Delivery Companies Research Articles

A realistic situation, proposed from data generated within the GeOrder Simulator to elicit the statistical reasoning

Construction and handling of important statistical concepts, such as statistical estimators computing, or organization of data in graphs and tables, are oftenly taught in a strict algorithmic manner. As consequence, students abilities regarding experimentation, data sampling, data collecting, data organization and results interpretation, are limited and their solutions lack of statistical sustain. In this work, in study class consisting in students of the Logistic and Transport Engineering program, it is developed and implemented a realistic problem situation: a sales e-commerce company has to decide, by analyzing received orders, whether to implement its own delivery system or hire a third-party delivery company. The software GeOrder Simulator is employed to generate random data for weight and location of simulated orders and the data are presented to students in tabular and iconic, onto a map geolocation, semiotic representation registers. The research aim of this work, is to analyze, from the perspective of Semiotic Representation Theory, the effect onto the statistical reasoning that is provoked in students when they solve the realistic problem situation with the data generated by the GeOrder Simulator. The results show that the addressing of the realistic problem situation together with the usage of the GeOrder Simulator, elicited the statistical reasoning in the students since it is observed the integration of statistical concepts within their argumentation, by means of the coordination of tabular, graphical and numerical semiotic representation registers.

Competitiveness Evaluation of Express Delivery Enterprises Based on the Information Entropy and Gray Correlation Analysis

With the rise of e-commerce in China, the express delivery industry has developed rapidly; however, in this stage of rapid growth, the development of express delivery enterprises is uneven. There are problems such as high loss, damage rates, and poor service attitudes. An evaluation of the competitiveness of express delivery enterprises can help these companies understand their shortcomings and learn from each other’s strengths, which can promote the long-term development of express delivery enterprises. In this study, the information entropy method and the gray prediction method were used to establish an index system and analyze the core competitiveness of four listed express delivery companies in China, using indicators such as service quality, price level, market position, guarantee ability, and comprehensive strength; the purpose was to study the competitive advantages of these express companies. By using the gray correlation analysis to calculate the degree of correlation and comparing the size of the degree of correlation, it can be concluded that among the four express delivery companies, ZTO Express shows the strongest competitiveness and that Shentong Express is the weakest. Through the evaluation and analysis of the competitiveness of express delivery enterprises, this study provides a reference basis for operation development and competitiveness improvement of express delivery enterprises in China.

Taking YTO and SF Express as Examples to Analyze the Operation Status of the Express Delivery Industry

In recent years, with the gradual prosperity of online shopping, the development of the express delivery industry has also flourished. The ensuing problem is that the market is saturated, and many express delivery companies will carve up the existing express delivery market together, so the ultimate profit of each company will be relatively meager. How an enterprise maintains its existing market size is very important. This article selects two companies, SF Express and YTO, with direct sales system and franchise system, for case analysis. In this research, the basic situation of the two enterprises is introduced, followed by a comparison of products, services and business levels of the enterprises to gain a picture of their economic benefits. The research results show that SF Express has better economic benefits and development level. It can be concluded tha at present, people not only pay attention to the price of delivery but also pay attention to the quality of goods in the process of transportation. In the future, the express delivery market will gradually be refined, but the price and service in each market will tend to be unified. If companies want to maintain the sustainable development of business, it is suggested to continue expending your own scale and learn from each other's strengths before the entire market stabilizes.

A Rebalancing of Financial Valuations and Expectations Moving Forward in the Telehealth Sector as the United States Moves Toward a Post-COVID-19 Reality.

