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While several warehouses are now technologically equipped and smart, the implementation of real-time analytics in warehouse operations is scarcely reported in the literature. This study aims to develop a practical system for real-time analytics of process monitoring in an internet-of-things (IoT)-enabled smart warehouse environment.

A modified system development research process was used to carry out this research. A prototype system was developed that mimicked a case company’s actual warehouse operations in Indonesia’s manufacturing companies. The proposed system relied heavily on the utilization of IoT technologies, wireless internet connection and web services to keep track of the product movement to provide real-time access to critical warehousing activities, helping make better, faster and more informed decisions.

The proposed system in the presented case company increased real-time warehousing processes visibility for stakeholders at different management levels in their most convenient ways by developing visual representation to display crucial information. The numerical or textual data were converted into graphics for ease of understanding for stakeholders, including field operators. The key elements for the feasible implementation of the proposed model in an industrial area were discussed. They are strategic-level components, IoT-enabled warehouse environments, customized middleware settings, real-time processing software and visual dashboard configuration.

While this study shows a prototype-based implementation of actual warehouse operations in one of Indonesia’s manufacturing companies, the architectural requirements are applicable and extensible by other companies. In this sense, the research offers significant economic advantages by using customized middleware to avoid unnecessary waste brought by the off-the-shelves generic middleware, which is not entirely suitable for system development.

This research’s finding contributes to filling the gap in the limited body of knowledge of real-time analytics implementation in warehousing operations. This should encourage other researchers to enhance and develop the devised elements to enrich smart warehousing’s theoretical knowledge. Besides, the successful proof-of-concept implementation reported in this research would allow other companies to gain valuable insights and experiences.

Motivated by recent research indicating that the operational performance of an enterprise can be enhanced by building a supporting data-driven environment in which to operate, this paper presents a simulation framework that enables an examination of the effects of applying smart manufacturing principles to conventional production systems, intending to transition to digital platforms.

To investigate the extent to which conventional production systems can be transformed into novel data-driven environments, the well-known constant work-in-process (CONWIP) production systems and considered production sequencing assignments in flowshops were studied. As a result, a novel data-driven priority heuristic, Net-CONWIP was designed and studied, based on the ability to collect real-time information about customer demand and work-in-process inventory, which was applied as part of a distributed and decentralised production sequencing analysis. Application of heuristics like the Net-CONWIP is only possible through the ability to collect and use real-time data offered by a data-driven system. A four-stage application framework to assist practitioners in applying the proposed model was created.

To assess the robustness of the Net-CONWIP heuristic under the simultaneous effects of different levels of demand, its different levels of variability and the presence of bottlenecks, the performance of Net-CONWIP with conventional CONWIP systems that use first come, first served priority rule was compared. The results show that the Net-CONWIP priority rule significantly reduced customer wait time in all cases relative to FCFS.

Previous research suggests there is considerable value in creating data-driven environments. This study provides a simulation framework that guides the construction of a digital transformation environment. The suggested framework facilitates the inclusion and analysis of relevant smart manufacturing principles in production systems and enables the design and testing of new heuristics that employ real-time data to improve operational performance. An approach that can guide the structuring of data-driven environments in production systems is currently lacking. This paper bridges this gap by proposing a framework to facilitate the design of digital transformation activities, explore their impact on production systems and improve their operational performance.

With the new generation Industry 4.0 coming, as well as globalization and outsourcing, products are fabricated by different parties in the distributed manufacturing network and enterprises face the challenge of consistent planning of semi-finished product in each manufacturing process in different geographical locations. The purpose of this paper is to propose a real-time operation planning system in the distributed manufacturing network to intelligently control/plan the manufacturing networks.

The feature of the proposed system is to model and simulate large distributed manufacturing networks to streamline the mechanical and production engineering processes with radio frequency identification (RFID) technology, which can keep track of process variants. To deal with concurrency and synchronization, the hierarchical timed colored Petri net (HTCPN) formalism for modeling is selected in this study. This method can help to model graphically and test the discrete events of concurrent operations. Fuzzy inference system can help for knowledge representation, so as to provide knowledge-based decision assistance in distributed manufacturing environment.

In this proposed system, there are two main sub-systems: one is the real-time modeling system, and the other one is intelligent operation planning system. These two systems are not parallel in the whole systems while the intelligent operation planning system should be embedded in any stage of the real-time modeling system as needed. That means real time modeling system provides the holistic structure of the studied distributed manufacturing system and realize real-time data transfer and information exchange. At the same time the embedded intelligent operation planning system fulfill operation plan function.

