The Role of Digitalization in Implementing Green Logistics Principles in Warehousing Operations: A Case Study

Abstract

Warehouses are energy-intensive nodes in a logistics chain and critical hotspots for decarbonization efforts. Digitalization and Industry 4.0 technologies are increasingly promoted as enablers of greener warehousing; however, environmental benefits are often implied rather than empirically quantified. This study examines how digitalization, automation, and robotization support the implementation of green logistics principles in warehousing operations. The research combines a scientific literature review and document content analysis with semi-structured interviews with company managers and logistics professionals. The results indicate that implementing a warehouse management system (Vision Equinox), integrating information systems, and adopting RFID technology reduce paper-based processes, improve picking accuracy and internal routing, shorten loading and unloading times, and may decrease the risk of human error. Consequently, these technologies enable more efficient resource use and can contribute to lower energy consumption and a reduced environmental footprint associated with warehouse activities. The study concludes that digital technologies already serve as a systematic enabler of green logistics within the organization; however, their environmental benefits have not yet been quantified. Future research should therefore focus on measuring changes in energy use and CO₂ emissions under different warehousing scenarios.

1. Introduction

The logistics sector is one of the most important branches of the modern economy; however, it is also a significant source of greenhouse gas emissions and energy consumption. Therefore, implementing green logistics principles has become a necessary condition for achieving sustainable development goals [1,2,3]. Warehousing is one of the key links in the logistics chain connecting the manufacturer and the consumer and includes not only the storage of goods but also in-warehouse transport, picking and packing, material preparation and sorting, as well as warehouse space planning and utilization [4,5]. Studies show that warehouses are energy-intensive facilities whose operations contribute substantially to the carbon footprint of the logistics chain, and poorly managed processes increase not only energy use but also waste generation and CO₂ emissions [6,7].

The scientific literature increasingly emphasizes that automation, digitalization, and robotization form the basis for a transition from traditional, labor-intensive warehousing processes to “smart” warehouses that can be greener in terms of both energy consumption and emissions. Lyu [7] and Marchuk, Harmash, and Ovdiienko [5] demonstrated that automated warehouses can reduce inventory levels, use storage space more efficiently, may decrease the required warehouse footprint, and lower heating and lighting costs and overall environmental impact. Automated systems help reduce the number of errors, improve inventory data accuracy, and minimize unnecessary internal transport, which is directly associated with lower CO₂ emissions [5,8]. Robotic handling systems and smart solutions reduce the risk of human error, increase operational speed, and enable more sustainable use of resources [9,10]. Empirical and modeling studies indicate that automated storage and retrieval systems (AS/RSs), autonomous mobile robots, automated sorting lines, and robotic handling systems can significantly reduce internal travel distances, optimize the use of storage space, and may decrease a warehouse’s carbon footprint [10,11].

Digitalization in warehousing is closely linked to automation and is considered one of the most important instruments for implementing green initiatives. Digital technologies—warehouse management systems (WMSs), Internet of Things sensors, big data analytics, and artificial intelligence—enable real-time monitoring of inventory status, the intensity of material and information flows, and energy consumption [2,12,13]. By eliminating paper-based documentation and implementing a WMS, time, labor, and financial resources are optimized, and the likelihood of errors is reduced [14,15]. Big data analytics and artificial intelligence make it possible to forecast demand, smooth peak hours, optimize routes, and thus reduce fuel consumption and emissions [13,16,17]. RFID technologies, integrated with WMSs and other digital tools, increase the accuracy of product identification, shorten operation times, and help reduce energy consumption through more efficient processes [3,18,19].

Robotization complements automation and digitalization by enabling not only the automation of individual operations but also the development of adaptive, data-driven warehouse systems. Robotic handling and order-picking systems, managed via WMSs and artificial intelligence algorithms, allow more accurate route selection, reduce error rates, prevent unnecessary product movements, and thereby reduce energy consumption and waste generation [13,20,21]. Integration of warehouse management systems and process consolidation allow employees to perform multiple operations within a single route, reducing unnecessary forklift movements by tens of percent, and thus may directly decrease energy use and CO₂ emissions [22]. Reviewing the links between Industry 4.0 technologies and sustainability in logistics and warehousing activities, the authors emphasize that automation, digitalization, and robotization solutions should be regarded as core technologies of green warehouses [2,3,17,23,24].

Despite a growing body of empirical and review studies, most research focuses on technological potential or case analyses of individual solutions, while comprehensive qualitative assessments showing how automation, digitalization, and robotization affect energy consumption and CO₂ emissions across different warehousing scenarios are still lacking [3,10,11,22,23]. This indicates a research gap and supports the scientific and practical relevance of this topic.

