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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]

Despite the exponential growth of artificial intelligence (AI) research in operations, supply chain, and productions management literature, empirical insights on how organisational behavioural mechanisms at the human–technology interface will facilitate AI adoption in small- and medium-sized enterprises (SMEs), and subsequent impact of the adoption on sustainable practices and supply chain resilience (SCR) is under-researched. To bridge these gaps, we integrate resource orchestration and knowledge-based view theoretical perspectives to develop a novel structural model examining antecedents to SCR and AI adoption, considering AI adoption as a pivot for facilitating SCR. The structural equation modelling technique was employed on the data collected from 280 Vietnamese manufacturing SMEs' operations managers. Our results demonstrate that leadership will drive AI adoption by creating a data-driven, digital and conducive culture, and strengthening employee skills and competencies. Furthermore, AI adoption positively influences CE practices, SC agility and risk management, which will help to achieve SCR. For managers, the importance of internal organisational employee-centric mechanisms to create value from AI adoption without impeding business value is highlighted. We reveal the enablers that will help in transforming SMEs to become resilient by deriving appropriate responses to unprecedented disruptions through data-driven decision-making leveraging AI adoption. [ABSTRACT FROM AUTHOR]

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]

Production companies are facing increasing production complexity and demographic change. Therefore, with its decreasing physiological and mental performance prerequisites, the aging workforce is often confronted with overload at the workplace leading to fatigue and work errors in the short term and increasing sick days in the long term. Integrating physiological and mental aspects into existing employee scheduling is one way to maintain employees' health and performance. Incorporating these aspects is often very complex due to the need for multiple disciplines and the lack of supporting methods and tools. Therefore, this paper provides a practical-oriented overview for including physiological and mental aspects in existing employee scheduling. The intercorrelation between workload, individual performance prerequisites and strain, their measurement methods, and standardised alternatives are explained to support industrial practitioners in production management, like planners. A four-step method for integrating physiological and mental aspects in employee scheduling is presented. With the help of this method, the interaction between the ergonomics/human factors departments and production management can be improved, resulting in a more human-oriented employee scheduling. A practical example from manual assembly illustrates the application of the presented approach. [ABSTRACT FROM AUTHOR]

In a competitive environment, effective management of production and inventory decisions is critical to maintain an efficient and balanced flow of the supply chain system. Manufacturing companies across supply chains have recognised that inventories can be managed more effectively through increased collaboration and better coordination. In practice, production planning and inventory control decisions are not separated, and supply chain members can significantly reduce costs through the coordination of production and inventory decisions. Research in this field has received much attention in recent years. This study aims to review coordination, cooperation, and collaboration (CCC) mechanisms in production-inventory studies. By using an established research methodology, a total of 118 articles were reviewed and the implemented CCC mechanisms were classified into three main categories: contractual practices, joint decision-making practices, and information-sharing practices. By examining the selected articles according to the implemented methodologies and their impact on the sustainably of supply chains, research gaps and future directions for academics and practitioners were identified. This study shows that more research is needed to discover the impact of combining different CCC mechanisms on supply chain efficiency in terms of economic, environmental, and social concerns. [ABSTRACT FROM AUTHOR]

Introduction: In the context of the deceleration of farmland scale management, a crucial question arises in agricultural development theory and practice: will service driven scale management in the production process become a viable alternative to farmland scale management, serving as the secondary pathway to achieve scale management in China? Methods: To address this issue, an empirical analysis on the impact of agricultural machinery services on farmland transfer was conducted in this article using data from the China Rural Revitalization Survey database comprising 302 villages and 2,871 micro-farmers in 2020 by utilizing the ordinary least squares method and instrumental variable method. Results and discussion: The results show that the implementation of agricultural machinery services will increase the rate of farmland transfer in villages. The effect of agricultural machinery services on increasing the profitability of large-scale farmers surpasses its effect on smallholder farmers, thereby motivating smallholder farmers to transfer farmland to large-scale farmers, consequently fostering the consolidation of farmland. In addition, power-intensive service links are more effective in increasing the rate of farmland transfer and fostering the concentration of farmland transfer compared to control-intensive service links. The impact of agricultural machinery services on enhancing the rate of farmland transfer and fostering the consolidation of farmland transfer is more pronounced on the plain areas compared to the hillmountainous areas. Consequently, this article demonstrates that service-driven scale management and farmland scale management are not merely alternatives but rather complementary strategies that mutually reinforce each other. [ABSTRACT FROM AUTHOR]

Ranking aggregation is an ancient problem with some characteristic elements: a number of experts, who individually rank a set of objects according to a certain (subjective) attribute, and the need to aggregate the resulting expert rankings into a collective judgment. Although this problem is traditionally very popular in fields such as social choice, psychometrics, and economics, it can also have several interesting applications in manufacturing, e.g., for customer-oriented design, reliability engineering, production management, etc. Through a case study related to a cobot-assisted manual (dis)assembly, the paper illustrates an operational methodology and various useful tools that assist in tackling the problem practically, effectively, and with a critical mind. Some of the proposed tools allow to estimate the degree of concordance among experts, and the collective judgment's consistency and robustness. The paper is aimed at scientists and practitioners in manufacturing. [ABSTRACT FROM AUTHOR]