The telehealth sector of health care delivery experienced significant growth at the start of the pandemic as web-based care quickly became essential for the ongoing safety of patients and health care providers, such as clinicians and other health care professionals. After vaccines were introduced, however, telehealth companies lost value as the need for web-based care appeared to lessen. Presently, both existing telehealth companies and new entrants to the space are seeking ways to innovate, gain investor and customer buy-in, and overcome competitors. New companies are hoping to be seen not as pandemic-era substitutes, but instead as reinforcements to in-person care, valuable in their own right thanks to the convenience and technological advancements they bring. This struggle to reframe the value proposition, or perceived benefit, of telehealth is reflected in fluctuating stock prices and dropping valuations. This viewpoint summarizes the market volatility seen in the telehealth sector since the start of the COVID-19 pandemic and suggests potential opportunities for growth in the space. This is accomplished through a qualitative secondary research approach, leveraging contemporary sources, financial references such as Yahoo! Finance, and peer-reviewed literature to support predictions for the future market. We found that, in 2020, the size of the US telehealth market rose to US $17.9 billion and is estimated to reach US $140.7 billion by 2030. Additionally, digital health venture funding nearly doubled in 2020 over the prior 2 years with total funding rising to US $14.1 billion. However, these factors produced an oversaturated market in which the volume of supply was higher than demand, resulting in a sharp drop in valuations for some as vaccination rates climbed in 2021. In the face of this rebalancing, or return to normal following excessively high or unsustainable valuations, we suggest a possible path forward for telehealth companies in the postpandemic era. Suppliers' current role in the telehealth space-whether health care industry incumbents, that is, traditional health care delivery systems and companies, or "telehealth-first" challengers-are especially relevant to the specific growth strategies they should pursue. Furthermore, consideration of the areas of medicine and characteristics that best lend themselves to web-based care may lead to a greater chance for long-term success in a postpandemic health care delivery system. In the future, we believe investors should expect a bullish market, that is, one characterized by growing share prices. Success is likely to occur in part through changing the actual models of care, as opposed to moving traditional care to a web-based format. The oversaturated market will likely condense into select established telehealth giants who were able to adapt to the changing landscape. While investors may be reasonably hesitant regarding individual telehealth companies, the industry can expect slowed but continued growth.

Proposal for the improvement of the integral logistics of a courier, shipping and express delivery company in the city of Villahermosa, Tabasco

Logistics activities currently represent variables of singular importance in the competitiveness of companies, which is evidenced by evaluating their impact on costs, their link with the level of customer service, their importance in the reliability of operations, safety, and their determining role with respect to many of the values perceived by the market. The purpose of this study is to evaluate logistics processes in an integral manner, considering the before, during and after of these processes. The methodology considers the Likert scale to collect qualitative aspects and transform them into quantitative aspects in order to obtain the necessary information to design a proposal that fits the requirements and needs of the company under study.

Solution Of Capacitated Vehicle Routing Problem For A Food Delivery Company With Heuristic Methods

A food delivery company operates in Balıkesir performs to the distribution for the products of a certain brand from the central warehouse to the customers located in the central districts of Altıeylül and Karesi by using two vehicles with high capacity. The company visits customers on certain routes to meet their daily demands and is able to meet all demands at the end of the day. In this study, the distribution of the company's products was considered as a Vehicle Routing Problem, and it was aimed to reconstruct the distribution routes of the vehicles with the help of various algorithms and to provide cost savings in terms of the distance traveled. In order to solve the problem, first of all, an appropriate capacity assumption was made for the vehicles by considering the daily demand amounts of the customers. Under this assumption, first new customer groups to be visited in daily periods were created, and then new routes were obtained for the relevant customer groups. In this process, the problem was designed as a Capacity Constrained Vehicle Routing Problem, and the results obtained using Fisher and Jaikumar's Algorithm and Clarke and Wright's Savings Algorithm were evaluated. When the results obtained are compared with the current route status of the company, it has been determined that it is possible to achieve a high rate of improvement by using the routes determined by algorithms.

The Profitability Puzzle of Digital Labor Platforms

By examining digital labor platforms that connect customers with gig workers, we attempt to solve the puzzle concerning why these labor platforms, such as ride-hailing companies and food delivery companies, continue to suffer weak profitability even though they promise a more efficient way to mobilize and utilize the platform’s labor force. Stylized facts on the profitability of the top ten global labor platforms and a descriptive analysis of their cost structures are presented. We further argue that the modes of production and competition inherent in digital labor platforms are critical to this profitability puzzle because they determine the upper limit of surplus value extraction in production and the heavy financial burden of profit generation for labor platforms. JEL Classification: P12, E24, D40

Incorporating Time-Series Forecasting Techniques to Predict Logistics Companies’ Staffing Needs and Order Volume

