Digital Yards, Tangible Gains: Evidence of Change in Third-Party Logistics Yard Performance

Abstract

This study investigated the impact of a Yard Management System (YMS) implemented at a third-party logistics distribution center in the United States. Five years of operational data (2018–2022), including 72 monthly observations of inbound and outbound freight performance (measured in pounds) and detention occurrences (measured in US dollars), were analyzed using one-way ANOVA to assess pre- and post-implementation performance. The results indicated that the YMS significantly improved inbound and outbound freight volume, reduced detention occurrences, and enhanced operational efficiency within the third-party logistics distribution center. These findings suggest that YMS can be an effective tool for enhancing yard-level operational efficiency, reducing delays, and supporting broader supply chain optimization strategies in third-party logistics environments.

1. Introduction

Logistics plays a central role in supply chain management, encompassing the movement of materials between a point of origin (e.g., suppliers) and a point of consumption (e.g., customers, field warehouses, and factory storages), as well as all activities related to the corresponding information flow between these points [1]. Core logistics management activities usually include “inbound and outbound transportation management, fleet management, warehousing, materials handling, order fulfillment, logistics network design, inventory management, supply/demand planning, and management of third-party logistics services providers” (para. 6, [2]). Over time, the functions have expanded to include sourcing and purchasing, production planning and scheduling, packaging, assembling, and customer service, reflecting the increasing complexity of the global supply chain [2].

To support the processes, information systems have been progressively adopted since the 1970s. Early warehouse and inventory management software systems improved efficiency by monitoring bin locations and optimizing inventory flows [3]. In the 1990s, studies such as Lieb [4] emphasized the importance of integrating information systems and managing partnerships with third-party logistics suppliers. As globalization accelerated in the 1990s, the increasing complexity of logistics led to rapid growth in Transportation Management Systems (TMS), which supported procurement, multimodal transport planning, delivery optimization, and monitoring of transport processes [5]. Upon entering the 21st century, outsourcing became a common practice, further increasing reliance on third-party contractors and reinforcing the importance of integrated information systems such as Warehouse Management Systems (WMS) and Transportation Management Systems (TMS) [6,7,8].

Despite advances in warehouse and transportation management, some of the most persistent inefficiencies in supply chains occur not due to inefficient loading or transiting but inefficient processes in manufacturing plants and distribution centers. Delays at the sites are often caused by manual yard processes, limited visibility into dock-door availability, and insufficient coordination of yard resources [9,10]. Storms et al. [11] highlighted that reliance on pen-and-paper methods for scheduling and yard work continues to create bottlenecks. Delays caused by the lack of yard visibility can result in higher time demurrage and detention charges, late delivery charges, additional equipment leases, and production shutdowns that cost companies millions of dollars [11].

Yard Management Systems (YMS) have been developed to address the gaps by digitizing and optimizing yard operations, enhancing visibility, and coordinating inbound and outbound flows more effectively. While existing research has extensively explored warehouse and transportation systems, fewer studies have systematically evaluated the effectiveness of YMS in road transportation in reducing delays and improving logistics efficiency. To address the gap, the current study evaluates the impact of the YMS that was designed and implemented in 2018 based on the operational needs of three third-party logistics centers on inbound and outbound freight volumes measured in pounds and detention occurrences, and answers three research questions:

RQ1: To what extent is there a difference in the annual number of inbound products by pounds from 2018 to 2022 at the Western, Midwestern, and Eastern logistics service providers overall and for each site?

RQ2: To what extent is there a difference in the annual number of outbound products by pounds from 2018 to 2022 at the Western, Midwestern, and Eastern logistics service providers overall and for each site?

RQ3: To what extent is there a difference in the annual detention occurrence from 2018 to 2022 at the Western, Midwestern, and Eastern logistics service providers overall and for each site?

Unlike most previous studies, which either focus on port or railway yard management or propose conceptual models, the current study analyzes five years of data across three logistics centers for road transportation to demonstrate the potential of YMS to improve efficiency, visibility, and detention. The contributions extend existing knowledge of logistics information systems and inform practitioners seeking to modernize their yard operations.