The aim of this article is to reveal the role of digital technologies in implementing green logistics principles in warehousing operations and to examine their relationship with resource use efficiency and environmental performance within the context of a case study.

2. Literature Review

The field of sustainable warehousing research remains fragmented, and environmental as well as social aspects are often addressed in the literature in an insufficiently consistent and comprehensive manner [25,26]. Nevertheless, recent years have seen a clear increase in review studies that aim to systematize the accumulated knowledge on green warehousing, identify research gaps in a structured way, and propose evidence-based directions for future research. Whereas early sustainable warehousing studies largely focused on improving operational efficiency—by optimizing lighting [27], reducing energy consumption [28], and improving warehouse material-handling systems [29,30] the discourse has increasingly shifted toward holistic sustainability management approaches that integrate individual “green” measures into coherent systems. In this context, recent research highlights the transformation of “green warehouse” initiatives through the deployment of advanced automation and data-driven solutions [31,32,33], alongside energy-efficient operating practices and the institutionalization of circular economy principles [34]. Empirical evidence further indicates that higher levels of automation and digitalization—particularly the Internet of Things, robotics, and artificial intelligence—can optimize warehouse flows while improving sustainability outcomes [35,36]. More specifically, robotics-enabled order fulfillment can support energy-efficient routing and task allocation through intelligent planning of automated guided vehicle (AGV) fleets, thereby reducing travel distances and idle time and lowering energy use and emissions [37].

Automation is consistently identified as a central enabler for implementing green logistics principles in warehouses. Automated warehousing can reduce inventory levels and costs while improving the efficiency of storage space utilization; consequently, the required facility footprint, energy consumption for heating and lighting, and overall environmental impact may decrease [7]. Automated storage and retrieval systems (AS/RSs) are also increasingly framed as a “green technology” because, when properly managed, they can increase throughput while reducing the number of required cranes, energy consumption, and total operating costs [25]. At a broader system level, systematic literature reviews report that the adoption of automation and Industry 4.0 technologies in warehouses—including robotic handling systems, autonomous vehicles, the Internet of Things, and advanced sensors—is directly associated with improvements in the economic, environmental, and social indicators of warehousing sustainability [3]. Beyond efficiency gains, automated systems can reduce errors, improve inventory data accuracy, and may decrease unnecessary in-warehouse transport, which is directly linked to lower CO₂ emissions [5,8]. Robotic handling systems and other smart solutions further reduce the risk of human error, accelerate operations, and enable more sustainable resource use [9].

Digitalization in warehousing is closely intertwined with automation and is widely regarded as one of the most important instruments for implementing green initiatives. Digital technologies—from warehouse management systems (WMSs) to big data analytics—enable real-time monitoring of inventory status, improved operational planning, and a reduction in paper-based processes [12,14]. By eliminating paper documentation and implementing WMSs, organizations can optimize time, labor, and financial resources while reducing the likelihood of errors [14,15]. At the same time, big data analytics, artificial intelligence, the Internet of Things, and blockchain technologies drive the digital transformation of logistics and strengthen the effectiveness of green logistics more broadly. Evidence from systematic literature reviews confirms that digital Industry 4.0 technologies in logistics and warehousing are among the key factors that simultaneously enhance operational efficiency and reduce negative environmental impacts [2,3,23,24]. In particular, big data analytics supports demand forecasting, peak smoothing, and route optimization, thereby reducing fuel consumption and emissions [9,16].

Complementing these findings, studies also show that integrated applications of big data analytics, artificial intelligence, and the Internet of Things can improve operational efficiency while reducing energy consumption and carbon dioxide emissions [38,39]. Related research identifies digital solutions for route optimization, intelligent planning, and real-time monitoring as effective means of reducing unnecessary energy use in logistics processes [40]. Furthermore, advanced supply chain process integration and improved energy management enabled by digitalization can increase the efficiency of resource allocation and strengthen firms’ capacity to systematically reduce emissions [41,42]. Accordingly, these studies provide both theoretical grounding and practical guidance for achieving more sustainable logistics and warehousing through digital technology integration.

Within the technology portfolio supporting sustainable warehousing, RFID is frequently highlighted for its operational and environmental benefits. RFID increases the accuracy of product identification, shortens operation times, and reduces energy consumption through more efficient processes [18,19]. Empirical research additionally indicates that when RFID is integrated with WMSs and other Industry 4.0 technologies, it enables real-time tracking of inventory and material flows, reduces stockouts and overstocking, and creates opportunities to optimize warehouse layout and vehicle movements [3,23]. In line with these findings, recent reviews on the relationship between WMSs and sustainability emphasize that WMSs are among the core technologies capable of decarbonizing warehouse operations. For instance, by optimizing internal transport, routes, and the sequence of operations, unnecessary forklift movements can be reduced by several tens of percent, directly decreasing energy use and CO₂ emissions [22]. Finally, analyses of Industry 4.0 integration into warehouse management further note that an advanced WMS—when integrated with automated equipment and the Internet of Things—is a key element in pursuing the United Nations Sustainable Development Goals in warehousing activities [23]. Collectively, these technologies create enabling conditions not only for more efficient warehouse operations but also for the consistent implementation of green logistics principles.

