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How Armstrong World Industries Focus on Executive Sponsorship, Business/IT Partnership, and Strategic Alignment in Governing Digital Acceleration Initiatives Improved Business Performance
Published in Philip Weinzimer, Strategic IT Governance 2.0, 2023
The executive-level consists of the C-Suite and other senior executives. The operational level, known as Digital Steering Committees, consists of senior and middle managers from both business and IT organizations. These teams focus on the Customer, Manufacturing and Supply Chain, Business Services, and the IT Foundation, which are considered the digital acceleration strategy’s four focus areas.Digital Customer focuses on improving the customer experience and driving speed for the customer.Digital Manufacturing integrates technology using predictive controls in manufacturing and maintenance, automates the warehouse, and provides visibility and control into each supply chain component.The Digital Business Services leverages data analytics and state of the art Enterprise Resource Planning (ERP) Tools, Human Resources Information Systems (HRIS) to improve employee experience and productivity.IT Foundational Services provide the solid foundation and infrastructure for collaboration, productivity, and security across the organization based on the Armstrong 5A principles: Anytime, Anywhere, Any Device, Always On, Always Available.
Industry 4.0: An Introduction in the Context of SMEs
Published in Ketan Kotecha, Satish Kumar, Arunkumar Bongale, R. Suresh, Industry 4.0 in Small and Medium-Sized Enterprises (SMEs), 2022
Priya Jadhav, Satish Kumar, Arunkumar Bongale
The scope of Industry 4.0 is to coordinate the activities of planning, producing, and managing manufacturing systems efficiently. Individual manufacturing entities and entire systems are reviewed in detail. Initial stage to successful solutions with subsystems of manufacturing as self-governing, yet closely related digital, implicit, and real provides efficient and effective manufacturing activities. Digital manufacturing has the potential to contribute to the effective planning, development, and operation of manufacturing systems. Other advanced Industry 4.0 technologies that enable smart production are described in the chapter. It also identifies common issues connected with digital transformation in manufacturing small and medium-sized enterprises (SMEs), as well as effective solutions for overcoming issues. Further research should concentrate on identifying the most advantageous clusters or pairings of Industry 4.0 technology applications. As an approach, it has the potential to raise awareness about areas where improvements are required.
Smart Product Development
Published in Uthayan Elangovan, Product Lifecycle Management (PLM), 2020
Product managers specify consumer requirements through a structured technique and equate them into certain plans to create products. The ideal journey is easy, smooth, comfortable, and inexpensive. Although prospects are good for standard product development process, with current technological improvement, manufacturing enterprise needs to work closely with consumers moving toward even more accountable and cost-effective modes of manufacturing to face new realities. Progressive Industry 4.0 opens up connected world to competition, and customers are more demanding, in terms of smooth running and value-added services. This increase of a brand-new competition paves the path to alternative modes of product development process, which is trembling up the typical value chain and considerably gaining market share. This digital manufacturing revolution allows manufacturing enterprises to release their vision, maximize equipment dependability, production, improve top quality, and drive ingenious business models.
An integrated methodology for the assessment of stress and mental workload applied on virtual training
Published in International Journal of Computer Integrated Manufacturing, 2023
Agnese Brunzini, Fabio Grandi, Margherita Peruzzini, Marcello Pellicciari
In the context of Industry 4.0, digitalization is considered one of the most important drivers of innovation, useful not only to save time and cost but also to optimize data and process management. In particular, digital manufacturing can be applied to different stages of the manufacturing process, such as design, prototyping, and assembly training (Abidi et al. 2019). Indeed, due to the importance of the assembly step in the manufacturing process, specific training should be provided to the operators, also to cope with the new technologies. In this context, the Operator 4.0 can be supported with different levels of cognitive automation, namely technical solutions helping the operator about how and what to assemble and to control the situation. Virtual Reality (VR) is categorized among these technological supports (Mattsson et al. 2020).
Industry’s 4.0 transformation process: how to start, where to aim, what to be aware of
Published in Production Planning & Control, 2022
Armando Calabrese, Manoj Dora, Nathan Levialdi Ghiron, Luigi Tiburzi
Digital manufacturing transformation (II) can be achieved by adopting technologies such as robots and drones, 3D printing, computing, and blockchain. The most relevant feature of this transformation is that manufacturers retain the traditional business paradigm (selling products) while at the same time improve their routines (Bibby and Dehe 2018; Lu 2017). Productivity and quality are the major goals of this type of transformation (Yadav, Shankar, and Singh 2020; Tortorella, Giglio, and van Dun 2019). The fact that digital manufacturers can do more with less has also positive consequences on environmental sustainability (Machado, Winroth, and Ribeiro da Silva 2020). For some scholars, given their efficiency improvements, the digital manufacturing transformation is seen as a cost equaliser opportunity to backshore the production of goods (Zhang and Chen 2020; Ancarani, Di Mauro, and Mascali 2019). This transformation is the most suitable in industries in which competition is based on cost, and value proposition is based on the offering of highly customised products packed with sensors and artificial intelligence and capable of performing autonomous tasks. The chief internal barrier for this kind of transformation is complex systems design and management (IB5), i.e. the creation of autonomous production lines which are flexible and resilient at the same time (Kim 2018; Jung et al. 2017).
Application of machine learning algorithm in the sheet metal industry: an exploratory case study
Published in International Journal of Computer Integrated Manufacturing, 2022
Ahm Shamsuzzoha, Timo Kankaanpaa, Huy Nguyen, Hoang Nguyen
The emergence of digitalization in manufacturing processes has attracted industries and evolved over recent decades. The traditional concept of computer-integrated manufacturing is transferred to digital manufacturing, where the technologies and tools facilitate the integration of product and process design before starting actual production and support the ramp-up phases . In digital manufacturing, particular emphasis is given to the optimization of networked production facilities, where real time data is necessary for the decision-making process. Machine learning, which is a diverse field of artificial intelligence, has the ability to automatically learn from data and make predictions based on data. This technique has been widely used in digital manufacturing for various purposes, such as predictive maintenance (Joseph et al. 2014; Dalzochio et al., 2020), demand forecasting (Huber and Stuckenschmidt 2020; Jayant, Agarwal, and Gupta 2021) to process monitoring (Cakir, Guvenc, and Mistikoglu 2021), and optimization (Weichert et al. 2019; Min et al. 2019).