Optimization and Management in Manufacturing Engineering

The manufacturing industry plays an important role in the economy and society. In traditional environment, the manufacturing industry is at a standstill or even in recession in the United States [1]. Thus, it is crucial to identify new drivers to boost the manufacturing industry. In recent years, the development of Internet of Things (IoT) has brought a great opportunity as well as a challenge for modern manufacturing enterprises. The application of IoT in manufacturing industry not only brings economic benefits for manufacturing enterprises, but also promotes the upstream and downstream industries. Unfortunately, the employment of the wireless transmission technologies in the IoT environment also introduces significant information security issues.

Global economic integration and information network have brought radical changes to the operational management of business processes. Emerging information technologies, such as the Internet of Things (IoT) and big data, have fostered customers’ changing personalized demands and accelerated the product updating speed, thereby impacting traditional production patterns. Empirical studies found that the IoT infrastructure can effectively support information systems of next-generation manufacturing enterprises [28]. More specifically, the requisition and sharing of a product’s life cycle (e.g., market demand, usage, and recycling) information in an IoT-based manufacturing enterprise have the following advantages over traditional manufacturing scenarios: (1) more comprehensive acquisition of product life cycle information, which would be impossible in a traditional manufacturing environment, (2) precise detection and analysis of on-site data through the perceptual and application layers of the condensed sensing network, and (3) faster information transmission in an intelligent manufacturing environment, so different hierarchies can conveniently access the needed information.

The Internet of Things (IoT) refers to the networking of physical items through the use of embedded sensors and other devices that gather and convey information about the items. The data collected from these devices can be used to optimize products, services, and operations. One of the earliest and best-known applications of such technology appears in the area of energy optimization: sensors deployed across the electricity grid can help utilities remotely monitor energy usage and make responses to account for peak times and downtimes. The IoT is also widely used in manufacturing enterprises to optimize production. For example, in factories, sensors enhance production efficiency by providing a constant flow of data to optimize production processes. The data collected from equipment can be used to determine the operating state of the equipment. This can greatly improve the accuracy of the equipment maintenance plan, reduce maintenance costs, and reduce unplanned downtime. The data collected from vehicles can be used to predict the arrival time of raw materials and product components.

Manufacturing companies cannot focus only on tradition manufacturing modes if they want to ensure profitability and competitiveness. Business process reengineering (BPR) is helpful for manufacturing companies who hope to benefit from new approaches to business. Manufacturers are confronted by two problems: defining what technology is important to the manufacturing company for reconstructing their processes and improving their mode and how to improve their management method to maximizing the benefit of the new mode. In this chapter, we highlight a new manufacturing mode, hybrid manufacturing, and propose a coordination management model that takes into consideration inventory and transportation as part of the comprehensive goal. We emphasize the innovational role of the IoT in these areas.

The cutting stock problem is representative of the combinatorial optimization problems that arise in industries such as steel, furniture, paper, glass, and leather. In a cutting plan, we must obtain the required set of smaller pieces (items) by cutting large pieces (objects) that are in stock. The objective is usually to minimize waste. In a real-life cutting process, there are some further criteria, e.g., the number of different cutting patterns (setups), capacity of the cutting equipment, and due dates. With the increasing scarcity of resources in the world, researchers are paying more attention to resource utilization.

Feigenbaum [218] developed the theory of total quality management in 1961. He held that total quality management (TQM) aims at fully satisfying customer requirements through market research, design, production, and services. He integrated the enterprise activities of designing quality, maintaining quality, and improving quality into an effective system. Shewhart [247] promoted the understanding of quality and quality management and accelerated the development of quality management. Johnson and Jack [224] indicated that TQM is “doing the right thing at the right time.” Deming [227] noted that the role of quality management in business is to create the constancy of purpose for the improvement of products and to create a system that can produce quality outcomes. Benson et al. [248] pointed out that quality is to “satisfy or delight the customer.” All quality improvement initiatives must start from an understanding of customer requirements. Samson and Terziovski [215] indicated that TQM must utilize techniques that improve product quality and processes to help a firm improve its competitive performance. Jeong et al. [240] thought total quality management means making sure everything and everyone in the organization realize continuous quality improvement. Hoanga et al. [244] summarized 11 measurements of the TQM model, including leadership and top management commitment, employee involvement, education and training, teamwork, employee empowerment, customer focus, process management, strategic planning, open organization, information and analysis system, and service. Many researchers systematically summarized the methods for quality management and tried to improve the concepts of quality management using the specific requirements of certain enterprises.

Life Cycle Assessment (LCA) is a tool that assesses the environmental impacts and resources used throughout a product’s life cycle, i.e., from raw material acquisition to the production and use phases and waste management [263]. Hellweg found that LCA is an important decision-support tool that, among other functions, allows companies to benchmark and optimize the environmental performance of products or for authorities to design policies for sustainable consumption and production [287].