Soil quality is pivotal for crop productivity and the environmental quality of agricultural ecosystems. Achieving sufficient yearly income and long-term farm continuity are key goals for farmers, making sustainable soil management an economic challenge. Existing bio-economic models often inadequately address soil quality. In this study, we apply the novel FARManalytics model, which integrates chemical, physical, and biological indicators of soil quality indicator, quantitative rules on how these indicators respond to farmers' production management over time, and an economic calculation framework that accurately calculates the contribution of production management decisions towards farm income. This is the first study applying this model on existing arable farms. FARManalytics optimizes crop rotation design, cover crops, manure and fertilizer application and crop residue management. Nine Dutch arable farms were analyzed with a high variation in farm size, soil type, and cultivated crops. First, we assessed farm differences in soil quality and farm economics. Second, we optimized production management to maximize farm income while meeting soil quality targets using farm-specific scenarios. Third, we explored the impact of recent policy measures to preserve water quality and to increase the contribution of local protein production. The results show that the case farms already perform well regarding soil quality, with 75% of the soil quality indicators above critical levels. The main soil quality bottlenecks are subsoil compaction and soil organic matter input. We show that even in front-runner farms, bio-economic modeling with FARManalytics substantially improves economic performance while increasing soil quality. We found that farm income could be increased by up to €704 ha−1 year−1 while meeting soil quality targets. Additionally, we show that to anticipate on stricter water quality regulation and market shift for protein crops, FARManalytics is able to provide alternative production management strategies that ensure the highest farm income while preserving soil quality for a set of heterogenous farms. [ABSTRACT FROM AUTHOR]

Organic management enhances the formation of distinct and stable soil microbial communities, however, its influence on the temporal recovery of microbiome and multifunctionality of sterilized soil remains poorly understood.We used amplicon sequencing and metagenomic sequencing to investigate the effects of microbial communities in long‐term organic and conventional managed soils on restoring soil microbiome and functionality. We calculated multifunctionality of soils at days 30 and 90 of recolonization using the averaging approach.Results showed that organic management (O) significantly increased alpha diversity, niche width and network complexity of microbial community compared to conventional management (C). The alpha diversity, niche width and network complexity of microbial community in soils with organic soil suspension were significantly increased compared to conventional management at days 30 and 90 of recolonization. Soil multifunctionality of sterilized organic managed soil inoculated with organic soil suspension (OO) was 14.6% to 70.6% higher than that of the rest treatments. Macrogenomic analysis revealed that O significantly enriched functional pathways of ABC transporters, carbon metabolism, biosynthesis of amino acids, two‐component and nitrogen metabolism as well as most of the functional genes for carbon degradation, carbon fixation, nitrogen cycling and phosphorus cycles compared to C. These functional pathways and genes were also significantly enriched in soils with organic soil suspension at day 30 and 90 of recolonization. Furthermore, alpha diversity, niche width, network complexity, functional pathways and functional genes of microbiome correlated positively with soil multifunctionality.Synthesis and applications. Our results emphasize the importance of organic management induced changes in diversity, network complexity, and functionality of microbial communities for promoting recovery to soil microbial and functional losses, providing the theoretical basis for sustainable impact of organic management in agronomic production on soil microbiome and function. Read the free Plain Language Summary for this article on the Journal blog. [ABSTRACT FROM AUTHOR]

One of the critical quandaries in real-world production management systems is uncertainty in the product demand, which is closely associated to the product price. This uncertain behavior of demand affects production decisions in a system. In this context, this study develops a decision support framework for an imperfect serial production management system under uncertainty. The proposed framework is analyzed considering price-dependent uncertain demand and random defective rate in the system, thus considering both internal and external uncertainties of the system. Three distinct production models are formulated considering different types of uncertain relations among the product demand and market price. Closed form solutions of the decisions are obtained through hybrid of simulation and analytical optimization and are tested through an experimental case analysis. @RISK of Palisade Suite is utilized for simulation and to capture the uncertainty in defective proportion and demand elasticity parameters by creating several possibilities. [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.)

Copyright of Cotton Textile Technology is the property of Cotton Textile Technology 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.)

Copyright of Packaging & Food Machinery is the property of Packaging & Food Machinery Magazine 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.)

Featured Application: This paper is intended to provide future researchers and users with an overview of concepts and modeling approaches for process-oriented tolerance and variation management. This should facilitate the identification and development of appropriate concepts. In the context of tolerance management, the consideration of manufacturing and assembly processes is becoming increasingly important. The main drivers for this are, above all, short development times and high-quality requirements, leading to tight tolerances. To overcome the resulting challenges, many publications address the process-oriented tolerance management. However, since multiple terms and definitions for describing activities that link tolerance management with the production process exist, it is hard to obtain a comprehensive overview on the topic. Therefore, this paper presents a review of existing approaches. The aim is to identify similarities and differences of existing approaches and present them with the help of a classification. For this purpose, among others, work from the areas of process-oriented tolerance management, stream of variation, state space modeling, and variation propagation in multistation manufacturing and assembly systems is considered. Based on the definition of the summarizing term "process-oriented tolerance and variation management", a classification of this thematic area will be introduced. [ABSTRACT FROM AUTHOR]

The research discusses management strategies for addressing production risks in the polyculture cultivation of milkfish and vaname shrimp in Bulukumba Regency. Effective management is necessary to minimize these risks and optimize profits for business operators. Production risk management is carried out using Value at Risk (VaR) analysis, based on observations and meetings with business operators and fisheries services. This approach helps identify and address sources of production risk in the polyculture of milkfish and vaname shrimp. The findings show that both the probability and impact of risk sources are higher for vaname shrimp than for milkfish. Feed is the largest source of risk for milkfish, while disease poses the greatest risk for vaname shrimp. Conversely, feed is a minor risk factor for vaname shrimp, and disease is a smaller concern for milkfish. Risk mitigation strategies include: (1) carefully monitoring the dosage of saponin poison used to eradicate pests; (2) ensuring the correct dosage and application of urea fertilizer, TSP, and feed; (3) controlling water quality; and (4) selecting high-quality seeds. [ABSTRACT FROM AUTHOR]

