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Industrial automation (IA) is a major manufacturing strategy which employs modern techniques and practices in manufacturing to achieve sustainable competitive advantage in the manufacturing industry. This research aims to categorize AI factors and examine its relative importance for better implementation in manufacturing industries. The relative importance and ranking of AI factors in manufacturing industry was done through a literature review, discussion with experts, and analytic hierarchy process (AHP) approach. In all, 13 IA factors were identified and further divided into three factor categories. Then their prioritization was done using AHP approach to assign the relative importance of these 13 factors affecting the industrial automation in manufacturing industries. The result will help the manufacturing industry mangers to work upon them based on their relative importance to improve their industrial automation performance. Finally, limitations, managerial implications, and scope of future study are presented at the end of the paper.

While many U.S. manufacturing operations utilize optimization for individual unit processes, smart manufacturing (SM) systems that integrate manufacturing intelligence in real time across an entire production operation are not pervasive in industry. A vendor-agnostic SM platform is under development that integrates information technology, models, and simulations driven by real-time plant data and performance metrics. By utilizing existing process control and automation systems, manufacturing organizations can manage systems at a much lower cost, optimizing process knowledge and improving energy productivity. Three case studies are presented: steam methane reforming to make hydrogen, optimization of a heat treatment furnace for metals processing, and a fuel cell system, all of which utilize high fidelity models as a starting point for optimization and control. The Smart Manufacturing Leadership Coalition has led the national effort in SM, and the recently established National Manufacturing Innovation Institute funded by DOE , private industry, and state governments will be described.

From Germany’s Industry 4.0 mission to Made in China 2025 and Make in India mission to British Factory of the Future in 2050, digital manufacturing (DM) is promoting in the world’s major industrial countries as a technology foundation of the future manufacturing. At the same time, in the different segments of the DM realm, different forms of information technologies (IT) are flourishing such as the following: computer-aided manufacturing, robotics control in manufacturing, and process simulation. This paper is aimed to review the latest initiatives of DM in the leading universities and major industrial countries. Along with, a critical literature review of various initiatives in the area of DM-assisted hybrid additive manufacturing (DM-HAM) has also been carried out. DM-HAM seems to be very promising for next generation multi-operational manufacturing as it is time saving and economical. The highlights of this review will provide a guide for the upcoming research activities in the area of DM-HAM.

As Additive Manufacturing becomes increasingly prevalent in commercial manufacturing environments, the need to effectively consider optimal strategies for management is increased. At present most research has focused on individual machines, yet there is a wealth of evidence to suggest competitive manufacturing is best managed from a systems perspective. Through 14 case studies developed with four long-established Additive Manufacturing companies this paper explores the conduct of Industrial AM in contemporary manufacturing environments. A multitude of activities, mechanisms, and controls are identified through this detailed investigation of Additive Manufacturing operations. Based on these empirical results a general four component Industrial Additive Manufacturing System is developed, together with the identification of potential strategic opportunities to enhance future manufacturing.

[EN] The urgent need for sustainable development is imposing radical changes in the way manufacturing systems are designed and implemented. The overall sustainability in industrial activities of manufacturing companies must be achieved at the same time that they face unprecedented levels of global competition. Therefore, there is a well-known need for tools and methods that can support the design and implementation of these systems in an effective way. This paper proposes an engineering method that helps researchers to design sustainable intelligent manufacturing systems. The approach is focused on the identification of the manufacturing components and the design and integration of sustainability-oriented mechanisms in the system specification, providing specific development guidelines and tools with built-in support for sustainable features. Besides, a set of case studies is presented in order to assess the proposed method., This research was supported by research projects TIN2015-65515-C4-1-R and TIN2016-80856-R from the Spanish government. The authors would like to acknowledge T. Bonte for her contribution to the NetLogo simulator of the AIP PRIMECA cell.

The purpose of this article is to collect and structure the various characteristics, technologies and enabling factors available in the current body of knowledge that are associated with smart manufacturing. Eventually, it is expected that this selection of characteristics, technologies and enabling factors will help compare and distinguish other initiatives such as Industry 4.0, cyber-physical production systems, smart factory, intelligent manufacturing and advanced manufacturing, which are frequently used synonymously with smart manufacturing. The result of this article is a comprehensive list of such characteristics, technologies and enabling factors that are regularly associated with smart manufacturing. This article also considers principles of “semantic similarity” to establish the basis for a future smart manufacturing ontology, since it was found that many of the listed items show varying overlaps; therefore, certain characteristics and technologies are merged and/or clustered. This results in a set of five defining characteristics, 11 technologies and three enabling factors that are considered relevant for the smart manufacturing scope. This article then evaluates the derived structure by matching the characteristics and technology clusters of smart manufacturing with the design principles of Industry 4.0 and cyber-physical systems. The authors aim to provide a solid basis to start a broad and interdisciplinary discussion within the research and industrial community about the defining characteristics, technologies and enabling factors of smart manufacturing.

