Purpose – The purpose of the paper is to explore the applicability of the Supply Chain Operations Reference (SCOR) model and to identify the key performance indicators (KPIs) for the service-oriented sector – namely a third-party logistics (3PL) service provider. Design/methodology/approach – The performance attributes of SCOR model (reliability, responsiveness, flexibility, cost measures and asset management efficiency) are used as the basis for defining the KPIs. A questionnaire was sent to relevant decision makers. Findings – This paper illustrates the use of the integrated approach of SCOR, fuzzy analytic hierarchy process (FAHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) for measuring the supply chain performance (SCP) in the light of a real life case study company. Research limitations/implications – This method forms the basis for performance measurement using the SCOR model to evaluate strategy. In this work, the performance of the company has been compared with its own previous performance. The work can be extended to external benchmarking and also to other sectors. Practical implications – The paper attempts to overcome the conflict between the top-down strategy and bottom-up implementation process. The paper links the strategic objective with operations which would aid managers at different levels of an organization with decision making. The KPIs, when implemented in a business intelligence (BI) tool, would result in real time performance measurement. Originality/value – The paper focusses on 3PLs. It provides a base for measuring the SCP using SCOR model. The paper also identified KPIs for three domains of 3PL, namely freight forwarding, customs and warehousing.

Purpose Portfolio optimization is the process of making an investment decision on a set of assets to realize high returns with low risk. It has three major stages: asset selection, asset weighting and asset management. Asset selection is an important phase because it influences asset allocation and ultimately affects the returns of a portfolio. Today, there is an increase in the number of listings on a stock exchange. Therefore, it is important for an investor to screen and select stocks for investment. This study focuses on the first stage of the portfolio optimization problem, namely, asset selection. The purpose of this study is to evaluate and select profitable stocks quoted on National Stock Exchange (NSE) for portfolio optimization. Design/methodology/approach Financial ratios are considered as the input and output parameters for evaluating the financial performance of the firms. This study adopts a hybrid principal component analysis (PCA) and data envelopment analysis (DEA) approach to evaluate the efficiency of the firms. Based on the efficiency scores, the firms are selected for the investment process. Findings The model helps to determine the relative efficiencies of the firms. The efficient firms are considered to be the potential stocks for investment. It helps the investors to screen the stocks from a large number of stocks quoted on NSE. Research limitations/implications One of the limitations of the standard DEA model is that it fails to discriminate the firms when the number of input and output parameters are larger than the number of firms. To overcome this problem, either a parameter can be ignored or weight-restricted DEA can be applied. When an input/output parameter is dropped, the information in that variable is lost. Weight-restricted DEA model uses expert opinion for measuring the relative importance of input and output parameters. Expert opinion is subjective and might be biased. The PCA-DEA model helps to identify the efficient firms by improving the discriminatory power of standard DEA without any loss of information and without the need for expert opinion, which might be biased. Practical implications Asset selection is an important stage in the investment process. Selection of stocks based on the efficiency score is an easier option available to the investors. But the misclassification of firms either due to biased expert opinion or discrimination inability of DEA can be costly to an investor. The PCA-DEA model overcomes both these limitations. Investors can select the potential candidates for asset allocation based on the efficiency scores obtained using the PCA-DEA model. Further, the relative efficiencies obtained can help the firms to benchmark their performance against the best performing firms within their industry. Originality/value This paper is one of few papers to adopt the PCA-DEA framework to select stocks in the Indian stock market.

With the advent of Internet of Things (IoT) and Web 2.0 technologies, there has been a tremendous growth in the amount of data generated. This chapter emphasizes on the need for big data, technological advancements, tools and techniques being used to process big data. Technological improvements and limitations of existing storage techniques are also presented. Since the traditional technologies like Relational Database Management System (RDBMS) have their own limitations to handle big data, new technologies have been developed to handle them and to derive useful insights. This chapter presents an overview of big data analytics, its application, advantages, and limitations. Few research issues and future directions are presented in this chapter.

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