A Structural Approach of Integrating Total Quality Management and Big Data Analytics in Supply Chain Management to Improve Business Performance in FMCG Industry

Abstract

The Supply Chain Management inside the industry of Fast-Moving Consumer Goods is considered one of the most complex supply chains due to high uncertainties and complexities due to involvement of different supplier, transportation problems and lead time issues. Therefore, this study examines the ability of integrating the concept of Total Quality Management which is considered one of the vital philosophies which tends to improve organizations in terms of achieving customer satisfaction and maintain continuous improvement which is highly needed inside the Supply Chain Management, as well as the Data Analytics tools which became very vital in decision making with the ability to clean, examine and analyze data to predict for future supply and demand, lead times and provide flexibility inside the Supply chain cycles, in improving the Supply Chain Management performance providing the Supply Chain Managers with high ability to predict future issues, selection of supplier, improve relation with costumers which will improve overall performance. Therefore, this study is conducted to identify areas of improvements in Supply Chain Management which can be improved through integrating the Supply Chain Management Practices with Total Quality Management Practices and Data Analytics tools. The study is based on collecting the main practices of each variable and collect data through preparing a questionnaire provided to experienced Supply Chain Managers in the field of Food Moving Consumer Goods. Structured Equation Modelling is used to test and validate the correlations between the variables examined and to test for the hypotheses developed. Thus, this study is considered very beneficial in terms of investigating the importance of integrating of the philosophies and practices to lead to better business performance and prepare organization for continuous improvements in every measure of the supply chain cycle.