3. Research Methodology

Three methods were applied in the study: scientific literature analysis, document content analysis, and a semi-structured interview.

3.1. Scientific Literature Analysis

The scientific literature analysis was conducted based on the most recent reviews of green and sustainable logistics, which emphasize the importance of systematic literature reviews in identifying research directions, gaps, and methodological approaches in green logistics research [26]. This enabled a theoretical substantiation of the application of green logistics principles in warehousing operations and of the role of technological solutions (automation, digitalization, and robotization) in reducing the environmental impact of logistics activities [27]. This study employed a semi-systematic scientific literature analysis to build the theoretical foundation for examining how digitalization supports the implementation of green logistics principles in warehousing operations. A semi-systematic review approach was selected because the evidence base on “green warehousing” and Industry 4.0-enabled sustainability is interdisciplinary and dispersed across logistics, operations management, information systems, and sustainability research. Thus, the approach combines systematic elements (predefined search strings, transparent screening, and documented inclusion/exclusion criteria) with sufficient flexibility to capture relevant sources that may not be consistently indexed under a single discipline or keyword set.

Search strategy. Searches were conducted in major academic databases (e.g., Scopus and Web of Science) and complemented by targeted searches in relevant journals and publishers’ platforms. Keyword strings combined (i) warehousing and intra-logistics terms with (ii) green logistics and sustainability terms and (iii) digitalization/Industry 4.0 terms, using explicit Boolean logic and parentheses. An example of the applied logic was: (“warehouse *” OR “warehousing” OR “in-warehouse” OR “intralogistics” OR “warehouse operations”) AND (“green logistics” OR “sustainab *” OR “decarbon *” OR “carbon footprint” OR “environmental performance”) AND (“digital *” OR “Industry 4.0” OR “IoT” OR “WMS” OR “warehouse management system” OR “RFID” OR “AI” OR “robot *” OR “automation” OR “AS/RS” OR “AGV” OR “AMR”). In addition to database queries, backward and forward citation tracking was applied to identify influential and frequently cited works and to reduce the risk of omitting relevant studies due to terminology differences.

Screening and selection. Records were screened in a staged process: (1) duplicate removal; (2) title–abstract screening; (3) full-text eligibility assessment. Screening decisions followed predefined criteria to ensure transparency and replicability. The final evidence base was used to synthesize (a) which digital technologies are most frequently linked to sustainability outcomes in warehouses and (b) through which mechanisms they support green logistics principles (e.g., internal transport optimization, reduced paper-based processes, improved inventory accuracy, and reduced unnecessary movements). The database search returned 760 records. After removing 180 duplicates, 580 records were screened by title and abstract; 470 were excluded at this stage. Full texts of 110 articles were assessed for eligibility; 70 were excluded based on the predefined criteria. Finally, 58 studies were retained for thematic synthesis.

Inclusion and exclusion criteria. Studies were included if they met all of the following conditions:

Topical relevance: The publication addressed warehousing/intra-logistics operations and explicitly linked digitalization (e.g., WMS, RFID, IoT, big data, AI, automation/robotics, AS/RSs, AGVs/AMRs) to sustainability/green logistics outcomes (e.g., energy use, emissions, resource efficiency, waste reduction, process optimization, circular practices).

Publication quality: Peer-reviewed journal articles were prioritized; high-quality review papers were included to map research directions and methodological approaches.

Timeframe and language: The review emphasized recent literature (primarily the last 10–15 years to align with the rapid development of Industry 4.0 technologies), while allowing the inclusion of seminal earlier works where they are foundational and frequently cited.

Studies were excluded if they focused on sustainability without warehousing relevance, discussed digitalization without a sustainability/green logistics link, or lacked sufficient methodological and empirical grounding (e.g., purely opinion-based texts).

Synthesis approach. The studies included were analyzed thematically. The synthesis focused on (i) technology groups (WMSs and integrated systems; RFID and identification/traceability; IoT/sensors; data analytics/AI; robotics/automation such as AS/RSs and AGVs/AMRs), (ii) green logistics principles operationalized in warehouses (energy efficiency, emission reduction, waste minimization, resource productivity, process optimization), and (iii) reported mechanisms and outcomes (e.g., reduction in internal transport distances, fewer handling errors, reduced paper documentation, improved space utilization). The results of the literature analysis informed the design of the empirical part of the study (document content analysis and semi-structured interviews) and supported the interpretation of how digital solutions operate as a systemic enabler of green logistics in the investigated company.

