The role of digital technologies in service business transformation is under-investigated. This paper contributes to filling this gap by addressing how the Internet of things (IoT), cloud computing ...(CC) and predictive analytics (PA) facilitate service transformation in industrial companies. Through the Data-Information-Knowledge-Wisdom (DIKW) model, we discuss how the abovementioned technologies transform low-level entities such as data into information and knowledge to support the service transformation of manufacturers. We propose a set of digital capabilities, based on the extant literature and the findings from four case studies. Then, we discuss how these capabilities support the service transformation trajectories of manufacturers. We find that IoT is foundational to any service transformation, although it is mostly needed to become an availability provider. PA is essential for moving to the performance provider profile. Besides providing scalability in all profiles, CC is specifically used to implement an industrialiser strategy, therefore leading to standardised, repeatable and productised offerings.
In light of the Fourth Industrial Revolution, the concepts of flexibility and re-configurability of manufacturing systems and the evolution of their control architectures are becoming increasingly ...important. The development of Cyber Physical Systems (CPS) and their flexibility and integrated capabilities have paved the way to the transition from centralized control to heterarchical (decentralized) control architectures. In this paper, a comparison between centralized and heterarchical control architectures in a virtual learning environment is presented. The control architectures of the assembly station and the materials handling system of modern manufacturing systems have been conceptualized and tested under different working conditions. The results show that centralized control is the best solution only for deterministic and predictable scenarios, which are very far from reality, whereas, in case of failures, a more flexible control is preferable.
Manufacturing has faced significant changes during the last years, namely the move from a local economy towards a global and competitive economy, with markets demanding for highly customized products ...of high quality at lower costs, and with short life cycles. In this environment, manufacturing enterprises, to remain competitive, must respond closely to customer demands by improving their flexibility and agility, while maintaining their productivity and quality. Dynamic response to emergence is becoming a key issue in manufacturing field because traditional manufacturing control systems are built upon rigid control architectures, which cannot respond efficiently and effectively to dynamic change. In these circumstances, the current challenge is to develop manufacturing control systems that exhibit intelligence, robustness and adaptation to the environment changes and disturbances. The introduction of multi-agent systems and holonic manufacturing systems paradigms addresses these requirements, bringing the advantages of modularity, decentralization, autonomy, scalability and re-usability. This paper surveys the literature in manufacturing control systems using distributed artificial intelligence techniques, namely multi-agent systems and holonic manufacturing systems principles. The paper also discusses the reasons for the weak adoption of these approaches by industry and points out the challenges and research opportunities for the future.
The layout of fixed-position assembly islands (FPAI) is widely used for producing fragile or bulky products. With the increasing customised demand and unique operation patterns, manufacturing ...practitioners are facing challenges on flexible and efficient production arrangement to meet customer demand, which lead to inappropriate assembly islands configuration, frequent setups and long waiting times in FPAI. Industry 4.0 comes with the promise of improved flexibility and efficiency in manufacturing. In the context of Industry 4.0, this paper proposes a 5-layer APICS (
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ssembly layer,
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erception layer,
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nteraction layer,
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ognition layer, and
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ervice layer) roadmap for transformation and implementation of Assembly 4.0. Following the 5-layer APICS roadmap, a Graduation Intelligent Manufacturing System (GiMS) is presented as the pioneering implementation in FPAI. A graduation-inspired assembly system is designed for FPAI at assembly layer. Internet of Things (IoT) and industrial wearable technologies are deployed for perception, connection, and collaboration among various manufacturing resources at perception and interaction layer. A self-configuration model is proposed at cognition layer for autonomously configuring optimal assembly islands and corresponding production activities to meet customer demand. Cloud-based services are developed for managers and onsite operators to facilitate their decision-making and daily operations at service layer. Finally, a demonstrative case is conducted to verify the feasibility of the proposed methods.
Configuring intelligent manufacturing systems (IMSs) is significant for manufacturing enterprises to take a step toward Industry 4.0. However, most current IMS is configured based on the Industrial ...Internet of Things (IIoT) with a centralized architecture, which results in poor flexibility to handle manufacturing disturbances and limits capacity to support security solutions. To solve the above issues, this article combines IIoT with the permissioned blockchain and proposes a novel manufacturing blockchain of things (MBCoT) architecture for the configuration of a secure, traceable, and decentralized IMS. Then, hardware infrastructures and software-defined components of MBCoT are designed to provide an insight into the industrial implementation of IMS. Furthermore, the consensus-oriented transaction logic of MBCoT is presented based on a crash fault-tolerant protocol, which empowers MBCoT with a strong but resource-efficient encryption mechanism to support the autonomous manufacturing process. Finally, the implementation of an MBCoT prototype system and its application examples justify that the proposed approach is practical and sound. The evaluation experiment demonstrates that MBCoT equips IMS with a secure, traceable, stable, and decentralized operating environment while achieving competitive throughput and latency performance.
•Methods to construct high-fidelity digital twin for automation systems is introduced.•Network of interfaces enabling communications among system components is built.•Manufacturing intelligence is ...realized by training Deep Reinforcement Learning.•A smart dynamic scheduler is developed for continuous process optimization.