Complexity management in manufacturing systems is crucial for the economic growth of countries, as efficient management can significantly improve business performance and ensure competitiveness in globalized markets. This research aims to develop a complexity management configurator that identifies critical effects, proposes solution strategies, and optimizes processes through a Lean Production and Industry 4.0 approach. To this end, its systematic design integrates the key stages of management: planning, organization, management and control. The research was structured as an applied study, implementing three main panels: general information, application of a diagnostic tool at each stage of the administrative process, and results focused on measuring the complexity and implementation of advanced technological solutions. The tool enables manufacturing companies not only to diagnose sources of complexity, but also to optimize their operations by adopting Lean methodologies and Industry 4.0 technologies. The findings show how the integration of these strategies contributes to reducing both static and dynamic complexity, resulting in greater operational efficiency and improved performance in an increasingly competitive industrial environment. In conclusion, the proposed configurator is positioned as a key tool to improve the competitiveness and sustainability of manufacturing companies by offering a comprehensive approach to complexity management that is tailored to the demands of modern industry. [ABSTRACT FROM AUTHOR]

In order to maximize the benefit of building supply chain, the topology optimization method of building digital supply chain based on genetic neural network is studied. According to the overall structural characteristics of the building digital supply chain, customer demand data, supplier sales data, manufacturer production management data, and environmental policy exception data are collected to form a building digital supply chain data set. As input data, a building digital supply chain demand prediction model based on improved genetic LSTM neural network is constructed. Capture the needs of the building digital supply chain; according to the demand of suppliers, manufacturers and customers in building digital supply chain, a nonlinear 0–1 mixed integer programming model based on the newsboy model is established. According to the various information provided by the supplier, the ant colony algorithm is used to calculate the optimal supplier. After all suppliers are identified, the topology of the digital supply chain is constructed. So far, the optimization of the supply chain has been completed. The experimental data show that this method can accurately predict the demand of construction projects, the maximum error is less than 1.5%, and can obtain the best supplier selection results. Compared with before optimization, the profit of the structural digital supply chain after topology optimization increases the most. [ABSTRACT FROM AUTHOR]

Simple Summary: Through the non-destructive recognition and tracking algorithm of multi-objective behaviors of beef cattle, practitioners are able to obtain beef cattle feeding information in an all-round way, which lays the foundation for intelligent farming. This study is based on the deep learning recognition algorithm YOLOv8 and tracking algorithm Deep SORT. Through optimizing the convolution module, introducing the attention mechanism, improving the re-identification network, and the trajectory generation and matching process, a new method for the non-destructive identification and tracking of multi-target behaviors of beef cattle is constructed. The average accuracy of nine behaviors can be up to 96.5%, and the accuracy of multi-target tracking is up to 92.1%, which can provide technical support for beef cattle management. The method proposed in this paper provides theoretical and practical support for the intelligent recognition and management of beef cattle. Accurate identification and tracking of beef cattle behaviors are essential components of beef cattle production management. Traditional beef cattle identification and tracking methods are time-consuming and labor-intensive, which hinders precise cattle farming. This paper utilizes deep learning algorithms to achieve the identification and tracking of multi-object behaviors in beef cattle, as follows: (1) The beef cattle behavior detection module is based on the YOLOv8n algorithm. Initially, a dynamic snake convolution module is introduced to enhance the ability to extract key features of beef cattle behaviors and expand the model's receptive field. Subsequently, the BiFormer attention mechanism is incorporated to integrate high-level and low-level feature information, dynamically and sparsely learning the behavioral features of beef cattle. The improved YOLOv8n_BiF_DSC algorithm achieves an identification accuracy of 93.6% for nine behaviors, including standing, lying, mounting, fighting, licking, eating, drinking, working, and searching, with average 50 and 50:95 precisions of 96.5% and 71.5%, showing an improvement of 5.3%, 5.2%, and 7.1% over the original YOLOv8n. (2) The beef cattle multi-object tracking module is based on the Deep SORT algorithm. Initially, the detector is replaced with YOLOv8n_BiF_DSC to enhance detection accuracy. Subsequently, the re-identification network model is switched to ResNet18 to enhance the tracking algorithm's capability to gather appearance information. Finally, the trajectory generation and matching process of the Deep SORT algorithm is optimized with secondary IOU matching to reduce ID mismatching errors during tracking. Experimentation with five different complexity levels of test video sequences shows improvements in IDF1, IDS, MOTA, and MOTP, among other metrics, with IDS reduced by 65.8% and MOTA increased by 2%. These enhancements address issues of tracking omission and misidentification in sparse and long-range dense environments, thereby facilitating better tracking of group-raised beef cattle and laying a foundation for intelligent detection and tracking in beef cattle farming. [ABSTRACT FROM AUTHOR]

With global economic integration and rapid IT advancements, enterprises face complex market environments and intense competition. Efficient management and coordination of supply chains and manufacturing are critical. This study explores optimization strategies for integrated supply chain and manufacturing management using system dynamics. Initially, it investigates the contractual collaboration model based on system dynamics, analysing dynamic behaviours and optimization strategies. Subsequently, a system dynamics simulation model of the integrated system is constructed. Simulation analysis reveals the interactive relationships and dynamic patterns within the system. While many studies focus on static analysis, they often overlook dynamic changes and complex interactions. This study enriches the theoretical framework and provides solutions for enhancing management efficiency and competitiveness in a volatile market. [ABSTRACT FROM AUTHOR]

