Big data analytics in flexible supply chain networks

Abstract

Supply chain responsiveness and Big Data Analytics (BDA) have incited an ample amount of interest in academia and among practitioners. This work is concerned with improving responsiveness in supply chain networks by extending production capacity to cope with changes and variations in demand. BDA helps researchers make sense of the current challenges of data: high volume, high velocity, and high variety. In this work, we will look at sales data and at large warehouses, which envelop all the said three characteristics of Big Data (BD). This is quite important as demand market data is increasingly shared with supply chain managers. Here, a working architecture is introduced to handle the challenges of BD. The work uses a neural network to detect patterns within the demand. The work combines deep learning with nonlinear programming to enable flexibility at supply chain production facilities to respond to the forecasted demand. The parameters in the neural network are analyzed and studied for each different product type. We see significant prediction improvements when the parameters are better tuned. Further, the work introduces a BD architecture that automates the acquisition of the data, data mining, and the storage of input and output files. Overall, the work utilizes a gradient search method, a genetic algorithm, ARIMA, a deep learning algorithm, and a mixed-integer nonlinear program.