3.2. Document Content Analysis

Document content analysis was used to assess, in practice, the application of green logistics principles in the logistics company under study by systematically analyzing internal corporate documents (CMR consignment notes, invoices, packing lists, customs declarations, and certificates of origin) and data from internal information systems (“Vision”, “SAP Logon”, “Logifly”). Such document analysis aligns with qualitative research recommendations, where real organizational documents are used as a reliable secondary data source to evaluate process execution, operational organization, and the implementation of sustainability practices [27,28]. Document analysis was selected due to the opportunity to obtain authentic information generated naturally in the course of operations, thereby reducing the bias associated with surveys and self-assessment [28].

The analyzed material comprised (i) operational and compliance documents created during day-to-day logistics and warehousing activities and (ii) structured records retrieved from internal information systems. Corporate documents were selected because they capture transaction-level evidence of processes, responsibilities, and control points (e.g., shipment characteristics, handling and packaging requirements, routing information, and formal compliance procedures). System outputs were included to complement paper/electronic documents with time-stamped process records (e.g., inventory movements, picking/packing events, order status changes, and exception logs), enabling cross-validation between sources.

Document selection followed a purposive logic to ensure relevance to warehousing operations and green logistics practices. Only documents directly linked to inbound, storage, internal movement, order fulfillment, and outbound processes were included. Documents unrelated to warehousing execution (e.g., purely financial summaries without operational identifiers) were excluded. Where multiple versions of the same record existed (e.g., corrected invoices or updated declarations), the final validated version was retained for analysis.

The primary unit of analysis was a single operational case (e.g., an inbound shipment, outbound consignment, or order fulfillment cycle) reconstructed through the document set and corresponding system records. For each case, the analysis focused on how green logistics principles are reflected in actual operational decisions and documented practices. Specifically, the document content was examined for related evidence.

In total, 18 operational cases were reconstructed and examined. The document corpus comprised approximately 120 internal and external documents (e.g., orders, invoices, packing lists, delivery/transport documentation, and relevant internal procedures) and six system extracts/reports from the Vision Equinox WMS and related information systems that supported process tracing across inbound, storage, picking, and outbound activities.

Resource efficiency and waste prevention (e.g., packaging type and quantity, consolidation indications, reuse or recycling-related notes, documentation practices affecting paper usage).

Energy- and emission-relevant process choices (e.g., handling requirements that influence internal transport intensity, consolidation patterns, and avoidable movements).

Process discipline and compliance supporting sustainability (e.g., completeness of documentation, standardization, traceability, and error/exception handling that may prevent rework and additional transport).

Digitalization-enabled process control (e.g., use of system-generated identifiers, scanning/traceability markers, and time-stamped confirmations supporting real-time monitoring).

This structure allowed the document analysis to link operational evidence to sustainability-relevant mechanisms rather than treating documents as descriptive artifacts only.

Data were handled in line with organizational requirements: sensitive information (e.g., client identifiers, commercial terms) was anonymized in the extraction matrix, and only aggregated insights are reported to prevent identification of specific shipments or customers.

3.3. A Semi-Structured Interview

Each interview lasted 40–75 min (average 55 min), resulting in approximately 7 h and 20 min of recorded interview material. Interviews were audio-recorded with consent, transcribed, and analyzed using thematic coding in two rounds (initial open coding followed by category refinement and theme consolidation). A codebook was maintained and iteratively updated. To enhance trustworthiness, inter-researcher validation was applied: a second researcher independently reviewed 25% of the transcripts and the corresponding codes; discrepancies were discussed until consensus was reached.

Semi-structured interviews were used as the primary qualitative method to obtain in-depth, context-specific insights into the application of green logistics principles in warehousing operations and the practical implementation of sustainability-oriented technological solutions, namely digitalization and automation. This method was selected because it enables the collection of detailed explanations of organizational processes and decision-making while ensuring a consistent thematic structure across participants. The semi-structured format also allows the interviewer to adapt the sequence of questions and use probing questions as needed, thereby enhancing the depth and relevance of the data.

The interview guide was developed based on the study’s theoretical framework and organized into thematic blocks reflecting the key analytical dimensions of the empirical section. It was designed to address: (i) the organization’s perspective and priorities regarding green logistics in warehousing; (ii) the practical implementation of green logistics principles in core warehousing processes; (iii) the role of technological solutions (digital systems, data use, and automation) in enabling “greener” practices.