PurposeThe purpose of this paper is to identify and analyze knowledge integration in manufacturing technology development projects required to build competitive advantages.Design/methodology/approachA longitudinal case study has been conducted at a Swedish manufacturing company by following a manufacturing technology development project in real time during a two-year period.FindingsThe results show that three different knowledge integration processes exist when developing unique manufacturing technology: processes for capturing, for joint learning, and for absorb learning. The findings of the current research suggest that the three knowledge integration processes are highly interrelated with each knowledge integration process affecting the other two.Research limitations/implicationsThe major limitation of the research is primarily associated with the single case, which limits generalizability outside the context that was studied.Practical implicationsThe findings are particularly relevant to manufacturing engineers working with the development of new manufacturing technologies. By using relevant knowledge integration processes and capabilities required to integrate the knowledge in manufacturing technology development projects, companies can improve design and organize the development of manufacturing technology.Originality/valuePrevious research has merely noted that knowledge integration is required in the development of unique manufacturing technology, but without explaining how and in what way. This paper’s contribution is the identification and analysis of three knowledge integration processes that contribute to the building of competitive advantages by developing unique manufacturing technology and new knowledge.

Our next generation of industry—Industry 4.0—holds the promise of increased flexibility in manufacturing, along with mass customization, better quality, and improved productivity. It thus enables companies to cope with the challenges of producing increasingly individualized products with a short lead-time to market and higher quality. Intelligent manufacturing plays an important role in Industry 4.0. Typical resources are converted into intelligent objects so that they are able to sense, act, and behave within a smart environment. In order to fully understand intelligent manufacturing in the context of Industry 4.0, this paper provides a comprehensive review of associated topics such as intelligent manufacturing, Internet of Things (IoT)-enabled manufacturing, and cloud manufacturing. Similarities and differences in these topics are highlighted based on our analysis. We also review key technologies such as the IoT, cyber-physical systems (CPSs), cloud computing, big data analytics (BDA), and information and communications technology (ICT) that are used to enable intelligent manufacturing. Next, we describe worldwide movements in intelligent manufacturing, including governmental strategic plans from different countries and strategic plans from major international companies in the European Union, United States, Japan, and China. Finally, we present current challenges and future research directions. The concepts discussed in this paper will spark new ideas in the effort to realize the much-anticipated Fourth Industrial Revolution.

This paper presents a new concept, Cloud Business Performance (CBP) and describes the method of measurement, data analysis, impacts to manufacturing and case studies about CPB. Three methods can be used for CBP with two case studies illustrated. The first case study presents a small and medium manufacturing enterprise that has adopted backup services for all manufacturing transactions and records. The second case study shows a software manufacturing organization’s forecasting on their business performance and risk. Methods used, results and analysis have been fully justifiable to support the case of CBP for manufacturing organizations. We demonstrate that the use of CBP calculation and prediction analysis is useful for manufacturing organizations that adopt Cloud Computing.

Advanced manufacturing uses emerging technologies to critically enhance not only the economic competitiveness of individual manufacturers but also the sustainability of the whole industrial sector. New materials and technologies require new manufacturing processes and novel analytical models for process controls and parameter optimization regarding cost, reliability, quality, product flexibility, energy consumption, and fossil carbon emissions. The successful adoption of advanced manufacturing for sustainability can only be realized by following a systematic approach from concept development, product design and manufacturing to product delivery and service as well as in forward and reverse supply chain management. This special volume reports on progress of advanced manufacturing on sustainability improvements along the whole life cycle and covers the six themes: 1. Design theory and methodology for sustainability with advanced manufacturing; 2. Energy efficiency assessment and control of mechanical manufacturing systems; 3. Parameter optimization for advanced manufacturing and remanufacturing; 4. Low fossil-carbon process planning and production scheduling; 5. Integration of supply-chain innovations and advanced manufacturing; 6. Sustainable innovation for product-service systems. In addition, this SV introductory article, highlights future research directions, such as the need for energy consumption and emission data for advanced manufacturing processes, optimization models and control schemes, supply chain innovations, and product-service integration.

Simulation of the business process in manufacturing is considered. The main attributes and stages of the business process are described. For the example of automated machining, the business process is broken down into its components. A logical relation is established between the subprocesses (functions). Their inputs and outputs controlling the implementation of the process are described. A process model of digital manufacturing is developed in the methodology of the IDEF0 standard.

In this work, the design and implementation of a set of practical activities in a flexible manufacturing cell is exposed, as well as the teaching methodology used, both in programming cell manually (guided programming) or remotely (off-line, using simulation software). These activities have been designed for a progressive learning, starting with simple tasks, like programming a robot arm movement between different points in space, and concluding with more complex tasks, such as programming of palletizing cycles or manufacturing a part by machining.

The purpose of this paper is to analyse the integration of discrete event simulation (DES) in operations management manufacturing tools. Due to the movement of the fourth industrial revolution (Industrie 4.0), the integration of manufacturing is a topic constantly discussed in many areas. Moreover, it presents great research and innovation opportunities. To achieve the objective of this study, a search was conducted using the main keywords found in papers related to manufacturing systems and operations management manufacturing tools. Also, academic databases were literature research to identify the keywords relevant to the study added to DES. We considered only articles from the last 8 years. At the end between the search, the integration between tools such as manufacturing execution system, enterprise resource planning, radio frequency identification, core manufacturing simulation data, e-Kanban with DES were analysed. Furthermore, it was observed that the tools cannot always be used separately, but in some cases, these tools should be used jointly to solve problems related to production systems. Another aspect observed was how the data collected in production systems are fed to the DES models. Through, it was possible to analyse an existing gap regarding how the data is used between DES and manufacturing systems, thereby enabling research development in this area.