This new intelligent real-time operation system realizes real-time modeling with RFID-based HTCPN and smart fuzzy engine to fulfill intelligent operation planning which is highly desirable in the environment of Industry 4.0. The new intelligent manufacturing architecture will highly reduce the traditional planning workload and improve the planning results without manual error interference. The new system has been applied in a practical case to demonstrate its feasibility.

In a complex semiconductor manufacturing system (SMS) environment, the implementation of dynamic production scheduling and dispatching strategies is critical for SMS distributed collaborative manufacturing events to make quick and correct decisions. The purpose of this paper is to assist manufacturers in achieving the real time dispatching and obtaining integrated optimization for shop floor production scheduling.

In this paper, an integrated model is designed under assemble to order environment and a framework of a real time dispatching (IRTD) system for production scheduling control is presented accordingly. Both of the scheduling and ordering performances are integrated into the days of inventory based dispatching algorithm, which can deal with the multiple indicators of dynamic scheduling and ordering in this system to generate the “optimal” dispatching policies. Subsequently, the platform of IRTD system is realized with four modules function embedded.

The proposed IRTD system is designed to compare the previous constant work in process method in the experiment, which shows the better performance achievement of the IRTD system for shop floor production dynamic scheduling and order control. The presented framework and algorithm can facilitate real time dispatching information integration to obtain performance metrics in terms of reliability, availability, and maintainability.

The presented system can be further developed to generic factory manufacturing with the presented logic and architecture proliferation.

The IRTD system can integrate the real time customer demand and work in process information, based on which manufacturers can make correct and timely decisions in solving dispatching strategies and ordering selection within an integrated information system.

As the technology evolves, the ways in which supply chain is coordinated improve. During a careful study on the intelligent wireless web (IWW) and its services for future applications, its great potentials for the implementation of a mobile real‐time system for supply chain coordination were realized. This paper seeks to introduce a development process for the IWW. In addition, it aims to explain the concept of mobile real‐time supply chain coordination, and propose and describe a practical model for this subject matter based on the most recent technologies including the IWW and agents.

Objectives were achieved through a thorough study on the IWW, agent technology, and the ways of applying them for mobile real‐time coordination in supply processes. As a method to conduct the research, first, the paper made out what the IWW services are and how one may develop them. Since mobile real‐time coordination is an absolutely innovative concept, the study prepared a comprehensive understanding of it and then, a practical framework was sketched and explained to implement the suggested system. The approach to this topic was a realistic one and an attempt was made to include all the prerequisites and details for the intended system.

In the course of the work, it was found that the IWW and other corresponding technologies have the greatest potentials ever available for the realization of a mobile real‐time supply chain coordination system and most of the chapters illustrate the claim.

Mobile real‐time coordination and its use in supply chains is something new. The development process for IWW proposed here is totally practicable and no other implementation scenario for the application of the IWW in mobile real‐time coordination has been suggested yet.

Creating a real-time data integration when developing an internet-of-things (IoT)-based warehouse is still faced with challenges. It involves a diverse knowledge of novel technology and skills. This study aims to identify the critical components of the real-time data integration processes in IoT-based warehousing. Then, design and apply a data integration framework, adopting the IoT concept to enable real-time data transfer and sharing.

The study used a pilot experiment to verify the data integration system configuration. Radio-frequency identification (RFID) technology was selected to support the integration process in this study, as it is one of the most recognized products of IoT.

The experimentations’ results proved that data integration plays a significant role in structuring a combination of assorted data on the IoT-based warehouse from various locations in a real-time manner. This study concluded that real-time data integration processes in IoT-based warehousing could be generated into three significant components: configuration, databasing and transmission.

While the framework in this research was carried out in one of the developing counties, this study’s findings could be used as a foundation for future research in a smart warehouse, IoT and related topics. The study provides guidelines for practitioners to design a low-cost IoT-based smart warehouse system to obtain more accurate and timely data to support the quick decision-making process.

The research at hand provides the groundwork for researchers to explore the proposed theoretical framework and develop it further to increase inventory management efficiency of warehouse operations. Besides, this study offers an economical alternate for an organization to implement the integration software reasonably.

