Management Of Manufacturing Systems Research Articles

Special Issue on Intelligent Computation in Design and Manufacturing

To solve problems underlying design and manufacturing we often rely on methodologies of computational intelligence such as machine learning, artificial neural networks, fuzzy logic, fuzzy inference systems and smart optimization algorithms. In this Special Issue of the International Journal of Automation Technology, original articles are presented with reference to the engagement of intelligent computation in diverse application areas of design and manufacturing, including manufacturing process monitoring, manufacturing systems management, scheduling, design theory and methodology. The six research papers in this Special Issue propose the use of intelligent computation methodologies to deal with various topics related to manufacturing and design. In particular, the first three papers focus on manufacturing process monitoring with reference to different manufacturing technologies, including tool wear monitoring in drilling of composite materials, sensor monitoring in CNC turning and residual stress prediction in welding. Diverse intelligent approaches such as artificial neural networks and adaptive neuro-fuzzy inference systems are proposed to support manufacturing process monitoring. The fourth paper deals with the manufacturing system level, proposing the employment of a solution algorithm combining metaheuristics and operation simulation for scheduling of production processes. The fifth paper aims at developing tools to guide the manufacturers to manage the technology investment and cost saving target for customer satisfaction based on the application of internet of things. The last paper proposes a methodology to support the introduction of customer requirements in product and service design via a decision support system which exploits artificial intelligence algorithms (machine learning) based on inductive inference, allowing knowledge related to product/service to be mapped, structured and managed to design the service and product semantic model. The editors deeply appreciate all the authors and anonymous reviewers for their effort and excellent work to make this Special Issue unique. We hope that future research on intelligent computation in manufacturing and design will advance manufacturing technology and systems as well as design methodologies.

Digital technologies in questions of automated manufacturing management systems

The article states that today in industry 4.0 are widely used automated and computerized control systems of industrial processes of the enterprise. It is stated that one of the types of such systems are MES-systems. The most popular MES-systems are presented, and it is revealed that the use of such systems in the service of the quality of the enterprise will improve the competitiveness of the enterprise. It is specified that the MES-system “FOBOS” is suitable for implementation in departments of quality service of the enterprise, the main advantages and opportunities of this MES-system are described. The article suggested that in order to exclude the possibility of unauthorized access to the MES-system, it is necessary to introduce an information security system. The main ways to ensure the security of data contained in the system are indicated. In conclusion, it is emphasized that the use of optimization and automation of the enterprise is an important step in the context of the transition to industry 4.0.

A software-defined framework for the integrated management of smart manufacturing systems

This paper introduces software-defined control (SDC) as a framework that enables integrated and programmatic management of smart manufacturing systems. SDC consolidates information from the production and enterprise levels in a central controller that monitors performance and detects changing conditions. The integrated view of the system provided by the central controller supports the development of applications, which supply the central controller with new information and reconfiguration recommendations. SDC is designed to be scalable and compatible with current automation technologies. A simulation shows that the incorporation of plant-floor information in management decisions, as supported by SDC, has the potential to improve profitability.

Influence of Traditional Business Practice on Firm Boundaries – Evidence from the Japanese Automotive and Steel Industries

The formation of supply chains in the extant literature has been based on the presupposition that individual companies in the network accumulate added value only on those products for which they are responsible, and they complete each sequential transaction with in-house quality assurance. This paper discusses a unique type of inter-firm relationship, which is based on a traditional business practice known as “Performance Guarantee,” in the Japanese automotive and steel industry. Such formations have not been discussed in the scholarly literature of supply chain management. In this system, multiple suppliers jointly bear the responsibility for product quality. It seems that this cooperation was built by a historical development between two industries, by a chain reaction of joint product development and quality assurance, as well as by specific manufacturing management systems in the supply chain. Exploring the background of this inter-firm cooperation, observed in major Japanese industries, is meaningful because it adds a new perspective to conventional research regarding firm boundaries and its supply chain management.