As semiconductor manufacturing processes are becoming more and more sophisticated, how to maintain their feasible production yield becomes an important issue. Also, how to build a smart manufacturing platform that can facilitate realizing smart factories is essential and desirable for current manufacturing industries. Aimed at addressing the above-mentioned two issues, in this letter, a five-stage approach for enhancing and assuring yield is proposed. Also, a smart manufacturing platform- Advanced Manufacturing Cloud of Things (AMCoT) based on Internet of Things, cloud computing, big data analytics, cyber-physical systems, and prediction technologies is designed and implemented to realize the proposed five-stage approach of yield enhancement and assurance. Finally, AMCoT is applied to a bumping process of a semiconductor company in Taiwan to conduct industrial case studies. Testing results demonstrate that AMCoT possesses capabilities of conducting total inspection in production, providing prognosis, and predictive maintenance on equipment, finding the root cause of yield loss, and storing and handling big production data, which as a whole is promising to achieve the goal of zero defects.

Summary Motivated by the rising costs of doing business overseas and the rise and implementation of digital technologies in production, new strategies are being explored to bring production and demand closer. While concepts like cloud computing, internet of things, and digital manufacturing increasingly gain relevance within the production activities of manufacturing companies, significant advances in three-dimensional (3D) printing technologies offer the possibility for companies to accelerate product development and to consider new supply chain models. Under this production scheme, material supply chains are redefined and energy consumption hotspots are relocated throughout the life cycle of a product. This implies a diversification of energy mixes and raw material sources that poses a risk of shifting problems between life cycle phases and areas of protection. This study compares a conventional mass scale centralized manufacturing system against a 3D printing-supported distributed manufacturing system on the basis of the production of one frame for eyeglasses using the life cycle assessment methodology. The study indicates clearly that the optimization potential is concentrated mainly in the energy consumption at the unit process level and exposes a close link to the printing material employed.

Efficiency and quality are major factors contributing to profits in manufacturing systems. Production downtime occurs during commissioning of a new system, adoption of new processes, system faults, or (un)planned maintenance; all of which result in reduced production and profit loss. Current techniques for evaluating change to a manufacturing system rely on simulation and modeling to verify processes, but ignore the physical interactions of the work parts on the system. Implementation techniques to evaluate commissioning focus on identifying issues with the cyber interfaces, ignoring the physical interfaces. To validate the cyber and physical interfaces simultaneously, physical work are sent through the system, resulting in significant costs from scrapped work parts and loss of production time. This research proposes a virtual fusion environment where the physical interfaces between a virtual work part and a manufacturing system can be investigated in real-time, on the physical system, without the...

The supply-demand matching and optimal-allocation of manufacturing resource and manufacturing capability, is always one of the key scientific issues to be addressed and the common objectives to be pursued for various advanced manufacturing systems (AMSs). Especially to the service-oriented manufacturing (SOM) systems (e.g., cloud manufacturing), which is a kind of typical and hot AMSs nowadays , it is extremely difficult to achieve the optimal allocation of large scales of different manufacturing services and the collaboration of manufacturing activities and business among massive manufacturing enterprises (no matter manufacturing service providers and consumers). Thus, under the current environment of social competition and collaboration, the most important challenge is how to integrate a variety of distributed manufacturing resources and capabilities in the form of manufacturing services efficiently and cost-effectively, as well as various demands or manufacturing tasks. In response to this challenge, the concept of supply-demand matching hypernetwork ( Matching_Net ) of manufacturing services in SOM system is put forward firstly in this paper. The proposed Matching_Net is constructed with manufacturing service network ( S_Net ), manufacturing task network ( T_Net ), and hyper-edges between those two networks which are revealing the matchable correlations between each service (supply) and each task (demand). Secondly, by comparing with the input or output information of manufacturing services and tasks from the functional view, the intelligent modeling of Matching_Net is illustrated and divided into those three constituent parts respectively. Finally, the simulation is carried out to validate and verify the proposed models and the modeling method. The model of hypernetwork (i.e., network of networks) is introduced into the supply-demand matching issues in service-oriented manufacturing systems.The concept and model of supply-demand matching hypernetwork ( Matching_Net ) of manufacturing services in SOM system are put forward considering both manufacturing services and manufacturing tasks.By comparing with the input or output information of manufacturing services and tasks, the intelligent modeling flow of Matching_Net is proposed and illustrated.The simulation is carried out to validate and verify the proposed models and the corresponding modeling method.

