1. Introduction
This presented research has significant practical significance, as it analyses particular consumer grievances about the quality of package delivery services offered by the state-owned enterprise State parcel company, using data from the years 2021–2023. An in-depth examination of consumer grievances elucidates persistent issues with package damage, erroneous deliveries, protracted delivery durations, and parcel misplacement. These results are crucial for the postal company’s management, as they facilitate the identification of prevalent issues and elucidate the interconnections among them. Research indicates that damaged items are often misdelivered or have prolonged delivery times. This suggests that enhancing handling and delivery operations in one aspect (e.g., delivery accuracy) might mitigate other issues (e.g., damage or loss).
This research may serve as a foundation for choices about operational enhancements, including the optimization of logistical procedures, the implementation of new shipment tracking systems, or the enhancement of staff training. Upon applying these guidelines, one might anticipate increased customer satisfaction and a reduced volume of complaints. Ultimately, emphasized correlations among categories of grievances may be significant not only for the administration of the State parcel company but also for other enterprises offering package delivery services encountering analogous issues. Consequently, this research offers significant insights that enhance service quality and provide a more efficient response to consumer requirements.
The correlation analysis conducted in the research demonstrates statistically significant links among many factors, including the association between damaged shipments and misdelivered shipments, as well as between missing shipments and prolonged delivery times. This indicates that these links are not arbitrary, facilitating an understanding of the interplay between various issues and the reasons for their concurrent occurrence. Despite many associations lacking statistical significance (e.g., the correlation between erroneous package delivery and lost parcels), the study offers valuable insights warranting additional investigation in further research.
The scientific uniqueness is evident in the chosen study period (2021–2023), enabling an evaluation of changes in service quality and issues post-pandemic. Throughout the pandemic, enterprises like State parcel company AB saw significant operational difficulties stemming from the heightened amount of online transactions and deliveries. This research offers fresh insights on the company’s adaptation to increasing workload and the potential changes in service quality over time. This paper goes beyond identifying individual practical issues such as delayed, damaged, or incorrectly delivered packages. The new thing is that these issues are being looked at in a planned way to see how they are connected and what that means for the general quality of service. The current logistics literature does not really go into these kinds of connections in depth; studies tend to treat operational problems as separate things. Our research shows that these problems often happen together and are linked, which means that fixing one problem might help lower the number of other problems that happen. As an example, making delivery more accurate might not only cut down on the number of things that get lost but might also lower the risk of damage because they will not have to be re-handled and rerouted as often. Logistics providers can make better decisions if they understand how these things work together. For example, companies can put more money into real-time tracking systems and better routing methods when they know that longer delivery times often mean that packages are lost. The correlation between failed deliveries and broken packages underscores the significance of modifying handling protocols and improving staff training.
Emphasizing ongoing operational issues in package delivery systems, this paper examines the links and impacts on consumer satisfaction. We highlight this focus by first posing the following study question:
How does the general quality of parcel delivery services and customer satisfaction suffer from linkages among important operational difficulties including delayed deliveries, misdeliveries, damaged parcels, and missing shipments? In conclusion, the paper enhances research on the quality of package delivery services both locally and internationally. Insights into the structure, frequency, and interrelationships of complaints help enhance delivery services for State parcel company AB and other organizations aiming to refine their logistics and customer service operations.
2. Materials and Methods
The study will consist of two stages: the analysis of complaints and the identification of the relationship between the variables of the obtained results using the Pearson correlation coefficient:
The example under consideration is based on the fact that the State parcel company delivers parcels in several directions: to the home, to the parcel locker, and to the company branch. Considering these delivery options, various challenges are encountered, which affect both customer satisfaction and customer logistics service. In order to achieve the set goals and objectives, customer complaints are investigated.
where ∆ is the error; N is the number of the studied population; and n is the sample size.
The study analyses the latest complaints from 2021 to 2023, pertinent to the research issue. This technique was selected due to insufficient statistical data from other research, and the examination of complaints is the most effective means to evaluate the company’s small package delivery issue.
- 2.
