Design and development of a student-initiated automated delivery system for potential use in e-commerce

The growth of e-commerce business – and the consequent faster delivery times – has demanded better services by overcoming problems such as high labor dependence and inefficiency at sorting and routes. This paper describes a student-initiated independent research and development project in which an automated system for the said delivery service involving a sorting mechanism enabled by Raspberry Pi and artificial intelligence-driven route optimization was designed and prototyped. The proposed system identified parcel destinations, efficiently sorted items, and calculated optimal delivery routes considering traffic, weather, and road conditions.In Singapore, e-commerce sales are expected to reach US$14 billion by 2027. This growth has increased the pressure on logistics providers to meet the rising customer expectations, especially in urban areas where same-day or next-day deliveries had been expected by the users. Late deliveries are one of the most common delivery problems. The traditional delivery services that rely on manpower slowly lose the ability to meet such criteria, causing more delay in deliveries and increase in operational costs. In manual sorting, it is exhausting and time-consuming to decode the packages and match them with information in the address database, increasing time taken in the sorting process and causing delay in deliveries.Our objective was to research and develop a prototype for an automated operations chain that spanned from inventory sorting to dispatching and final delivery. This involved:•designing an automated operations chain and evaluating the practical feasibility and logistics of achieving each stage of operation;•setting up an image-text algorithm using a web camera as an input device, capable of identifying the destination of the package and formatting it into a spreadsheet (xlsx). This allows for future reference by the management for further tracking or the product; and•comparing and evaluating wayfinding algorithms to plan an efficient route from the recorded destinations in the excel sheet.Key findings suggested that automation minimizes human error, enhances operational efficiency, and reduces environmental impact through optimized fuel consumption. Additionally, the data-driven approach taken by the system enhanced traceability and transparency, thus potentially building trust in customers. While there were still some challenges to be faced – for instance, restricted data access for validation – this project underlined the potential of AI and robotics in improving delivery logistics, hence providing a scalable and sustainable framework that might meet the increasing demands within the e-commerce landscape.