Time-series analysis is a widely used method for studying past data to make future predictions. This paper focuses on utilizing time-series analysis techniques to forecast the resource needs of logistics delivery companies, enabling them to meet their objectives and ensure sustained growth. The study aims to build a model that optimizes the prediction of order volume during specific time periods and determines the staffing requirements for the company. The prediction of order volume in logistics companies involves analyzing trend and seasonality components in the data. Autoregressive (AR), Autoregressive Integrated Moving Average (ARIMA), and Seasonal Autoregressive Integrated Moving Average with Exogenous Variables (SARIMAX) are well-established and effective in capturing these patterns, providing interpretable results. Deep-learning algorithms require more data for training, which may be limited in certain logistics scenarios. In such cases, traditional models like SARIMAX, ARIMA, and AR can still deliver reliable predictions with fewer data points. Deep-learning models like LSTM can capture complex patterns but lack interpretability, which is crucial in the logistics industry. Balancing performance and practicality, our study combined SARIMAX, ARIMA, AR, and Long Short-Term Memory (LSTM) models to provide a comprehensive analysis and insights into predicting order volume in logistics companies. A real dataset from an international shipping company, consisting of the number of orders during specific time periods, was used to generate a comprehensive time-series dataset. Additionally, new features such as holidays, off days, and sales seasons were incorporated into the dataset to assess their impact on order forecasting and workforce demands. The paper compares the performance of the four different time-series analysis methods in predicting order trends for three countries: United Arab Emirates (UAE), Kingdom of Saudi Arabia (KSA), and Kuwait (KWT), as well as across all countries. By analyzing the data and applying the SARIMAX, ARIMA, LSTM, and AR models to predict future order volume and trends, it was found that the SARIMAX model outperformed the other methods. The SARIMAX model demonstrated superior accuracy in predicting order volumes and trends in the UAE (MAPE: 0.097, RMSE: 0.134), KSA (MAPE: 0.158, RMSE: 0.199), and KWT (MAPE: 0.137, RMSE: 0.215).

Siuntų pristatymo sektoriaus įmonių veiklos efektyvumo vertinimas COVID-19 pandemijos kontekste

The paper examines the impact of COVID-19 pandemic on operational effectiveness of parcel delivery sector companies operating in Lithuania. As demand of parcel delivery sector‘s services has increased, it is important to find out whether the impact of COVID-19 pandemic on operational effectiveness could be considered as positive even though the sector faces difficulties related to parcel delivery. The aim of the research is to evaluate the efficiency of parcel delivery sector companies operating in Lithuania during the COVID-19 pandemic. After the literature analyses, it was decided to examine the financial statements of 27 parcel delivery companies during the COVID-19 pandemic (2020-2021) and before the pandemic (2017-2019). During the investigation the impact of pandemic was analysed using the methods of correlation, regression, and logistics analysis. The main purpose of correlation analysis was to find connections between variables and by regression analysis to find causality between dependent and independent variables. The results of pandemic ware compared with results of pre-pandemic. After this, the results of pandemic have been applied in logistics analysis in forecasting process. Overall, it became clear that during the pandemic operational profit was more effective and costs were less effective comparing with results of pre-pandemic. In addition, the results of logistics analysis showed that there is a possibility that in the future operation profit will be ineffective and costs will be effective.

Routing and scheduling decisions for a single-hub same-day delivery network

The present research addresses the problem of routing and scheduling in a home delivery network with distinct pickups and deliveries. A fleet of capacitated vehicles belonging to the only hub center of the network transports the demands between the network nodes. The proposed mixed integer programming model minimizes the total transportation costs in a situation where demand splitting is possible. This model can be used in general hub networks and many-to-many networks. Due to the complexity of the problem, valid inequalities are used to reinforce the formulations. Then, a genetic algorithm serves to solve problems of various sizes. The performance of the reinforced model along with the valid inequalities and the genetic algorithm is examined through a case study of a same-day postal delivery company. The computational results show the good performance of the solution methods.

