Search

With the escalating integration of renewable energy sources (RESs) within the manufacturing sector, the synergy between energy and production management has gained paramount importance. To harmonise energy efficiency with production effectiveness, the concept of energy-aware scheduling, comprising off-line, on-line, and hybrid scheduling, has been introduced. While each approach has merits and drawbacks in handling unexpected disturbances, recovering optimal performance, and managing computational costs, their designs often tend to be ad hoc. Currently, there is a lack of a systematic design that effectively balances computational costs, robustness to unexpected disturbances, and optimal performance. Moreover, existing results do not fully leverage the flexibility in factories integrated with Renewable Energy Sources (RESs). In response, this study introduces the paradigm of event-triggered hybrid scheduling (ETHS) to achieve a balance among optimality, robustness, and computational cost by fully exploring the system's flexibility. The efficacy of ETHS is empirically demonstrated through a comprehensive case study involving a real-world manufacturing facility. Diverse scenarios are scrutinised, accompanied by sensitivity analyses pertaining to parameter choices. The outcomes conspicuously underscore the potency of ETHS in facilitating the harmonious alignment of energy and production management within the ambit of manufacturing industries. [ABSTRACT FROM AUTHOR]

Lean manufacturing (LM) applies different tools that help to eliminate waste as well as the operations that do not add value to the product or processes to increase the value of each performed activity. Here the main motivation is to study how quantitative modelling approaches can support LM tools even under system and environment uncertainties. The main contributions of the article are: (i) providing a systematic literature review of 99 works related to the modelling of uncertainty in LM environments; (ii) proposing a methodology to classify the reviewed works; (iii) classifying LM works under uncertainty; and (iv) identify quantitative models and their solution to deal with uncertainty in LM environments by identifying the main variables involved. Hence this article provides a conceptual framework for future LM quantitative modelling under uncertainty as a guide for academics, researchers and industrial practitioners. The main findings identify that LM under uncertainty has been empirically investigated mainly in the US, India and the UK in the automotive and aerospace manufacturing sectors using analytical and simulation models to minimise time and cost. Value stream mapping (VSM) and just in time (JIT) are the most used LM techniques to reduce waste in a context of system uncertainty. [ABSTRACT FROM AUTHOR]

Production management serves as a pivotal component in the operational activities of oilfield sites, with the effectiveness of management practices directly influencing the success of developmental outcomes. To enhance the maintenance-free operational period of oil production systems, elevate management standards, and reduce overall operational costs, advanced technologies such as artificial intelligence (AI) and big data analytics have been strategically integrated into oilfield operations. These technologies are able to incorporate data resources from all stages of oilfield production, thus providing a comprehensive view of oilfield production and guidance for production. This study uses a series of diagnostic and predictive methods to construct a management system that allows for the comprehensive monitoring and fault diagnosis of oil production systems, which can ensure the intelligent management of oil production systems at multiple levels throughout their life cycle. Automated monitoring workflows and proactive analytical processes are at the heart of the framework, enabling real-time monitoring and predictive decision-making. This not only minimizes the likelihood of system failure but also optimizes resource allocation and operational efficiency. [ABSTRACT FROM AUTHOR]

This article is devoted to a system solution for the integration and automation of production management of an industrial enterprise, expanding the space of its adjustable parameters and accelerating the response to current production events. The analysis and development of the architecture of an integrated information system providing integrated operational management was carried out in the conditions of the industrial revolution "Industry 4.0." The objects, processes, and relations of production were studied in relation to other functional areas of the enterprise, from the point of view of both economic content and management and the logical structure of the information system. [ABSTRACT FROM AUTHOR]