Introducing Flexibility to Complex, Resilient Socio-Ecological Systems: A Comparative Analysis of Economics, Flexible Manufacturing Systems, Evolutionary Biology, and Supply Chain Management

In this paper, a framework incorporating flexibility as a characteristic is proposed for designing complex, resilient socio-ecological systems. In an interconnected complex system, flexibility allows prompt deployment of resources where they are needed and is crucial for both innovation and robustness. A comparative analysis of flexible manufacturing systems, economics, evolutionary biology, and supply chain management is conducted to identify the most important characteristics of flexibility. Evolutionary biology emphasises overlapping functions and multi-functionality, which allow a system with structurally different elements to perform the same function, enhancing resilience. In economics, marginal cost and marginal expected profit are factors that are considered to be important in incorporating flexibility while making changes to the system. In flexible manufacturing systems, the size of choice sets is important in creating flexibility, as initial actions preserve more options for future actions that will enhance resilience. Given the dynamic nature of flexibility, identifying the characteristics that can lead to flexibility will introduce a crucial dimension to designing resilient and sustainable socio-ecological systems with a long-term perspective in mind.

Information exchange standards for design, tolerancing and Additive Manufacturing: a research review

Nowadays, the implementation of digital thread for Additive Manufacturing (AM) and product data management system strongly depends on specified file formats and used interoperability standards in order to integrate geometric dimensioning and tolerancing (GD&T) information directly in the 3D CAD model. The consideration of geometric deviations and variations is a key issue for design and tolerancing, inspection and management of manufacturing part information in AM through product and manufacturing information (PMI). PMI has been used to describe GD&T and non-geometric data, such as surface texture, surface finishing requirements, material specifications, process data, and other annotations, based on ISO standards. Standards play an important role in enabling the interoperability and efficiency of AM systems through the development of AM standard formats. This paper reviews current challenges of geometric and tolerancing model and formats for AM processes, which largely impede the advancement of AM technologies. Two criteria to enhance AM geometric and tolerancing model and standards are presented in this paper: GD&T management and compliance with PMI. STL, AMF and STEP formats are discussed on their characteristics of product definition and manufacturing specification for AM. The review presented here highlights that STEP standard can be a good basis for future research work to integrate and standardise material information, geometric and tolerancing model, and process planning for AM. Finally, new specifications of STEP-AM format are proposed in this work.

Succeeding in the Shale Business With a Lean Well Manufacturing Management System

Management The current oil and gas commodity prices have posed several challenges to oil and gas companies operating in shale plays trying to generate revenue or even becoming cash neutral. Shale drilling, as opposed to conventional drilling, requires many more wells to produce similar volumes of oil or gas due to the significantly smaller wellbore drainage area and the tight nature of the reservoir. For these projects to be economically viable, a high volume of low-cost and quickly drilled and completed wells is required, which cannot be achieved with a conventional oil and gas mindset. By implementing a Lean Well Manufacturing Management System and by having a dedicated Productivity Improvement team embedded in the operations driving changes, BHP Billiton was able to reduce drilling and completion (D&C) costs by 49% in the Eagle Ford Basin and 50% in the Permian Basin over an 18-month period (Fig. 1). These results are similar to those reported by other operators in similar plays that also incorporated lean management principles to their operations. A Lean Well Manufacturing Management System can be defined as a system to drill and complete a large number of wells per year in a factory-like setting using a lean manufacturing mind-set. The performance of the “factory” is measured by the ability to deliver wells at low capital costs while still performing safely, reliably, and meeting local regulatory requirements. To be successful, this model requires predictable and repeatable performance using highly dependable and fit-for-purpose equipment that is easy to operate and maintain. Value is maximized by addressing the technical specifications from the customer and optimizing the use of existing and new technology to realize the lowest achievable well cost. In turn, this translates into reduction of the unit development costs, increase in short-term production, and more efficient resource utilization. Waste can be encountered in many areas in the well delivery process, which can increase D&C costs, jeopardizing the project economics. For this reason, the key objective for a lean organization is to identify areas where waste needs to be reduced and eliminated without compromising safety, quality, and performance using the resources (people), processes, and technology available.