- The Pearson correlation coefficient is one of the most widely used statistical tools for determining the strength and direction of a linear relationship between two quantitative variables [31]. This ratio helps researchers analyze how closely datasets are related and how a change in one variable may be related to a change in another variable. The methodology for calculating this coefficient and the interpretation of the obtained results are as follows: (1) Data collection and preparation—To calculate the Pearson correlation coefficient, it is necessary to have two sets of variable data that are continuous and quantitative (e.g., number of complaints, satisfaction score). The data on shipment problems presented in the article are suitable for this analysis because it covers specific categories, such as the amount of damaged or overdue shipments. It is important to ensure that pairs of data for each variable are collected independently and accurately. (2) Calculating data averages—This is the first step in normalizing the data and preparing it for a further analysis. (3) The calculation of deviations from the average—The next step is to calculate the deviations from the mean for each pair of data. These deviations help determine how each data point differs from the mean of the entire set, which is important for further correlation calculations. (4) The application of the correlation coefficient formula. (5) Interpretation of coefficient values—The value range of the Pearson correlation coefficient is from −1 to +1; (1) r = +1r = +1r = +1—a perfect positive relationship; when one variable increases, the other also increases proportionally. (2) r = −1r = −1r = −1—a perfect negative relationship; when one variable increases, the other decreases proportionally. (3) r = 0r = 0r = 0—no linear relationship; the variables are not directly related in a linear fashion. The closer the value is to +1 or −1, the stronger the relationship between the two variables. (6) Assessment of statistical significance—To confirm that the obtained correlation coefficient is not random, it is necessary to perform a test of statistical significance, usually using the ppp-value. This helps to determine whether the correlation is statistically significant at a certain level of significance (often α = 0.05\alpha = 0.05α = 0.05); (1) if p < 0.05p < 0.05p < 0.05, it leads to the conclusion that the correlation is statistically significant and unlikely to be due to chance. (2) If p > 0.05p > 0.05p > 0.05, it can be said that the correlation value is not statistically significant, and the presence of a relationship cannot be firmly stated.
In summary, Pearson’s correlation coefficient is a powerful tool for examining the direct relationship between variables, but it also has limitations. This method is sensitive to extreme values (i.e., outliers) and only applies to linear relationships. If the relationship between the variables is nonlinear, the Pearson correlation coefficient may not reflect the true nature of the relationship. In such cases, it is recommended to use other correlation methods, such as Spearman’s correlation coefficient, for investigating nonlinear relationships.
3. Results
The Results Section delineates the gathered data and the outcomes of the conducted study, enabling comprehensive knowledge of the quality of parcel delivery services offered by State parcel company AB and the associated issues. The research examined 375 customer complaints submitted from 2021 to 2023 to identify the predominant breaches and difficulties encountered by customers. The investigation revealed that the primary issues prompting consumer complaints include incorrect package deliveries, damaged parcels, extended delivery delays, and missing shipments. This section examines the categories of these complaints, their prevalence, and the statistical correlations among these issues, along with graphical depictions of the data to highlight significant findings from the research.
A study of customer complaints performed by the State parcel company indicated that 5% of consumers had issues with their goods being delivered and collected by incorrect recipients. Eleven percent of consumers said that their packages were damaged as a result of couriers’ negligence during delivery. Thirteen percent of consumers reported that, despite having paid for delivery to a designated location of a post machine, the couriers of the State parcel company delivered the items to an entirely separate place, specifically to post machines that were not advantageous to the client. Furthermore, subsequent to the client’s payment for home delivery, the couriers of State parcel company AB deposited the items in the post machines without notifying the consumer. Twenty-five percent of clients said that their goods were concealed when using “State parcel company” parcel delivery services. The State parcel company received the highest volume of complaints regarding prolonged delivery times. Despite the company’s assertion that shipments within Lithuania typically arrive within one working day, an examination of customer grievances revealed that 46% of recipients experienced delays exceeding two weeks.
The quality of the goods utilized for packing is also quite crucial. The State parcel company routinely employs standardized packaging. Things like moisture, temperature fluctuations, or pressure during shipping could more readily harm this form of packaging. Handling items wrapped in inexpensive cardboard or plastic increases their likelihood of breaking, cracking, or bending. Even if the contents are still good, the outside can be harmed with just a little physical contact—that is, when packages are packed into a moving car or kept in poor condition.
Fifty clients lodged complaints about faulty delivery by State parcel company AB. Among these complaints, 52% were about instances when AB “State parcel company” couriers delivered items to an entirely different postal machine situated in an inconvenient location for the client, despite the customer having paid for delivery to a designated postal machine. Thirty-two percent of consumers said that “State parcel company” couriers delivered items to a post machine or post office without any notification to the client, despite the customers having paid an extra cost for home delivery. Ultimately, 16% of consumers reported that the products were delivered to places entirely different from those specified.
