, which enables mass customization [2,3].	Customization of products and components, spare part management, value-added services.	[10,14,27]
Big data and analytics	Collection and analysis of large amounts of data from various sources to support real-time decision-making [3,28].	Data collection and reporting, performance optimization, decision-making.	[5,7,12,14,15]
Simulation	Real-time data to create a virtual model that mirrors the physical world, which can include machines, products, and humans [28].	Warehouse planning and decision-making.	[5,10,11,12]
Cloud	Systems that provide online storage services for applications, programs, and data on virtual servers without requiring installation [3].	Enterprise integration, real-time monitoring, data storage, management, and analysis, process optimization, inventory management.	[5,7,14]
Cybersecurity	Protecting critical industrial systems through secure and reliable communications and sophisticated identity and access management for machines and users [28].	Risk management.	[5]
Mobile technologies	Devices with Internet access that can receive and process large amounts of information and feature high-quality cameras and microphones for recording and transmitting information [2].	Information/data collection and exchange, decision-making.	[5,15,16]
RTLS and RFID technologies	Auto-ID technologies for identification, location detection, and condition monitoring of objects and resources within organizations and across companies, which support organizational integration and enable self-decision-making by machines and smart devices [2].	Tracking, flow analysis, pallet management, information/data collection and exchange, inventory management.	[5,10,12,15]
Blockchain	Shared, distributed, tamper-proof digital ledgers with timestamps of blocks maintained by every participating node [3,29].	Data management in the supply chain, decision-making support, stock monitoring, smart contract, inventory management.	[5,10,12,14,15,30]

.

4. Research Method

To classify W4.0 technologies, a case study was carried out, as this approach facilitates a deep and comprehensive understanding of the phenomenon studied [31,32]—in this case, an understanding of the current and future applications of W4.0 technologies. It also allows for a relatively full answer to the what, why, and how [33], which is essential for understanding the challenges to adopting these technologies. Through the case company it is possible to validate the technologies derived from the literature review and assess the validity of L4.0 and I4.0 technologies included in the initial classification.

The company selected for this study is an international transport and logistics firm operating in over 80 countries globally with a workforce of over 70,000. The company provides air, sea, and road transport services, along with contract logistics. This case study focused on contract logistics in the warehousing segment.

Data Collection and Analysis

This study was based on semi-structured interviews with stakeholders in the case company (see

Table 3. Overview of interviews.

Participant	Responsibility	Duration (min)
Director of industrial technology	All divisions, globally	60
Application security manager	All divisions, globally	60
Senior manager of technology and automation	All divisions, globally	60
Senior manager of automation and warehousing design	Warehousing, EMEA	60
Lead solution architect of technology and automation	Warehousing, globally	60
Operation manager	Warehouse site, Denmark	30
Business development manager	Warehouse sales, Denmark	60
Manager of digital products	Warehousing, globally	60
Manager of ML operations	All divisions, globally	60
Senior business change manager	Warehousing, globally	60
Senior director of innovation	All division, globally	30
Director of continuous improvement	Warehousing, EMEA	30
Team lead	Warehouse site, Denmark	30
Senior design engineer	Warehousing, Denmark	60
Senior manager of material handling equipment	All divisions, globally	30
Director of automation	Warehousing, North America	30

). The participants included experts in different technologies, as well as employees involved in day-to-day operations, both with global and regional responsibility in Denmark, Europe, the Middle East and Africa (EMEA), and North America. Initially, the director of industrial technology was interviewed to gain an overview of the relevant technologies and stakeholders. The participants were chosen to cover a wide range of technologies and application areas. Subsequently, relevant stakeholders were interviewed using an interview guide. The guide included (1) an introduction to the participants and their understanding of L4.0, (2) a detailed review of the technologies listed in Table 2, along with their experiences with these technologies, (3) general questions about the motivation for implementing these technologies, (4) their perceived barriers and enablers to adoption, and (5) a conclusion and outlook. The interviews were either recorded and later transcribed or transcribed in real time during the meetings. The transcripts were then consolidated into a structured data table, where responses to the interview guide were grouped by the technologies from Table 2. For each interviewee, it was recorded whether a specific technology had been used or not used, or if a relevant application area could be identified. Using the structured data table, it was possible to ensure validity and robustness of the interviews, as data was triangulated across the stakeholders, ensuring a diverse view across organizational responsibilities and regions. This made it possible to identify technological patterns on a global and full-service level, ensuring more consistent definitions of the technology and allowing for improved assessment of a technology’s relevance.