Participants were selected through purposive sampling, targeting employees whose roles provide direct knowledge of warehousing operations, technology use, and sustainability-related decision-making, including the management of warehousing processes through digitalization. The study involved informants representing different organizational levels and perspectives: the company director, the warehouse manager, and logistics specialists/managers. This participant configuration was considered appropriate for a case-study design, as it captures strategic, tactical, and operational viewpoints and supports a comprehensive understanding of how green logistics principles are interpreted and implemented within the company.

Interviews were conducted using the interview guide, while maintaining flexibility to adjust the order of questions and ask follow-up questions when necessary. To ensure accurate capture of responses, notes and/or audio recordings were used (with participant consent), and the data were transcribed for subsequent analysis. To protect confidentiality, potentially identifying information (e.g., customer names, shipment identifiers, and commercially sensitive details) was removed or anonymized during transcription and reporting. Interview data were stored securely and used exclusively for research purposes.

Interview transcripts were analyzed using thematic coding, combining predefined categories derived from the theoretical section (e.g., green logistics practices in warehousing; digitalization-enabled monitoring and control; process optimization and the reduction in unnecessary movements) with inductively generated themes reflecting company-specific practices and constraints. To strengthen trustworthiness, interview findings were compared with evidence from document content analysis and internal information system records, following the principle of methodological triangulation. This cross-source comparison enabled the identification of convergent patterns and potential inconsistencies (e.g., differences between documented procedures and practices described by informants), thereby increasing the robustness of the study’s conclusions.

4. Results and Interpretation

This section presents empirical findings derived from interviews and document analysis, organized across themes related to applied robotic solutions, information system integration, and the role of digital technologies in supporting green logistics practices in warehousing.

To determine which robotic solutions are currently applied in warehousing operations, what development directions are envisaged, and which factors limit a more active deployment of robotization, study participants were asked: “Have robotic solutions been implemented in warehousing operations to support the implementation of green logistics principles? If not, what are the reasons, and are such solutions planned for implementation in the future?” The participants’ responses were systematized and presented in

Table 1. Robotic solutions in warehousing operations.

Category	Subcategory	Supporting Statements
Robotic solutions in warehousing	Applicable solutions	D. 1 “There are certain robotic solutions <..> that help to stack one pallet on top of another <..>” D. 3 “<..> we have a production line where goods come straight from packaging without forklifts <..> human intervention is no longer necessary <..>”
	Robotic and automated solutions in the future	D. 2 “<..> we are analyzing robotic solutions.” D. 3 “<..> specifically, our company has initiated proposals for robotization to the SBA group.” D. 3 “<..> we plan to install rollgangs (roller conveyors) <..>”
	Financial challenges	D. 1 “<..> the main obstacle is not technological <..> but profitability, because we are still not satisfied with it.” D. 2 “<..> it is an expensive solution and the payback period is quite long.” D. 3 “<..> due to high costs <..> we will probably have to pause and have already paused <..>”

, which summarizes the robotic solutions currently in use, identifies the main barriers to their implementation, and outlines the planned directions for further robotization in the company.

Based on the study results presented in Table 1, the company can be assessed as having taken initial yet meaningful steps towards implementing robotic solutions in warehousing operations. The existing technological solutions create opportunities to automate part of the loading and internal logistics processes, thereby reducing manual work and the likelihood of human error. The integration of production and warehousing processes, where part of the output from production units reaches the warehouse without direct human intervention, contributes to more consistent management of product flows, shorter movement chains, and fewer unnecessary operations—consistent with the green logistics objective of optimizing material flows and resource use.

The data indicate that the company associates robotization not only with productivity gains but also with environmental objectives. Planned solutions, such as installing roller conveyors in high-traffic zones, are aimed at shorter internal transport distances, less forklift operating time, and lower energy consumption, thereby indirectly reducing the negative environmental impact associated with warehousing operations. The development of robotic and automated solutions can be viewed as a measure to increase the energy efficiency of logistics processes and, in the long term, to support the implementation of green logistics principles.

At the same time, clear factors limiting robotization were identified. The main barriers relate to high upfront investments in hardware and software, integration solutions, and employee competency development, as well as a long payback period. As a result, the deployment of robotic technologies is assessed through the lens of economic rationality, seeking a balance between sustainability objectives and financial capacity. This situation shows that implementing green logistics principles through robotization depends not only on technological factors but also on the company’s economic and strategic decisions.

With rapid advances in digital technologies, the logistics sector is increasingly adopting advanced solutions, particularly the application of artificial intelligence (AI). AI systems enable automated analysis of large data streams, optimization of storage location assignment, forecasting of inventory needs, and real-time decision-making. Such technologies are especially relevant in the context of green logistics because they allow more efficient planning of material flows, reduction in unnecessary transport, optimization of energy and other resource use, and systematic reduction in emissions associated with warehousing operations.