In summary, it can be said that the most problematic things are the following: long delivery; late shipments; lost shipments; and damaged shipments. Considering the obtained results, it is important to assess how the problem areas interact with each other and what consequences they bring.
4. Discussion
The discussion part aims to assess the study findings and examine their importance regarding the quality of package delivery services provided by the State parcel company. This section analyses the interplay of highlighted concerns from customer complaint data, including damaged shipments, prolonged delivery delays, misdelivery, and missing shipments, and their potential impact on total customer satisfaction. The study’s results are examined within the broader context of the logistics industry, focusing on prospective enhancements in business processes and methods to elevate customer service quality. The section examines the study’s shortcomings and proposes potential avenues for further research to solve the highlighted concerns.
The trend analysis splits the complaint data into monthly or quarterly periods to find swings and possible trends including seasonal fluctuations or systemic changes brought on by outside events like the post-pandemic rise in parcel deliveries. This research seeks to determine if the correlation between delayed deliveries and missing shipments increases during peak delivery seasons or decreases during quieter periods. The temporal dimension will provide important insights into the operational challenges encountered by the State parcel company, especially in recognizing times of increased risk or enhancement. This trend analysis uses time-series visualization techniques, such as line graphs and stacked bar charts, to show the frequency of each operational issue over time. Furthermore, we will compute and analyze correlation coefficients for important linked concerns (e.g., damaged shipments and misdelivery) over a range of time intervals, revealing how these interactions evolve over time. This approach will elucidate critical trends and assist in determining whether specific operational enhancements, including new logistics strategies or improved tracking systems, have significantly influenced the mitigation of these issues.
Defective shipments are often linked to misrouted shipments, extended delivery durations, and missing shipments. This demonstrates that shipments that encounter certain issues are more prone to experiencing further difficulties. For instance, goods that have been damaged are often delivered to an incorrect destination or experience delays beyond the anticipated timeframe. This might heighten the likelihood of further damage owing to further handling or inadequate storage conditions. Shipments that have been damaged are also more prone to being lost, most often as a result of inadequate management.
Instances of shipments being misplaced, as well as experiencing extended delivery durations and missing shipments, are often seen. The delivery of a shipment to the wrong place may result in a delay as it has to be redirected to the proper destination. Furthermore, a package that is sent to the wrong recipient might be more susceptible to being misplaced or mistreated, hence increasing the likelihood of it being lost. Frequently, these goods are also erroneously sent to an incorrect destination as a result of mishandling issues.
Extended delivery durations are often linked to instances of misplaced goods and items being delivered to incorrect recipients. Shipments with extended transit lengths have a higher probability of being misplaced or lost, and prolonged delivery durations amplify the likelihood of mistakes that may lead to a package being delivered to an incorrect receiver. Additionally, there exists a link between extended delivery durations and the occurrence of package damage, since longer delivery periods might elevate the likelihood of package deterioration.
Missing packages often coincide with items being delivered to the incorrect recipient. This implies that shipments that go missing often end up being sent to the incorrect recipient as a result of mistakes in addressing or handling. Erroneously addressed or mishandled shipments often result in the delivery of damaged parcels to the incorrect addressee.
To summarize, it may be said that the primary issues faced by consumers of AB “State parcel company” are interconnected and often occur simultaneously. Instances of damaged, misdirected, lost, long-delayed, and misdelivered shipments often coincide, suggesting that resolving one issue might mitigate the occurrence of other issues. Hence, it is important to holistically tackle the concerns about the quality of package delivery in order to enhance customer satisfaction and diminish the quantity of complaints.
The occurrence of damaged shipments is highly positively correlated with misdelivered shipments (r = 0.74252, p = 0.03486), suggesting that a rise in the number of misdelivered shipments typically leads to an increase in the number of damaged shipments. There is a modest positive association between lengthy delivery times, with a correlation coefficient of 0.58035 and a p-value of 0.13149. However, this correlation is not statistically significant. Furthermore, there is a moderate positive relationship between damaged shipments and lost shipments (r = 0.27837, p = 0.5044), and a very significant positive relationship between damaged shipments and misdelivered shipments (r = 0.93835, p = 0.000589911). This suggests that in the event of package damage, there is a high probability that it was also delivered to an incorrect receiver.