In the face of increasingly severe global environmental problems, green transformation of the manufacturing industry has become a key issue. This study aims to explore the interaction between green innovation, intelligent decision-making and risk management in manufacturing industry. The purpose of this study is to explore the relationship between intelligent decision-making, risk management and green innovation, as well as the role and impact of risk management in the process of green innovation in manufacturing companies by building a statistical model. In this study, we investigated the intelligent decision-making problem and the risk management problem in the green transformation process through the literature survey method and statistical analysis. The results of the study show that intelligent decision-making positively affects the green innovation capability of manufacturing enterprises, there is a negative correlation between the complexity of risk management and the green innovation capability of manufacturing enterprises, and when manufacturing enterprises rely more on intelligent decision-making, the impact of risk management (positively or negatively) on green innovation is amplified, indicating an interactive effect between intelligent decision-making and risk management. This study provides valuable insights for the manufacturing industry to enhance smart decision-making in the face of environmental challenges. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to validate the link between production manipulation and a firm's performance variability (fundamentals and stock returns). It explores whether executives' risk-taking incentives encourage production deviations around the normal level during uncertainty. Design/methodology/approach: Utilizing panel data of manufacturing firms from Compustat over three decades, the study investigates production management practices during economic uncertainty. The Economic Policy Uncertainty Index (EPU) is employed as a key metric. The empirical strategy involves documenting the effect of economic uncertainty on overproduction and underproduction, examining the role of executive compensation and assessing the impact on risk. Findings: The research finds that risk-taking incentives increase over/underproduction, particularly amplifying the extent of underproduction during uncertainty. Production deviation rises, indicating that firms take greater risk by engaging in abnormal business operations. The study's results are robust against various econometric methods, emphasizing the influence of risk-taking incentives on corporate production decisions. Research limitations/implications: While providing valuable insights, the study acknowledges inherent limitations, including factors influencing production decisions beyond risk-taking incentives. Further research could explore additional determinants for a comprehensive understanding. Practical implications: The findings highlight the potential dark side of executive compensation that motivates suboptimal risk-taking decisions, impacting risk, cost of capital and firm performance. Policymakers and compensation committees can use these insights to design efficient systems that mitigate moral hazard problems associated with productivity changes. Social implications: The study emphasizes the broader social implications of production manipulation under uncertainty. It prompts discussions on the ethical considerations of managerial opportunism, its potential consequences for stakeholders and market dynamics. Originality/value: This study contributes to the literature by examining the role of economic uncertainty on production manipulation and the influence of risk-taking incentives. It extends the earnings management literature by considering real activity manipulation and emphasizing the importance of decomposing production deviation into positive and negative values. [ABSTRACT FROM AUTHOR]

Purpose: the article is an attempt to present topics related to management system auditing, as well as presenting the history of auditing in the world. The article also emphasizes the importance of conducting risk analysis in the context of a properly functioning quality management system. Design/methodology/approach: the presented considerations indicated the growing position of management systems audit and certification, as well as risk analysis. Findings: the article presents the history of the development of auditing and its industry universality as a way to verify the correct functioning of the organization. The conclusions also highlighted the growing importance of systems implemented into organizations. Practical implications: the number of certificates issued indicate the great importance of international standards in the area of management systems of production and service organizations. Originality/value: the information indicated shows the great importance of quality management, which provides many benefits for the organization. Additionally, the article presents the importance of audit in modern organizations, divides the audit and pays attention to the issue of risk and opportunity management, thus complementing the literature on the subject. [ABSTRACT FROM AUTHOR]

Firms increasingly harness data that are created as by-products of information systems usage to evaluate and manage employees. However, such "trace data" can be a double-edged sword. The data can provide a whole new visibility into work practices but also, make work less transparent if the employees start to change their behavior to shape the data. We study this dilemma in the context of knowledge work that has traditionally eluded behavioral measurement. We show that when knowledge workers become aware of data collection and have an interest in how their work may be represented by the data, they start to actively perform the data. We identify different patterns by which employees shape work practices to produce trace data. The changes affect not only the actions and data of the focal employee but also, the actions and data of their colleagues and subordinates. Therefore, to fully realize the potential of trace data, managers may need to get involved in designing the data and to set a trace data policy that states how the data will be used in the organization. The digitalization of organizational settings can provide seemingly unprecedented visibility into knowledge work practices through trace data. Although trace data could be a boon for monitoring knowledge work that has traditionally eluded detailed oversight, the broader literature on data suggests that increasing behavioral visibility through trace data can also motivate reactions that reduce the transparency of work practices and question the validity of the data. To reconcile these contrasting views, we draw from performativity and conduct an in-depth case study of trace data production in a learning management and analytics system at an international business school. We find that once knowledge workers become aware of trace data in the system, they start to shape not only their own actions against the anticipated uses of the data but also the actions of their colleagues, subordinates, and even their students. We conceptualize the changes as first- and second-order prefiguration and discuss how prefigurative changes go beyond gaming, impression management, and transfiguration work discussed in the literature. Finally, we theorize configuration work as metawork that emerges from the need to pay constant attention to how knowledge work practices produce trace data and to keep aligning the practices with data-based monitoring. The results contribute to the literature by shedding light on when, how, and to what effect trace data from knowledge work are performed. History: Accepted by Suprateek Sarker, Senior Editor; Monideepa Tarafdar, Associate Editor. Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2019.0357. [ABSTRACT FROM AUTHOR]

Copyright of Automobile Technology & Material is the property of Automobile Technology & Material 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 sole treatment for snakebite envenomation (SBE), the anti-snake venom (ASV), suffers from considerable drawbacks, including side effects and limited species specificity. Additionally, despite its existence for more than a century, uniform availability of good quality ASV does not yet exist. The present review describes the journey of a SBE victim and highlights the global crisis of SBE management. A detailed analysis of the current ASV market has also been presented along with the worldwide snake distribution. The current production of country specific licensed ASV throughout the globe along with their manufacturers has been examined at the snake species level. Furthermore, a detailed analysis of on-ground situation of SBE management in antivenom manufacturing countries has been done using the most recent literature. Additionally, the export and import of different ASVs have been discussed in terms of procurement policies of individual countries, their shortcomings, along with the possible solution at the species level. It is interesting to note that in most countries, the existence of ASV is really either neglected or overstated, implying that it is there but unsuitable for use, or that it is not present but can be obtained from other countries. This highlights the urgent need of significant reassessment and international collaborations not just for development and production, but also for procurement, distribution, availability, and awareness. A PROMISE (Practical ROutes for Managing Indigenous Snakebite Envenoming) approach has also been introduced, offering simple, economical, and easy to adopt steps to efficiently alleviate the worldwide SBE burden. [ABSTRACT FROM AUTHOR]

