Developing Smart Supply Chain Management Systems Using Google Trend’s Search Data: A Case Study

Abstract

Future manufacturing companies require smarter solutions to compete in the economy. Smart supply chain management systems are one of the most effective solutions. Use of previous information can help companies to predict the demands of the market and react in an agile manner to sudden changes. Google receives over 63,000 searches per second on any given day. This huge amount of data provides us with the opportunities to investigate researches in multiple subjects and extract useful information from the raw data that is available through Google Trend. In this research, we investigate the possible relationships between searches that are made in Google for two manufacturing capability terms, namely, Precision Machining (PM) and Electric Discharge Machining (EDM). Time-series oriented research is conducted on these two datasets in order to find the dynamics characteristics as well as interesting hidden relationships between these two search items to help us build a smarter supply chain management system. Two different methods namely ARMA and ARMAV models are be applied to fit a representative model to these datasets. The order of the both models are evaluated based on AIC statistic. In addition, multiple seasonal trends are detected in the datasets. Finally, Using ARMA model, we predict the datasets for one-step ahead in order to validate our models. Recognition of seasonalities and correlations between two datasets could lead to better prediction and smarter supply chain creation and management.