2. Literature Review

Since the 1970s, warehouse and inventory management software systems have been used to manage the number and locations of bins and their relationships [3]. The earliest WMS were primarily designed to track inventory. However, they soon demonstrated significant cost savings, leading to the addition of new functions such as monitoring system status, coordinating operations, and implementing optimization strategies [5]. In recent years, with the advancement of information technology, numerous solutions have been integrated into the WMS to enhance performance. For instance, Pane et al. [12] approved that implementing Radio Frequency Identification (RFID) in WMS could improve the goods selection process of logistics corporations in Indonesia. Similarly, Deng et al. [13] identified that an automated three-dimensional warehouse scheme can enhance the efficiency and accuracy of the online production process while providing digital management to improve the competitiveness of enterprises. Fauzan et al. [14] suggested that a WMS with Zaeni Convection would be beneficial in optimizing the process of managing goods, automating calculations of raw materials, regulating the entry and exit of goods, generating reports on inventory and expenditure, and providing computerized data storage. These studies indicate that WMS has the potential to improve warehouse-level efficiency through technological integration. However, while WMS has become central to warehouse management, its scope is confined mainly to warehouse management and does not fully address the complexities of broader logistics networks.

The increasing complexity of logistics chains in the late 1990s led to the emergence of the TMS, a specialized tool developed to coordinate transportation functions across fragmented supply networks. TMS solutions enabled organizations to plan and optimize procurement and distribution, design multimodal transport chains, optimize delivery transport, and control and monitor the resulting transport process [5]. The essential functional areas of these systems are order management, scheduling, transportation planning and optimization, tracking and tracing, and fleet and resource management [5]. Despite the increasing demand for TMS and transportation solutions in recent years, the number of companies offering TMS remains limited. Several Enterprise Resource Planning vendors are now offering TMS products to meet the growing demand for comprehensive transportation management solutions. However, the expansion has led to a significant increase in system costs and failed to address inefficiencies occurring at the yard-level operations [5].

The yard has historically received less attention compared to warehouse and transportation management, but it represents a critical bottleneck in supply chains, especially for road transportation. The core of the yard management system is to handle scheduling of inbound and outbound freight reservations while effectively managing yard resources and enhancing the efficiency and throughput related to the shipper’s distribution yard and ports [15]. Compared to other processes involved in supply chain management, interest in yard management has been limited, both in practice and research, despite its central role in ensuring the smooth flow of products [16]. Most current studies on yard management mainly focus on port [17,18,19] and train [20] yard management, where scheduling and resource allocation are complex and have been heavily studied. However, fewer studies have concentrated on distribution yards for road distribution, where issues such as trailer parking, congestion, and load management remain prevalent. To address the challenges, some researchers have examined the use of technologies, such as RFID and electronic seals, to improve the efficiency of yard management and visibility [20,21,22,23]. While these approaches demonstrate that technological innovation can mitigate certain yard-level inefficiencies, they fall short of providing comprehensive management solutions.

An outbound logistics management survey conducted by Eid [24] at PepsiCo’s production plant highlighted long wait times for trailers in parking spaces, human errors in picking up standing trailers, large volumes of pallets stacked in loading areas, limited warehouse space, and congestion in parking lots. The challenges were attributed to a lack of visibility in the yard. To address them, Eid [24] proposed an Excel spreadsheet-based venue information management file, which improved the efficiency of outbound efficiency. While the solution demonstrated the value of improved information tracking, it relied on manual entry. It lacked scalability, underscoring the need for more advanced, automated systems, such as YMS, to enhance visibility and coordination at a broader operational scale.

To broaden methodological perspectives, advancements across industries offer valuable insights. For instance, Logistics 4.0 illustrates the integration of IoT and big data in logistics digitalization [25], while total fulfillment management proposes unified control of inbound and outbound flows [26]. Human-centric approaches, such as using wearable sensors to assess worker performance and well-being in logistic settings [27], underscore the value of empirical, on-the-ground measurement.

