On-Demand Service Delivery Apps for Efficient Last-Mile Delivery

On-Demand Service Delivery Apps for Efficient Last-Mile Delivery

This study aims to pursue a SWOT analysis to unearth the improved strategies leverage on strengths and opportunities and remedy the weaknesses and threats on Malaysian on-demand delivery (ODD) apps towards better last-mile delivery services. This study was conducted by conducting detailed field observations, unrecorded interviews, author experiences and extensive literature reviews. Numerous ODD apps related concerns were identified and explored. By utilizing the reducing methods, it is organized and categorized into the four SWOT clusters. It demonstrates the way for future work to establish an effective and successful ODD apps management. A variety of strategies are suggested that can give a favourable impact on the ODD apps service provider. Findings and suggested strategies can add considerable value to current academic work, deepen knowledge along with the development of a successful integrated ODD network. This study is among a few of this kind that offers a new strategy for diversifying the ODD apps business by venturing into the last-mile delivery market. It is hoped that this study able to contribute to future studies and act as a resource for ODD practitioners. This study will also add to the existing ODD literature and expand the understanding of the last-mile delivery On-Demand Service Delivery Apps for Efficient Last-Mile Delivery

It is very important to predict the demand of APP-based taxi service. In this paper, we proposed a study to analyze and forecast the demand of APP-based taxi service by using two different machine learning approaches, linear and sinusoidal regression. The Wujin District of Changzhou City in China is the study area in the experiment. The experimental result shows that the linear regression is better than the sinusoidal regression, although the request of APP-based taxi service have the periodic variation naturally.

Secure Payment Integration: Our on-demand service application comes with secure and reliable payment integration. Users can easily make payments using various methods, including credit/debit cards, digital wallets, or even cash on delivery. The application ensures the safety of sensitive user information through robust encryption protocols.

Review and Rating System: Build trust and credibility with a review and rating system. Users can provide feedback and rate service providers, helping others make informed decisions. This feature promotes transparency and accountability, enhancing the overall user experience.

Under the background that the country advocates green travel and vigorously promotes the development of new energy industry, electric vehicles are bound to be gradually popularized in China, and the demand of electric vehicle users for charging service is also increasing. In order to let electric vehicle users know the location and availability of the charging pile station nearby in real time, it is a feasible scheme to build an intelligent pile station management platform by applying the rapid development of cloud computing technology and mobile app based on map navigation function. However, with the increase of the number of charging piles and charging stations, it is bound to put forward higher requirements for the performance of the management platform. This paper proposes an optimization scheme based on LBS cloud retrieval technology to update the available status of a large number of piles.

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