A healthy lifestyle encourages people to regularly eat more fruits and nuts, including nutritious snack bars, which provide excellent nutritional and energy value and are exceptional convenience foods with the necessary content of macro- and microelements, dietary fibre and biologically active compounds. The article analyses and proves the usefulness of fruits and nuts, which have a positive impact on human health in snack bars. The consumption of nuts demonstrates a positive effect on health, preventing and/or creating a therapeutic effect on a person by reducing some risk factors associated with chronic diseases. The simulation, which reflects the structure of the population's diet, when all snacks between meals, with the exception of drinks, were replaced by nuts, records a significant decrease in added sugar (-17.8 %), solid fats (-21.0 %), saturated fat (-6.6 %) and sodium (-12.3 %), and an increase in monounsaturated (+35.4 %) and polyunsaturated fats (+42.0 %), vegetable omega-3 fatty acids (+53.1 %), dietary fibre (+11.1 %), magnesium (+29.9 %) and a slight increase in potassium (+1.5 %). The available scientific evidence shows that nut consumption (15 to 30 g per week for 3 to 16 weeks) does not provide additional health benefits for children aged 8 to 18 years, but replacing high-calorie, low-nutrient snacks with nuts can help to promote healthy dietary habits as children grow into adults. A survey of 188 respondents (39.3 % male, 60.7 % female) was conducted to investigate the attitudes of Ukrainian youth towards nut consumption and preferences for snack bar flavours. While protein bars can be considered a speciality food for consumers seeking to increase muscle mass, snack bars based on nuts and dried fruits are seen as snacks with an overall positive impact on the body. When eating, a person has a certain sequence of chemosensory sensations that influence the aesthetic pleasure of eating certain dishes, and the taste preferences of respondents show that the most desirable aroma and taste is chocolate (39.3 %), followed by hazelnut (32.8 %) and strawberry (13.1 %). The least desirable flavours for consumers are blueberry (2.6 %), cereal (2.0 %) and banana (2.0 %). In addition to attention to the vitamin content (44.3 %), respondents are interested in the absence of chemical additives (27.9 %) and sugar (16.4 %) in snack bars. Despite the scientifically proven positive effects of antioxidants and dietary fibre on the human body, only 1.6 % of respondents consider their presence to be a determining factor in choosing a food product. Based on the study conducted by the authors, it was found that using a mixture of nuts and dried fruit for snacking creates a balance between monounsaturated and polyunsaturated fatty acids, provides the body with essential vitamins and is desirable for consumption. Snack bars containing nuts and dried fruits are organoleptically the most desirable for consumers and may be useful in preventing a number of diseases. This study can be used to make managerial decisions in the production of healthy food products.

The sustainable use and disposal of carbon materials without affecting the profit (gain) of an industry is an important task of the policy makers recent times. The present study deals with a vendor–buyer inventory model for deteriorating and imperfect quality items considering the carbon emissions under different environments, namely general fuzzy and triangular interval type-2 fuzzy environments. In fact, we develop a vendor-buyer inventory model under a fuzzy bi-matrix game approach and construct a joint payoff function along with a joint effective emission cost function. Carbon emission is related to the fuel consumption during transportation, disposal of the deteriorating items and warehouse energy consumption per unit item. The notion of this study is to optimize the total average inventory cost along with the amount of carbon emission cost under flexible demand rate and uncertain cost parameters. Basically, we have developed a new optimization problem incorporating vendor–buyer's objective function with carbon emission based on the expected payoff function of the bi-matrix game. Numerical findings reveal that a type-2 fuzzy system could be able to optimize the average inventory cost as well as total emission cost all the time. Finally, sensitivity analysis graphical illustrations are made to validate the model. [ABSTRACT FROM AUTHOR]

Copyright of Metal Working (1674-165X) is the property of Metal Working Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The study provides a data-driven approach for the Work-in-Process of bottlenecks in Semiconductor manufacturing operation. Semiconductor manufacturing is a repetitive and time-consuming process, with cycle times ranging from one to three months. To improve market responsiveness and reduce operational costs, minimizing work-in-process (WIP) throughout the manufacturing process is crucial, especially for the bottleneck process because it determines the efficiency of the entire production system. To achieve this goal, we utilize optimization and statistical techniques based on real-world data to determine the optimal level and threshold bounds for managing the WIP of the bottleneck process. Our results show a 35% reduction in the average WIP level of the bottleneck process without compromising its productivity, demonstrating the effectiveness of our approach in practical applications. History: This paper was refereed. [ABSTRACT FROM AUTHOR]

