Smart factory is a fully-integrated of firm’s facilities (i.e., sensors, smart machines, and robots) and information system architecture (i.e., IoT, ICT, and cloud computing) to enable high degree of automation in manufacturing processes. IT knowledge capability is the IT knowledge organization that how employees understand IT knowledge in different dimensions, i.e., general management, product design, production planning, data analysis, information security, and automation system. Since the system of smart factory depends on the massive of data collecting (big data) and the firm’s advance analyzing approach (analytics). The big data in manufacturing include the data from production planning, quality control, procurement, inventory control, human resource management (HRM), and delivery. The purpose of this study is to examine the role of IT knowledge capability and big data and analytics on the degree of smart factory. Survey data from 141 Thai manufacturing firms from the list of the ministry of industry and industrial zones were collected during March–April 2019. The multiple regression result shows that both IT knowledge capability and big data and analytics have a positive impact on the degree of smart factory. In addition, we use a firm’s age and firm’s size (based on the number of employees and total asset) as control variables. The results show that firm’s size have a positive effect on hypothesis model.
This study aims to provide the solution for call center providers to overcome the problem of optimizing the call center's efficiency, while ensuring satisfactory customer service. By formulating a simulation to represent call center model based optimization approach, helps eliminating the cost of uncertainty that might occur when the call center model is actually applied to the real life situation. The simulation model is generated via Arena12, and locates the separation of the agent's obligation into 2 types: The first type is constructed as each agent will handle only inbound or outbound calls separately, whereas the second is considered an agent needed to handle both inbound and outbound calls individually. The simulation model to discover the factor that affects the efficiency of the call center the most.
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