5. Case Study Results

The findings from the interviews are shown in

Table 4. Relevant W4.0 technologies in the case company.

Main Technologies	Examples of Related Technologies in the Case Company	Areas of Application	Potential Future Applications
IoT	Industrial sensors, smart labels, RFID, AR, cameras	Tracking of sensors or tags on warehouse equipment (e.g., forklifts, conveyors, AS/RS) or objects that are connected to the cloud Collection of data, e.g., temperature, humidity, shock, weight, or location of objects	Tracking CO2 impact in warehouses Automated preventative maintenance for clients Cameras on AGVs for pick verification Cameras to monitor warehouse operations and automatically log information in the WMS Use sensor data and cameras to determine why impacts on goods happen in warehouses RFID tags on pallets to track empty pallet spaces, which enables the company to charge clients for consumed spaces instead of fixed spaces RFID on items to reduce the sorting of mixed inbound orders RTLS provides extra safety around blind areas, using sensors or flashing lights to avoid collisions.
Automation and robotics	(1) Crane/automated forklifts: AS/RS; (2) Carousels and dispensers: carousels and vertical lifts; (3) AGVs: autonomous forklifts, autonomous narrow-aisle forklifts; (4) Shuttles: pallet shuttles; (5) Robotics: collaborative robots, AMRs, RMFSs; (6) Drones.	Semi- or full automation of manual tasks in warehouse operations, e.g., pick and pack Inventory management, e.g., by drones for cycle counts Automated replenishment to pick locations	Automation and robotics are utilized with AI to automate inbound/outbound activities, e.g., loading containers Fully automated picking with robotics from AS/RS
Augmented reality	Smart glasses	Order picking (pick-by-vision) by overlaying virtual orders from the WMS onto physical environment	Quality control with AS/RS Determination of optimal traveling route in warehouse operations and displaying orders, e.g., stock keeping unit (SKU), picture of SKU, and quantity, to pickers
Horizontal and vertical system integration	Enterprise applications: WMS, warehouse control system, warehouse execution system APIs Web apps/applications	Enterprise integration and software development Operational technology and process optimization Omnichannel Enterprise data platform and reporting Public APIs to communicate with clients Tracking shipments in web apps Applications for uploading pictures of shipments to clients	Forecasting of preventative maintenance for clients Forecasting of seasonality and other patterns
Additive manufacturing	3D printing	Value-added services, e.g., printing spare parts for clients	Automated preventative maintenance for clients
Big data and data analytics	Big data, data analytics algorithm, AI, ML, data mining	Collection of data to enterprise data platform Optimization of warehouse layout Decision-making Reporting Automation of manual processes, e.g., customs clearance AI to manage customer inquiries	AI to manage customer inquiries and perform root-cause analysis AI integrated with automation and robotics, e.g., for decision-making in warehouse operations Chatbot and agentive AI for smaller clients Algorithms for data collection to measure the profitability of warehouse operations
Simulation	Simulation, digital twins	Warehouse planning Decision-making Simulation with drones	Use real-time data for digital twins to forecast resource demand in operations Simulation of congestion when designing warehouse operations and automation solutions, e.g., to simulate congestion
Cloud	Hybrid cloud, edge computing.	Enterprise integration and process optimization Automation warehouse control system in the cloud Real-time monitoring, e.g., of temperature and data storage, management, and analysis. Inventory management
Cybersecurity	Encryption	Risk management, e.g., assessment of suppliers Operational technology and office equipment (access management) Compliance with regulations. Secure by design in software development
Blockchain		Smart contracts

, which summarizes the main technologies, examples of related technologies in the company, areas of application, and potential future applications. The case study revealed that the category “mobile technologies”, along with smart glasses, referred to web apps or applications for tracking or documenting orders in warehouse operations. However, this category overlaps with other technologies, making it difficult to distinguish between categories. To ensure clarity, “Mobile technologies” were removed from the classification. Similarly “RTLS and RFID technologies” were merged into the “IoT” category, as they fall within the definition of each other, and as the areas of application are closely related. These adjustments ensure a robust classification of technologies by reducing potential overlap.

