Demand forecasting and Route Optimization in Supply chain industry using Data Analytics

Logistics industry is one of key pillars of the global economy, which involves interdisciplinary domains. Manufacturing companies need to devise strategies in order to deliver best-quality products on time, meeting customers' growing expectations is becoming increasingly important. Demand forecasting and route optimization are key challenges which needs to be solved because it is interdependent on Fleet usage, analyzing the safety stock, and last mile connectivity in large cities where the density of orders to be delivered is high and any miscalculation in the above two aspects will lead to a domino effect in the industry, where the losses are unacceptable. This paper leverages two important algorithms, one is time series analysis i.e. Auto regressive integrated moving average and simulated annealing algorithm for demand forecasting and route optimization respectively. Both the models help in addressing the key problems by formulating a solution by means of simulation and providing the best possible results, that can be directly incorporated in the industry for the benefit.