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January 21, 2021

The online e-commerce industry has seen unprecedented growth in the year 2020. As access to a vast selection of products is just a few clicks away, the top priority for retailers is to ensure customer satisfaction with flexible and speedy order fulfillment. The need is for digital transformation in logistics and for last mile distribution companies to offer flexible, faster and on-time deliveries.

This requires managing parcel volumes, improving resiliency, optimizing costs, and boosting process improvements. However, as the parcel distribution value chain consists of multiple players (see Figure 1) across B2C, B2B and the logistics domain offering geography specific services, it is a challenge to meet shipper and consumer demand for flexible and fast last mile distribution.

Figure 1: Parcel distribution and logistics value chain

In addition, lack of visibility and traceability across the value chain makes it difficult to ensure optimized delivery. It is also not easy for sorting and fulfillment centers to drive robust and resilient operations with last mile channels meeting varying demands and preferences for traceability. With the increase in parcel volume and decline rate in mail volumes, an AI-led transformation in logistics is critical to offer increased end consumer value. Here are four ways to do it:

Forecast for improved operational efficiency

Leveraging AI in logistics to anticipate and predict incoming volumes at terminal, geography, and retailer level is a great way to enhance operational efficiency. Companies can also offer chat bots and self-service applications for proactive and just in time communication. This will help anticipate call volumes and types of customer queries such as parcel tracking or change of delivery preferences and timings to improve on-time parcel delivery. In addition, pro-active customer communication will not only help improve customer satisfaction scores but also enable first time successful delivery.

Enhance supply chain visibility

Control tower automation and real time intelligence equips companies to improve asset utilization and enables preventive maintenance. Companies can enhance operational efficiency by getting real time insights into workforce health, parcels, and fuel consumption. To optimize workforce utilization, companies can also enable real-time navigation and eco-driving and gain insights to their last mile distribution.

Reduce CO2 emission

Implementing machine vision for improved operational efficiency helps monitor chute jam congestion, classify parcel and dimensions, measure fill rate in trucks and conduct vehicle checks. This also helps to monitor social distancing amongst resources for improved compliance. For instance, companies can support drone-based delivery and offer frictionless payments as part of contactless delivery experience. Network optimization aligned to dynamic demand and planning is an effective way for dynamic routing on the day of delivery. Companies can also get a real-time view into freight transport Co2 emission, last mile distribution emission, and facility energy consumption.

Slash costs and increase capacity

Forecasting and scenario planning can be improved by leveraging digital twin technology. This can involve the sorting terminal, line haul network, parcel and last mile distribution. Companies can offer environment friendly service offerings such as reusable and trackable bags and solutions for operational efficiency such as last mile crowd sourcing platform and marketplace for assets, where assets can be shared to increase throughput/capacity. This helps reduce cost. Finally, leveraging autonomous robots, 3D printing and AR assisted loading and unloading will enable hyper automation for increased capacity and throughput.

AI in Logistics: The key to build resilience

Proactive and responsive operations enabled by a real-time data platform and a core AI engine is imperative for digital transformation of logistics companies. Being future-ready is not just about efficient last mile distribution or gaining competitive advantage. It is about democratizing insights and collaborating with experienced partners in the ecosystem to bring in more robustness and resiliency and drive business outcomes in a seamless manner. It is about offering more contextualized insights. It is accelerating the adoption of technology-led interventions insights to reach the right people at the right time in the right channels for timely decision making.

Raghavendran V, Transformation Strategist and Enterprise Data Architect, TTH, TCS has over 18 years of experience and his expertise includes data strategy, consulting and delivering enterprise grade solutions. He is a Bachelor of Technology in Information Technology. 

Vigram Raja leads the IoT unit at TCS, with more than 15 years of experience. Based out of Sweden, Vigram is a Master of Engineering specializing in engineering design.


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