Nowadays, with the introduction and application of new information technologies in manufacturing, various advanced manufacturing modes and national strategies have been put forward and paid more and more attention, such as Industry 4.0, Industrial Internet, Cyber-Physical System or Cyber Manufacturing, Made in China 2025, Internet Plus Manufacturing, Cloud Manufacturing, etc. For these modes and strategies, how to realize the effective and intelligent supply–demand matching (SDM) of various manufacturing resources and capabilities (MR&C) in the form of service is one of the common issues and aims. In order to provide a uniformed research platform for related researchers both in academic and industry, the concept of manufacturing service SDM simulator (SDMSim) is proposed in this paper. A hypernetwork based architecture for the simulator is designed, as well as its seven key functions and subsystems, including manufacturing service management, manufacturing task management, manufacturing service SDM hypernetwork, manufacturing service SDM problem formulation and configuration, matching and scheduling algorithms/strategies selection and design, statistical analysis, and visualization. It illustrates that SDMSim has the potential to serve the users of manufacturing service provider, manufacturing service consumer, manufacturing service operator in the field of SoM, as well as the related researchers.

Modern manufacturing industry calls for a new generation of production system with better interoperability and new business models. As a novel information technology, Cloud provides new service models and business opportunities for manufacturing industry. In this research, recent Cloud manufacturing and Cloud robotics approaches are reviewed. Function block-based integration mechanisms are developed to integrate various types of manufacturing facilities. A Cloud-based manufacturing system is developed to support ubiquitous manufacturing, which provides a service pool maintaining physical facilities in terms of manufacturing services. The proposed framework and mechanisms are evaluated by both machining and robotics applications. In practice, it is possible to establish an integrated manufacturing environment across multiple levels with the support of manufacturing Cloud and function blocks. It provides a flexible architecture as well as ubiquitous and integrated methodologies for the Cloud manufacturing system. A ubiquitous manufacturing system based on Cloud manufacturing.Industrial robots utilized as implementation applications in the Ubiquitous manufacturing environment.Systematic analysis and guidelines for adpoting advanced manufacturing technologies.Multiple deployment models for private, community and public scenarios.Proposed system is implemented and evaluated in the near-real manufacturing environment.

Given the significant requirements for transforming and promoting the process industry, we present the major limitations of current petrochemical enterprises, including limitations in decision-making, production operation, efficiency and security, information integration, and so forth. To promote a vision of the process industry with efficient, green, and smart production, modern information technology should be utilized throughout the entire optimization process for production, management, and marketing. To focus on smart equipment in manufacturing processes, as well as on the adaptive intelligent optimization of the manufacturing process, operating mode, and supply chain management, we put forward several key scientific problems in engineering in a demand-driven and application-oriented manner, namely: ① intelligent sensing and integration of all process information, including production and management information; ② collaborative decision-making in the supply chain, industry chain, and value chain, driven by knowledge; ③ cooperative control and optimization of plant-wide production processes via human-cyber-physical interaction; and ④ life-cycle assessments for safety and environmental footprint monitoring, in addition to tracing analysis and risk control. In order to solve these limitations and core scientific problems, we further present fundamental theories and key technologies for smart and optimal manufacturing in the process industry. Although this paper discusses the process industry in China, the conclusions in this paper can be extended to the process industry around the world.

Automation and advanced manufacturing technology solutions (AAMT) that present tangible and intangible benefits remain key drivers for manufacturers from both the developed and developing countries...

This paper presents a framework to support the full life-cycle of extended manufacturing enterprises, from creation to operation and dissolution phases. The deployment and operation of such enterprises can be compared with the concept of 'plug-and-play', as the internal processes and legacy systems of the companies involved are smoothly integrated within an overall business process designed, validated and executed according to a specific business opportunity. During the plug phase, the specific business requirements are elicited and integrated with the design of the extended business processes. On the other hand, in the play phase, those predefined processes are executed in order to run the extended enterprise successfully. The paper describes an application case regarding an engineer-to-order and one-of-a-kind engineering product. This scenario is common to a large number of technology-driven SMEs, and illustrates the value of the framework to exploit business opportunities that require a combination of skills and resources that do not exist in-house. The case shows how the platform addresses the two main challenges in the deployment of an extended enterprise. The first challenge is finding the right set of partners to address a new business opportunity and the design of the underlying collaborative processes. The second challenge is mostly technical, and focuses on the integration of the legacy systems of the partners participating in the network so that cooperation can take place quickly and seamlessly. This paper presents a framework to support the full lifecycle of extended manufacturing enterprises developed in the scope of large trans-national European project.In this research project, several emerging technologies were explored such as Cloud and SOA manufacturing and internet-of-things applied in complex distributed manufacturing environments.The main result of the project was the framework presented in the paper was full demonstrated against several real manufacturing cases. One of those cases address the one-of-kind manufacturing environment and is described in the paper.It is important to highlight that the framework addresses two main challenges in the deployment of an extended enterprise. The first challenge addresses the finding of the right set of partners to address a new business opportunity and the design of the underlying collaborative processes. The second one is mostly technical and stays on the integration of the legacy systems of the partners participating in the network so that cooperation can take place quickly and seamlessly.To our best knowledge this is the first framework covering both technical and business challenges.