Controllable processing time policy in job shop manufacturing systems: design and evaluation by simulation modelling

The research proposed in this paper concerns the management of job-shop manufacturing systems with machines characterised by controllable process times. The process times are controlled by the selection of cutting speed. It has been developed a multi-domain simulation environment by ExtendSim package linked to the cutting parameters' database of the tools. Two combined simulation models have been developed. The first simulation model uses the cutting tool parameters to define the relation between the cutting speed and the waiting time of the parts in queue. The output of this model is the knowledge used by the control time policy. It is controlled the waiting time of the parts in each machine; based on the information provided by the first simulation model it is selected the cutting speed of the tools, and the process time is modified. The second simulation model is used to test the proposed policy. The numerical results highlight the benefit in terms of performance measures obtained.

Controllable processing time policy in job shop manufacturing systems: design and evaluation by simulation modelling

The research proposed in this paper concerns the management of job-shop manufacturing systems with machines characterised by controllable process times. The process times are controlled by the selection of cutting speed. It has been developed a multi-domain simulation environment by ExtendSim package linked to the cutting parameters' database of the tools. Two combined simulation models have been developed. The first simulation model uses the cutting tool parameters to define the relation between the cutting speed and the waiting time of the parts in queue. The output of this model is the knowledge used by the control time policy. It is controlled the waiting time of the parts in each machine; based on the information provided by the first simulation model it is selected the cutting speed of the tools, and the process time is modified. The second simulation model is used to test the proposed policy. The numerical results highlight the benefit in terms of performance measures obtained.

ORGANIZACJA I ZARZĄDZANIE ELASTYCZNYMI SYSTEMAMI 5 PRODUKCYJNYMI

ORGANIZACJA I ZARZĄDZANIE ELASTYCZNYMI SYSTEMAMI 5 PRODUKCYJNYMI

A basic study on analysis of heart rate variability in workers for dynamic production management in cellular manufacturing systems

A basic study on analysis of heart rate variability in workers for dynamic production management in cellular manufacturing systems

Integration of lean manufacturing and quality management system through structural equation modelling

Both the lean manufacturing (LM) and the quality management system (QMS) are clear initiatives with a goal of improving effectiveness and efficiencies. This review paper aims at exploring the impact of LM tools on QMS. Many organisations tackle lean philosophy, ISO 9001 standard individually. But this kind of attempt lacks focus on the synergy and the advantage from the potential collaboration. Few studies were conducted to explore the linkage between LM and QMS and whether they can coexist in the Manufacturing Industries or not. Though LM is generally proposed as a driver for QMS, a conceptual model is necessary to be developed in order to investigate the integration between LM and QMS. The model is proposed in this paper with a future direction of empirical investigation using AMOS 22 structural equation modelling to test the developed proposition. The originality of this paper lies in observing and measuring the impact of 11 LM tools on the QMS implementation with eventual contribution to operations management by focusing on hard LM aspects and their interrelationships with QMS in one integrated model.

Integration of lean manufacturing and quality management system through structural equation modelling

Both the lean manufacturing (LM) and the quality management system (QMS) are clear initiatives with a goal of improving effectiveness and efficiencies. This review paper aims at exploring the impact of LM tools on QMS. Many organisations tackle lean philosophy, ISO 9001 standard individually. But this kind of attempt lacks focus on the synergy and the advantage from the potential collaboration. Few studies were conducted to explore the linkage between LM and QMS and whether they can coexist in the Manufacturing Industries or not. Though LM is generally proposed as a driver for QMS, a conceptual model is necessary to be developed in order to investigate the integration between LM and QMS. The model is proposed in this paper with a future direction of empirical investigation using AMOS 22 structural equation modelling to test the developed proposition. The originality of this paper lies in observing and measuring the impact of 11 LM tools on the QMS implementation with eventual contribution to operations management by focusing on hard LM aspects and their interrelationships with QMS in one integrated model.