Analyzing Production-Inventory Systems with General Demand: Cost Minimization and Risk Analytics Frequent production rate changes are prohibitive because of high setup costs or setup times in producing such items as sugar, glass, computer displays, and cell-free proteins. Thus, constant production rates are deployed for producing these items even when their demands are random. In "Production Management with General Demands and Lost Sales," Han, Li, Sethi, Siu, and Yam obtain the optimal constant production rate for a production-inventory system with Lévy demand for long-run average and expected discounted cost objectives, explicitly in some cases and numerically in general with a Fourier-cosine scheme they develop. This scheme can help in computing risk analytics of the inventory system, such as stockout probability and expected shortfall. These measures are particularly significant for assessing supply resilience, especially for emergency products or services like medicines and healthcare equipment. This study's analytical and numerical findings contribute to enhancing efficiency and decision making in production management. We consider continuous-review, single-product inventory systems with a constant replenishment rate, Lévy demand, general inventory holding cost, and general lost-sales penalty. The Lévy demand encompasses various demand dynamics used in the inventory literature. We obtain optimal replenishment rates that minimize the time-average cost and expected discounted costs. We can solve this problem explicitly for the optimal replenishment rate by utilizing the renewal theorem for the time-average cost objective. For a more complex expected discounted cost minimization problem, we first obtain the Laplace transform of the cost objective in terms of the unique positive root of the corresponding Lundberg equation. Then, we devise a Fourier-cosine scheme to numerically compute the original cost objective together with a detailed error analysis to determine the optimal production rate. In particular cases, we obtain closed-form expressions of the optimal replenishment rates. The numerical examples further illustrate our numerical method's accuracy, stability, and robustness. Finally, our Fourier-cosine method can be applied to compute risk analytics, including but not limited to the stockout probability and expected shortfall of the production-inventory system. Funding: S. P. Sethi acknowledges financial support from the Eugene McDermott Chair Professorship. C. C. Siu acknowledges financial support from the Research Grants Council of Hong Kong [Grant "Generalized Sethi Advertising Model and Extensions" with Project UGC/FDS14/P02/20]. S. C. P. Yam acknowledges financial support from [Grant HKGRF-14301321 with Project "General Theory for Infinite Dimensional Stochastic Control: Mean Field and Some Classical Problems" and Grant HKGRF-14300123 with Project "Well-Posedness of Some Poisson-Driven Mean Field Learning Models and Their Applications"]. Supplemental Material: The e-companion is available at https://doi.org/10.1287/opre.2022.0191. [ABSTRACT FROM AUTHOR]

To support growth, short-cycle horticultural crops require readily available nutrients. However, this often leads to nutrient leaching. Implementing best management practices in production decisions like incorporating fertilizer retaining amendments to substrates or modifying fertilization programs can mitigate nutrient losses to the environment and associated costs. This study examined using an activated aluminum (AA) material as a substrate amendment to retain phosphorus (P) within containers while also assessing methods to reduce P fertilization in Tagetes production over a six-week production cycle. A commercial peat moss substrate (PL) pre-loaded with nutrients was amended with AA, enabling comparisons between substrates with and without AA. Enhanced fertilizer practices involved supplementing the initial nutrients by applying a weekly fertigation solution including nitrogen and potassium over the six weeks, but P for either 0, 2, 4, or 6 weeks. The incorporation of AA significantly reduced P leaching losses by 89.5–97.7%, compared to the PL substrates receiving P the entire six weeks. Regardless of substrate or fertilizer management, all Tagetes had equivalent sizes (growth index) and aboveground biomass. The results indicate that amending substrates with AA and/or reducing additional P inputs are effective strategies to minimize P leaching without compromising Tagetes quality. [ABSTRACT FROM AUTHOR]

With the advancement in global market integration, manufacturing enterprises face increasingly fierce competition, making the development of intelligent manufacturing systems a key factor in enhancing market competitiveness. However, manufacturing systems are characterized by varying characteristics of manufacturing resources and strong interdependencies, which make production control more complex. A bottleneck refers to the issue where the manufacturing system's actual production capacity is less than or equal to the demand placed on a resource. After extensive research, scholars have concluded that the definition, identification methods, and related studies on bottlenecks are not fixed but depend on the specific research subject and the type of bottleneck. Therefore, scholars suggest that only by accurately defining the bottlenecks within a system can appropriate models be employed for prediction, thereby avoiding issues such as inefficient resource allocation and delivery delays, or implementing measures to minimize these negative impacts. Particularly under unstable production conditions, dynamic bottlenecks are likely to occur, making the prediction and identification of bottlenecks under dynamic environmental conditions crucial. Currently, there is still a lack of research in real-time state sensing and integration, as well as a lack of systematic review of related research. To fill this research gap, this review comprehensively introduces the current state and achievements in bottleneck research within manufacturing systems, focusing on bottleneck categories, identification, shifting, and management. It also provides an outlook on future research trends and directions in the study of manufacturing system bottlenecks. [ABSTRACT FROM AUTHOR]