Filling the gaps between virtual and physical systems will open new doors in Smart Manufacturing. This work proposes a data-driven approach to utilize digital transformation methods to automate smart manufacturing systems. This is fundamentally enabled by using a digital twin to represent manufacturing cells, simulate system behaviors, predict process faults, and adaptively control manipulated variables. First, the manufacturing cell is accommodated to environments such as computer-aided applications, industrial Product Lifecycle Management solutions, and control platforms for automation systems. Second, a network of interfaces between the environments is designed and implemented to enable communication between the digital world and physical manufacturing plant, so that near-synchronous controls can be achieved. Third, capabilities of some members in the family of Deep Reinforcement Learning (DRL) are discussed with manufacturing features within the context of Smart Manufacturing. Trained results for Deep Q Learning algorithms are finally presented in this work as a case study to incorporate DRL-based artificial intelligence to the industrial control process. As a result, developed control methodology, named Digital Engine, is expected to acquire process knowledges, schedule manufacturing tasks, identify optimal actions, and demonstrate control robustness. The authors show that integrating a smart agent into the industrial platforms further expands the usage of the system-level digital twin, where intelligent control algorithms are trained and verified upfront before deployed to the physical world for implementation. Moreover, DRL approach to automated manufacturing control problems under facile optimization environments will be a novel combination between data science and manufacturing industries.
This paper discusses production systems with a focus on the relationships between product supply and customer demand in the context of Industry 2.0-4.0. One driver of production evolution is changes ...in customer demand over time, which is categorised into several dimensions. Major production systems - flow line, Toyota production system (TPS), job shop, cell, flexible manufacturing system and seru - have been developed and applied to supplies to match different demand dimensions over time. For each production system, two questions are addressed: what and how. Comparisons between seru with TPS and cell are given. The possibilities of a future smart factory equipped with internet of things are discussed. The demand dimensions of Industry 4.0, the product architecture change in the automobile industry and the impact of 3D printing are elaborated. Potential applications of lean and seru principles for Industry 4.0 are presented.
The demand for distributed manufacturing systems (DMS) in the manufacturing sector has notably gained vast popularity as a suitable choice to accomplish sustainability benefits. Manufacturing ...companies are bound to face critical barriers in their pursuit of sustainability goals. However, the extent to which the DMS attributes relate to sustainable performance and impact critical barriers to sustainability is considerably unknown. To help close this gap, this article proposes a methodology to determine the relative importance of sustainability barriers, the influence of DMS on these barriers, and the relationship between DMS attributes and sustainable performance. Drawing upon a rich data pool from the Chinese manufacturing industry, the best-worst method is used to investigate the relative importance of the sustainability barriers and determine how the DMS attributes influence these barriers and relate to sustainability. The study findings show that "organizational barriers" are the most severe barriers and indicate that "reduced carbon emissions" has the highest impact on "organizational" and "sociocultural barriers" whereas public approval" has the highest impact on "organizational barriers." The results infer that "reduction of carbon emission" is the DMS strategy strongly linked to improved sustainable performance. Hence, the results can offer in-depth insight to decision-makers, practitioners, and regulatory bodies on the criticality of the barriers and the influence of DMS attributes on the sustainability barriers, and thus, improve sustainable performance for increased global competitiveness. Moreover, our study offers a solid foundation for further studies on the link between DMS and sustainable performance.
Innovation and transformative changes in products, manufacturing technologies, business strategies, and manufacturing paradigms have profoundly changed the manufacturing systems. In addition to being ...environmentally, economically socially sustainable, manufacturing systems are increasingly using intelligent technologies to be even more resilient, responsive, and adaptable. A new Adaptive Cognitive Manufacturing Systems (ACMS) paradigm, its drivers, enablers, and characteristics, including cognitive adaptation, is presented. Classification and definitions of four types of adaptability in manufacturing systems are included. Human-centric collaboration of workers and intelligent machines and applications, and the future of work in cognitive adaptive manufacturing systems are outlined. Cognitive Digital Twins (CDT), their features, evolution, and their use to support humans in intelligent, collaborative manufacturing settings are discussed. Industrial applications and case studies are used to illustrate the presented concepts and paradigms. Challenges and future research directions to achieve the ACMS paradigm and implement more intelligent, more adaptive, and sustainable manufacturing systems are presented. The presented novel concepts and technologies make significant contributions to the fast-evolving field of manufacturing systems. This pioneering research sheds light on many important future research topics and provides a road map and motivation for researchers in this field.
•A dynamic control architecture (ORCA) solves the manufacturing system scheduling.•ORCA allows an optimized and reactive control.•ORCA was implemented on a real flexible manufacturing ...system.•Different scenarios highlighted the scalability and the robustness of the approach.•Results are compared to the solutions of a mixed integer linear model.
Reactive and effective hybrid manufacturing control architectures, combining hierarchy and heterarchy adapted to the current constraints of the industrial market and its environment were created. In this article, a new generic hybrid control architecture called ORCA (dynamic Architecture for an Optimized and Reactive Control) is first proposed. This hybrid architecture is able to dynamically and partially switch between a hierarchical predictive architecture and a heterarchical reactive architecture, if an event forbidding the planned behavior to be followed occurs. In this article, this architecture was applied to a Flexible Manufacturing System (FMS) problem and denoted ORCA-FMS. ORCA-FMS was tested on an existing manufacturing cell with simulations and real experiments to prove the applicability and the effectiveness of this kind of hybrid architecture in an industrial environment.
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