PROSPECTS AND PROBLEMS OF ROBOTIC DELIVERY OF THE LAST MILE. THE RELEVANCE OF THE PROBLEM AND THE CONCEPT OF THE SOLUTION. PART I.

The express delivery market in recent years has been growing at the level of 3-4%, and even in these conditions, not only is it not saturated, but the demand for it is growing. According to Oxford Economics, the growth of the air cargo market, which determines the volume of the express delivery market, accelerated at times up to 7% per year from 2013 to 2018 [1]. The biggest changes took place in 2016-17 due to a technological breakthrough in the field of logistics with the introduction of services such as drone delivery, processing orders on the blockchain, calculation of the delivery mode using artificial intelligence, etc. It was expected that due to the growing demand on fast delivery guaranteed, the number of express delivery employees worldwide will grow to 4.5 million over the next few years. But the coronavirus pandemic has accelerated this process. In the study “The Future of Freight Transportation. How new technologies and new thinking can change the movement of goods”, presented by the international network of consulting companies Deloitte in 2017, states that carriers have already solved many of the problems associated with the transportation of goods. But the “last mile delivery” stage has remained limiting the development of the delivery service. At this stage, companies suffer losses due to the concentration of logistics, algorithmic and kinematic tasks that cannot be automated with modern means and technologies for replacing human labor. Consequently, the use of alternative, unconventional technologies at this stage is a key condition for the mass development of delivery.

Mining Express Service Innovation Opportunity from Online Reviews

In order to further meet the diversified needs of customers and enhance market competitiveness, it is necessary for express delivery enterprises to carry out service innovation. From the perspective of customer demand, we propose a framework for mining service innovation opportunities. This framework uses text mining to analyze user generated content and tries to provide a scientific service innovation scheme for express enterprises. Firstly, we crawl online reviews of express companies and use LDA model to identify service attributes. Secondly, customer satisfaction is calculated by sentiment analysis, and simultaneously, the importance of each service attribute is calculated. Finally, we carry out an opportunity algorithm with the results of text mining to quantify the innovation opportunities of service attributes. The results show that the framework can effectively identify service innovation opportunities from the perspective of customer demand. This study provides a new way to explore the direction of service innovation from the perspective of customer demand.

Mining Express Service Innovation Opportunity From Online Reviews

In order to further meet the diversified needs of customers and enhance market competitiveness, it is necessary for express delivery enterprises to carry out service innovation. From the perspective of customer demand, we propose a framework for mining service innovation opportunities. This framework uses text mining to analyze user generated content and tries to provide a scientific service innovation scheme for express enterprises. Firstly, we crawl online reviews of express companies and use LDA model to identify service attributes. Secondly, customer satisfaction is calculated by sentiment analysis, and simultaneously, the importance of each service attribute is calculated. Finally, we carry out an opportunity algorithm with the results of text mining to quantify the innovation opportunities of service attributes. The results show that the framework can effectively identify service innovation opportunities from the perspective of customer demand. This study provides a new way to explore the direction of service innovation from the perspective of customer demand.

Risk Assessment of Express Delivery Service Failures in China: An Improved Failure Mode and Effects Analysis Approach

With the rapid growth of express delivery industry, service failure has become an increasingly pressing issue. However, there is a lack of research on express service failure risk assessment within the Failure Mode and Effects Analysis (FMEA) framework. To address the research gap, we propose an improved FMEA approach based on integrating the uncertainty reasoning cloud model and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. The cloud model describing randomness and fuzziness in uncertainty environment is adopted to achieve the transformation between the qualitative semantic evaluation of occurrence (O), severity (S), and detection (D) risk factors of FMEA and the quantitative instantiation and set up the comprehensive cloud of risk assessment matrix for express delivery service failure (EDSF). The TOPSIS method calculates and ranks the relative closeness coefficients of EDSF mode. Finally, the rationality and applicability of the proposed method are demonstrated by an empirical study for the express delivery service in China. It is found that among 18 express delivery service failure modes, six service failure modes with high risk are mainly located in the processing and delivery stages, while six service failures with the relatively low risk are involved in the picking-up and transportation stages. This study provides insight on how to explore the risk evaluation of express delivery service failure, and it helps express delivery firms to identify the key service failure points, develop the corresponding service remedy measures, reduce the loss from service failures, and improve the service quality.

Impact of Express Delivery Industry’s Development on Transportation Sector’s Carbon Emissions: An Empirical Analysis from China

In recent years, China’s express delivery industry has developed rapidly. According to a rough estimate in this paper, carbon emissions caused by express parcel transportation in China account for 1/7 of the transportation sector’s carbon emissions. However, considering the possibility of a scale effect, it is unclear whether the express delivery industry’s development will inevitably lead to more carbon emissions. Therefore, this paper uses the panel data of 30 Chinese provinces from 2008 to 2017 to explore the complex relationship between the express delivery industry’s development and the transportation sector’s carbon emissions, and also conducts regional heterogeneity analysis. The main findings are as follows: (1) There is a significant U-shaped relationship between per capita express delivery amounts and the transportation sector’s CO2 emissions, especially in the Central region. (2) At the national level, the number of per capita postal outlets significantly promotes the transportation sector’s CO2 emissions. (3) The impact caused by the number of per capita postal workers varies regionally. Increasing postal worker numbers in the Western region can significantly reduce carbon emissions, while the result in the Central region is the opposite. (4) The Express Comprehensive Development Index (ECDI) has a significant U-shaped effect on the transportation sector’s carbon emissions at the national and sub-regional level.