The occurrence of lost shipments is highly correlated with extended delivery durations (r = 0.97356, p = 0.0000045307), suggesting that misplaced shipments often coincide with lengthy delivery periods. Additionally, there is a substantial and strong positive connection (r = 0.80985, p = 0.01483) between missing packages and a moderate positive correlation (r = 0.52158, p = 0.18493) between packages sent to the incorrect receiver. However, it should be noted that the correlation between packages delivered to the wrong recipient is not statistically significant.
There is a significant and positive association between long delivery times and misplaced shipments (r = 0.97356, p = 0.0000045307), as well as a strong positive correlation between long delivery times and lost shipments (r = 0.9188, p = 0.00126). This suggests that extended delivery durations are often linked with missing shipments. Furthermore, there is a modest positive connection between shipments sent to the incorrect recipient, with a correlation coefficient (r) of 0.31394 and a p-value of 0.4489. However, it is important to note that this correlation is not statistically significant.
Lost shipments have a moderate positive association with damaged shipments (r = 0.27837, p = 0.5044), a strong positive correlation with misplaced shipments (r = 0.80985, p = 0.01483), and a very high positive correlation with extended delivery times (r = 0.9188, p = 0.00126). Additionally, there is a negligible inverse correlation seen between shipments sent to the incorrect recipient (r = −0.04982, p = 0.90675), suggesting that there is no statistically significant association between these variables.
Shipments delivered to the incorrect recipient have a highly significant positive correlation with damaged shipments (r = 0.93835, p = 0.000589911). There is a moderately positive correlation between shipments delivered to the wrong recipient and misplaced shipments (r = 0.52158, p = 0.18493), as well as long delivery times (r = 0.31394, p = 0.4489). However, these correlations are not statistically significant. Additionally, there is a negligible negative connection seen between missing shipments, with a correlation coefficient (r) of −0.04982 and a p-value of 0.90675.
Including these reliability measures highlights the study’s statistically significant findings while also acknowledging areas where further investigation may be needed due to limited significance. This enhancement strengthens the transparency and credibility of the results, allowing readers to critically assess the validity and practical implications of the observed interrelations.
The research identified several robust and statistically significant relationships, including the correlation between delayed deliveries and lost shipments (r = 0.9188, p = 0.00126), and between damaged and misdelivered parcels (r = 0.93835, p = 0.00059). It is imperative to acknowledge that not all correlations exhibited statistical significance. It is imperative to explicitly address these limitations in order to conduct a fair and transparent analysis.
The correlation between damaged and lost shipments (r = 0.27837, p = 0.50440) is not statistically significant, which is a significant limitation. The relationship between misdelivered and delayed shipments (r = 0.31394, p = 0.44890) exhibited a weak association, indicated by a wide confidence interval, suggesting a lack of reliability. The results suggest that the observed relationships may be due to random variation rather than a significant underlying association. This limitation may stem from various factors, such as the characteristics of the sample, external influences, or the intricate nature of the operational issues under investigation.
A further limitation is the possibility of multicollinearity, in which certain variables might be interconnected through a third factor, thereby weakening the direct correlation between them. Delayed deliveries and damaged parcels may appear to have a weak correlation; however, both can be independently influenced by external factors such as inadequate storage conditions or poor handling practices, which are not directly addressed in the current analysis. Such indirect relationships may contribute to the lack of statistical significance in some correlations.
Also, group size is a very important factor in figuring out the statistical power of the analysis. Paniotto’s method was used to figure out that the sample of 375 customer complaints was representative. However, because the data are so variable, a bigger sample might be needed to find small correlations. The correlation (r = 0.58035, p = 0.13149) between late and damaged shipments suggests a potentially small effect size that may remain undetected due to inadequate statistical power. This suggests that subsequent research should utilize larger datasets to validate these findings and perform a more thorough analysis of smaller effect sizes.