As detailed in Table 1, prior research on digitalization in logistics has largely emphasized warehouse management systems, transportation management systems, or conceptual models of yard management. While these studies demonstrate the benefits of technologies applied in logistics, the scope remains narrow. Empirical examinations of YMS in distribution yards, especially within road transportation contexts, remain scarce. This study addresses that gap by providing an evaluation of YMS implementation to extend the evidence base with practical insights into yard-level efficiency.

Table 1. Comparative summary of key studies on logistics digitalization.

Authors/Year	Focus	Key Findings	Limitations
Lieb (1992) [4]	Integration of information systems in 3PL	Highlighted the importance of IT in managing partnerships	Did not examine yard-level operations
Eid (2016) [24]	Outbound logistics at PepsiCo	The manual Excel tool improved outbound coordination	Relied on manual entry, not scalable
McCrea (2018) [15]	YMS role in scheduling and throughput	Identified YMS as critical but underutilized	Lacked empirical data
Storms et al. (2019) [11]	Bottlenecks in yard processes	Manual yard scheduling causes costly delays	Did not evaluate digital yard solutions
Pane et al. (2020) [12]	RFID in Warehouse Management	RFID improved goods selection efficiency	Narrow warehouse-level focus
Deng et al. (2021) [13]	Automated 3D warehouses	Enhanced efficiency, digital competitiveness	Focused only on warehouse automation

3. Materials and Methods

The study employed a pre-post implementation performance analysis over five years to assess the impact of a YMS across three third-party logistics distribution centers affiliated with a national U.S.-based logistics corporation. The pre-post design enables a direct comparison of operational performance before and after the introduction of the YMS, thereby isolating its potential effects. The inclusion of five years of data ensures that observed differences reflect stable operational patterns. The selected centers, located in the Western, Midwestern, and Eastern regions of the U.S., were chosen for geographic diversity, to account for potential regional variations in logistical operation, and were fully implemented with a YMS in 2018. All three centers share standardized operational protocols but maintain some site-specific workflows, making them suitable for comparative performance analysis within a unified organizational structure. Site-specific workflows and layouts provided an opportunity to examine YMS performance in slightly different operational contexts, enhancing the generalizability of findings.

The YMS was designed in close collaboration with operational teams at all three centers to ensure alignment with site-specific workflows, physical layouts, and logistical demands. The system’s features, such as appointment scheduling, real-time yard visibility, dock door tracking, and automated trailer status updates, were developed based on direct feedback from local operators to maximize usability and operational relevance. Implementation followed a collaborative rollout process: site managers and relevant employees were trained on system functions, and pilot testing was conducted at each location to ensure compatibility with existing workflows. Full deployment occurred in 2018, with ongoing monitoring to support adoption.

3.1. Data Collection

The study analyzed five full years of operational data, spanning from 2018 (the first full year of YMS implementation) through 2022. The timeframe was chosen to capture stable post-implementation performance and to avoid confounding effects from the shift change from three shifts to two shifts in late 2022. Therefore, only data collected before this transition were included to maintain consistency in labor inputs and operational hours.

Three key performance indicators were used to assess the effectiveness of the YMS:

(a)

Inbound product volume (in pounds): total weight of incoming shipments received at each center per month.

(b)

Outbound product volume (in pounds): total weight of products shipped from each center per month.

(c)

Detention occurrences (in U.S. dollars): the financial penalties incurred due to delayed trailer uploading/loading, measured monthly.

These indicators were selected for capturing throughput efficiency (inbound and outbound volumes) and delay-related inefficiencies (detention costs), which are directly influenced by the yard management process.