Vehicle routing (VR) is critical in successful logistics execution. The emergence of technologies and information systems allowing for seamless mobile and wireless connectivity between delivery vehicles and distribution facilities is paving the way for innovative approaches to real‐time VR and distribution management. This paper investigates avenues for building upon recent trends in VR‐related research towards an integrated approach to real‐time distribution management. A review of the advances to‐date in both fields, i.e. the relevant research in the VR problem and the advances in mobile technologies, forms the basis of this investigation. Further to setting requirements, we propose a system architecture for urban distribution and real‐time event‐driven vehicle management.

This paper reviews the real‐time enterprise from a technology point of view, with a strong focus on the challenges of building globally scalable technology. We analyze the different aspects of the real‐time enterprise and the link between the real‐time functionality and event driven systems. We then review the major bottlenecks in achieving scalability and robustness for event‐driven systems when they need to support global enterprise systems. At a conceptual level the real‐time enterprise is extremely promising but neither research nor product groups have the technologies to operate at a global scale in a manner that will allow the real‐time enterprise to become a reality, unless a number of significant hurdles are removed. This paper examines the impact of the concept of the real‐time enterprise on research agenda of academia as well as product groups, and lays out an agenda for achieving scalability and reliability goals.

The purpose of this paper is to propose customer behavior analysis based on real-time data processing and association rule for digital signage-based online store (DSOS). The real-time data processing based on big data technology (such as NoSQL MongoDB and Apache Kafka) is utilized to handle the vast amount of customer behavior data.

In order to extract customer behavior patterns, customers’ browsing history and transactional data from digital signage (DS) could be used as the input for decision making. First, the authors developed a DSOS and installed it in different locations, so that customers could have the experience of browsing and buying a product. Second, the real-time data processing system gathered customers’ browsing history and transaction data as it occurred. In addition, the authors utilized the association rule to extract useful information from customer behavior, so it may be used by the managers to efficiently enhance the service quality.

First, as the number of customers and DS increases, the proposed system was capable of processing a gigantic amount of input data conveniently. Second, the data set showed that as the number of visit and shopping duration increases, the chance of products being purchased also increased. Third, by combining purchasing and browsing data from customers, the association rules from the frequent transaction pattern were achieved. Thus, the products will have a high possibility to be purchased if they are used as recommendations.

This research empirically supports the theory of association rule that frequent patterns, correlations or causal relationship found in various kinds of databases. The scope of the present study is limited to DSOS, although the findings can be interpreted and generalized in a global business scenario.

The proposed system is expected to help management in taking decisions such as improving the layout of the DS and providing better product suggestions to the customer.

The proposed system may be utilized to promote green products to the customer, having a positive impact on sustainability.

The key novelty of the present study lies in system development based on big data technology to handle the enormous amounts of data as well as analyzing the customer behavior in real time in the DSOS. The real-time data processing based on big data technology (such as NoSQL MongoDB and Apache Kafka) is used to handle the vast amount of customer behavior data. In addition, the present study proposed association rule to extract useful information from customer behavior. These results can be used for promotion as well as relevant product recommendations to DSOS customers. Besides in today’s changing retail environment, analyzing the customer behavior in real time in DSOS helps to attract and retain customers more efficiently and effectively, and retailers can get a competitive advantage over their competitors.

The purpose of this study is to propose a real-time lecture supporting system. The target of this study is on-site classrooms where teachers give lectures and a lot of students listen to teachers’ explanations, conduct exercises, etc.

The proposed system uses an e-learning system and an e-book system to collect teaching and learning activities from a teacher and students in real time. The collected data are immediately analyzed to provide feedback to the teacher just before the lecture starts and during the lecture. For example, the teacher can check which pages were well previewed and which pages were not previewed by students using the preview achievement graph. During the lecture, real-time analytics graphs are shown on the teacher’s PC. The teacher can easily grasp students’ status and whether or not students are following the teacher’s explanation.

Through the case study, the authors first confirmed the effectiveness of each tool developed in this study. Then, the authors conducted a large-scale experiment using a real-time analytics graph and investigated whether the proposed system could improve the teaching and learning in on-site classrooms. The results indicated that teachers could adjust the speed of their lecture based on the real-time feedback system, which also resulted in encouraging students to put bookmarks and highlights on keywords and sentences.

Real-time learning analytics enables teachers and students to enhance their teaching and learning during lectures. Teachers should start considering this new strategy to improve their lectures immediately.