Enterprises around the world are facing increasingly severe global competition, the shorter life cycle of new products, and changing customer demands. Therefore, they must transform their business operations to provide greater product variety and customization through flexibility and quick responsiveness, and also to remove the data latency, analysis latency, as well as decision latency as much as possible (Hackathorn 2003). Mass customization often requires firms to manufacture and deliver customer-specific products or services with the same price and efficiency as mass-produced products. In response to the new business model, they must adapt new information systems that can manage dynamic manufacturing activities and take immediate action to resolve any events that disrupt production or cause customer dissatisfaction (Byrd et al., 2006). Coupling mass customization, just in time (JIT), and lean production with real-time business intelligence will enable a firm to compete in today’s hyper competition environment (Du et al., 2006). In other words, firms must re-engineer their current business practices to a real-time enterprise (RTE) operational model, which uses up-to-date information to eliminate business process delays (Kopitsch 2005). However, in the mass customization environment, the execution process of a production system is frequently disrupted by internal and external dynamics, such as equipment failure and changing customer orders (Qu et al., 2016). The term production logistics (PL) describes these execution processes as logistics activities related to material transfer between production stages and PL often accounts for nearly 95% execution time of the entire manufacturing process (Qu et al., 2016). To effectively employ mass customization and JIT production for RTE models, auto-ID methods are required for near real-time process control (Hansen and Gillert 2008). Many manufacturing firms already adopted auto-ID to manage their PL activities. The enabling technologies for auto-ID that attracted the most attention in recent years include Radio Frequency Identification (RFID) and Internet of Things (IoT). More specifically, IoT extends into our everyday lives through a wireless network of uniquely identifiable objects and forms a global infrastructure of networked physical objects (Welbourne et al., 2009). This article (part B of the research) extended the implementation architecture proposed in Part A of this research. Part A of the research proposed an implementation architecture employing IoT technologies and comprising one IoT cloud and several iNodes, where each iNode manages multiple IoT devices We called the proposed implementation architecture an IoT-based CPS for PL and supply chain applications. Therefore, this aritcle is clearly link to Part A of this research. IoT technology has been adopted by a wide range of industries in both indoor assets tracking (Thiesse et al., 2006; Zhang et al., 2007; Wang et al., 2010) and outdoor assets tracking (Choi et al., 2012). Recent studies also show that integrating IoT technology, such as RFID, in shop floor operations can greatly optimize and improve manufacturing and PL operations (Qiu 2007, Zhou et al., 2007; Huang et al., 2008; Ruey-Shun et al., 2008; Wang et al., 2012; Zhong et al., 2013). The basic infrastructure of IoT consists of Electronic Product Code (EPC) and EPCglobal network (Thiesse et al., 2009; Yan and Huang 2009), which provide a flexible and scalable information system architecture for implementing a range of applications, such as anti-counterfeit (Kwok et al., 2010) and information sharing (Yan et al., 2016). To fully realize the potential benefits of IoT technology, firms must adopt a new IT infrastructure that can better track and manage a large volume of distributed objects within their organizations and beyond. As we are moving towards the world of IoT, millions of embedded devices and industrial machines empowered with Internet technologies will be able to communicate, collaborate, and offer their functionality as a machine to machine (M2M) service (Karnouskos et al., 2009).