Recently, sustainable development has obtained increasing attentions from governments, industry, and academia owing to the limited natural resources. In the area of energy consumption, manufacturing accounts for a major portion of the total energy usage in industry. There is a clear necessity for energy-efficient manufacturing by optimizing manufacturing activities. Condition monitoring is the technology that provides runtime information for optimization. This paper aims to provide a better understanding of past achievements and future trends of condition monitoring towards energy-efficient manufacturing. Since there are a variety of sensors and technologies that can be used for condition monitoring towards energy-efficient manufacturing, this paper divides manufacturing activities into three levels, namely unit process level, shop-floor level, and supply chain level, and summarizes and discusses the sensors and technologies required to enable energy-efficient manufacturing on each level. With the advancement of technology, condition monitoring shows the characteristic of intelligence. Intelligent sensors that can be applied to condition monitoring in energy-efficient manufacturing are also reviewed. This paper can be helpful to manufacturers who are willing to improve energy efficiency in own manufacturing practice.

Information integration and interoperability among different design and manufacturing application software systems are important in smart manufacturing implementation and product life cycle management. Information models are foundational and critical to the specification of common terms, interfaces, and software architectures for information interoperability. The manufacturing process is one of the key processes in a product life cycle. This paper presents a methodology of using the metamodels as the modeling basis in manufacturing process information modeling. A manufacturing process information metamodel (MPIMM), which consists of object classes, relationship classes, and relationships, defines the information and relationships of processes, operations, resources used, and product/part manufactured of manufacturing process planning. The MPIMM is built by unified modeling language (UML) while the semantics of the process elements and their attributes are specified referencing the process specification language. A four-layer framework is proposed to construct manufacturing process information model (MPIM) following the rules of metamodeling, and including meta-metamodeling, manufacturing process information metamodeling, and manufacturing process domain information modeling; thus, manufacturing process plan data can be represented. The UML profile extension mechanism realized by defining Stereotype, TaggedValue, and Constraint is used in extending MPIMM to MPIM. A case study of applying MPIM in the product life cycle data management system for port hoisting equipment manufacturing is provided to demonstrate and verify the modeling capability and efficiency of the proposed methodology. This work provides a systematic and standardized modeling methodology and information expression mechanism for manufacturing process information.

This paper presents an effective product design and manufacturing process based ontology for manufacturing knowledge reuse. While a number of related efforts exist in the literature, there lacks a granular, interconnected product design and manufacturing process based ontology that can lead to greater industry adoption and knowledge reuse. In particular, the proposed ontology leverages an established industry standard to connect product design and manufacturing process knowledge in a logical and effective manner. The resulting effort is a general ontological framework that can be widely employed by the manufacturing industry. Additionally, the feasibility of implementing the ontology enabled knowledge reuse framework is demonstrated through a real-world case study.

Many developed countries are making various efforts to innovate their own manufacturing industries, through initiatives such as Manufacturing innovation 3.0, Industry 4.0, and Manufacturing 2025. Innovation in the manufacturing industry, represented by the so-called “smart factories,” is being developed through the latest technologies such as Internet of things (IoT), cloud, and big data. However, as the application of these technologies requires a lot of cost and time, small and medium-sized enterprises (SMEs) are often hampered in their efforts to take full advantage of them. For an enterprise that operates a manufacturing information system, the integrated management of information between systems is essential to the application of a new technology. If the enterprise lacks the relevant experts, it will have difficulty applying a new technology in the field. This study suggests the application of a cloud-based applications’ integration and operation platform (AIOP) in order to resolve those problems. The AIOP must accept a large volume of data at IoT-based manufacturing fields, interconnect between manufacturing fields and AIOP, and provide application contents in the form of service. The suggested study contents are applied to a company that produces electric components using plastic injection molds to verify their effects. It is expected that this study can be used as a reference model for applying smart factory technologies to other SMEs in the future.

With the developments and applications of the new information technologies, such as cloud computing, Internet of Things, big data, and artificial intelligence, a smart manufacturing era is coming. At the same time, various national manufacturing development strategies have been put forward, such as Industry 4.0 , Industrial Internet , manufacturing based on Cyber-Physical System , and Made in China 2025 . However, one of specific challenges to achieve smart manufacturing with these strategies is how to converge the manufacturing physical world and the virtual world, so as to realize a series of smart operations in the manufacturing process, including smart interconnection, smart interaction, smart control and management, etc. In this context, as a basic unit of manufacturing, shop-floor is required to reach the interaction and convergence between physical and virtual spaces, which is not only the imperative demand of smart manufacturing, but also the evolving trend of itself. Accordingly, a novel concept of digital twin shop-floor (DTS) based on digital twin is explored and its four key components are discussed, including physical shop-floor, virtual shop-floor, shop-floor service system, and shop-floor digital twin data. What is more, the operation mechanisms and implementing methods for DTS are studied and key technologies as well as challenges ahead are investigated, respectively.