An improved matheuristic for solving the electric vehicle routing problem with time windows and synchronized mobile charging/battery swapping

The shift towards low-emission vehicles in transportation activities, electric vehicles (EVs) in particular, has accelerated lately due to the growing concerns in modern societies regarding greenhouse gas emissions and climate change. Delivery companies have started using EVs in their fleets to reduce their dependency on fossil fuels and improve their carbon footprints. However, range anxiety, long recharge durations and insufficient recharging infrastructure still restrain the wider adoption of EVs in the sector. As a remedy, battery swapping vans (BSVs) were proposed in the literature to supply energy to EVs at points of need and the arising problem was referred to as the Electric Vehicle Routing Problem with Time Windows and Synchronized Mobile Battery Swapping (EVRPTW-SMBS). However, the use of BSVs is limited to small commercial vehicles. In this study, we generalize the problem and present the Electric Vehicle Routing Problem with Time Windows and Mobile Charging Stations (EVRPTW-MCS). In this problem, EVs serve the customers within their time windows and electric trucks/vans are employed to recharge or swap their batteries at selected customer locations during their visits. The objective is to minimize the total operational cost with the minimum fleet size. First, we present the mathematical model of the EVRPTW-MCS. Next, we propose a matheuristic approach that combines the Variable Neighborhood Search with exact method to solve it. Then, we perform an extensive numerical study to validate the performance of the proposed approach and present new best solutions for two related problems in the literature. We also investigate the potential benefits of utilizing MCSs and provide several trade-off analyses. Finally, we provide a case study based on real data to present managerial insights.

How platform businesses mobilize their users and allies: Corporate grassroots lobbying and the Airbnb ‘movement’ for deregulation

Abstract This article analyzes and theorizes the political strategies of businesses in the new digital ‘platform’ economy. Airbnb, Uber and meal delivery companies have transformed travel, urban space and repertoires of everyday exchange; they are also transforming norms around governance. Central to platforms corporate political strategies is the use of corporate grassroots lobbying (CGL), the selection, mobilization, resourcing and coordination of ordinary users and grassroots allies to influence the public and policy-making process. The article argues that platforms build on, and make five innovations to, the most common existing repertoires of CGL. Four main approaches of CGL among these businesses are also identified: temporary mobilization; curated storytelling; front groups; and grassroots alliances. The article demonstrates how these approaches to CGL are deployed and combined, using Airbnb as the main case study. Finally, I reflect on the implications of the findings for the platform economy, corporate political activity and socioeconomic change.

Determinants of satisfaction with online food delivery providers and their impact on restaurant brands

研究目的本调查旨在分析在线食品配送提供商 (OFD) 绩效与餐厅品牌之间的溢出效应。 它提出了 OFD 电子满意度的三个决定因素模型, 并将这些变量与餐厅的品牌满意度、形象和忠诚度相关联。研究设计/方法/途径本研究设计了一项调查, 并收集了来自厄瓜多尔交付应用程序用户的 332 份回复。 应用PLS结构方程模型。研究发现OFD 满意度的三个拟议决定因素非常重要：电子服务质量、送货员的个人方面和感知的食品质量。 关于溢出效应, 电子服务质量和个人方面对感知食品质量有影响, 调节对餐厅品牌满意度、形象和忠诚度的连锁影响。研究研究局限性/影响本研究数据是在 COVID-19 之前收集。 由于预计送货员和顾客之间的接触最少, 因此需要在 COVID-19 大流行的背景下进行进一步研究。 此外, 食品质量认知可能包括生物安全规范的新担忧。实践意义本研究建议采取旨在改善 OFD 服务体验的做法, 包括适当的培训、监督和改善送货工人的条件。 另一方面, 由于餐厅不控制 OFD 的绩效, 因此他们的合同协议应着重于避免服务失败而损害餐厅的品牌资产。研究原创性/价值本文满足了研究协作经济背景下溢出效应的需要, 在这种情况下, 送货公司、送货工人和餐馆共同提供服务, 其中一方的表现会影响消费者的 对方的看法。

Research on express transport based on the ARIMA model

Based on the express transportation data of express companies, this paper makes an in-depth study of express transportation problems. First, the evaluation model to rank the importance of cities. Second, we predict the number of express deliveries between site cities with the time-series ARIMA model. Then, the random forest model of the CART decision tree was used to predict normal shipments and express delivery quantity. Next, the planning model is established based on the two parameters of railway transportation fixed cost and rated loading volume to plan the transportation scheme with the lowest cost of the express delivery company. Finally, the delivery requirements and model parameter estimates were estimated through the KDE model.