As shown in Table 4, in a warehouse context, all the technologies in the initial W4.0 framework were in use at the company, and further applications were considered.

First, IoT was used in the form of sensors or tags on warehouse equipment or objects that are connected to the cloud and can collect data for analytics. According to the participants, this can produce benefits such as improved efficiency and reduced costs from increased traceability and optimization of processes and warehouse layout. However, the interviews showed that the company had difficulty fully utilizing IoT because of challenges related to accessing data from suppliers of equipment. Specifically, some suppliers fail to understand the importance of providing data, and the data are stored in the suppliers’ cloud solutions. Furthermore, RFID is not a commonly applied technology in warehousing because clients’ goods are rarely received with RFID tags. Thus, the company would need to attach them, which would require more handling, resulting in increased costs. However, in the future, the company believes that IoT will become more common in warehousing, as LSPs need to use more real-time data to improve efficiency and reduce costs but also to meet new requirements from clients (e.g., to provide numbers on CO₂ emissions).

Automation and robotics are used to automate warehouse activities, particularly pick and pack, inventory management, and replenishment. These technologies can improve the efficiency and productivity of warehouse operations by automating tasks that are traditionally heavy on manual labor. The challenge with these technologies lies in scalability. Short-term contracts and long payback periods for investments necessitate that solutions are scalable to more clients. However, the lack of standardized processes, along with inadequate strategy and customer segmentation, can hinder adoption and prevent the realization of expected benefits. In the future, automation and robotics could be used together with AI, for example, to optimally load containers to increase space efficiency, and activities such as picking could be fully automized to improve efficiency.

Augmented reality is not used in the company, but smart glasses have been tested for order picking to overlay orders from the WMS onto the physical environment. The challenge with this technology, particularly smart glasses, was the ergonomics of employees. At the time, the glasses were too heavy and uncomfortable to wear for longer periods of time. Similar to previous technologies, the company could not create a viable business case. The benefit of this technology could be faster and more accurate picking using hands-free equipment. Future areas of application include combining smart glasses with picking from AS/RS for quality control or determining the optimal traveling route of employees in warehouse operations, which is relevant in large warehouse facilities.

Horizontal and vertical system integration refers to IT systems that connect the value chain horizontally and vertically. In the company, these are enabled by different enterprise applications, APIs, and web apps. This technology is highly relevant in enterprise integration to enable communication between people and digital environments. Several participants emphasized the need for scalable systems and applications that can integrate into other systems seamlessly, which is relevant when designing IT architectures and developing software. Operational technology enables the company to collect data from warehouse operations, for example, for process optimization or reporting to other departments or clients. Furthermore, omnichannels are more prevalent with e-tailers, and integration into different external systems plays a critical role. The adoption of these technologies is challenged by the diversity of IT systems, especially as a result of a wide client base. Future applications could include value-added services, such as forecasting of preventative maintenance for clients, that is, a system of sensors (IoT) that could predict when a part needs maintenance and automatically order spare parts from the LSP, or forecasting seasonality and other patterns. Although the company already has forecasts of demand, these could be sold as specific services to create partnerships with clients.

Additive manufacturing has been used to produce spare parts from 3D models. This technology allows the company to print spare parts for different clients. The benefit is that the LSP can print and ship spare parts faster than other manufacturers while also enabling mass customization. However, the company could not identify a client base or create a business case. In relation to future areas of application of the previous technologies, the LSP could offer automated preventative maintenance for clients and print spare parts to reduce lead time from production. This could open new markets to the company.

Big data and data analytics include the collection and analysis of large amounts of data from many different sources to support real-time decision-making and automation of manual processes. The company uses these technologies to collect data for optimization and reporting and to automate manual processes and customer inquiries, thus reducing the need for manual labor and extra costs. The challenges to adopting these technologies include a lack of standardization (e.g., documentation, IT infrastructure, and resistance to change). There must be a certain level of standardization to automate manual processes, while the IT infrastructure should be able to handle these technologies. Resistance to change arises from the elimination of manual labor and, likely, some employees’ tasks. Future applications of ML and AI include automating customer service to enhance efficiency and responsiveness while delivering personalized experiences. Additionally, AI integrated with robotics can be utilized for decision-making in warehouse operations, significantly improving efficiency.