The minimization of energy, waste, and emissions in operations are the foremost sustainability goals in industry. The shift from a linear product lifecycle to a circular one is required, along with increased energy efficiency and reduced resource use, to achieve these goals. This paper examines how the use of Re-Distributed Manufacturing (RdM) and a Product-Service System (PSS) approach, while leveraging the latest digital manufacturing technologies, enables the shift to this new economic model. A case study illustrates this new approach and relates it to the shoe manufacturing industry. The ShoeLab case study includes an outline of the business model options supporting this new approach to sustainable production highlighting the circularity that may be achieved in employing RdM and the latest digital manufacturing technologies in the form of 3-D printing. The research conducted indicates that using IDEF0 modelling could help to realize the full potential of RdM such as the manufacturing and transport of products involving less material, energy and waste.

Ever increasing customer choices, environmental concerns and competitiveness among manufacturers across the globe has engaged the industry to embrace newer manufacturing strategies. Predominantly, there are three dimensions to modern manufacturing systems viz. economic, environmental and social. The integration of lean-green-agile manufacturing strategies would be a complete and comprehensive manufacturing system which is the need of the 21st century. The adoption of Lean-Green-Agile Manufacturing System (LGAMS) would be facilitated by few enablers. The influence of these enablers is a matter of investigation which is addressed by the present study. An attempt has been made to prioritize the facilitating capacity of each enabler. The outcome of the research would facilitate the policy makers in the industry and government to frame policies.

The cloud manufacturing paradigm has been known as one of the best solutions for today's competitive manufacturing environments. This paradigm insists on using a manufacturing service oriented approach for fulfilling the manufacturing enterprise resource management by the access to global availability of manufacturing services. The proposed service oriented approach covers a wide area of manufacturing resource management from infrastructures to platforms and applications. However, due to wide global distributions of manufacturing clouds, the service oriented architectures which are designed in the clouds cannot communicate with each other. This is mainly due to concentration of cloud manufacturing paradigm to the application of the detailed manufacturing resource acquisitions through a services oriented approach. To solve this problem a wide area of researches have been established which can be named as the interoperability of manufacturing clouds. In this paper, a novel solution for enabling the interoperability among the manufacturing clouds has been proposed. This solution applies the service oriented approach of the cloud manufacturing to enable the interoperability services. These services are proposed by Information Technology related service provider companies. These firms can provide the different manufacturing clouds with the interoperability services. These services are acquired by the manufacturing cloud members to communicate with other cloud members. The proposed solution enables two important mechanisms. The first mechanism uses service integration functions for enabling an integrated service data model and the second mechanism uses service mapping functions. Finally, the paper has elaborated the architecture of the platform and outlined future works.

Increasingly the convergence of natural environment (land, water, air and life), built environment (housing, buildings, transportation and infrastructure) and digital environment (computing power, the internet, big data, and technology) is shaping the economies and societies. Smart living is taking root. Mass customization of products and services is preferred over mass production. Businesses wish to serve an individual customer at a competitive cost comparable to the mass production cost, with shortest possible development time and production time. This requires manufacturing to change from a more labour intensive processes to information technology enabled mechanical processes. Digital manufacturing is a broader concept of manufacturing innovation in which the digital and material advancements enable the company to conceive products in a desired style and quantity in time scales shorter than the conventional methods while efficiently managing the entire product lifecycle. It is about defining manufacturing processes and managing manufacturing process information via full digital product definition. It encompasses visualization, manufacturing simulation, ergonomic and human factor analyses, holistic view of product and process design, and product design sensitive to the process constraints and capabilities. This article emphasizes the need and driving forces for adopting digital manufacturing, transformation of manufacturing to smart manufacturing, present applications and future scope of digital manufacturing.

Products become more complex as the general technology development reaches new levels. These new technologies enable manufacturing companies to offer better products with new functionalities to their customers. Complex products require adequate manufacturing systems to cope with changing product requirements. In general, manufacturing of this type of products entails complex structured and rigid IT systems. Due to the system’s complexity and comprehensive structure, it becomes challenging to optimize the information flow. There are improvement potentials in how such systems could be better structured to meet the demands in complex manufacturing situations. This is particularly true for the vehicle manufacturing industry where growth in many cases has occurred through acquisitions, resulting in increased levels of legacy IT systems. Additionally, this industry is characterized by high levels of product variety which contributes to the complexity of the manufacturing processes. In manual assembly of these products, operations are dependent on high quality assembly work instructions to cope with the complex assembly situations. This paper presents a current state analysis of data and information handling in assembly information systems at multiple production sites at a case company manufacturing heavy vehicles. On basis of a certain set of characterizing manual assembly tasks for truck, engine and transmission assembly, this work focuses on identifying what data that is used in manufacturing engineering processes and IT systems to produce assembly work instructions. This work aims to identify gaps in the information flow between manufacturing engineering and shop floor operations.

Throughout this paper it will present the benefits of the latest trends in sustainable manufacturing, relating them to the managerial practices and the standards proposed by the World Class Manufacturing methodology. A special emphasis will be given to Environment and Logistics pillars, focusing on how to improve the environmental performance of logistics processes. The ultimate purpose would be to suggest a catalog of measures to implement on manufacturing industry that could be easily adopted in the area of Supply Chain generating added value for the companies and their environmental and social surroundings.