Cucumber (Cucumis sativus L.) and celery (Apium graveolens L.) are among the most widely cultivated vegetable crops, belonging to the melon and leafy vegetable categories, respectively. This study aims to provide predictions for the quality formation of greenhouse cucumber and celery, as well as technical support for intelligent vegetable production management. Based on the light and temperature response characteristics of cucumber and celery growth and development and using the experimental varieties 'Jinsheng 206' for cucumber and 'Juventus' for celery, the observational data from a five-cropping season trial over 2 years were utilized. By analyzing the relationship between the growth and development of vegetables and key meteorological factors (radiation and temperature), simulation models for quality formation were constructed using the day–night temperature difference method (ATD) and the product of thermal effectiveness and the photosynthetically active method (TEP) as independent variables. The model parameters were determined and the model was validated using independent trial data. The results showed the following: (1) There exist quantifiable relationships between different quality indicators and ATD or TEP. These relationships are mainly presented through linear functions, exponential functions, logarithmic function, and logical functions. (2) The normalized root mean square error (NRMSE) of the cucumber quality model ranges from 1.13% to 29.53%, and the NRMSE of the celery quality model ranges from 1.63% to 31.47%. (3) Based on two kinds of normalization methods, the average NRMSE of the VQSM model is 13.72%, demonstrating a relatively high level of accuracy in simulation. These results demonstrate that the proposed model can dynamically and accurately simulate the quality formation process of vegetables, providing a theoretical basis and data support for the research on productivity and the economic benefits of greenhouse crops. [ABSTRACT FROM AUTHOR]

Adopting the concept of Industry 4.0 enables manufacturing companies to gain advantages from digital production management, though issues have been identified related to human resources, management and organisation. Problem areas concern workplace ergonomics to the general wellbeing and satisfaction of workers, and social responsibility. Industry 5.0 promotes progress based on human-machine collaboration and sustainable, human-centred value creation. The research model of the degree of technological humanism, which aims to ensure that technological progress improves human wellbeing in manufacturing companies in the transformation towards Industry 5.0, is a demanding area that has not been sufficiently researched or unambiguously defined so far. The presented model can facilitate the digital transition of manufacturing companies towards Industry 5.0, as it contains the components necessary for the transformation of organisational processes, thereby enhancing and accelerating the implementation of smart manufacturing. [ABSTRACT FROM AUTHOR]

Objective: This study aims to evaluate the effectiveness of Integrated Soil-Crop System Management (ISSM) and provide technical support for sustainable high yield and efficiency in regional agriculture. Methods: The study compared the effects of no fertilization (Control), conventional farmer practices (FP), high-yield management (HY), and ISSM on maize yield and plant nutrient uptake. Measurements included grain yield, plant biomass, plant nutrient absorption, and soil nutrient content across different management strategies. Results: Over the 12-year experimental period, a significant decline in grain yield was observed under the Control treatment, with a slight decrease in the FP treatment. In contrast, consistent yield increases were noted for the HY and ISSM treatments. The ISSM approach significantly enhanced the average yield and plant uptake of P and K by 26%, 24%, and 32%, respectively, approaching 98%, 91%, and 85% of the levels achieved in the HY treatment. Furthermore, the average use efficiency of P and K fertilizers in the ISSM treatment exceeded those in the FP treatment by 18.7% and 1.2%, respectively, and those in the HY treatment by 17.4% and 24.8%, respectively. The adoption of ISSM led to a significant increase in total and available P and K content within the 0-20 cm and 20-40 cm soil layers and enhanced the available P and K content across all aggregate size fractions within the 0-20 cm soil layer. Conclusion: ISSM is capable of achieving long-term high and stable yields for spring maize, enhancing the uptake and utilization of P and K in plants, and bolstering the soil's capacity to supply these nutrients, thereby fostering the sustainable development of the entire soil-crop system. [ABSTRACT FROM AUTHOR]

In Indonesia, the development of the poultry industry is facing numerous challenges. Major constraints include high disease burdens, large fluctuations in farm input and output prices, and inadequate biosecurity. Timely and reliable information about animal production and health can help stakeholders at all levels of the value chain make appropriate management decisions to optimize their profitability and productivity while reducing risks to public health. This study aimed to describe the challenges in the Indonesian poultry industry, assess stakeholders' needs and capabilities in terms of generating and using poultry information for making production and health management decisions, and identify levers for improvement. Interviews were conducted with a diversity of key informants and value chain actors in five Indonesian provinces. Thematic analysis was applied with an interpretivist approach to gain an in-depth understanding of the lived experiences of various stakeholders and their opinions as to what might constitute appropriate solutions. Our findings indicate that market and political instability, ineffective management of poultry data, and limited inter-sectoral collaboration are limiting the development of the sector. Increased intersectoral cooperation is needed to implement standards for data collection and sharing across the industry, provide education and practical training on the use of information technologies for farm management, and accelerate research and innovation. Our study can contribute to the development of data-driven tools to support evidence-based decision-making at all levels of the poultry system. [ABSTRACT FROM AUTHOR]

ESG (Environmental, Social and Governance) management practice is an important part of promoting sustainable operation and development of manufacturing enterprises. Currently, traditional evaluation methods have limitations such as low efficiency and lack of objectivity. To improve the efficiency and accuracy of ESG evaluation and promote the optimization of ESG performance in manufacturing enterprises, this article combined data mining and analytic hierarchy process (AHP) to conduct effective research on ESG management practice evaluation in manufacturing enterprises. This article adopted the best priority search strategy to collect and process enterprise ESG data. By using AHP to construct hierarchical and segmented objectives for target problems, a performance evaluation index system for management practices was built based on the evaluation objectives and hierarchical priority order. Finally, based on the performance evaluation of ESG management practices, the K-nearest Neighbor algorithm was applied to analyze historical data of key indicators. According to the weights, various key indicators were re-integrated, achieving practical evaluation and decision support for enterprise ESG management. To verify the effectiveness of data mining and AHP, this article took Z enterprise as the research object and conducted empirical analysis on it. The results showed that in terms of evaluation accuracy, the method proposed in this article achieved the highest evaluation accuracy of 92.51%, 91.16%, and 91.75% in environmental, social, and governance dimension data use case evaluation, respectively. The conclusion indicated that data mining and AHP could improve the accuracy of ESG management practice evaluation in enterprises, provide reliable decision support for enterprise development, and help promote sustainable development of enterprises. [ABSTRACT FROM AUTHOR]

