# Toward a Modern Last-Mile Delivery: Consequences and Obstacles of Intelligent Technology
## 3 Feasibility of Modern LMD  
Uses of AI-improved tangible or intangible technologies in LMD causes significant outcomes. [PERSON] [88] and [PERSON] [89] suggest that to be competitive in modern LMD, vehicles must be directed to the right consumer location, quantity, loaded with the right order, and deliver on time, so detecting and following the shortest, most economical route when directing vehicles is crucial. [PERSON], [PERSON], and [PERSON] [90] discuss how route optimization using autonomous robots from local facilities to customers can solve LMD operational challenges. The authors describe a situation in which goods are unloaded from trucks and delivered by robots. From findings, AI's optimization can reduce the delay issue. AI optimization and operations involve the application of technologies, such as advanced analytics and machine learning. This is to automate processes and problem-solving in operations and network, as well as to improve both design and optimization capabilities [91]. This study optimizes facility number and location and route, and as more facilities use robots to deliver goods, late deliveries are decreasing [90]. [PERSON] and [PERSON] [40] found that sidewalk autonomous delivery robots save time and increase customer satisfaction by delivering products faster. According to [PERSON] and [PERSON] [92], finding a solution to expanding product quantities and consumer expectations is vital, and employing autonomous robots for modest deliveries over short distances would minimize late deliveries. Logistics operators are seeing an increase in unsuccessful delivery efforts and product returns, necessitating complex delivery planning to achieve timeliness and cost effectiveness, and logistics service providers are using novel approaches to solve the issues [88], while it is evidenced that self-driving delivery robots can make faster and more efficient deliveries, according to [PERSON] et al. [41] and [PERSON] and [PERSON] [92]. In fact, time and cost efficiency are linked by [PERSON] and [PERSON] [40]. Similarly to previously discussed studies, [PERSON] et al. [41] argue for the benefits of smart robots in faster parcel delivery, fewer vehicles on the road, and improved delivery performance. Finding the best route by robots and drones needs AI, as AI-powered platforms uses real-time environmental, geographical, traffic, shipment, and delivery data to highlight the best route, so the vehicle's navigation system can display the optimized route [93]. Personnel costs should be saved because robots do not require a salary, unlike traditional trucking, which pays the driver. They would not require breaks because of their independence [92]. AI is capable of eliminating human communication errors, as AI-powered tools can help companiesimprove warehouse and transportation visibility by simplifying distribution networks and mapping capacity and equipment availability [93]. AI-powered technologies also assist firms in analyzing data, such as total daily deliveries, vehicle types utilized for deliveries, number of deliveries to specified zones/regions, route congestion, and spotting trends or patterns; as a result, companies, such as Walmart, Tesco, DHL, UPS, and Fedex, use AI-powered route planning tools [93]. DHL has deployed the \"Greenplan\" dynamic tour optimization tool to deal with delivery complexity. The process of determining the most cost-efficient route is route optimization. It is far more complex than simply finding the shortest path between distinct points, and needs to include full relevant variables, such as the location and number of necessary stops on the route, plus the time frame for all deliveries. Using this tool instead of traditional route optimization techniques saved 20% on delivery costs and a lot on computational time [94, 95]. Additionally, Tesco's AI-powered vehicle routing and scheduling system has helped them save 11.2 million miles and 8% fuel per order [96].