The direction of current literature in addressing sustainability issues in the manufacturing sector highlights some models and approaches that are usually based on the concept of the triple-bottom line. However, as a functional unit in a manufacturing organization, the role of the manufacturing function in creating competitiveness has been outdone by the current demands of sustainability such that integrating sustainability and competitiveness remains a significant gap. This paper proposes a unifying framework in formulating a manufacturing strategy which espouses sustainability with due consideration of the manufacturing's internal and external competitive functions. The proposed framework integrates the features based on the classical theories of manufacturing strategy and the other features that must be considered to transform a firm's manufacturing strategy into a sustainable manufacturing strategy. This framework serves as a guide for decision-makers in identifying policies in various manufacturing decision areas that would comprise a sustainable manufacturing strategy. Results of recent empirical studies that are based on the models generated from the framework are reported in this paper.

Means of faster and better technological preparation for multiproduct manufacturing are considered. The possibility of technological preparation in parallel with design and organizational preparation is demonstrated. An approach to organizational and technological design in the development of manufacturing processes is proposed. In this approach, the dynamics and goals of production systems are taken into account. A timeline for the introduction of the proposed approach is outlined, and the conditions for its successful implementation are specified.

Model of enterprise manufacturing environment as an integrated dynamic interacting parallel and/or serial manufacturing objects system is discussed. In this paper we consider what the approach of dynamic model building gives for the identification, organization and management of the enterprise in the case of a serial arrangement of production areas. The results were used to construct new methodology of engineering manufacturing control algorithms for integrated manufacturing executing systems.

The study proposes a business model for energy efficiency and provides a tool that describes how manufacturing companies create, deliver and capture value, and at the same time evidencing the energy inefficiencies, wastes and manufacturing performances that are critical to the business model. Accordingly, we analyze cause-effect relationships between manufacturing performances and business model performances, and patterns of business models that can be adapted to develop a new structure focused on energy efficiency. To achieve this, a case study in the rubber manufacturing industry is carried out for the development and validation of concepts.

The use of data-mining as an analytical tool has been increasing in recent years; and the emergence of new manufacturing paradigms such as the Industry 4.0 initiative have led many smaller manufacturers to look at utilizing these powerful techniques; however, practical applications are still in their infancy, and remain out of reach for many of these small manufacturing enterprises (SME's). This paper focuses on methods to integrate these emerging paradigms into existing manufacturing processes, specifically, how data-mining principles may be used to begin to explore the concept of Intelligent Manufacturing under Industry 4.0; with a focus on improving product and process quality. In collaboration with an industrial partner; a respected manufacturer of household electronic appliances, techniques were developed using open-source and freely-available software, running on readily available hardware and using only existing data-collection points, that were able to provide actionable feedback which could be used to make improvements to the manufacturing operations; and to increase product quality. This paper serves as evidence that the ability to utilise these techniques is now within reach of numerous smaller manufacturing operations, and provides a further understanding of how moves towards fully Industry 4.0 ready factories may be made in the years to come.

The modern industry requires the next generation of manufacturing system to be intelligent, flexible, and interoperable. The manufacturing system also needs to offer better performance with lower cost. The huge success of cloud computing brings the opportunity to the manufacturing systems with high scalability, productivity and agility. It also provides practical solutions to the small and medium-sized enterprises with elastic investment solutions. In a cloud manufacturing system, the manufacturing resource and capability are provided in the service-oriented architecture as cloud services. In this research, the practical models of the cloud-based system are investigated, and distributed process planning services are developed accordingly. The proposed infrastructure is evaluated via a case study in which the industrial demonstrators are practised in the cloud as one specific type of process planning service.

The materials and manufacturing processes are evolving very rapidly subject to today’s high performance, shorter lead times, and high part complexity needs. With over 80,000 materials, various manufacturing technologies (additive and traditional), diverse streams of application (aerospace, motor vehicles, health care, etc.), and high cost incurred due to manufacturability of the part, it has become essential to choose the right compromise of technology resource in early stages of design considering the Design for Manufacturing (DFM)/Design for Additive Manufacturing (DFAM) guidelines. The concerned literature to date focuses on manufacturing technology selection by being either part specific or application specific. As multiple criteria are involved for decision making, this paper provides a thorough review on the following questions: (1) What are the common design criteria used in literature for additive and traditional manufacturing technologies with respect to product-process integration? (2) What is the literature contribution for material and manufacturing process selection strategies with special focus on comparison of additive and traditional manufacturing technologies? The paper attempts to provide as a result a basic generic methodology for resource selection (RS) which will not only take into account all of the areas of application, DFM/DFAM guidelines, and three design criteria (function, cost, and environment), but will also discuss avenues for collaborative product development. A complex industrial case study is also presented to test the proposed methodology.

This paper suggests an extended product data model that integrates product design and manufacturing data to support personal manufacturing based on 3D printing services. To produce highly customized products with limited resources, personal manufacturers require data management tools for integrated product design and manufacturing. However, the current separated and dedicated design and manufacturing information systems for mass production cannot satisfy the requirements of personal manufacturing. To solve this problem, this study introduces an extended product data model that has data objects for bills of processes (BOP), customer orders and manufacturing operation linked to 3D printing services through Internet of things (IOTs) technologies. The extended product data model can support product design, process planning, production planning, and execution of manufacturing operation in a unified single user environment. To demonstrate the applicability of the model, this study implemented it as a prototype product data management (PDM) system and applied the system to an example product development.