The matrix-based methodology "Design Structure Matrix (DSM)" which enables to evaluate dismantling performance of automotive structures is introduced. After the component of DSM and the numerical operation of its elements to make the evaluation graph shall be explained, the systematic scheme of matrix re-ordering and expressions of order constraint would be proposed. Finally, practical dismantling example of automotive door structure with modular design shall make us convinced that the DSM is reasonable and intuitionally comprehensible tools. [ABSTRACT FROM AUTHOR]

Purpose: The work aims to assess the impact of the implementation of CNC laser technology on the efficiency and quality of machine parts production, using the example of a company in the metal industry. The introduction of the CNC laser is intended to increase the precision and automation of production processes, which will translate into increased efficiency and quality of manufactured products, in line with the idea of industry 4.0 promoted in the modern economy. Design/methodology/approach: As part of the research, a detailed analysis of the impact of CNC laser implementation on production was carried out, taking into account aspects such as production efficiency, product quality and process optimization. The study used a methodology based on observations of the production process, analysis of technical documentation and interviews with employees involved in the implementation process. Findings: The results of the analysis indicate significant benefits resulting from the implementation of CNC technology, such as shortening production time, increasing the machine utilization rate (OEE) and reducing production waste. At the same time, potential challenges related to the transformation in line with the idea of Industry 4.0 were identified, such as the need to constantly monitor and analyze the effectiveness of the implemented technological solutions. Practical implications: The work is a valuable contribution to the literature on production management and technological innovation in industry. Providing empirical evidence of the benefits of automation and implementation of modern technologies in production processes. Originality/value: The original contribution of this work is the implication of the data on real- world research models. These data allowed the authors to make calculations and indicate directions for design improvements in the machinery sector in line with the promotional idea of industry.4.0. [ABSTRACT FROM AUTHOR]

The paper presents a novel MCDM model aimed at enhancing satisfactory quality-environmental decisions in product development. The model integrates FAHP, FTOPSIS, Pareto-Lorenz and global sensitivity analyses. It enables to study products with respect to quality level, environmental impact throughout its life cycle and simultaneous consideration on quality level and environmental impact. Results of the research on the example of a smartphone demonstrate that the model successfully identified specific criteria for improvement, offering a valuable tool for enhancing customer satisfaction and promoting environmentally friendly product development. The originality of the research lies in the calculation of the average weights of product criteria with triangular fuzzy numbers, based on the principles of fuzzy logic and FAHP. As a compact, ready-to-use solution, our innovative MCDM model can be employed by organizations to enhance the quality and environmental impact of their products. [ABSTRACT FROM AUTHOR]

This research aimed to identify methodologies for the optimization of production management in restaurants. The PRISMA 2020 systematic review method was employed, using relevant meta-search engines such as ScienceDirect, Scopus, and Web of Science. The search query was determined, followed by reading and analysis of the results to identify improvement methodologies. Subsequently, a synthesis of the most relevant and innovative articles was conducted. The research yielded 40 articles encompassing various methodologies that optimize all aspects of production management in restaurants. [ABSTRACT FROM AUTHOR]

Companies have implemented Lean to increase efficiency and competitiveness. However, the importance of Ergonomics is often neglected, resulting in ergonomic problems and lower profitability and acceptance of Lean. This study presents a comprehensive approach to Operations and Production Management (OPM) considering sociotechnical synergies. For Lean and Ergonomics, literature-based main methodologies and categories are defined. These main methodologies/categories are used as search-term combinations in a further literature search. This literature is divided into "Production worker" (PW), "Physical environment" (PE), "Industry 4.0 technology" (i4.0), "Company culture" (CC), and "Manufacturing methods" (MM) based on a metric, the sociotechnical system (STS) concept. This makes it possible to determine the percentage of participation in Lean and Ergonomics articles by STS category. The main differences can be seen in PE (Lean: 10%; Ergonomics: 24%) and i4.0 (Lean: 29%; Ergonomics: 15%). However, for PW (Lean: 18%; Ergonomics: 21%), CC (Lean: 19%; Ergonomics: 20%), and MM (Lean: 26%; Ergonomics: 20%), there are similarities between Lean and Ergonomics. The OPM user should manage the PW, CC, and MM factors equally with Lean and Ergonomics, as the objective is the same. For PW, CC, and MM measures, a professional separation into Lean/OPM and Ergonomics/Occupational Medicine does not make sense. Concerning i4.0, there is a danger that the human factor in (especially innovation-oriented) OPM will be unjustly neglected and that too much emphasis will be placed on supposedly human-free technology. [ABSTRACT FROM AUTHOR]