Using Green Chemistry and Engineering Principles To Design, Assess, and Retrofit Chemical Processes for Sustainability.

The concepts of green chemistry and engineering (GC&E) have been promoted as an effective qualitative framework for developing more sustainable chemical syntheses, processes, and material management techniques. This has been demonstrated by many theoretical and practical cases. In addition, there are several approaches and frameworks focused on demonstrating that improvements were achieved through GC&E technologies. However, the application of these principles is not always straightforward. We propose using systematic frameworks and tools that help practitioners when deciding which principles can be applied, the levels of implementation, prospective of obtaining simultaneous improvements in all sustainability aspects, and ways to deal with multiobjective problems. Therefore, this contribution aims to provide a systematic combination of three different and complementary design tools for assisting designers in evaluating, developing, and improving chemical manufacturing and materials management systems under GC&E perspectives. The WAR Algorithm, GREENSCOPE, and SustainPro were employed for this synergistic approach of incorporating sustainability at early stages of process development. In this demonstration, simulated ammonia production is used as a case study to illustrate this advancement. Results show how to identify process design areas for improvements, key factors, multicriteria decision-making solutions, and optimal trade-offs. Finally, conclusions were presented regarding the tools' use in more robust sustainable process and material management designs.

Cloud-based cyber-physical systems and quality of services

Purpose – The purpose of this paper is to review and explore the evolution, advances and future trends of cloud manufacturing, placing the focus on the quality of services. Moreover, moving toward the new trend of cyber-physical systems (CPS), a cloud-based cyber-physical system (CBCPS) is proposed combining the key enabling techniques of this decade, namely Internet of Things (IoT), cloud computing, Big Data analytics and CPS. Design/methodology/approach – First, an extensive review is made on cloud computing and its applications in manufacturing sectors, namely product development, manufacturing processes and manufacturing systems management. Second, a conceptual CBCPS which combines key enabling techniques including cloud computing, CPS and IoT is proposed. Finally, a review on the quality of the services (QoS) presented in the second step, along with the main security issues of cloud manufacturing, is conducted. Findings – The findings of this review indicate that the combination of the key enabling techniques presented in the CBCPS will lead to a new manufacturing paradigm capable of facing the new challenges and trends. The opportunities, as well as the challenges and barriers of the proposed framework are presented, concluding that the transition into this whole new era of networked computing and manufacturing has a valuable impact, but also generates several security and quality issues. Originality/value – The paper is the first to specifically study the QoS as a factor in the proposed manufacturing paradigm.

New approach based on group technology for the consolidation problem in cloud computing-mathematical model and genetic algorithm

Resource management is a hotspot issue in distributed systems like cloud computing (CC). It means how to prepare the computational resources, i.e., servers and virtual machines (VMS), to execute the tasks. This paper offers a new approach based on Group Technology (GT)—known as a powerful philosophy for the resource management in cellular manufacturing systems—to deal with the resource management problem in CC. We develop a mathematical model to optimally consolidate the VMs, servers and tasks simultaneously to control several important factors such as task migrations and server load variation, as well as the number of VMs. To test the validity of our proposed model, several small problems are generated randomly and solved by LINGO 9 software. Furthermore, to cope with larger problems, which cannot be solved optimally, a genetic algorithm is proposed. We, finally, compare our methods with the most well-known algorithms in this context, round robin (RR) and first-come, first-served (FCFS) algorithms.

Energy consumption awareness in manufacturing and production systems

Industry is a major energy consumer and therefore, manufacturers are continuously trying to reduce manufacturing costs. The improvement of energy usage in manufacturing has been receiving large attention during the last years due to the increasing energy cost and environmental awareness. However, it is still difficult to identify the most energy demanding manufacturing phases in the whole manufacturing process, and which ones can and should be energetically improved. This paper proposes a standard-based infrastructure to collect and monitor energy data in real time for manufacturing and production systems, along with a manufacturing energy management system (MEMS). The collected data improves energy consumption awareness and allows the MEMS to make further analysis and to identify where to take actions in the manufacturing process in order to reduce the energy consumption. The developed MEMS enables energy consumption monitoring with different granularities, depending on where the monitoring devices are placed, that is, per machine, per product process, or for the factory as a whole. The developed MEMS also includes the control of energy related devices to support energy-based decisions and enables the interoperability with a generic manufacturing management system simulation software that works with real monitored energy data in order to test and validate modifications made to the manufacturing process.