The hydrocarbon extraction process is complex and involves numerous design variables and mitigating risk. Numerous time-consuming simulations are required to maximize objective functions such as NPV from a particular field while contemplating a significant representation of uncertainty scenarios and various production strategies. Production strategies searches may result in a high-dimensional search space which can yield sub-optimal reservoir economical exploration. As a solution, appropriate optimization algorithms selection and tuning may provide good solutions with lesser simulations. This paper presents a methodology to calibrate, develop, and select optimization algorithms for oil production strategy applications while quantifying the dimension and optimum location effects. Global optimum location altered the best method to be selected. It presents a novel algorithm (ASLHC) and a modification of the Nelder-Mead method (NMNS) to improve its high dimensionality performance. Performances of six pre-calibrated techniques were compared using novel normalized mathematical functions. Optimizations were limited to a 500 evaluation functions computational budget. The PSO, ASLHC, NMNS, and IDLHC were selected and implemented to perform production strategy improvements regarding two parameterizations of the reservoir management variables for a real reservoir model with restricted platform. Results showed the implemented algorithms successfully improved NPV by at least 8% at each of the 24 real-case optimizations. After upscaling the selected techniques for a 115 variable parameterization, the NMNS and IDLHC demonstrated good resilience against local convergence and each technique kept improving during all iterations of the process. An optimization method recommendation chart is presented based on the computational budget of the application. [ABSTRACT FROM AUTHOR]

Adaptive management, a process of planning, implementing, and evaluating management strategies, is often recommended for monitoring ecological systems. However, few examples of successful implementation and retrospective case studies exist. We provide a case study of adaptively managing hatchery-assisted protection and recovery for Chinook salmon (Oncorhynchus tshawytscha) and winter steelhead trout (O. mykiss) during and after the removal of two large mainstem dams in the Elwha River, WA. We summarize key aspects of the monitoring and adaptive management plan over the last decade and highlight successes, challenges, and complications during the plan's implementation. The Elwha Monitoring and Adaptive Management Guidelines included a trigger-based system for moving through four phases of recovery that included preservation, recolonization, local adaptation, and viable natural population, each with differing levels of hatchery production as the management actions. The monitoring component of the plan has been very successful, providing critical data to guide management actions that otherwise may not have occurred and, opportunistically, provided data for other native species in the Elwha River. Implementing adaptive management provided mixed results and was at times hindered by divergent management goals among project partners, the inflexibility of the Endangered Species Act regulatory requirements as implemented for this project, and conflicting information among guidance documents. We learned that some metrics and triggers in the plan were ill-defined or too difficult to measure in the field. In some cases, the performance indicators and/or triggers were successfully modified to incorporate what was learned; however, in other cases, we were unable to revise the values due to differing opinions among partners. The ability to reach consensus on revised triggers appeared to be influenced by the recovery trajectory of the species involved. The implemented adaptive management strategy resulted in substantial collaboration and learning, which resulted in revised management strategies, but was imperfect. Sufficient long-term funding is necessary to implement a well-designed monitoring program and could benefit from including a defined leadership position to shepherd and facilitate a multi-stakeholder adaptive management program. Additionally, incorporating adaptive management into legally binding conditions under the Endangered Species Act is feasible, but requires substantial pre-planning in close coordination with regulatory agencies. [ABSTRACT FROM AUTHOR]

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Manufacturing management and operations place heavy emphasis on monitoring and improving production performance. This supervision is accomplished through strategies of manufacturing performance management, a set of measurements and methods used to monitor production conditions. Over the last 30 years, the most prevalent measurement of traditional performance management has been overall equipment effectiveness, a percentile summary metric of a machine's utilization. The technologies encapsulated by Industry 4.0 have expanded the ability to gather, process, and store vast quantities of data, creating the opportunity to innovate on how performance is measured. A new method of managing manufacturing performance utilizing Industry 4.0 technologies has been proposed by McKinsey & Company (New York City, NY, USA), and software tools have been developed by PTC Inc. (Boston, MA, USA) to aid in performing what they both call digital performance management. To evaluate this new approach, the digital performance management tool was deployed on a Festo (Esslingen, Germany) Cyber-Physical Lab (FCPL), an educational mock production environment, and compared to a digitally enabled traditional performance management solution. Results from a multi-day production period displayed an increased level of detail in both the data presented to the user and the insights gained from the digital performance management solution as compared to the traditional approach. The time unit measurements presented by digital performance management paint a clear picture of what and where losses are occurring during production and the impact of those losses. This is contrasted by the single summary metric of a traditional performance management approach, which easily obfuscates the constituent data and requires further investigation to determine what and where production losses are occurring. [ABSTRACT FROM AUTHOR]

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Digital transformation is driving the current technological trends in manufacturing. An integral constituent is a communication between machines, between machines and humans, or between machines and products. This extensive communication involves large volumes of data. Many manufacturers apply data analytics (e.g., for quality management or improvement purposes) to translate the data into a business value. However, isolated, rigid, and area-specific IT solutions often carry this out. Today's complex manufacturing requires quality management approaches that constitute a holistic view of and understanding of process–product interactions along the process chain instead of focusing solely on single processes. A novel platform approach to support quality management in manufacturing systems is proposed in this paper to overcome this deficit. It integrates state-of-the-art concepts of IT with modeling approaches for planning and operation of quality management. A conceptual framework and the technical architecture for implementing a digitalization platform are presented in this regard. Moreover, the approach is validated and implemented within a web application based on a use case of data-driven quality management in electronics production. [ABSTRACT FROM AUTHOR]