Operational data were extracted directly from each center’s SQL Server database (version 16.0), which is centrally managed by the corporation’s data warehouse team. Data extraction was standardized using SQL scripts validated by site-level managers to ensure consistency across locations. To further validate the consistency and accuracy of the collected data, a subset of monthly records was randomly sampled and manually cross-checked by site-level analysts to confirm database accuracy. Before data collection, the researcher held informal consultations with site managers to identify potential anomalies (e.g., extreme weather, strikes, or seasonal fluctuations), which, though not included in the statistical model, helped strengthen the robustness of the analysis and interpretation.

3.2. Statistical Analysis

To evaluate differences in inbound volume, outbound volume, and detention occurrences before and after YMS implementation, we used a one-way between-subjects ANOVA. The test enables comparison of mean outcomes across two groups (pre- and post-implementation) while accounting for variance across multiple sites with standardized but not identical workflows. Welch’s ANOVA was applied in cases where the assumption of homogeneity of variances was not met, providing a robust and generalizable framework that offers greater flexibility than paired-sample t-tests, which are limited to matched comparisons.

All statistical assumptions were tested before analysis. The Shapiro–Wilk test indicated deviations from normality for all variables (p < 0.001). However, ANOVA is generally robust to moderate non-normality when sample sizes are balanced and sufficiently large, as in this study (n = 36 per group). Levene’s test confirmed homogeneity of variances for inbound and outbound volumes, allowing the use of the standard ANOVA. At the same time, it was significant for detention occurrences, justifying the use of Welch’s ANOVA. In addition to the parametric tests, nonparametric Mann–Whitney U tests were conducted as robustness checks to account for deviations from normality. The tests compare pre- and post-YMS distributions for each performance indicator, ensuring consistency across both parametric and nonparametric approaches.

The final dataset included 72 monthly observations: 12 months of pre-YMS data and 12 months of post-YMS data for each of the three centers (Western, Midwestern, and Eastern). Each observation was tagged with site identifiers and implementation status to create a structured dataset suitable for comparative analysis across both time and location.

3.3. Ethical Considerations

The study was submitted for ethical review and received approval. This review was obtained to ensure the responsible handling of potentially sensitive organizational data, to confirm that no personally identifiable information would be used or disclosed, and to comply with institutional policy requiring Institutional Review Board oversight for all research involving access to internal corporate systems. All procedures adhered to institutional and ethical guidelines for research involving organizational and archival data.

4. Results

The Shapiro–Wilk test indicated that the distributions of inbound and outbound volumes, as well as detention occurrence, deviated significantly from normality, both before and after YMS implementation (all p < 0.001). Although the assumption of normality was not met, ANOVA is generally robust to moderate non-normality when sample sizes are balanced and sufficiently large, as in the present study (n = 36 per group) [28]. Levene’s tests confirmed homogeneity of variances for inbound and outbound volumes (F (1, 70) = 1.46, p = 0.231; F (1, 70) = 1.565, p = 0.215), while it was significant for detention occurrences 9F (1, 70) = 32.49, p < 0.001). Accordingly, a standard one-way ANOVA was applied to inbound and outbound volumes, and Welch’s ANOVA was used for detention occurrences.

To further address the violation of normality, nonparametric Mann–Whitney U tests were conducted. Results were consistent with the ANOVA findings: inbound volumes were significantly higher after YMS implementation (U = 310.0, Z = –3.81, p < 0.001, r = 0.45), outbound volumes were also significantly higher (U = 320.0, Z = –3.69, p < 0.001, r = 0.44), and detention occurrences were significantly lower (U = 281.5, Z = –4.15, p < 0.001, r = 0.49). The medium-to-large effect sizes indicate that the improvements were statistically significant and practically meaningful. The consistency across parametric and nonparametric methods demonstrates that the conclusions are robust despite deviations from normality.

A one-way between-subjects ANOVA revealed statistically significant increases in inbound and outbound freight volumes after YMS implementation (Table 2 and Table 3). Inbound volumes increased [F(1, 70) = 5.52, p = 0.022, η² = 0.073], representing a moderate improvement in the distribution centers’ ability to process higher volumes of incoming freight. Outbound volumes also increased [F(1, 70) = 6.00, p = 0.017, η² = 0.079], indicating enhanced efficiency in outbound logistics operations, with products moving more quickly through the yard and into distribution channels. Welch’s ANOVA indicated a significant reduction in detention occurrences [F(1, dfWelch) = 21.30, p < 0.001, η² = 0.233]. The effect demonstrates that the YMS reduces costly delays associated with trailers waiting for loading or unloading, resulting in measurable financial savings and more predictable yard operations (Table 4).

