An Analysis on Sustainable Supply Chain Management in Thermal Power Plants

Research on sustainable supply chain management (SSCM) has been garnering interest because of its multi-approach in nature. SSCM has emerged as an essential method for organizations to develop and to enhance their competitive strategy through innovative ways in order to satisfy customer basic needs. It facilitates competitive advantage, faster flow of information, material, less response time, speeding up delivery action, better relation and coordination among partners, easy way of information sharing, and increasing order fulfilment rate. Implementing SSCM in organizations like thermal power plants has other benefits such as increasing attention about environmental performance intending the integration of social as well as economic performance. In this chapter, the artificial neural network (ANN) method is used to measure the customer satisfaction after implementing SSCM by thermal power industries.

Supplier Selection Criterion for SSCM in Indian Thermal Power Plant

All companies are dependent on their raw material providers. The same applies in the case of thermal power plants. The major raw material for a thermal power plant is the coal. There are a lot of companies which in turn provide this coal to the thermal power plant. Some of these companies are international; some are local, whereas the others are localized. The thermal power plants look into all the aspects of the coal providing company, before settling down for a deal. Some people are specifically assigned to the task of managing the supply chain. The main motive is to optimize the whole process and achieve higher efficiency. There are a lot of things which a thermal power plant looks into before finalizing a deal, such as the price, quality of goods, etc. Thus, it is very important for the raw material providers to understand each and every aspect of the demands of the thermal power plant. A combination of three methods—Delphi, SWARA, and modified SWARA—has been applied to a list of factors, which has later been ranked according to the weight and other relevant calculations.

Occupational Health and Safety Hazards in Agriculture

Within the agricultural sector, it becomes essential worldwide to analyze the magnitude of OHS problems. However, there is a lack of study in Odisha (India) to assess the prevailing situations. Hence, an attempt was made in this study to explore the issues related to OHS among the farmers of Odisha in India. There is a dual main contribution in this study. At first the occupational health and safety issues of farmers of Odisha in India were analyzed based on the literature review and the data collected by personal interaction and questionnaires. In the second part, the step-wise weight assessment ratio analysis (SWARA) method was used to rank the different farming processes, as well as different risks involved in various farming activities.

Leadership 5.0 in Industry 4.0

In today's competitive environment, agile firms tend to be more successful. If today's technology companies, which are leaders in their sector, may fail in that competitive environment, it would be possible that they might lose their market leadership in the future. Some companies which were in the top in market in their own sector in the past are likely to be stand back from their competitors for not adapting to market change conditions. Fast process of technology and digital world are taking place in all organizational authoritative in all area and in all kind of sectors because the business world is transformed by the postmodern revolution-Fourth Industrial Revolution. In this dynamic environment, leaders should learn new management behaviors, with which they can communicate both internal and external environment of their enterprises by the strategies of being agile and innovative organizations. This can be by being aware of changes in environment and having the ability to manage these changes for the company's favor.

Using Big Data Analytics to Forecast Trade Volumes in Global Supply Chain Management

As the supply chains become more global, the operations (such as procurement, production, warehousing, sales, and forecasting) must be managed with consideration of the global factors. International trade is one of these factors affecting the global supply chain operations. Estimating the future trade volumes of certain products for specific markets can help companies to adjust their own global supply chain operations and strategies. However, in today's competitive and complex global supply chain environments, making accurate forecasts has become significantly difficult. In this chapter, the authors present a novel big data analytics methodology to accurately forecast international trade volumes between countries for specific products. The methodology uses various open data sources and employs random forest and artificial neural networks. To demonstrate the effectiveness of their proposed methodology, the authors present a case study of forecasting the export volume of refrigerators and freezers from Turkey to United Kingdom. The results showed that the proposed methodology provides effective forecasts.

Forecasting

The chapter covers a study on forecasting stock prices, which can be a challenging task due to the amount of information and variability involved. The test approach, research, and results cover 50 companies on the US stock market over a 6-year period. Company quarterly and annual financial reports, along with daily stock prices, form the data set analyzed. The financial ratios were tested as independent variables against stock price as the dependent variable. Also, ratio type comparisons and timing scenarios for leading or lagging indicators were covered. Correlation and multiple-regression tests were used to eliminate some ratios, and to find a combination of 12 ratios that successfully account for 35% of the variability in stock prices. The results point to leading indicators, statistically significant ratios, and a predictive model for forecasting stock price.

A Handbook for ITF R&D Project Management

Hong Kong's Innovation and Technology Fund (ITF) from the Hong Kong Special Administrative Region's Government supports collaborative R&D projects in industry, academia, and research centers. This chapter has been developed for the ITF project teams and collaborative organizations specifically, but it is a useful reference for project teams working with multiple partners or stakeholders. This chapter is the result of the author's Engineering Doctorate research on “Enhancing the Commercialization Success of Innovation and Technology Fund (ITF) R&D Projects in Hong Kong's Logistics and Supply Chain Industry.” It takes the readers through the process from project identification to commercialization. The process described in the chapter has been effectively implemented in a recent ITF project in Hong Kong's logistics and supply chain industry. The chapter is a useful standard operations procedure (SOP) for collaborative R&D project teams.

Managing Relationships With Suppliers

In recent years, growing market competition has made companies increasingly dependent on their suppliers, which requires effective management of their supply chains. To establish and maintain relationships with the best suppliers, companies apply strategic supplier selection, evaluation, and development criteria. The literature review showed the importance of supply chains and the use of strategic criteria in managing relationships with suppliers. Based on the model proposed by Oflac, the authors studied a local subsidiary of a global company of components for the automotive industry. Through this case study, it was possible to understand how a firm establishes strategies and performs in the practice its supply relationship management activities. Results suggest that the existence of strong and continuous relationships creates advantages for companies, allowing them to remain competitive in the markets. The strategies of selection, evaluation, and development of suppliers enable companies to filter the best partners and develop their capabilities to achieve greater profits.

Framework for Measuring the Performance of Production Operations in the Dairy Industry

For the business operations in the dairy industry, the quick, safe, and hygienic production processes are of crucial importance. The chapter is aimed to develop a framework for evaluating the performance of production operations in dairy industry. An optimum production model is developed through factor analysis and structural equation modelling (SEM) methods applied to the responses collected from different dairy industries. The hypotheses testing suggests that all five factors positively affect the production operations of dairy supply chain. The outcome of the study reveals that the dairy industry needs significant improvement in their production operations to attain competitiveness. Further, this study is helpful for the dairy industry in handling the demand fluctuation, execution of effective production and information systems, improved product quality, process flexibility, etc.

The Role of Electronic Supply Chains and ERP Systems in the Realm of E-Commerce

Businesses around the world experience many challenges to acquire raw materials, parts, subassemblies, and the other necessary inputs to their production systems. As businesses are all moving into the e-commerce platform to gain market share, they realize that electronic supply chain management (e-SCM) powered by enterprise resource planning systems (ERPs) are the new norms and no business organization can operate without both in the new world of e-commerce. Little attention has been devoted to e-SCM dynamic with ERP and the challenges they pose to organizations. In the e-commerce environment, e-SCM is among the most important factors to organizational success. Effective e-SCM can enhance competitiveness and increase market share leading a higher profitability. Nevertheless, the new e-SCM professionals and other actors must understand the factors that undergird e-SCM performance, their drivers, and the necessity of fully functional ERPs for an effective e-SCM.