Ananda S. Jeeva

Supply Chain Flexibility Assessment by Multivariate Regression and Neural Networks

This paper compares two vastly different methods of analysis – multiple regression and neural networks, in supply chain flexibility assessment. Data of manufacturing firms evaluating their prominent suppliers were analysed by multiple regression and simulated using three-layer multilayer perceptron (MLP) neural networks. Our study shows that NN can accurately determine a supplier’s flexibility capability within an error of 1% The incorporation of these two methods can lead to better understanding and dynamic prediction of supply chain flexibility for buyers.

A taxonomic approach to supplier intelligence in manufacturing: managing components of strategic procurement planning

This paper is used to explore requirements for achieving sustainability and survival of manufacturing organisations by means of ensuring continuous supplies for production. Under the current environmental uncertainty due to the global financial crisis and consequent demise of many companies, the contention is that manufacturers require more information and data regarding their suppliers and use supplier intelligence (SI) to reduce supply risk. Current research literature indicates there is little information regarding supplier intelligence; in the context of the suppliers supplier, suppliers financial health in terms of capital investment, debt, share market performance, financial reporting, accounting practices, auditing, corporate governance and operating environment. Consequently, a conceptual framework is proposed for gathering supplier intelligence to improve supplier relations and their management. The impetus for a planned approach to increasing information about a trading partner has become more critical since the collapse of well-known and stable MNCs and the demise of tier 2 suppliers. Supplier intelligence enables the buyer to advance their operations and long term strategies to succeed with more certainty, less supply risk, increased competitive advantage and greater innovative capabilities. Finally, a taxonomy for collecting SI data and information is proposed.