Express Delivery and Logistics Courier Company Marketing Analysis (Case Study: JNE)

JNE is a company engaged in the delivery of goods in Indonesia. Goods delivery is not only done within the country but also provides overseas goods delivery services. PT JNE Express has an extensive network and professional services that have helped in product distribution and increased competitiveness in marketing. Freight forwarding service companies have specific marketing strategies in capturing markets and competing for customers with different marketing strategies, strategies and determining the right marketing mix will make it easier for companies to create, build and carry out good marketing strategies. This study aims to find out how the marketing strategy is implemented in PT JNE Express.This study uses a qualitative descriptive method approach. Description method to explain aspects related to research collected through literature study. This study uses research journals, books, theses, and trusted online news channel sources to collect data, reduce and search for and select variable descriptions.Based on the results of marketing strategy research conducted by PT JNE Express, namely using the application of segmenting, targeting, positioning and marketing mix. This strategy has superior products that suit customer needs which include good service and information communication and the process strategy includes fast and precise technology-oriented contact service services.

Model-Based Vehicle-Miles Traveled and Emission Evaluation of On-Demand Food Delivery Considering the Impact of COVID-19 Pandemic

The market for on-demand food delivery (ODFD) has increased considerably, especially during the COVID-19 pandemic. It is crucial for transportation and environmental agencies to understand how ODFD has reshaped the travel patterns of people, affecting vehicle-miles traveled (VMT) as well as pollutant emissions in the transportation system. However, the lack of public data from food delivery companies makes it challenging to quantify the impact of on-demand delivery on the real-world transportation network. In this research, we propose a comprehensive framework to quantify the VMT and emissions incurred by ODFD with three main components: (i) a daily activity generation tool, Comprehensive Econometric Micro-simulator for Daily Activity-travel Patterns, to create a simulation scenario of ODFD behaviors based on a real-world roadway network and population demographics in the City of Riverside, California; (ii) an efficient order dispatching and routing algorithm, adaptive large neighborhood search, to obtain a high quality order dispatching and routing plan; (iii) an emission evaluation model, emission factor (EMFAC), to evaluate pollutant emissions from all dining-related trips. Both short-term and long-term impacts of the COVID-19 pandemic are evaluated. Experimental results show that ODFD has great potential to reduce the dining-related VMT and emissions. The total dining-related VMT in the during-pandemic case decreased by 38% and in the after-pandemic case reduced by 6% to 9%, and the corresponding environmental impacts were reduced accordingly. Meanwhile, emissions reduced significantly with more electric vehicles involved in food delivery. With 100% electric delivery fleet, the ODFD service can save 14% to 22% of emissions after the COVID-19 pandemic.

Research on Shared Logistics Decision Based on Evolutionary Game and Income Distribution

As a green, efficient, and feasible solution, logistics resource sharing has received increasing attention in urban last-mile delivery. Instability in cooperation and unequal income distribution are significant constraints to logistics resource sharing. In this paper, we investigate the logistics resource sharing decision-making process among express delivery companies. First, according to the characteristics of the express delivery companies, symmetric and asymmetric game models based on evolutionary game theory are proposed, respectively. We examine the express delivery company’s choice of strategy and the major determinants of collaboration. Then, we examine the income distribution problem for subjects sharing logistics resources and propose an improved Raiffa solution that takes enterprise scale into account. Finally, certain management insights are offered for the express delivery companies to support the realization of logistics resource sharing. The results show that the evolution direction of the model is influenced by the initial state, enterprise scale, income distribution coefficient, and default penalty coefficient. Furthermore, the improved Raiffa solution takes into account the asymmetry of resource contribution of participating subjects and is more reasonable.

