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Manufacturing Excellence in Ceramic Industry
Published in Debasish Sarkar, Ceramic Processing, 2019
Mithugopal Mandal, Debasish Sarkar
Most of the refractory companies, sanitary wares, bath tubs, tiles (wall, floor and synthetic granite) and manufacturing systems are examples of discrete manufacturing. However, glass and steel manufacturing can be categorized as continuous manufacturing. Therefore, the Lean implementation approach would be different in these two categories of the industries. By nature of the manufacturing process, discrete manufacturing may have multiple work stations and works in process (WIPs) are stored in the buffer stations. Production may have different batches and batch-wise semi-finished stocks are also stored in the intermediate stations to feed in the next stations. An example of this is in the tile industry: the dried tiles are stored in sufficient quantities to feed in the next stations like the firing process. If the stocks fall below the critical stock, the kiln output may get impacted in either firing strength or defects that eventually demand sorting for quality and reliable product. But in case of the continuous manufacturing, there is very little or no chance to store the WIP in between the work stations. The intermediate products are continuously fed to the next stations automatically and production run uninterruptedly. The continuous manufacturing is less flexible in nature; on the contrary, the discrete manufacturing is more flexible, line balancing, capacity management and customization.
Computer-Integrated Manufacturing in the Food Industry
Published in Gauri S. Mittal, Computerized Control Systems in the Food Industry, 2018
In practice many foods are manufactured from food components, which in turn may consist of subcomponents, and so on. This is shown in Fig. 8 for lasagna, which is assembled from pasta sheets, tomato sauce, and cheese. Tomato sauce consists of canned tomatoes, baked minced beef, cream, and an herbs mix. This can be continued down to the ingredient level, where ingredients are defined for this purpose as the food components that are purchased by an external supplier. In the discrete manufacturing processes, for example, an automobile assembly line, the term Bill of Materials (BOM) is often used to list the components and subassemblies that make up a product.
The State of Manufacturing IT Today
Published in Kevin Ake, John Clemons, Mark Cubine, Bruce Lilly, Information Technology for Manufacturing, 2003
Kevin Ake, John Clemons, Mark Cubine, Bruce Lilly
Now here is the downside, and it is serious. In most manufacturing environments outside the pure discrete industries like automotive, ERP has been abysmal in its production management functions. Put bluntly, ERP simply doesn’t understand the plant as it exists today. Twenty years ago, the bulk of manufacturing in this country was “discrete” manufacturing — the production of items like chairs, PCs, automobiles, and consumer electronics, for example. In the past two decades much of that work has gone to China, Taiwan, Mexico, and Thailand. Our manufacturing is now largely “process” and “hybrid” manufacturing.
Data continuity and traceability in complex manufacturing systems: a graph-based modeling approach
Published in International Journal of Computer Integrated Manufacturing, 2021
The configuration of the data model and the translation of the model into a physical architecture results in different MES design considerations, which are aligned to the characteristics of the manufacturing flow and its requirements concerning data processing, storage, and analysis. When analyzing the literature about data models for manufacturing traceability, it becomes evident, that the vast majority of publications address process industries with high traceability maturity, such as in the works of Wu, Meng, and Gray (2017) or Jansen-Vullers, Van Dorp, and Beulens (2003). Process-driven industries follow a linear process sequence on a lot-basis. As their manufactured products consist of a few parts, MES for process industries are usually based on static process plans, which define the logic to produce the final product. Central entities of the underlying data models are the production recipes, which create the items and sequence the operations (Jansen-Vullers, Van Dorp, and Beulens 2003). In contrast to process industries, discrete manufacturing industries developed traceability solutions, in which the underlying data model focuses on the final aspired product hierarchy. In discrete manufacturing, identifiable products consisting of items are produced and assembled according to a product structure. The underlying data model is product-driven and centers around the final products’ bill of material (BoM). Illustrative data models have been developed for different applications and industries (Tang and Yun 2008; Li, Wan, and Xiong 2011; Jansen-Vullers, Van Dorp, and Beulens 2003; Helo et al. 2014).
Lagrangian relaxation method for optimizing delay of multiple autonomous guided vehicles
Published in Transportation Letters, 2018
Important drivers for the manufacturing and discrete manufacturing industries are delivering products on time while improving quality and reducing production costs. AGV systems comprise intelligent scheduling software to meet just-in time deliveries and to save costs related to moving products internally. As they manoeuver very carefully and handle your goods with care damage to products and equipment is minimized. Typical issues of the discrete manufacturing business include the complexity of the product mix, small batch sizes, a variety of treatments per component, and last but not least the cyclical fluctuations in the market demand. Transport of such products requires a flexible logistic solution. AGV systems provide a maximum of free space on the shop floor and are easily adoptable to changes in the plant layout and process equipment.
An energy-aware cyber physical system for energy Big data analysis and recessive production anomalies detection in discrete manufacturing workshops
Published in International Journal of Production Research, 2020
Chaoyang Zhang, Zhengxu Wang, Kai Ding, Felix T.S. Chan, Weixi Ji
In a discrete manufacturing workshop, the complexity and dynamics during manufacturing processes are tremendous challenges hindering companies from maintaining good manufacturing quality and productivity as well as the best energy performance. Meanwhile, production systems and cutting tools are also prone to degradation, which causes dimensional and geometric deviations of the workpieces. The dynamics generates unexpected breaks and unnecessary inspection, standby, repairing and maintenance of production systems, leading to time, energy and resource waste (Wang et al. 2015). Thus, many studies have been conducted to detect production anomalies and can be divided into two aspects, i.e. the machine layer and workshop layer.