Streamlining logistics and transportation using AI

Automating Logistics and Transportation

The demand for faster delivery or same-day delivery of goods and services is stretching resources and increasing the complexity of supply chain operations. In addition, commercial vehicles are contributing to the already rising carbon emissions.

AI-driven decision-making and automation along with the use of cleaner energy sources can drive sustainability in transportation, while balancing costs and improving profitability. Real-time information and responsive algorithms are better at responding to dynamic demand scenarios. Automation in logistics helps optimize vehicle trips, labor usage, inventory levels, warehousing, and supplier management. This will in turn, reduce wastage, drive efficiencies and support sustainability in logistics. In the long run, businesses can increase revenue at lower operating costs, enhance customer service and achieve their sustainability goals.

Towards a sustainable supply chain enterprise

With the rapid increase in e-commerce, improving efficiencies of logistics and transportation operations has become a major concern for enterprises. The increased complexity of operations (including same-day delivery) has stretched resources such as material and labor beyond capacity. Enterprises are looking for sustainable solutions to not only ease operations but also improve profitability in the long run.

Advanced technologies such as artificial intelligence (AI) and automation, can help enterprises optimize the use of resources while streamlining supply chain operations. This white paper accentuates the potential of improved efficiency in decision-making for reducing resource consumption of logistics and transportation in enterprises. Combined with human-in-the-loop automation and cleaner energy sources, the enterprise of the future can lead sustainability transformation.

Reasons to adopt sustainable measure

E-commerce and faster delivery services have grown like never in the last five years. The COVID-19 pandemic further accelerated the trend. With more than half the global population living in cities due to rapid urbanization,these trends lead to extreme congestion and increased carbon emissions. Transportation activities constitute more than 60% of the world’s oil consumption, with road transport accounting for 70% of that amount. Commercial vehicles are essential for fast e-commerce deliveries, and these are responsible for a disproportionately larger fraction of carbon and noxious gas emissions compared to their contribution to overall vehicles journeys.

Consequently, these factors slow down and hamper our progress in reducing global greenhouse gas emissions by 7.6% each year until 2030. Enterprises need a balanced approach that enhances the customer shopping experience while optimizing costs of logistics and transportation and leading to overall reduction in carbon emissions.

The growing need for advanced technologies

The overall process of managing the supply chain and transportation consists of multiple computationally challenging stages including loading of parcels into containers or trucks, crew scheduling, sorting, and packaging goods for intended destinations, managing inventory, and optimal routing of delivery vehicles. The interconnected nature of these stages makes the problem of balancing costs with sustainability increasingly complex.

Consider the schematic diagram of an enterprise supply chain as shown in Figure 1. Like most complex systems-of-systems, a decision taken anywhere in the supply chain has a cascading effect throughout. For example, a small reduction in inventory of one product leads to lower availability in stores and higher demand in regional warehouses to replenish the shortage. We believe a combination of AI-driven decision-making, automation, and the use of cleaner energy resources will help businesses achieve the dual objective of efficiency and sustainability.

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The introduction of electric vehicles (EVs) has added a whole new dimension to sustainable deliveries. These EVs offer certain distinct advantages such as reduced carbon emissions, subsidies and incentives from governments, and access to parts of cities (especially in European countries) that are otherwise inaccessible for traditional delivery vehicles.

AI for sustainable logistics and transportation

Given the complexity of the operations involved in supply chains, enterprises must go beyond deploying EVs and using eco-friendly packaging material to ensure sustainability. Though these new technologies reduce the impact of transportation and logistics on the environment, they are not sufficient to achieve ambitious sustainability goals. A more consummate revolution is taking place in algorithms that manage these functions, and this can lead to leaner and efficient enterprises.

Real-time information and responsive algorithms enable enterprises to adjust to changing requirements. Simultaneously, enterprises must work with flexible vendors and put in place more aggressive inventory policies to ensure efficient utilization of labor and transport resources.

Figure 2 depicts the enterprise of today. Despite the dynamic nature of customer demand, real-time information does not resonate throughout the supply chain; it is generally siloed in the last mile portion. This leads to less efficient operations and in extreme cases, can cause severe disruptions because of a mismatch between expectations and reality.

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Assuming the physical entities in the supply chain remain constant, Figure 3 depicts the use of algorithms to improve the responsiveness of the enterprise.

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The two examples below show the potential impact of such AI-driven algorithms on the enterprise:

1. Optimized Transport Routes: In e-commerce doorstep delivery, reducing the number of vehicles in operation saves fuel and manpower costs with the added benefit of lower carbon emissions. Identifying opportunities for combining routes for multiple vehicles with dynamic optimization and AI can reduce the number of trips without compromising on service levels. This improves coordination between logistics providers so that the same neighborhoods are not repeatedly visited by the same vehicles. This further aids in smoothing supply chain operations by anticipating demand and mobilizing resources proactively. 
2. Efficiency in Distribution: In sorting and distribution centers, employing automation and cost minimization algorithms throughout the distribution process (sorting, workforce allocation, scheduling, and routing) can produce superior and more structured solutions to demand unpredictability and cost-related problems. Such approaches can reduce the number of containers used, and consequently the number of vehicles for transportation. Further, with the advent of faster deliveries, new customers may need to be added to the existing solutions resulting in dynamic and complex problems. Automated routing solutions using machine learning (reinforcement learning) and search heuristics can solve large-scale dynamic problems within minutes.

Building the sustainable enterprise

Organizations around the world are realizing that being sustainable does not necessarily mean losing profits. Using a combination of automation, AI-based optimization and alternate energy sources can help strike a balance between economic and ecological sustainability. The COVID-19 pandemic has accelerated the trend of automation, which is expected to last for a long time in the future. We believe that it is high time for enterprises to upgrade or overhaul their existing infrastructure and invest in re-skilling their employees. Doing so will provide long-term benefits of increased revenue at lower operating costs, better customer service, and collectively enable the move towards a greener planet. For the logistics and transportation functions, AI can provide a smooth transformation from the existing carbon-intensive processes to a 100% green fleet, without losing sight of economic realities.