Research background: Generative artificial intelligence (AI) and machine learning algorithms support industrial Internet of Things (IoT)-based big data and enterprise asset management in multiphysics simulation environments by industrial big data processing, modeling, and monitoring, enabling business organizational and managerial practices. Machine learning-based decision support and edge generative AI sensing systems can reduce persistent labor shortages and job vacancies and power productivity growth and labor market dynamics, shaping career pathways and facilitating occupational transitions by skill gap identification and laborintensive manufacturing job automation by path planning and spatial cognition algorithms, furthering theoretical implications for management sciences. Generative AI fintech, machine learning algorithms, and behavioral analytics can assist multi-layered payment and transaction processing screening with regard to authorized push payment, account takeover, and synthetic identity frauds, flagging suspicious activities and combating economic crimes by rigorous verification processes. Purpose of the article: We show that edge device management functionalities of cloud industrial IoT and virtual robotic simulation technologies configure plant production and route planning processes across cyber-physical production and industrial automation systems in multi-cloud immersive 3D environments, leading to tangible business outcomes by reinforcement learning and convolutional neural networks. Labor-augmenting automation and generative AI technologies can impact employment participation, increase wage and wealth inequality, and lead to potential job displacement and massive labor market disruptions. The deep learning capabilities of generative AI fintech in terms of adaptive behavioral analytics and credit scoring mechanisms can enhance financial transaction behaviors and algorithmic trading returns, identify fraudulent payment transactions swiftly, and improve financial forecasts, leading to customized investment recommendations and well-informed financial decisions. Methods: Machine learning-based study selection process and text mining systematic review management software and tools leveraged include Abstrackr, CADIMA, Colandr, DistillerSR, EPPI-Reviewer, JBI SUMARI, METAGEAR package for R, SluRp, and SWIFT-Active Screener. Such reference management systems are harnessed for methodologically rigorous evidence synthesis, study selection and characteristic extraction, predictive document classification, machine learning-based citation and record screening, bias assessment, article retrieval automation, and document classification and prioritization. Findings & value added: Industrial IoT and 3D augmented reality technologies can create business value by streamlining virtual product and remote asset management across extended reality-based navigation and robotic autonomous systems in smart factory environments by generative AI and machine learning algorithms, articulating business organizational level and theory of management implications. 3D simulation and operational modeling tools can execute and complete complex cognitive task-oriented and knowledge economy jobs, producing first-rate quality outputs swiftly while leading to unemployment spells, labor market disruptions, job displacement losses, and reduced earnings by machine learning clustering and spatial cognition algorithms. Generative AI decentralized finance, interoperable blockchain networks, cash flow management tools, and asset tokenization can mitigate fraud risks, enable digital fund and crypto investing servicing, and automate treasury operations by integrating real-time payment capabilities, routing and configurable workflows, and lending and payment technologies. [ABSTRACT FROM AUTHOR]

Agricultural business methods are no longer possible without computer technology, which helps to identify animals, record productivity and monitor their health. Therefore, the study aims to conduct a comparative analysis of the functionality of the main dairy farm management software for their objective assessment from the point of view of milk producers. For this purpose, information from the official websites of software products was analysed and scientific articles analysing this software were collected. As a result, all software products were divided into 3 categories: software for controlling dairy productivity and managing processes on a dairy farm from dairy equipment manufacturers; such software products include DairyPlan C21 (GEA), DATAFLOW II (Milkline), AfiFarm (Afimilk), DelPro (DeLaval) and FARMSOFT (Farmtec a. s.); dairy farm management software DairyComp 305 and UNIFORM-Agri, as well as farm software from post-Soviet developers – MilkCentre (CIAB Expert). Programmes from dairy equipment manufacturers were almost at the same level and had virtually all the necessary blocks for identifying, controlling and managing animals on a dairy farm. The software developed for dairy farm management is more effective in tandem with one of the milking parlour programmes. This class of software expands the possibilities for managing reproductive and veterinary work on the farm, as well as reduces the workload on staff by generating automatic reports for regulatory authorities and management. The third class of programs is the least developed compared to the above, but it compensates for this shortcoming by constant improvement due to the continuous contact of developers with the manufacturer. Thus, there is a significant number of software products on the software market that can provide dairy management for farms of various sizes