Simulation is currently used when warehouse operations are planned but are not used with real-time data. Simulation is also used in connection with drones. Simulations can save time during the design and planning of warehouse operations and can be used to monitor operations. Future applications include the use of real-time data in digital twins to forecast resource demand in operations or when warehouses and automation solutions are designed. However, this requires large amounts of data, which ties back to the challenges associated with IoT.

Cloud technologies are used for the online storage of applications and back-up of data, for example, in hybrid computing platforms. This enables performance to be decentralized from the physical site. The cloud also plays a critical role in enterprise integration and allows the company to perform real-time monitoring, process optimization, and inventory management. The lack of effective cloud strategies can hinder the optimal utilization of these technologies. Additionally, accessing and managing data in cloud solutions can be challenging when they are controlled by suppliers who often prefer to retain ownership.

Cybersecurity in the company covers preventive and detective IT security that mitigates the risks of cybersecurity threats, which can result in downtime and significant costs. This includes, but is not limited to, secure and reliable communication, IT systems, access management, and operational technology. Cybersecurity is also an integral part of how software is developed (secure by design). Regulations also require LSPs to comply to avoid fines. While cybersecurity is sparsely described in the L4.0 literature, it is a central topic in the development and implementation of other technologies in the company.

Finally, most participants were not familiar with blockchain or actual use cases in the logistics industry, but it had previously been tested with smart contracts in the company. Although the technology worked well, the implementation required other parts of the value chain to participate, which was challenging to establish.

6. Toward a Definition of W4.0

Based on the insights obtained from the W4.0 and L4.0 literature, as well as the case study carried out, a general classification of W4.0 and its applications is proposed, as shown in

Table 5. General classification of W4.0 technologies and applications.

Category	Included/Related Technologies	Areas of Application	N *	C *	Source(s)
IoT	Industrial sensors, smart labels, RFID, AR, cameras, actuators, pick-to-light, pick-by-voice, web and smart phone apps/applications, wireless sensor network, 4G communication devices, location (GPS), beacon.	Tracking of warehouse equipment (e.g., forklifts, conveyors, AS/RS) or goods using sensors or tags that are connected to the cloud		X	[5,10,14,15,20]
		Information/data collection and exchange, e.g., temperature, humidity, shock, weight, etc., or location of objects		X	[5,10,11,14,15,20,21]
		Flow analysis based on tracking of equipment/goods movements		X	[5,11]
		Pallet management based on tracking of goods movements		X	[5]
		Inventory management based on tracking of goods movements		X	[5,10,20]
		Decision-making based on tracking of equipment/goods movements		X	[5,15]
Automation and robotics	(1) Crane/automated forklift, e.g., AS/RS and automated storage and retrieval rack (AS/RR) mini-loads; (2) Carousels and dispensers, e.g., carousels, vertical lifts, and A-frames; (3) AGV, e.g., autonomous forklifts, autonomous narrow-aisle forklifts; (4) Shuttles, e.g., pallet shuttles and autonomous vehicle-based storage and retrieval (AVS/R); (5) Robotics, e.g., collaborative robots, AMRs, RMFS; (6) Drones.	Automation of warehouse activities from inbound to outbound, e.g., pick and pack, inventory management, and replenishment		X	[5,7,8,10,11,14,15,19,20,21]
Augmented reality	Smart glasses Screen-based AR (smartphones, tablets, etc.)	Order picking (pick-by-vision)		X	[5,10,16,22,23,30]
		Quality checks			[5]
		Stock monitoring			[30]
		Driving assistants			[5]
		Remote maintenance and repairs of warehouse equipment			[30]
Horizontal and vertical system integration	Enterprise applications, e.g., WMS, warehouse control system, warehouse execution system APIs Web and smart phone apps Cloud	Enterprise integration		X	[5,7,11,21]
		Process optimization and reporting	X
Additive manufacturing	3D printing	Customization of products and components as a value-added service		X	[10]
		Spare part production	X		[27]
Big data and data analytics	Big data, data analytics algorithms, AI, ML, data mining	Data collection and reporting		X	[7,14]
		Optimization of warehouse layout or processes		X	[5,7]
		Decision-making		X	[7,15]
		Customer service	X
Simulation	Simulation, digital twins	Warehouse planning and decision-making		X	[5,10,11]
Cloud	Hybrid clouds Edge computing Web and smart phone apps APIs	Enterprise integration		X	[7]
		Data storage, management, and analysis		X	[5,14]
		Real-time monitoring, e.g., of temperature		X	[5]
		Process optimization		X	[5]
		Inventory management		X	[5]
Cybersecurity	Encryption.	Risk management		X	[5]
		Access management	X
		Compliance	X
		Secure by design in software development	X
Blockchain		Transactions, e.g., smart contracts		X	[5]
		Data management in the supply chain			[30]
		Decision-making support			[10,30]
		Inventory management			[10]
		Stock monitoring			[5]

