Driven by a team of computer scientists and electrical engineers from IIT Delhi and IIT Madras, Shipsy aims to create platforms for data-driven decision making with the vision of bringing visibility and operational efficiency to the Supply Chain industry.
Most of our paying clients are in the Supply Chain industry and we are enabling new business models, swifter operations using algorithms and machine learning. We are processing ~10 million transactions per month through our system.
We have a Dashboard along with a companion Android application. The Dashboard is used by Operations managers and the Central Strategy team. It encompasses functionalities such as real-time operations monitoring, customer care, operations management, booking and strategic data-driven decision making.
The data science team works in multiple areas such as geo-intelligence, machine learning, discrete optimisations & simulations.
This is a huge India specific challenge, primarily because of the unstandardized way of writing addresses and poor quality of road/pincode data in smaller towns/villages. We process a large amount of location data from our devices on the ground to learn locality boundaries, routes taken by ground staff, etc.
Machine Learning: We use a range of machine learning techniques to automate decision making at the ground level, e.g., identifying whether a shipment is safe to fly based on its product description, identifying which shipments have a high probability of being returned, etc.
Discrete Optimisation: We rely on a range of optimisation methods to ensure that our partners’ networks are designed for cost efficiencies and scale, e.g., Vehicle Routing Problem to optimise the shipment collection process from clients, network optimisations to ensure that the hubs are suitably located, etc.
Simulation: The scale of the logistics networks often makes many problems intractable due to the existence of millions of variables and how they interact with each other over time. We run our simulator to measure the impact of changes put over time and ultimately propose optimal designs to the systems that can be “self-aware” ad “self-improving”.
– Contribute to the development/ deployment of machine learning algorithms, operational research, semantic analysis, and statistical methods for finding structure in large data sets
– Use advanced statistics and machine learning on large-scale multidimensional data and generate actionable insights
– Disseminate original research in peer-reviewed journals and conferences
Understanding and knowledge of CS basics and principles. Experience with Machine Learning is a plus.