To assess the potential of AI implementation in warehousing operations and its links to the implementation of green logistics principles, the semi-structured interview included the following question: “How do you assess the potential of implementing artificial intelligence in warehousing operations when implementing green logistics principles, and would the use of this technology be justified in pursuing sustainability goals?”. The responses made it possible to identify how organizational representatives perceive AI’s contribution to sustainable warehouse process management, what benefits and risks they associate with these technologies, and what preconditions they see for their practical application. The informants’ insights were systematized and are presented in

Table 2. Potential for implementing artificial intelligence.

Category	Subcategory	Supporting Statements
Potential for implementing artificial intelligence	Use of artificial intelligence	D. 2 “We already apply some artificial intelligence tools in daily work <..> for meeting transcripts, as well as for analysis, data presentation, in Teams meetings, and for Outlook email management.”
	Assessment of artificial intelligence	D. 1 “<..> a human can evaluate only a limited part of the information, whereas artificial intelligence, today, can handle astronomical amounts <..>” D. 1 “<..> I think this is the future.”
	Possible applications of artificial intelligence	D. 2 “<..> it exists in IT systems such as <..> electronic document exchange systems, order systems, yard management systems <..>” D. 3 “<..> tools that speed up the issuing of certain invoices <..> digitalized accounting tools.”

.

Based on the study data presented in Table 2, it can be stated that the organization has already begun a systematic integration of artificial intelligence (AI) into logistics processes. At present, AI tools are used for data analysis, meeting transcription, and email management; however, their potential is seen in a broader context—from electronic document exchange and order management to yard management systems and the automation of accounting processes. AI technologies can process far larger data volumes than humans, identify patterns, and generate decision alternatives in real time, thereby creating opportunities to optimize warehousing operations and logistics flows.

From a green logistics perspective, AI integration can be considered a significant instrument for achieving more sustainable warehouse management. Automated data processing and smarter decision-making enable more accurate planning of resource use, reduction in unnecessary operations and internal movements, and optimization of energy and material consumption. The use of AI increases operational transparency and data-driven management and reduces reliance on repetitive manual processes, which in turn improves efficiency and contributes to reducing the ecological footprint—one of the core objectives of implementing green logistics principles.

In today’s logistics environment, companies increasingly seek to optimize operational processes using digital technologies. With rising customer expectations, expanding product assortments, and tightening environmental requirements, effective supply chain management becomes particularly important, with a strong focus on warehousing operations. To increase efficiency, accuracy, speed, and flexibility while implementing green logistics principles, the company deploys and uses digitalized management systems in its warehouse. Digital solutions enable real-time monitoring of inventory levels, analysis of performance indicators, and data-driven decision-making. This not only accelerates day-to-day warehousing operations but also increases their transparency, allows faster responses to market changes, and helps meet environmental requirements. In addition, digital systems facilitate real-time data analysis and decision-making, contributing to more efficient resource use and a lower environmental impact. For managing warehousing and logistics processes, the company uses four main information systems: “Vision Equinox”, “SAP Logon”, “Logifly”, and “MaxLoad”, which are used in an integrated manner to manage operational, information, and planning processes.

One of the key tools enabling green warehousing practices is the advanced warehouse management system “Vision Equinox”. This system covers all core warehousing activities—from receiving and storage to order picking and dispatch—and is oriented not only towards process optimization, cost reduction, and increased labor productivity but also towards more efficient resource use and reduced negative environmental impact. A key advantage of “Vision Equinox” is comprehensive warehouse process management and flexible integration with other enterprise management systems, enabling consistent synchronization of data flows and operations, reducing duplication of information flows, and decreasing the need for paper documents.

The system structures the picking logic in a way that minimizes the distance traveled by forklifts and other internal transport vehicles; therefore, electricity consumption and related CO₂ emissions may decrease. In this way, “Vision Equinox” acts as one of the technologies enabling green logistics by allowing more rational planning of internal flows, route optimization, improved inventory management, and reduced material consumption (e.g., paper). To assess this system’s contribution to implementing green logistics principles in more detail, the semi-structured interview asked how the warehouse management system contributes to reducing energy use, optimizing routes, improving inventory management, reducing paper use, limiting the costs of product movement, and monitoring environmental indicators. The responses were systematized and are presented in

Table 3. Warehouse management system.

Category	Subcategory	Supporting Statements
Warehouse management system	Reducing paper use	D. 1 “<..> no need for paper sheets <..>” D. 3 “<..> paper use in the company is generally decreasing more and more <..>”
	Route optimization	D. 1 “<..> selects the most optimal routes <..> so that there are no unnecessary drives <..>” D. 2 “<..> the forklift driver no longer needs to wander around the whole warehouse <..> the number of kilometers driven is reduced.” D. 3 “<..> selects the path properly <..> we can load more goods in less time.”
	Assessing handling efficiency	D. 1 “<..> helps calculate <..> handling efficiency, how efficiently we can work <..>”
	Inventory management efficiency	D. 2 “<..> has a specific location you must go to—in one case to place and store a pallet, in another case to take a pallet and load it for the customer.”
	Monitoring environmental indicators	D. 1 “<..> it does not help monitor environmental indicators” D. 3 “<..> it contributes to monitoring environmental indicators.”