Zero-point fixture systems as a reconfiguration enabler in flexible manufacturing systems

ABSTRACTToday's manufacturing systems need to be able to quickly adapt to customer demands, ranging from high volumes of mass production to high volumes of mass customization. Flexible Manufacturing Systems provide a high degree of flexibility to cope with these challenges. They consist of machine tools capable of executing a wide range of machining operations while the use of pallets to reference and block the parts allows the decoupling of the setup operations from the machining centers activity. This paper presents an ontology-based framework to support the design and management of flexible manufacturing systems, aimed at integrating the various involved activities including the pallet configuration and process planning, the management policies for short-term production planning and the pallet checking to verify the correct configuration of the physical pallet.

Automatic identification of constraints and improvement actions in production systems using multi-objective optimization and post-optimality analysis

Manufacturing companies are operating in a severely competitive global market, which renders an urgent need for them to explore new methods to enhance the performance of their production systems in order to retain their competitiveness. Regarding the performance of a production system, it is not sufficient simply to detect which operations to improve, but it is imperative to pinpoint the right actions in the right order to avoid sub-optimizations and wastes in time and expense. Therefore, a more accurate and efficient method for supporting system improvement decisions is greatly needed in manufacturing systems management. Based on research in combining simulation-based multi-objective optimization and post-optimality analysis methods for production systems design and analysis, a novel method for the automatic identification of bottlenecks and improvement actions, so-called Simulation-based Constraint Identification (SCI), is proposed in this paper. The essence of the SCI method is the application of simulation-based multi-objective optimization with the conflicting objectives to maximize the throughput and minimize the number of required improvement actions simultaneously. By using post-optimality analysis to process the generated optimization dataset, the exact improvement actions needed to attain a certain level of performance of the production line are automatically put into a rank order. In other words, when compared to other existing approaches in bottleneck detection, the key novelty of combining multi-objective optimization and post-optimality analysis is to make SCI capable of accurately identifying a rank order for the required levels of improvement for a large number of system parameters which impede the performance of the entire system, in a single optimization run. At the same time, since SCI is basically built atop a simulation-based optimization approach, it is capable of handling large-scale, real-world system models with complicated process characteristics. Apart from introducing such a method, this paper provides some detailed validation results from applying SCI both in hypothetical examples that can easily be replicated as well as a complex, real-world industrial improvement project. The promising results compared to other existing bottleneck detection methods have demonstrated that SCI can provide valuable higher-level information to support confident decision-making in production systems improvement.

A Systematic Approach to Quality Oriented Product Sequencing for Multistage Manufacturing Systems

Product sequencing is one way to reduce cost and improve product quality for multistage manufacturing systems (MMS). However, systematically evaluating the influence of product sequence on quality performance for MMS is still a challenge. By considering the rate of incoming conforming product, manufacturing system quality transition between batch to batch, and quality propagation along stages, this paper investigates the appropriate batch policies and product sequencing for MMS so that satisfied quality performance can be achieved. A model to analyze the relationship between the product sequencing and quality performance is conducted just by using the quality inspection data and the complex engineering knowledge used in the variation method is avoided. Based on Markov Chain processes methodology, quality performance is modeled as a function of transition states jointly determined by multistage condition, product sequencing, incoming part quality, and propagation of the rate of conforming products among multistage. Quality related batch strategies are discussed for optimal quality performance. Two kinds of quality efficiency are put forward to facilitate the modeling and the discussion. The results of the model will lead to guidelines for quality management in multistage manufacturing systems.