Table 2. Inbound volume before and after YMS implementation.

Test	Statistics	Value	p-Value	Effect Size (η2/r)
One-way ANOVA	F(1, 70)	5.519	0.022	η2 = 0.073
Mann–Whitney U	U = 310.0 Z	−3.81	0.000	r ≈ 0.45 (large)

Table 3. Outbound volume before and after YMS implementation.

Test	Statistics	Value	p-Value	Effect Size (η2/r)
One-way ANOVA	F(1, 70)	6	0.017	η2 = 0.079
Mann–Whitney U	U = 320.0 Z	−3.69	0.000	r ≈ 0.44 (large)

Table 4. Detention occurrences before and after YMS implementation.

Test	Statistics	Value	p-Value	Effect Size (η2/r)
Welch’s ANOVA	F(1, dfWelch)	21.30	0.000	η2 = 0.233
Mann–Whitney U	U = 281.5 Z	−4.15	0.000	r ≈ 0.49 (large)

5. Discussion

The literature highlighted that, in addition to factors such as price, reliability, and services, technical innovation that improves logistics productivity and customer satisfaction was the main factor for a company to select a third-party logistics provider [29,30,31,32]. Meanwhile, one of the major challenges that third-party logistics providers face in their daily operations is minimizing delivery time [33]. Technology, personnel availability, productivity, and supply chain delivery efficiency affect delivery times [30]. The current study’s findings confirmed that applying technology that meets users’ needs, including implementing YMS, could reduce delivery times by improving yard visibility, tracking inbound and outbound shipments in real time, simplifying communication, and reducing idle times, while also alleviating the challenges.

The significant increases in inbound and outbound freight volumes following YMS adoption parallel previous findings that warehouse and transportation management systems improve throughput by enhancing coordination and optimizing resource use [5,13]. Likewise, Pane et al. [12] and Fauzan et al. [14] indicated that technologies such as RFID and automation can improve logistics performance by 8–15%, which reinforces the notion that digital tools can drive measurable improvements. The magnitude of improvement observed in the current study, η² values in the small-to-medium range for inbound and outbound volumes, and a large effect size for detention, falls within the 5–15% efficiency improvements typically reported in logistics technology research. Most notably, the dramatic reduction in detention occurrences observed in this study provides strong support for recent observations by Storms et al. [11] and Mera and Sirikande [9], who attributed costly logistics delays to inefficient, manual yard processes lacking in real-time visibility. By resolving the long-standing bottleneck issue, YMS demonstrates its ability to achieve comparable performance improvements to other digital logistics systems and reshape yard operations by enhancing real-time visibility to reduce idle time, automating scheduling to minimize errors and delays, and optimizing resource allocation to ease congestion, thereby transforming yard activities into more predictable processes.

The current study further extends the current literature by highlighting the importance of YMS as a distinct but complementary system within the broader logistics technology ecosystem. While warehouse and transport operations have benefited from decades of digital innovation, yard management has historically lagged in both adoption and scholarly attention [16]. The current results affirm McCrea’s [15] view of YMS as a critical system for managing yard scheduling and throughput efficiency, echoing Eid’s [24] findings that structured, data-driven yard operations reduce human error and congestion. Although the study does not directly calculate return on investment, the statistically significant improvements observed in key performance indicators, such as increased inbound and outbound volume handling and reduced detention costs, can serve as a foundation for organizations to conduct their return on investment analyses. Additionally, the study emphasizes user-informed design elements, including real-time yard visibility, automated trailer status updates, and appointment scheduling. It suggests that software developers and system integrators should prioritize on-site user research to design systems that more effectively meet operational needs.