Production ramp-up refers to the period between the completion of product development and full capacity utilization. In semiconductor industry, engineering lots are given higher priority in the manufacturing process in order to improve the manufacturing process and/or to facilitate the new product development. However, the implementation of engineering lots can disrupt the smoothness of the manufacturing process and thus increase the cycle time of normal lots. Because semiconductor manufacturing is a very complicated manufacturing process, the determination of the appropriate product mix is a difficult task for many semiconductor companies, especially when risks are taken into consideration. We propose a model to characterize this hard tradeoff and apply an efficient simulation optimization method to obtain the optimal product mix where risks are taken into consideration. An extensive numerical study shows that the proposed method has satisfactory performance and is capable of identifying the nearly optimal product mix in reasonable computing time. An empirical study conducted in collaboration with a semiconductor company further validates the viability of this research in practical settings.

Manufacturing software services provisioned as unbundled “apps” could be significantly more flexible and less expensive to use than the current monolithic manufacturing applications. However, integrating such heterogeneous apps is not a trivial job. National Institute of Standards and Technology (NIST) has initiated a research and development program to advance science and engineering towards the vision of composable Smart Manufacturing systems. In April 2016, NIST hosted a workshop, called Drilling down on Smart Manufacturing – Enabling Composable Apps, to work with industry and academia on needed technical and standards-based solutions. This paper reports on key outcomes of this workshop.

Confronted with growing sustainability awareness, mounting environmental pressure, manufacturers are seriously striving to address sustainability-related issues without sacrificing customers’ needs and market competitiveness. A new manufacturing system called cybermanufacturing system (CMS) has great potential in addressing sustainability issues by handling manufacturing tasks differently from and better than traditional manufacturing systems. CMS is a vision for future manufacturing where physical components are fully integrated and seamlessly networked with computational processes, forming an on-demand, intelligent, and communicative manufacturing resource and capability repository with optimal, sustainability-oriented manufacturing solutions. The recent developments in the Internet of things, cloud computing, fog computing, service-oriented technologies, etc., all contributed to the development of CMS. In this new manufacturing paradigm, every manufacturing resource or capability is digitalized, registered, and networked to each other directly or through the Internet, thus enabling intelligent behaviors of manufacturing components and systems such as self-awareness, self-prediction, self-optimization, and self-configuration, among others. In this research, a comprehensive definition of CMS has been developed, a suggested architecture of CMS has been constructed, and important functions of CMS have been identified. Simulation models have been developed and used to investigate the sustainability benefits of CMS. The simulation results show promising sustainability benefits of CMS over traditional manufacturing systems.

Based on the phenomenal changes that additive manufacturing (AM) has brought to industries and markets, managerial approaches should be re-examined and developed to take advantage of emerging opportunities. This revolutionary technology is dramatically changing business and innovation models, shrinking supply chains and altering the global economy. For example, 3D printing shifts production locations closer to customers and leads to free-form product design as well as sustainable manufacturing. Several studies have been conducted on AM technology, but the research stream on AM management is still developing, with studies dispersed in journals across different research areas. Our study presents both systematic and quantitative analyses of the literature, including co-citation analysis, factor analysis and multidimensional scaling, to explore the structure of the AM research domains in the scope of management, business and economics. We found eight main research streams: AM technology selection, supply chai...

Sensor-packed manufacturing systems will become ubiquitous.Cybersecurity aspects are gaining importance within the manufacturing domain.Manufacturing cyber-physical systems are expected to follow the trend set by other domains that benefited from the Internet of Things, Cloud Computing, and Big Data.Outcomes of the implementation of manufacturing cyber-physical systems could be transformative to the extent that predictive manufacturing systems can become a reality. The recent advances in sensor and communication technologies can provide the foundations for linking the physical manufacturing facility and machine world to the cyber world of Internet applications. The coupled manufacturing cyber-physical system is envisioned to handle the actual operations in the physical world while simultaneously monitor them in the cyber world with the help of advanced data processing and simulation models at both the manufacturing process and system operational levels. Moreover, a sensor-packed manufacturing system in which each process or piece of equipment makes available event and status information, coupled with market research for true advanced Big Data analytics, seem to be the right ingredients for event response selection and operation virtualization. As a drawback, the resulting manufacturing cyber-physical system will be vulnerable to the inevitable cyber-attacks, unfortunately, so common for the software and Internet-based systems. This reality makes cybersecurity penetration within the manufacturing domain a need that goes uncontested across researchers and practitioners. This work provides a review of the current status of virtualization and cloud-based services for manufacturing systems and of the use of Big Data analytics for planning and control of manufacturing operations. Building on already developed cloud business solutions, cloud manufacturing is expected to offer improved enterprise manufacturing and business decision support. Based on the current state-of-the-art cloud manufacturing solutions and Big Data applications, this work also proposes a framework for the development of predictive manufacturing cyber-physical systems that include capabilities for attaching to the Internet of Things, and capabilities for complex event processing and Big Data algorithmic analytics.