The article analyses the role of immersive technologies in increasing productivity in enterprises. Economic growth is determined not only by productive forces, but also by the rational use of human labour and achievements of scientific and technological progress. The author has examined the experience of using digital tools relevant to the market that improve productivity – virtual and augmented reality. These technologies have a significant impact on labour productivity and contribute to enhancing performance indicators of production operations. The global practice of applying immersive technologies in enterprises demonstrates a wide range of areas of their use. Thus, augmented and virtual reality are in demand in mechanical engineering, energy extraction, logistics, design of facilities and production lines. In essence, these technologies expand the boundaries of human capabilities in interaction with virtual and augmented spaces, providing unique management tools for solving tasks of varying complexity, and have significant potential in the context of better productivity. Based on the results of the analysis, the key functions of augmented and virtual reality in industry have been identified, and the structure of the functional usage of immersive technologies to increase productivity has been developed.

Copyright of Dictamen Libre is the property of Universidad Libre Seccional Barranquilla and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Signos is the property of Universidad Santo Tomas and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Organizations increasingly recognize the need to act in accordance with the idea of sustainable development. In the context of a production system, this is based not only on minimizing material losses but also on human support in the work process. The activities described in this article verify the operation of the system by collecting data on problems occurring during the production process, and the presented case study shows not only that companies do not always have to base their analyses on expensive solutions constructed on big data but they also misinterpret the causes of disruptions, which leads to further losses. This study presents an innovative system supporting the identification of downtime, and the results obtained allowed the examined organization to introduce changes based on specific premises, which led to reducing the waste of resources. The research carried out showed that it is not the excessive load on machines that is the problem but unnecessary downtime resulting from, for example, poor-quality materials for packaging goods. [ABSTRACT FROM AUTHOR]

The paper aims to study digital marketing models and their impact. Partly in response to the new digital age and partly due to the emergence of new marketing methods. Companies need to use different models to control their digital marketing plans. Due to the growing need for digital marketing models, they have investigated what models are in use today and whether it is possible to offer a new model to the market to make things easier for marketers. A new model was created based on one of the companies that use different digital tools. Marketers can learn helpful information from models when developing a digital marketing strategy, conducting an audit, or reviewing online communications. Company leaders must be aware that the marketing strategies they have adopted in the past year will not work under current conditions, which means they should be adapted to current trends. [ABSTRACT FROM AUTHOR]

Agricultural business methods are no longer possible without computer technology, which helps to identify animals, record productivity and monitor their health. Therefore, the study aims to conduct a comparative analysis of the functionality of the main dairy farm management software for their objective assessment from the point of view of milk producers. For this purpose, information from the official websites of software products was analysed and scientific articles analysing this software were collected. As a result, all software products were divided into 3 categories: software for controlling dairy productivity and managing processes on a dairy farm from dairy equipment manufacturers; such software products include DairyPlan C21 (GEA), DATAFLOW II (Milkline), AfiFarm (Afimilk), DelPro (DeLaval) and FARMSOFT (Farmtec a. s.); dairy farm management software DairyComp 305 and UNIFORM-Agri, as well as farm software from post-Soviet developers - MilkCentre (CIAB Expert). Programmes from dairy equipment manufacturers were almost at the same level and had virtually all the necessary blocks for identifying, controlling and managing animals on a dairy farm. The software developed for dairy farm management is more effective in tandem with one of the milking parlour programmes. This class of software expands the possibilities for managing reproductive and veterinary work on the farm, as well as reduces the workload on staff by generating automatic reports for regulatory authorities and management. The third class of programs is the least developed compared to the above, but it compensates for this shortcoming by constant improvement due to the continuous contact of developers with the manufacturer. Thus, there is a significant number of software products on the software market that can provide dairy management for farms of various sizes. [ABSTRACT FROM AUTHOR]