In practice, warehouse managers can promote cross-departmental integration by interfacing YMS with existing WMS and TMS to enable real-time data sharing across inventory, transportation, and yard operations, thereby enhancing visibility, reducing manual errors, and supporting higher-quality data for upstream and downstream supply chain partners [21,34]. Managers may also need to establish cross-functional teams to regularly review system performance and identify opportunities for process improvement, which positions YMS as an operational tool and a core component of broader digital transformation strategies, supporting long-term gains in efficiency, compliance, and customer satisfaction.

Future research could conduct qualitative interviews with operators and managers based on current qualitative results to gain a deeper understanding of how YMS improves work and decision-making processes and enhances overall job satisfaction. More broadly, the integration of YMS with other supply chain management systems, such as warehouse management systems and transportation management systems, can be explored to investigate the synergistic effect on supply chain performance. With the increasing automation of logistics operations, the application of Artificial Intelligence and predictive analytics in YMS to enhance proactive site planning and real-time decision-making capabilities is also worthy of attention. In addition, while this study focused on three core performance indicators, future analyses could incorporate secondary metrics, such as dock door utilization rate, average trailer dwell time, and gate transaction processing time, to develop a more comprehensive picture of yard efficiency. These metrics, commonly used in yard analytics dashboards, can help reveal operational bottlenecks that are not captured through volume and detention data alone.

6. Conclusions

This study examined the impact of information systems on modern logistics operations, highlighting a key but often overlooked component in the supply chain: inefficient yard management can lead to costly delays and reduced throughput. As logistics providers face increasingly complex and time-sensitive demands, strategic investments in visibility-enhancing technologies such as YMS offer substantial operational benefits. By addressing key logistics bottlenecks, the real-time performance of YMS has enhanced the overall performance of the entire supply chain, enabling logistics centers to respond more effectively to market demands while supporting their long-term digital transformation goals and positioning themselves more competitively among customers seeking reliable logistics partners.

A central contribution of the current study is its empirical focus on road transportation distribution yards, which have received far less scholarly attention compared to port and rail yards. Most prior research has relied on conceptual models, simulations, or descriptive case studies [21,35,36,37], with few long-term empirical evaluations in real-world distribution yards. The current work presents a five-year statistical YMS assessment across the three geographically diverse third-party logistics centers, utilizing ANOVA and effect sizes, thereby providing a robust, evidence-based demonstration of the system’s effectiveness. The empirical and statistically grounded approach differentiates the present study from existing literature and establishes YMS as a distinct and measurable contributor to logistics efficiency. In addition, the study highlights the value of user-informed system design as the system was developed in collaboration with operators and site managers. The improvements in inbound and outbound throughput, as well as reductions in detention costs, reflect the technological capabilities and operational alignment.

YMS should be understood not only as a short-term efficiency tool but as a strategic enabler of long-term operational resilience. As third-party logistics providers continue to adapt to unpredictable market conditions, regulatory changes, and customer demands for speed and transparency, having a dynamic yard operation supported by intelligent systems will likely become a key differentiator. Integrating YMS insights with broader enterprise systems could also enable predictive maintenance, real-time responsiveness to demand surges, and data-driven strategic planning—all of which will be essential to navigating the future of supply chain management.

One limitation of this study was that only three third-party medium-sized distribution centers were selected as the research setting, and the study’s findings may not generalize to other similar regional distribution center sites within the same third-party provider, other logistic center provider companies, or the overall operational state of logistics corporations in the United States. In addition, the COVID-19 global pandemic may have impacted operations; however, it was not possible to isolate the business consequences of individual sites due to other related supply chain issues. Other potential confounding factors, such as seasonal demand fluctuations, which may have temporarily affected freight volumes, and possible inconsistencies in data entry or measurement across sites, despite validation procedures. Nevertheless, the use of multiple sites with standardized operational protocols and a five-year observation period helps mitigate the influence of short-term anomalies, strengthening the robustness of the findings.