.

Interview data indicate that the warehouse management system in use contributes significantly to implementing green logistics principles. A large share of processes is carried out digitally, which reduces paper use and resource consumption associated with document processing. The system automatically selects the optimal picking and movement route; as a result, unnecessary driving is reduced, employee movement becomes less chaotic, and the productivity of warehousing operations increases. In addition, the warehouse management system enables precise control of inventory movements—specific locations are assigned for picking and put-away, handling efficiency can be assessed more easily, and the likelihood of human error is reduced. This increases process transparency, allows better monitoring of warehousing performance indicators, and supports the development of more sustainable, resource-based logistics practices. The implementation of the warehouse management system in the company can be considered a significant step towards greater process efficiency, digitalization, and sustainability.

In warehousing operations, RFID technology also plays an important role in managing product movement and inventory accounting and in increasing operational efficiency. RFID tags can be read without direct contact or line of sight, which makes it possible to automate part of the operations, reduce manual work, speed up processes, and increase data accuracy. To analyze the potential of RFID technology in the context of green logistics, informants were asked how they assess the use of RFID in warehousing operations and to what extent, in their opinion, this technology contributes to implementing green logistics principles. The responses were systematized and are presented in

Table 4. Use of RFID technology in warehousing operations and its contribution to implementing green logistics principles.

Category	Subcategory	Supporting Statements
Use of RFID technology in warehousing operations	Time savings in warehousing operations	D. 1 “This allows saving time without getting out of forklifts, without scanning each pallet, but simply passing through the gates and bringing the products in <..>” D. 2 “<..> speeds up both loading and unloading processes.” D. 3 “<..> shortens pallet loading and unloading time.” D. 3 “<..> saves a lot of time; expensive technology, but good.”
	Reducing manual work	D. 2 “<..> no longer need to <..> stand and physically count pallets; no need to scan each pallet with a scanner <..> in terms of work efficiency.” “With the scanner, we beep-scan once, whereas otherwise it would be necessary <..> three times <..>”
	Assessment of RFID technology	D. 3 “I evaluate RFID very positively <..>”
Implementation of green logistics principles	Reducing energy consumption	D. 1 “<..> in terms of energy consumption, I truly think it would contribute something to green logistics.”

.

An analysis of the informants’ responses suggests that the application of RFID technology significantly increases the efficiency of warehousing processes. This technology speeds up both loading and unloading operations by eliminating the need to scan each pallet separately. The amount of manual work decreases—employees no longer need to physically count pallets or use a scanner for each unit—thus simplifying the day-to-day logistics chain and reducing the likelihood of human error. Although the initial investment in RFID technology is higher, informants evaluate it positively due to its long-term efficiency and reliability. It is also noted that RFID can contribute to lower energy consumption (due to shorter operation times and more optimal handling organization); therefore, this technology can be regarded as suitable in the context of green logistics. In summary, RFID technology in the company is viewed as an advanced tool that helps pursue both operational efficiency and sustainability objectives simultaneously.

5. Discussion

This study strengthens the sustainable warehousing evidence base by showing how concrete digital solutions such as WMSs, RFID, and the early use of AI tools enable the practical implementation of green logistics principles in a real warehousing context [25]. This emphasis is warranted because warehouses are a material sustainability hotspot. Warehouses account for about 11% of emissions related to logistics activities [43], indicating that operational improvements in warehouses can meaningfully support decarbonization agendas. This argument is consistent with the conceptual framework proposed in [44,45,46], which emphasizes that energy efficiency and environmental impact reduction in warehouses require systematic integration of technological, operational, and managerial levers rather than isolated technical improvements.

First, the Vision Equinox warehouse management system contributes to greener operations through process dematerialization and movement optimization. In the investigated company, the system reduced paper-based documentation and supported automated selection of picking and movement trajectories. Interviewees linked these functions to shorter internal travel and fewer redundant forklift trips. From a sustainability perspective, this provides a direct pathway to lower energy demand and associated emissions. The case also shows that these benefits are not yet captured through systematic environmental KPI monitoring. This finding reflects the gap identified in [46], where energy saving initiatives are often implemented operationally but remain weakly connected to structured environmental performance measurement systems.

