1. Introduction

With the widespread adoption of big data technology across industries, an increasing number of organizations recognize that data has become an essential component of their strategic resources. In 2021, according to the statistics [1], big data applications will represent 5.2% of China’s manufacturing industry. This demonstrates that big data has an impact on the manufacturing industry. Moreover, as a data-intensive industry, logistics generates a large amount of location data, transaction data, and equipment operation data daily. If effectively utilized, these data resources will aid in enhancing operational efficiency and service level, thereby promoting the sustainable development of logistics enterprises. Despite the fact that all aspects of logistics have begun to use big data technology extensively, the perception in the pertinent research field is low. The majority of businesses and individuals have not yet developed a clear and systematic comprehension of how big data facilitates the sustainable development of logistics. Theoretically and empirically clarifying the substantial impact of big data on the sustainable development of logistics is crucial.

Through literature review and case analysis, this paper mainly discusses the following: First, it outlines the application scenarios of big data in various aspects of logistics. Second, it selects several typical cases for in-depth analysis and discusses the technical means and impacts of utilizing big data to achieve the desired outcomes. This paper hopes to clarify the central role of big data technology in fostering the sustainable development of logistics.

2. Big Data in Logistics Operation

In the realm of transportation, big data technology can be used to optimize routes and improve fuel efficiency. For example, real-time traffic congestion can be predicted to optimize transportation routes, avoid congestion and poor driving conditions, and thus improve transportation efficiency. Regarding storage side, big data technology can help manufacturers analyze historical data about product demand and sales to help predict future demand patterns. This information can be used to optimize inventory levels, reduce the cost of over-stocking or under-stocking, and avoid the bullwhip effect. As for loading and unloading, the demand for loading and unloading can be predicted by large technical means. This allows for efficient scheduling of personnel and equipment, reducing downtime and increasing overall productivity.

In terms of packaging, by analyzing data on packaging handling and damage rates, companies can optimize packaging design and methods to reduce damage and increase customer satisfaction. When it comes to distribution, big data technology can be used to predict distribution demand and summarize distribution time, so as to improve distribution schedule and improve distribution efficiency to reduce costs.

3. Impact of Big Data on Sustainable Logistics

3.1. Enhancing Operational Efficiency

One of the most significant impacts of big data on the logistics industry is the ability to boost operational efficiency. According to data from the State Post Bureau, the express delivery business volume increased by 15.5% from January to July of 2023, reaching 70.3 billion items [2]. In addition, logistics companies deal with enormous quantities of data daily, including transportation, distribution, packaging, and storage information. With so many business and data processing duties to complete. Clearly, humans find it difficult to comprehend and analyze these data sets in a timely manner. With big data analytics, however, businesses are able to effectively process this data, derive insights from it, and make strategic decisions.

As a type of big data technology, blockchain technology is extensively used in logistics transportation due to its decentralization and immutability, which enables real-time tracking and monitoring of goods in transit [3]. This tracking can not only improve the user’s consumption experience and satisfaction with logistics delivery, but it can also provide valuable information about the status of the products, allowing the company to make timely decisions to avoid potential issues. In addition, sophisticated algorithms and machine learning are used in conjunction with big data analytics. In general, there are three categories of big data: descriptive, predictive, and prescriptive [4]. Companies can use prescriptive analytics, the first application of big data analytics, to prescribe the optimal route based on a variety of variables, such as current traffic and weather conditions, road closures, and delivery deadlines. In addition, predictive analytics, the most prominent type of big data analytics, can assist in predicting potential problems, such as vehicle failures or shipment delays, allowing businesses to take preventative measures to minimize disruptions.

By increasing operational efficiency, big data can contribute to the sustainability of logistics operations by reducing waste and resource consumption. Optimizing routes, for instance, can reduce fuel consumption and carbon emissions, thereby promoting environmental sustainability.

3.2. Reduce Environment Impact

The concept of sustainability encompasses economic, social, and environmental factors. Consequently, sustainable logistics entails not only efficient operations, but also a reduction in the environmental impact of logistics activities. Big data can play an important role in this regard.

The logistics industry is a major contributor to global CO₂ emissions due to the extensive use of vehicles, ships, and aircraft for transport. Here, large data can contribute significantly. Route optimization, for instance, can not only increase operational efficiency, but also decrease fuel consumption, thereby minimizing emissions. At the same time, big data can also promote the implementation of the concept of “green logistics”. Big data can simultaneously foster the implementation of “green logistics”. In response to climate change, at the 21st session of the Conference of the Parties in 2016, 197 countries adopted the Paris Agreement, which seeks to significantly reduce global greenhouse gas emissions [5]. To reach an agreement, carbon credits were created. However, monitoring carbon credits requires an excessive amount of process data, so big data enables licensing companies to track and monitor their carbon footprint and identify areas in which they can reduce their environmental impact.

