Fusion-Based Supply Chain Collaboration Using Machine Learning Techniques

Application of machine learning techniques for supply chain demand forecasting

Full collaboration in supply chains is an ideal that the participant firms should try to achieve. However, a number of factors hamper real progress in this direction. Therefore, there is a need for forecasting demand by the participants in the absence of full information about other participants’ demand. In this paper we investigate the applicability of advanced machine learning techniques, inc...

Forecasting Supply Chain Demand Using Machine Learning Algorithms

Managing supply chains in today’s complex, dynamic, and uncertain environment is one of the key challenges affecting the success of the businesses. One of the crucial determinants of effective supply chain management is the ability to recognize customer demand patterns and react accordingly to the changes in face of intense competition. Thus the ability to adequately predict demand by the parti...

Secure Supply-Chain Collaboration

CERIAS and Department of Computer Sciences, Purdue University, 656 Oval Drive, West Lafayette, Indiana 47907-2086, USA Krannert School of Management, Purdue University, 100 S. Grant St., West Lafayette, Indiana 47907-2076, USA CERIAS and Department of Computer Sciences, Purdue University, 250 N. University St., West Lafayette, Indiana 47907-2066, USA Krannert School of Management, Purdue Univer...

Supply Chain Collaboration

Predicting stock market index using fusion of machine learning techniques

The paper focuses on the task of predicting future values of stock market index. Two indices namely CNX Nifty and S&P Bombay Stock Exchange (BSE) Sensex from Indian stock markets are selected for experimental evaluation. Experiments are based on 10 years of historical data of these two indices. The predictions are made for 1–10, 15 and 30 days in advance. The paper proposes two stage fusion app...