Leveraging Analytics for Supply Chain Optimization in Freight Industry

We live in a country whose logistics industry is slated to be worth $160 billion. Several start-ups have emerged in India trying to crack this globally yet unsolved problem statement. Moreover, the freight scene, being age old, has its segment of bottlenecks to deal with which are inclusive but not limited to problems like fragmentation, inflated costs, lack of visibility into lanes, limited digital capabilities and back hauling. Tech-driven start-ups provide solutions to overcome these challenges for the fleet owners in terms of optimized demand–supply matching, brokerage eliminations by connecting supply with relevant demand thereby reducing costs, expose truckers and fleet owners to unchartered lanes and also facilitate reverse loads to optimize costs for these businesses. This is where companies are leveraging data science and analytics to tackle these issues and help the businesses grow. Companies like Uber Freight, BlackBuck and Rivigo are using the best of technologies to monetize this industry. Data when logged in the right manner can help industries understand the intricacies of issues and help them overcome the same. A typical example of implementation would be using a simple regression technique to predict demand in a specific region so that supply can be exposed well within time in order to avoid idling period by these truckers. Tracking key metrics like supply turn around time (TAT), truck in transit duration (TiT), placement index and others can help organizations determine and optimize on these metrics to maximize revenue. Being convoluted of a system, this industry has been a tough nut to crack, especially in a country like India. This chapter discusses how companies set up the entire data platform and infrastructure, thereby facilitating the usage of data for advanced analytics techniques to solve some crucial supply chain problems in the freight industry. The chapter also talks about some of the use cases for analytics and machine learning to solve problems related to the freight industry. Firstly, we demonstrate some visualizations and representations as to how insights are drawn through analytics to solve these kinds of problems. We follow this up by discussing how data infrastructures are set up in organizations to collect freight data and then finally we showcase some ML techniques that are used in the freight sector of businesses. This in turn would help users understand the nuances of decision science and analytics with its capabilities in scaling businesses.