* N: New application, that is, identified in case study but not in the existing literature; C: Confirms literature, that is, found in the literature and in the case study.

. This classification is derived from existing research outlined in the literature and confirmed by empirical findings, which also reveal new application areas and technologies, thus creating a distinction between general I4.0 technologies and those relevant for logistics and warehousing by including practical insights.

6.1. Identified Novel Application Areas

As shown in Table 5, this study identified several I4.0 technology application areas in a 3PL warehouse context that were not described in previous studies. These insights are a result of the practical insights gained from the interviews, which allowed for a deep dive into technological application areas currently in use in warehousing. These concern horizontal and vertical system integration, big data and data analytics, and cybersecurity.

Within horizontal and vertical system integration, area process optimization and reporting were identified. These areas of application describe using technologies to enhance process optimization and reporting by integrating systems, improving efficiency, and providing comprehensive data insights. Although this area is related to enterprise integration, these are two distinct areas. Process optimization and reporting are also closely related to big data and data analytics and cloud, as technologies within horizontal and vertical system integration are dependent on other technologies.

Within big data and data analytics, customer service was identified as a distinct area of application. Specifically, data analytics technologies, such as ML and AI, allow for improved customer service by automating previously manual processes, thus improving the speed and quality of services delivered to the LSP’s clients. Again, while this can be related to the other areas of application that were identified in the literature, the case study showed that this is an area of great interest in warehousing because of the technology’s ability, particularly with ML and AI, to handle diverse tasks due to the large volume of clients.

Lastly, within cybersecurity, three new areas of application were identified: access management, compliance, and secure by design in software development. Access management involves controlling and monitoring the access of machines and users to systems and resources, such as operational technology and office equipment, such as laptops. Compliance describes adhering to company standards, laws, and regulations on cybersecurity to prevent cybersecurity incidents. Secure by design in software development describes the integration of security strategies and requirements into software design and architecture. Both areas are related to risk management but emphasize two distinct aspects of cybersecurity.

6.2. Non-Confirmed Application Areas

Several application areas identified in the literature for augmented reality, additive manufacturing, and blockchain were not observed in the case study. Specifically, quality checks, stock monitoring, driving assistants, and remote maintenance and repairs of warehouse equipment were not confirmed in the case study. While it can be argued that both quality checks and stock monitoring are related to order picking, these application areas were not identified independently. This can be attributed to the case company’s limited use of smart glasses or screen-based augmented reality. Thus, the remaining areas of application could not be confirmed. However, both quality checks and driving assistants were mentioned as potential future applications in terms of quality control with AS/RS and the determination of optimal travel routes in warehouse operations. Remote maintenance and repairs of warehouse equipment were not identified in the case study.

Additive manufacturing for spare part management in the LSP, as described by Bowersox et al. [27], was confirmed in the case study. Although this was not observed, it was perceived as a potential use of the technology. While it was not found to be financially viable for value-added services at present, its feasibility for spare part management needs to be reassessed in the future.

Regarding blockchain, this study did not confirm data management in the supply chain, decision-making support, inventory management, or stock monitoring. However, only one blockchain application was identified, which was limited by adoption challenges.

Finally, based on the discussion above and the definitions provided by Winkelhaus & Grosse [10,16] the following definition of W4.0 in 3PL is proposed:

3PL warehousing 4.0 refers to a highly integrated system that leverages advanced digital technologies and automation to achieve efficient and effective 3PL warehousing operations. It is designed to meet individualized client demands sustainably, without increasing costs, and to adapt to the dynamic challenges of 3PL warehousing.

