# Toward a Modern Last-Mile Delivery: Consequences and Obstacles of Intelligent Technology
## 2 Toward Modernity
### AI-Supported Intangible Technologies toward Modern LMD  
The use of groundbreaking AI in LMD may be employed as a data-driven decision-making technique [32; 72; 73]. Because of the recent surge in online sales, the LMD process now creates millions of data points from each client that requires delivery of their items. LMD companies may use consumer data in AI to make medium- and short-term projections for management and stakeholders [73]. The customer's data is analyzed, grouped, and categorised using pattern recognition in the first phase of the data mining process. The ML system then processes this data further to provide precise projections [73]. In addition to customer data, the LMD process may yield a variety of important information from daily delivery traffic, delivery zones, and driver performance [73]. AI will process occasional traffic and area topography to schedule deliveries with optimal performance by providing the best available route and vehicle; this will also assist drivers in deciding which alternative route to take during real-time delivery when traffic is suddenly blocked by an accident/other road congestions [73]. Furthermore, documented driver performance in the data system will help determine which driver is better in different areas to enhance the LMD process [73].  
All of these data-driven AI capabilities will provide the basis for dynamic decision-making, supporting businesses and stakeholders in improving performance, forecasting,capability, and even predictive maintenance [72; 73]. This data-driven AI in LMD will also assist in addressing systematic and strategic operational demands depending on the preferences of each stakeholder, including carriers, end users, local governments, and even citizens [72; 73]. Traditional logistics and non-data-driven delivery procedures that are inflexible and do not incorporate accurate AI and ML algorithms created from a broad variety of data will become outdated as they fail to solve the numerous present and future operational difficulties [72; 73]. The organization that eventually advances toward data-driven AI will profit from more precise dynamic decision-making for management, allowing it to participate meaningfully in the fast-paced LMD battle [72; 73].  
System integration may include the addition of new technology to an existing process or system, enabling the whole supply chain to exchange real-time data and link systems [74]. Through comprehensive information in LMD, logistics management software can coordinate the whole logistics system network and enhance value. Software as a service (SaaS) allows conventional data centers or business services to be inclusive and aligned with corporate KPIs by facilitating communication across organizational systems.  
Real-time information is provided via digital service systems that link all users, which may increase consumer expectations by making information more accessible and interpretable [75]. Other benefits might include real-time tracking, smart scheduling, and route optimization, which are all tied to the previously stated IoT and cloud computing technology [75; 76]. Intelligent transportation systems, for example, can automatically adapt routes and scheduling to prevent congestion and meet user patterns [32; 77]. Loading bay booking systems and public infrastructure improvements may enable the real-time interaction between cars and roadside infrastructure, enabling not only for the allocation of loading and unloading space but also the resolution of unforeseen delivery difficulties [32; 78].