3.3. Fostering Innovation in Logistics Solutions

Big data encourages innovation in logistics solutions in addition to operational efficiency and environmental sustainability. Big data can inspire new ideas to enhance existing processes and develop new solutions by providing a wealth of information on all aspects of logistics operations.

For instance, big data can facilitate the development of complex algorithms for logistics optimization. To predict the risks encountered by logistics in multiple links in the actual process, it is necessary to consider the impact of multiple variables, and the combination of these variables can yield tens of thousands of results. It is difficult for humans to precisely predict risk. With the aid of big data algorithms, however, many variables, such as demand patterns, delivery times, and environmental factors, can be considered, resulting in more precise solutions. This strategy can result in a more sustainable logistics enterprise.

Second, shared logistics describes the reconfiguration of dormant resources [6]. With the emergence of the concept of shared logistics, the demand for large amounts of data in shared logistics is progressively growing. To attain enhanced logistics system efficiency, downsizing information interconnection plays a crucial and indispensable role in achieving low logistics costs. Leveraging big data technology, the logistics industry has developed new modes of conveyance.

Simultaneously, large data can facilitate the development of AI-driven technologies, such as drones. The majority of vans used for last-mile deliveries are powered by internal combustion engines. However, with the assistance of big data technology, drone logistics could become an advantageous and innovative mode of transportation. Drones can not only solve the problem of last-mile delivery, but also transport products to difficult-to-reach locations, promoting sustainability by reducing the need for infrastructure such as roads and bridges [7]. This type of change is desirable for cities with complex geographical conditions and inadequate traditional transportation.

Moreover, big data analytics can facilitate the development of logistics Internet of Things (IoT) applications. IoT devices are able to capture vast amounts of data in real time, providing valuable insights for logistics operations optimization [8]. IoT sensors in vehicles, for instance, can monitor fuel consumption and vehicle performance, allowing businesses to optimize vehicle use and maintenance, thereby enhancing sustainability.

4. Big Data Challenges in Sustainable Logistics

The application of big data to sustainable logistics has tremendous potential, but it also faces a number of obstacles and challenges. Data can be a double-edged sword in terms of operational decision-making. Inadequate or excessive data can lead to ineffective decision making. Excessive data results can contribute to data bias, which can result in “data fraud” [9]. Additionally, the efficient implementation of big data in logistics may present some ethical challenges. Logistics, a pillar of the national economy, has generated a substantial number of employment opportunities. Nonetheless, it is now evident that big data applications can supplant human labor in areas such as transportation and loading and unloading with greater efficiency. Social satisfaction will inevitably decline as a consequence of this process.

At the same time, there are ethical concerns regarding data security and privacy. For example, the construction of shared logistics is dependent on big data technology. However, logistics data frequently contains confidential information, such as consumer data and transaction records. There are not enough objective standards and institutions to oversee the sharing process. Consequently, preventing data intrusions and protecting privacy is a major challenge in sustainable logistics at present.

5. Conclusion

In summary, this paper examines the outcomes of empowering big data for sustainable logistics, focusing on the function of big data in various logistics process components. The application of big data in logistics is examined through a literature review and case study, and the implementation and decision-making experience of a sustainable supply chain is analyzed. The authors discovered that big data enhances operational efficiency in multiple ways, including route optimization, demand forecasting, and improved scheduling. It also reduces its impact on the environment. Through route optimization and real-time monitoring, companies can reduce fuel consumption, carbon emissions and overall energy use. This contributes to environmental sustainability and is in line with global efforts to combat climate change. Moreover, big data encourages innovation in logistics solutions by facilitating the creation of complex algorithms, shared logistics models, and AI-driven technologies such as drones. While big data offers numerous advantages, there are also some challenges that need to be addressed. An excess of data can result in ineffective decisions, and data biases can lead to deceptive results that result in data fraud. The automation of specific duties by means of big data applications may also raise ethical concerns, such as the displacement of local workers. Moreover, assuring data security and privacy in shared logistics models remains a significant obstacle, necessitating the development of objective standards and systems. Overall, Big data plays a pivotal role in promoting sustainable logistics, enhancing efficiency, minimizing environmental impact, and nurturing innovation.