Compared to the existing definitions [10,16], this definition of W4.0 in 3PL integrates warehousing and 3PL, focusing on the emerging role of technologies in warehousing within L4.0. It also highlights the importance and influence of clients and competition in 3PL—elements that are not prominently featured in the general W4.0 definition [10].

7. Conclusions

7.1. Research Implications

This study makes two main research contributions: (1) the classification of W4.0 technologies in the 3PL industry and (2) insights into the adoption and areas of applications of W4.0 in the 3PL industry.

First, the literature review revealed a need to classify W4.0 technologies and for more empirical studies into L4.0 and LSPs, which is backed up by Perotti et al. [13] and da Silva et al. [14]. The results show that W4.0 technologies could be classified into tenmain technologies, which correspond to the nine technologies described in the I4.0 literature [28]. While there are numerous classifications within I4.0 (e.g., Rüßmann et al. [28], Zheng et al. [3], Tsaramirsis et al. [29], and Ustundag & Cevikcan [2]), it was evident that these describe the same number of technologies, with some being combined within W4.0.

Second, this study shows that the primary technologies in the physical world adopted by the company were automation and robotics, while technologies such as horizontal and vertical system integration, big data and data analytics, cloud, and cybersecurity, which fall into the digital realm, play a critical role in enterprise integration and infrastructure. These results contradict other studies stating that RFID is the primary IoT technology used in warehousing [20] and that blockchain is commonly used by LSPs [30]. It is also debatable whether IoT is one of the most applied technologies in warehousing, as found by Custodio and Machado [11]. This study further shows that technology not included in previous classifications, for example, 3D printing, is also relevant to LSPs. Thus, the proposed classification of W4.0 technologies reflects technologies that are currently relevant in the warehousing industry.

Compared to other studies on I4.0 in warehouse management, this study provides a broader overview of the general technologies that are relevant and applicable within 3PL. This contrasts with the findings of other studies, which either focus on specific warehousing technologies (e.g., Azadeh et al. [8]; Epe et al. [22]), do not specifically address 3PL (e.g., Winkelhaus and Grosse [10]), where the findings highlight the role of clients in adoption, or lack empirical evidence (e.g., Perotti et al. [13]; da Silva et al. [14]).

7.2. Practical Implications

The W4.0 classification provides practitioners with a list of relevant technologies, along with definitions and suggestions for applications and future applications. This study shows that most technologies in the I4.0 and L4.0 literature are relevant to warehousing, but some are less adopted, for example, blockchain and RFID, because of the costs or other challenges associated with their implementation or adoption. The different technologies were validated through interviews and grounded in real-life use cases to ensure that the definition and taxonomy used from the literature were relevant, while also ensuring that technologies not previously associated with L4.0 or W4.0 were represented.

7.3. Limitations and Future Research

The classification developed in this study was based on I4.0, which includes relevant technologies. However, the list of relevant technologies in W4.0 may not be complete, because it was based on the literature and a single case study. Furthermore, many of the identified application areas are interconnected, making it debatable whether they are truly distinct.

Other limitations are connected to the choice of case study. While a single case study allowed for in-depth analysis and thorough interviews with experts on the subject matter, this also limited generalizability to the warehousing industry and might not adequately represent technological maturity levels and technological application areas for smaller LSPs. It can be argued that this study is representative, as it was conducted with a world-leading LSP and with participants with years of experience in the logistics industry and other industries. While certain areas of application were not identified in the case study but in the literature, this does not imply that they lack relevance in the warehousing industry. This discrepancy may be attributed to the maturity level of the case company. Consequently, these areas of application could be addressed through the future applications discussed in the interviews.

Future research should focus on the adoption of W4.0 technologies to understand why the warehousing industry struggles to adopt these technologies. Likewise, future studies could examine the maturity of LSPs and the success factors for adopting W4.0 technologies. To strengthen the classification framework, it would be relevant to investigate other companies to further validate the empirical findings presented in this study. Exploring a broader range of LSPs may reveal new application areas for emerging technologies and highlight technological maturity levels for existing technologies, ensuring a comprehensive and adaptable framework.