It is crucial to recognize that operational issues in logistics are influenced by a variety of external factors, including differences in geography, seasonal fluctuations, and the working methods of couriers. These factors can introduce additional noise into the data. This type of variation can obscure genuine relationships, particularly for associations that are not of significant importance. This issue demonstrates the necessity of acquiring more detailed data, such as partitioning the analysis by area or delivery method, to more accurately reflect these external factors and improve the accuracy of the results. To summarize, there are notable relationships among several kinds of complaints. An exceptionally robust positive link is shown between shipments that have been damaged and packages that are delivered to an incorrect receiver. Similarly, there is a substantial association between products that are sent to an incorrect location and extended delivery durations. These findings indicate a strong correlation and potential interconnectedness between these issues. These connections highlight the need for effectively dealing with problems related to the quality of package delivery. This will help decrease the amount of customer complaints and enhance the overall quality of service.
In order to reduce these parcel delivery problems, the following strategic decisions could be taken:
First, to reduce parcel delivery problems, the parcel transportation network can be expanded to achieve greater geographical coverage and improve the quality-of-service delivery. This would allow faster and more efficient delivery of parcels, reduce long delivery times, and solve the problem of non-delivered parcels. In addition, more parcel lockers would ensure a lesser number of filled parcel lockers, which would make the work of couriers easier. Increasing geographic coverage would provide more choice for customers, improve business opportunities, and stimulate economic growth, especially in smaller cities and rural areas. It would also increase operational efficiency and profitability for companies and entrepreneurs. As an example of practical applicability, the company examined in this article has 353 parcel machines and 171 post offices throughout Lithuania, but not all cities have the same number of parcel delivery points compared to the number of residents, so parcel delivery times often increase. This problem is especially felt in smaller cities, where there are only a few or even one post office or parcel lockers for a large number of residents. For example, in a city with a population of 17,385 people, there are only five parcel lockers and one post office. The situation is similar in another city, where only one post office and three parcel lockers serve 19,586 residents. This means that each parcel locker and post office have to serve a large number of customers, which can cause great difficulties in delivering parcels. This type of problem can be solved precisely by developing the parcel transport network. In order to solve this problem, in a city with a population of 19,586, at least two more parcel lockers should be built closer to residential areas or shopping centers, since compared to a city with a population of 17,385, there are five parcel lockers, while in this city with a population of 19,586, there are only three parcel machines. Such an expansion of parcel lockers would allow couriers to reach the parcel lockers chosen by customers faster and more efficiently to deliver parcels. Such an expansion of the parcel transport network would allow for improving the quality of parcel delivery services and reducing long parcel delivery times and delays. The implementation of this proposal may also have problematic moments, because this is a state-owned company, where strategic decisions are implemented more closely than in private equity companies (and there are at least several private equity competitors providing small parcel transportation services).
Second, apply “real-time feedback” tools. This tool would allow customers to provide immediate and easy feedback on courier performance and delivery, while giving employees the opportunity to receive feedback on their actions and take immediate corrective action if necessary. The “Realtime feedback” tool would be applied directly after the package is delivered or picked up from the post office. When the customer receives the package, they would be asked to rate the courier’s performance by marking “good delivery service” or “bad delivery service” on the courier’s scanner or post machine screen, and leave a comment if desired. This tool would allow employees to receive feedback on their actions and take corrective action if necessary. Also, a real-time feedback tool would help reduce the risk of damaged shipments. If a courier discovers that a customer has received a damaged or spoiled shipment after delivering it, they could take immediate corrective action and pass the information on to the company’s customer service department so that an appropriate resolution can be reached and similar incidents can be avoided in the future. This department could take corrective action, such as contacting the sender for additional information or compensation. Also, if necessary, the customer service department could inform the company’s logistics department so that the shipment’s travel route can be checked and where and how the shipment may have been damaged or spoiled. Finally, the logistics department can take measures to ensure a better delivery process in the future. This would help improve staff qualifications and competence, reduce the risk of damaged shipments, and improve the overall customer experience. In addition, the implementation of real-time feedback tools would help them achieve higher standards of customer service and increase their reputation in the market; because of better delivery and handling of shipments, customers would be more satisfied and inclined to choose it as their reliable shipping partner. All this could be implemented using modern information tools and technologies, such as courier scanners and tablets in parcel lockers with integrated “real-time feedback” systems. Such a solution would become a long-term investment in the company’s activities, which could help them maintain competitiveness in the market and improve their business activities. Considering that sometimes customers simply do not want to evaluate services here and now or there are internet disruptions—these could be challenges with which the implementation of this tool would be associated.
