Business intelligence dashboard for driver performance in fleet management

Transportation is at the center of logistics as it represents the physical movement of materials between points in a supply chain. The problem involve in the transportation industry is fleet management. Fleet management is the broad topic that involve vehicles maintenance, operation capacity, driver selection and so on. This project focus on performance of the driver in fleet management. Imbalance driver contribution in fleet management decline the productivity of the organization especially in transportation industry. Each driver should be evaluated and analysed on their productivity and contribution towards the organization based on their performance. The aim of this project is to identify the factors influencing driver performance in logistic transportation and provide business intelligence dashboard for visualize driver performance for organization in their decision making. One transportation industry has been selected for the case study relying on business intelligence framework and tools for the development of dashboard. As the finding of this project, a conceptual model representing factors influencing driver performance is proposed. A dashboard was developed to provide business insight and help the organization in decision making based on the conceptual model proposed. The dashboard comprises of four main components which are summary, delivery, driver profile and driver behaviour. The dashboard was evaluated with respondents who involved in fleet management

Big data management capabilities in the hospitality sector: service innovation and customer generated online quality ratings.

Despite the wide usage of big data in tourism and the hospitality sector, little research has been done to understand the role of organizations’ capability of managing big data in value creation. This study bridges this gap by investigating how big data management capabilities lead to service innovation and high online quality ratings. Instead of treating big data management as a whole, we access big data management capabilities at the strategic and operational level. Using a sample of 202 hotels in Pakistan, we collected the primary data for big data capabilities, knowledge creation and service innovation; the secondary data about quality rating were collected from Booking.com. Structural equation modelling through SmartPLS was used for data analysis. The results indicated that big data management capabilities lead to high online quality ratings through the mediation of knowledge creation and service innovation. We contribute to the current literature by empirically testing how strategic level big data capabilities enable the firm to add value in innovativeness and positive online quality ratings through acquiring, contextualizing, experimenting and applying big data