A Framework for Urban Last-Mile Delivery Traffic Forecasting: An In-Depth Review of Social Media Analytics and Deep Learning Techniques

The proliferation of e-commerce in recent years has been driven in part by the increasing ease of making purchases online and having them delivered directly to the consumer. However, these last-mile delivery logistics have become complex due to external factors (traffic, weather, etc.) affecting the delivery routes’ optimization. Intelligent Transportation Systems (ITS) also have a challenge that contributes to the need of delivery companies for traffic sensors in urban areas. The main purpose of this paper is to propose a framework that closes the gap on accurate traffic prediction tailored for last-mile delivery logistics, leveraging social media analysis along with traditional methods. This work can be divided into two stages: (1) traffic prediction, which utilizes advanced deep learning techniques such as Graph Convolutional and Long-Short Term Memory Neural Networks, as well as data from sources such as social media check-ins and Collaborative Innovation Networks (COINs); and (2) experimentation in both short- and long-term settings, examining the interactions of traffic, social media, weather, and other factors within the model. The proposed framework allows for the integration of additional analytical techniques to further enhance vehicle routing, including the use of simulation tools such as agent-based simulation, discrete-event simulation, and system dynamics.

An Online Deep Reinforcement Learning-Based Order Recommendation Framework for Rider-Centered Food Delivery System

As an important part of intelligent transportation systems, On-demand Food Delivery (OFD) becomes a prevalent logistics service in modern society. With the continuously increasing scale of transactions, rider-centered assignment manner is gaining more attraction than traditional platform-centered assignment among food delivery companies. However, problems such as dynamic arrivals of orders, uncertain rider behaviors and various false-negative feedbacks inhibit the platform to make a proper decision in the interaction process with riders. To address such issues, we propose an online Deep Reinforcement Learning-based Order Recommendation (DRLOR) framework to solve the decision-making problem in the scenario of OFD. The problem is modeled as a Markov Decision Process (MDP). The DRLOR framework mainly consists of three networks, i.e., the actor-critic network that learns an optimal order ranking policy at each interaction step, the rider behavior prediction network that predicts the grabbing behavior of riders and the feedback correlation network based on attention mechanism that identifies valid feedback information from false feedbacks and learns a high-dimensional state embedding to represent the states of riders. Extensive offline and online experiments are conducted on Meituan delivery platform and the results demonstrate that the proposed DRLOR framework can significantly shorten the length of interactions between riders and the platform, leading to a better experience of both riders and customers.

Green meets food delivery services: consumers' intention to reuse food delivery containers in the post-pandemic era

PurposeThe coronavirus disease (COVID-19) has increased food delivery service demand, which generates massive amounts of solid waste, specifically plastic material. Therefore, this study aims to examine the determinants of consumers' intention to reuse food delivery containers (ITR) using the extended theory of planned behavior (TPB). Moral obligation was included as an antecedent, while behavioral expectation (BE) ahead of behavioral intention was an immediate predictor of consumers' pro-environmental behaviors.Design/methodology/approachThe hypotheses were tested on 348 food delivery service users in Malaysia and analyzed using the partial least squares structural equation modeling (PLS-SEM).FindingsThe findings indicated that consumers' ITR is directly influenced by perceived behavioral control and attitude. Perceived behavioral control and attitude had a positive partial indirect effect on ITR through BE. Meanwhile, subjective norms and moral obligation had a positive full indirect effect on ITR through BE.Research limitations/implicationsThe findings can be directly applied to practical situations of food delivery companies and environmental protection organizations managing solid waste among food delivery services.Practical implicationsUnderstanding consumers' ITR could promote practical environmental sustainability. Practically, the study provides insights to the food delivery service industry, policymakers and relevant stakeholders to encourage consumer behavior change by reusing food delivery containers in line with Sustainable Development Goal 12.Originality/valueThe study enhances the existing literature by extending TPB with two psychological variables: moral obligation (independent variable) and BE (mediating variable). To the best of the authors' knowledge, this study is the first attempt to empirically investigate BE in consumers' pro-environmental behavioral intention in a high-context culture and developing economy. This study could benefit food and beverage merchants, food delivery companies, governments, non-governmental organizations and pro-environmental behavior researchers in this industry.