5. Conclusions
A survey of customer complaints revealed that 5% of customers have encountered a problem when their parcels were delivered and picked up by the wrong recipient. In total, 11% of customers reported that their parcels were damaged due to couriers’ irresponsibility during delivery. In total, 13% of customers said that, despite having paid for delivery to a specific location of a parcel locker, the couriers delivered the parcels to a parcel locker located in a completely different location, unfavorable to the customer. In addition, after the customer paid for home delivery, the couriers delivered the parcels to the parcel locker without informing the customer. In total, 25% of customers have reported that their packages have been damaged when using package delivery services. Long delivery times were the number one complaint, and while the company says that domestic shipments typically arrive in just 1 business day, a review of customer complaints revealed that 46% of recipients had to wait more than two weeks.
The research demonstrated a robust positive connection (r = 0.93835, p = 0.000589911) between damaged parcels and misdelivered goods. This indicates that misdelivered items are more prone to harm. This may result from supplementary cargo handling or mismanagement when items are sent to incorrect locations.
A robust positive connection (r = 0.9188, p = 0.00126) was seen between extended delivery times and missing shipments. This indicates that the prolonged duration of a package’s transit correlates with an increased likelihood of loss. This association indicates that extended delivery delays may elevate the likelihood of package loss attributable to inadequate tracking and handling protocols.
Damaged shipments correlate with prolonged delivery times (r = 0.58035, p = 0.13149); yet, this link lacks statistical significance. Nonetheless, this pattern may suggest that prolonged cargo delivery times elevate the risk of damage owing to increased handling or substandard storage conditions.
The relationship between misdelivery and lost shipments is substantial (r = 0.80985, p = 0.01483). This suggests that misdelivered parcels are more likely to be lost. This may be attributable to faults in shipment management, resulting in redirection or misdelivery, hence increasing the risk of loss.
The significant connections shown across issues such as erroneous deliveries, damaged shipments, and prolonged delivery times indicate that these problems are often interconnected, and addressing one issue may mitigate others. This underscores the need for comprehensively addressing package delivery quality concerns to enhance customer service and diminish complaints.
From a logistics provider’s point of view, the study provides valuable information that can improve operations and customer satisfaction. By understanding the interconnected nature of operational challenges, businesses may get to the bottom of problems rather than merely masking their symptoms. For example, if transportation routes were optimized and advanced tracking systems were put in place, delays might be reduced and goods would likely be lost less often. It would be possible to reduce the number of damaged and lost parcels simultaneously with improved training for employees on proper package handling. Logistics businesses can examine their own complaint data, identify patterns, and implement targeted adjustments to make their operations more reliable and earn consumers’ trust. This data-driven approach was employed in the research. By analyzing the numerical connections between operational issues in package delivery services, this work addresses a significant gap in the existing literature. Single issues, such as delays or damages, have been the primary focus of prior study. By investigating the interconnections between these issues, our research adopts a broader perspective. A methodological contribution that demonstrates how statistical techniques may be utilized to identify detailed linkages in logistics operations is the use of a Pearson correlation analysis on real-life complaint data. The application of a Pearson correlation analysis to real-life complaint data represents a methodological enhancement, illustrating the capacity of statistical tools to uncover complex relationships within logistics operations. Additionally, the research underscores the value of integrating customer feedback into operational assessments, providing a solid basis for further studies on customer-centered logistics strategies and data-driven decision making in supply chain management. Several directions for future research emerge from this study. First, future investigations could explore the potential nonlinear relationships between operational issues by applying advanced methods such as Spearman’s rank correlation or machine learning algorithms. These methods could reveal more nuanced patterns that are not captured by linear models. Second, conducting a comparative analysis of private and public parcel delivery companies would help determine whether similar interrelations exist across different organizational structures and business models. Third, future research could incorporate a larger and more diverse dataset, accounting for seasonal variations and regional differences, to capture a broader range of operational challenges. Lastly, longitudinal studies could examine the long-term impact of implemented improvements, tracking whether addressing specific issues leads to sustained reductions in related problems over time. In summary, this research bridges the gap between theory and practice, offering both immediate and long-term benefits for the logistics industry while contributing to the academic understanding of service quality improvement in supply chain management. By addressing the interconnections between operational issues, the findings provide a foundation for logistics companies to enhance service delivery and customer satisfaction, while also advancing scholarly discussions on optimizing logistics performance.
Source link
Kristina Čižiūnienė www.mdpi.com