Second, RFID implementation illustrates how digital identification can simultaneously increase process accuracy and reduce resource waste. Participants reported shorter loading and unloading cycles, reduced manual handling, and fewer errors because pallets are registered without contact and without scanning each unit separately. In green logistics terms, fewer delays and mistakes reduce rework and may lower energy use associated with idle time and unnecessary movements. The actual magnitude of energy and emission reductions has not yet been quantified in the organization [16,18]. From a performance evaluation perspective, this limitation resonates with [47], which argues that sustainability initiatives in warehouses must be assessed through clearly defined and measurable performance indicators integrating environmental, operational, and economic dimensions. Without such structured assessment, digital solutions risk being recognized only for efficiency gains while their environmental contribution remains implicit.

Third, AI currently contributes to sustainability mainly indirectly. The company uses AI tools primarily for information management, management of email and document flows. AI potential to optimize physical warehousing processes has not yet been integrated into operational systems [2,17,20]. This indicates an opportunity to move from indirect benefits such as time savings and fewer coordination errors to direct environmental impacts by connecting AI to WMSs and related systems for forecasting, inventory planning, and resource aware decision support. In line with [46], advanced digital technologies can support energy efficiency when embedded into broader system level optimization, for example by improving forecasting accuracy and aligning inventory decisions with resource consumption patterns.

Fourth, the findings suggest that digitalization does not automatically translate into sustainability performance without organizational enablers. Prior research shows that sustainable logistics performance is strengthened by a green organizational culture and that enabling capabilities such as a big data driven supply chain help explain this relationship [44]. Consistent with our results, respondents recognized sustainability co benefits of digital tools. Environmental indicators were not systematically measured or linked to digital functionalities, which implies a need for governance routines that institutionalize measurement, learning, and accountability for green outcomes. This observation aligns with [47], which highlights that sustainability performance in warehouses improves when initiatives are supported by formal evaluation frameworks, cross functional coordination, and clearly assigned managerial responsibility.

Fifth, the case reveals a typical constraint in the form of high upfront investments and long payback periods that slow down robotization and advanced digitalization. Decision making remains predominantly economically driven, while environmental benefits are harder to monetize in the absence of systematic measurement. This finding also resonates with sustainability decision making research that relies on structured multi criteria evaluation. Previous studies emphasize the role of a decision-making matrix and a more realistic treatment of criteria when evaluating sustainable warehouse options, which in practice requires reliable operational and environmental data as inputs [45]. In addition, ref. [46] underline that energy efficiency investments in warehouses often face justification barriers unless environmental benefits are translated into measurable cost and performance indicators, reinforcing the importance of integrating sustainability metrics into capital budgeting processes.

Overall, the study confirms that WMSs and RFID already enable greener warehousing through reduced paper use, shorter process duration, route optimization, and fewer unnecessary internal movements. AI currently supports sustainability indirectly and holds clear potential for deeper operational integration [3,12,22]. The results also expose a critical evidence gap. Environmental outcomes are not yet monitored systematically, which limits the ability to quantify energy and emission impacts. Consistent with the frameworks proposed in [46,47], two research directions follow. The first is quantitative assessment linking specific digital interventions to changes in energy use and CO₂ emissions across operating scenarios using structured sustainability performance indicators. The second is analysis of how digital solutions can be embedded into organizational sustainability management systems so that green benefits are demonstrated with empirical evidence rather than assumed.

6. Conclusions

This study demonstrates that digital solutions create tangible preconditions for more sustainable warehousing operations. However, their environmental impact depends on the level of technological integration and the presence of systematic performance measurement. The empirical findings indicate that WMS and RFID technologies contribute to process dematerialization, movement optimization, and error reduction, which are associated with shorter process times, fewer unnecessary internal movements, and potentially lower energy consumption.

The current use of AI is largely limited to information management functions and has not yet been integrated into core warehousing operations. This reveals significant development potential in areas such as demand forecasting, inventory planning, and resource-aware decision support, where AI could generate direct environmental benefits through enhanced operational efficiency.

The study further shows that environmental outcomes are not systematically monitored within the organization. Consequently, the sustainability benefits of digital technologies remain primarily process-based and perception-driven rather than quantitatively verified. This limitation reduces the ability to accurately assess energy savings and CO₂ emission reductions and constrains the integration of environmental criteria into investment decision-making processes.

Overall, the findings suggest that digitalization functions as an enabler of sustainable warehousing only when supported by clearly defined environmental indicators, structured data governance, and embedded management practices. In the absence of these elements, digital initiatives tend to be evaluated mainly from an economic efficiency perspective, leaving their environmental potential underexploited.

Future research should focus on quantitatively linking specific digital interventions to measurable changes in energy consumption and emissions and on examining how digital technologies can be systematically embedded into organizational sustainability management frameworks to ensure evidence-based environmental performance improvements.