Artificial Intelligence to improve Customer Experience in Logistics!


This is a case of an upcoming logistic company looking to offer services of last mile deliveries for third parties and warehousing facilities. The handling involves large number of shipments per day for thousands of clients across multiple cities.


The biggest challenge that is presented is demand supply forecast and customer service requests to manage stocks at various locations so that the deliveries are handled and issues resolved within stipulated time with lesser dependency on agents.

This is an industry that is driven on stringent SLAs and requires extremely high standard of customer/stakeholder interaction. There is a substantial movement of activities at any given point of time and hence large amount of transactions involving multiple stakeholders and handshakes with different functions. The complexities are multifold with the spread across cities, the cultural diversity and capabilities/work ethics of manpower.


The recommendations at this stage to client are initiating data clean up and normalization; collate demands and supplies, service requests and feedback from multiple channels to a single platform.

 There is a need to draw trends of demands/supplies; the most time consuming transactions and those requiring human intervention. The various structured and unstructured issues across different mediums like email, helpdesk, social media, phone or chats are streamlined.

·         Issue resolution and demand forecast is automated using ML, NLP.

·         Automatic allocation of tickets to appropriate agents basis the ticket type,

·         Location or client can be enabled that could help in improving TAT and first time right ratio can be impacted remarkably.

Operational efficiencies can be drawn from automated workflows across functions etc. With the help of sophisticated analytics they could easily predict inventory demand at a particular location, anticipate issues due to local legislations, performance of different functions and agents, resource optimization.