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Real-Time Management-Based Production Scheduling for Sustainability
Published in Vijaya Kumar Manupati, Goran D. Putnik, Maria Leonilde Rocha Varela, Smart and Sustainable Manufacturing Systems for Industry 4.0, 2023
Smart manufacturing has been promoted as a new manufacturing paradigm with the development of advanced technologies, such as the Internet of Things (IoT), artificial intelligence (AI) and cyber-physical systems, among others (Qu, Ming, Liu, Zhang, & Hou, 2019; Mittal, Khan, Romero, &Wuest, 2019). Research in advanced ICT technologies, under the 4th Industrial Revolution (so-called Industry 4.0), can create a competitive, digital, low-carbon and circular industry (European Commission, 2021) and should be aligned with the Sustainable Development Goals (SDGs) from the United Nations. Industry 4.0 is seen as a powerful instrument for achieving sustainability goals (Varela, Araújo, Ávila, Castro, & Putnik, 2019). Advanced information and communication technology (ICT), in the scenario of Industry 4.0, allows data collection, processing, and decision-making in real time to control (in real time) manufacturing (Alves & Putnik, 2019).
* Strategy: The Catalyst for Organizational Change
Published in Martin Stein, Frank Voehl, SM Management, 2020
Supply chain management is frequently cited as an important new catalyst for change. Because it is comprehensive and because it cuts across organizational lines, it qualifies to be categorized as a Macrologistics strategy. The definition of supply chain management is the creation of a management process for integrating decisions, plans and information systems from customer requirements through the manufacturing process to the suppliers of materials. Components of the process are purchasing, manufacturing, distribution, transportation, product handling and customer service. For some products, this process can also include recycling and disposal (also known as “reverse logistics”). The major advantage of this management approach is that because it is comprehensive, there are substantial opportunities to reassess the way that customers obtain value-added from the company. If all elements of the value chain are identified, barriers to meeting and exceeding customer expectations are identified. See the case studies of Becton Dickinson and Xerox which show the benefits of this approach (see Profiles 4.1 and 12.1).
Designing, Developing, and Deploying Integrated Lean Six Sigma Certification Programs in Support of Operational Excellence Initiatives
Published in Harriet B. Nembhard, Elizabeth A. Cudney, Katherine M. Coperich, Emerging Frontiers in Industrial and Systems Engineering, 2019
Jack Feng, Scott Sink, Walt Garvin
Six Sigma BB training provides an industry-leading approach that can tackle the complex problems and deliver breakthrough performance. Six Sigma is a highly specialized training involving the use of quantitative tools for process improvement. Most of the tools taught as part of the BB certification are data-driven tools and are heavily based on statistical concepts. Six Sigma is a disciplined, data-driven approach and methodology for eliminating defects in any process—from manufacturing to transactional and from product to service. It focuses on process improvement and variation reduction through application of Six Sigma improvement projects. In Jabil, Six Sigma BBs are developed through continuous Explain–Practice–Apply–Review learning cycle on in-depth knowledge of Six Sigma methodologies and tools. The development program consists of four 5-day training sessions over a 4-month period including one application project for each candidate.
Combining lean and agile manufacturing competitive advantages through Industry 4.0 technologies: an integrative approach
Published in Production Planning & Control, 2023
Bingjie Ding, Xavier Ferràs Hernández, Núria Agell Jané
Many tools are utilized in assessing and improving lean manufacturing activities, among which the most common ones are Value Stream Mapping (VSM), Hoshin Kanri and planning, Lean Office, Lean Metrics, Push and Pull systems, Kaizen events, Visual control and management, 5S, Jidoka – automation, Kanban and Total productive maintenance (Chiarini 2011). A large number of studies have shown a positive effect of these lean practices on operational, financial, and environmental performance (Negrão, Filho, and Marodin 2017; Vinodh, Kumar, and Vimal 2014; Alcaraz et al. 2014). Lean manufacturing focuses on eliminating waste to improve processes, speed up the production time, and deliver the quantity demanded by the customer. Lean manufacturing relies on the optimization of processes and inventory to reduce manufacturing time, which leads to better utilization of resources and time and eventually results in quality production at the lowest cost for manufacturing organizations (Potdar, Routroy, and Behera 2017).
Machine learning based fault detection approach to enhance quality control in smart manufacturing
Published in Production Planning & Control, 2023
Manufacturing is the process of turning raw materials into finished products on a massive scale. One of the most significant sectors of the global economy, it produced $13.9 trillion in global output in 2019 and contributed almost 16% of the world’s GDP (Afrasiabi et al. 2019). Producing more high-quality products at the lowest possible cost is one of the most crucial production goals. However, for firms that lack the tools and resources required to create top-notch items, manufacturing things could be an expensive and challenging task. The history of manufacturing has undergone amazing changes during the last few centuries. The Industrial Revolution began in the eighteenth century when businesses looked for machinery to take the role of physical labour in the production of commodities. Three technology trends—connectivity, intelligence, and flexible automation—define the fourth Industrial Revolution, also referred to as ‘Industry 4.0’, which started in 2016. The industrialization of computers is encouraged by this revolution.
A system dynamics approach to measure the effect of information sharing on manufacturing/remanufacturing systems’ performance
Published in International Journal of Computer Integrated Manufacturing, 2022
Hamed Delavar, Hani Gilani, Hadi Sahebi
Supply chain management is business cooperation that consists of manufacturing systems configuration, production planning, and material shipment control from the supplier to the final customer (Ghamari and Sahebi 2017). Some issues related to closed-loop supply chain strategic management were investigated in the work of Fleischmann et al. (2000), which shows that only two case studies had been conducted on reverse logistics modeled by SD about recyclable material flow from a manufacturing and recovery supply chain. The first study on system dynamic modeling of supply chain management had been presented by Forrester (1958). In this model, the downward commodity flow starts from the factory to the factory warehouse, the distributor, then the retailer, and ultimately the customer. Orders (information flow) go from the lower element to the higher ones upwardly. There are also some delays at each chain level while sending and receiving orders (Forrester 1961). Also, a case study is provided for reverse logistics models by Fleischmann et al. (1997). In that study, Fleischmann et al. reviewed all reverse logistics models.