Authors: Soumya M Achari, Pakhi Singha, Nithin Ramakrishnan
Abstract: On demand delivery began as a competitive edge in the consumer market. Quick commerce sites provided customers access to products within the shortest possible time to stand out from rivaling brands. However, this fast growth of 10 minute and 1 day delivery services leave traditional delivery services irrelevant. Due to the customer’s opting for convience and speed, retailers selling stock struggle to meet these expectations and lose profitability. Access to real time data updates and optimisation has hence become significant in ensuring delivery to correct locations, punctually and efficiently. Current local systems struggle to respond to dynamic data, leading to missed delivery time slots, manual intervention requirement, excessive fuel and time wastes, poor customer feedback and so on. In order to remain competitive in such consumer markets, business require real time updates on demand and supply chains, delivery agent availability, client shopping patterns and traffic volume information. To counter these challenges artificial intelligence can be used to understand real time data and set parcel delivery time slots automatically while routing delivery agents through optimal pathways and monitoring the system of the agents and customer to align with their available schedules. The AI will utilise previous ETA, traffic congestion, pattern recognition in relation to prior on time articles that were received and user presence to define schedules for delivery and update the consumers, drivers and supervisors accordingly. This proposed intelligent system would solve the common E-Commerce problems faced by traditional delivery systems by ensuring routes are mapped to avoid redundancy, increase time efficiency, deliver as per consumer availability, especially for cash on delivery where the client is required at the home for payment, provide real time transportation status of the products to supervisors and customers, therefore increasing the trust of the user in the brand and providing an avenue for the manager to handle mismanaged deliveries. Such a system would bolster customer satisfaction and also reduce fuel and time consumption for the drivers, enhancing their work life balance. Deliveries that are more likely to be missed or routes that could result in accidents would be information sent to the supervisor, customer and delivery agents respectively, hence, preventing missed deliveries, injuries and delays. Such systems have been applied experimentally at a smaller scale and proven successful in reducing time, fuel, costs and injury risk, while improving customer satisfaction, making them a worthwhile subject of research.
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