The application effect of D/CD yarn clearing function and cross yarn clearing function of USTER Quantum 4 electronic yarn cleaner was discussed. The main yarn clearing function of USTER Quantum 4 electronic yarn cleaner was introduced, its D/CD yarn clearing function was verified and tested. Its cross yarn clearing function was used to monitor the cloth defects reported by users. The production practice showed that by setting the D/CD clearing function of the electronic yarn cleaner reasonably, the yarn density difference defects could be monitored effectively. The application of cross yarn clearing function could effectively strengthen the monitoring of yarn defects. It is considered that using USTER Quantum 4 electronic yarn clearing system to monitor and control data, which can effectively control yarn defects, guide the optimization and improvement of quality management in spinning production. [ABSTRACT FROM AUTHOR]

Distribution, as one of the components of the marketing mix, in the simplest case, carries the task of transferring the product from the place of production to the place of purchase of the customer in the distribution channel. In other words, the most important task of distribution management is to make the goods available to potential customers at the right time and place. The purpose of this study is to identify the pattern of non-financial perceived values among distribution channels and manufacturing companies. For this purpose, a mixed method was used. At first, relying on the research literature, factors related to values were identified, and then closed and open polls were conducted among experts about the factors, and new factors were also proposed. Data analysis was done using fuzzy Delphi methods and hierarchical analysis. The findings of the research showed that the components of continuity commitment, customer expectations, pricing policy, product specifications, competitive features, brand awareness, perceived quality, customer expectations, product innovation, comprehensive quality management and participation in decisionmaking from the functional dimension, components of emotional commitment, Customer satisfaction, customer trust, customer loyalty, brand personality, brand value and value communication from the emotional dimension and normative commitment components, word-of-mouth communication, communication management, social media marketing activity, brand image and customer-seller relationship from the social dimension were placed at the first level and the market characteristics component from the social dimension at the second level. Finally, it was found that except for the component of market characteristics from the social dimension, which played the role of an independent variable, all the components of the functional, emotional and social dimensions played the role of a linked variable. [ABSTRACT FROM AUTHOR]

The production management of lactating sows is a crucial aspect of pig farm operations, as their health directly impacts the farm's production efficiency. The feeding behavior of lactating sows can reflect their health and welfare status, and monitoring this behavior is essential for precise feeding and management. To address the issues of time-consuming and labor-intensive manual inspection of lactating sows' feeding behavior and the reliance on breeders' experience, we propose a method based on the improved YOLO (You Only Look Once) v5s algorithm and image segmentation for detecting the feeding behavior of lactating sows. Based on the YOLOv5s algorithm, the SE (Squeeze-and-Excitation) attention module was added to enhance the algorithm's performance and reduce the probability of incorrect detection. Additionally, the loss function was replaced by WIoU (Weighted Intersection over Union) to accelerate the model's convergence speed and improve detection accuracy. The improved YOLOv5s-C3SE-WIoU model is designed to recognize pre-feeding postures and feed trough conditions by detecting images of lactating sows. Compared to the original YOLOv5s, the improved model achieves an 8.9% increase in mAP@0.5 and a 4.7% increase in mAP@0.5 to 0.95. This improvement satisfies the requirements for excellent detection performance, making it suitable for deployment in large-scale pig farms. From the model detection results, the trough remnant image within the detection rectangle was extracted. This image was further processed using image processing techniques to achieve trough remnant image segmentation and infer the remnant amount. Based on the detection model and residue inference method, video data of lactating sows' feeding behavior were processed to derive the relationship between feeding behavior, standing time, and residue amount. Using a standing duration of 2 s and a leftover-feed proportion threshold of 2% achieves the highest accuracy, enabling the identification of abnormal feeding behavior. We analyzed the pre-feeding postures and residual feed amounts of abnormal and normal groups of lactating sows. Our findings indicated that standing time was significantly lower and residual feed amount was higher in the abnormal groups compared to the normal groups. By combining standing time and residual feed amount information, accurate detection of the feeding status of lactating sows can be realized. This approach facilitates the accurate detection of abnormal feeding behaviors of lactating sows in large-scale pig farm environments. [ABSTRACT FROM AUTHOR]

Copyright of Coal Geology & Exploration is the property of Xian Research Institute of China Coal Research Institute 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 Nuclear Safety is the property of Nuclear & Radiation Safety Center 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 Nuclear Safety is the property of Nuclear & Radiation Safety Center 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 International Journal of Professional Business Review (JPBReview) is the property of Open Access Publications LLC 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.)

Purpose: The purpose of the present article was to refine the definition of management by values for production organizations in high-tech and automotive industries, based on a systematic literature review. Design/methodology/approach: An inductive approach in grounded theory was used. The study had two main stages. In the first stage, the PRISMA protocol was used to conduct a systematic review of the literature on the subject. In the second, critical content analysis was used to develop own definition, taking into account the specificity of production organizations in high-tech and automotive industries (Managing by Values, MBV). Findings: The authors’ definition of MBV for production organizations in high-tech and automotive industries was formulated. The research methods used in MBV research in various industries were reviewed and the directions of future research were presented. It was determined that there had been no previous research in the area of MBV for production organizations in high-tech and automotive industries. The analysis of abstracts allowed to determine that using keywords such as “management by values” and “managing by value” one obtains publications in which the concept of MBV does not appear and the values themselves are understood as creating the value of a product or company or creating value for the customer. Originality/value: The content concerning the concept of MBV was structured, the authors’ definition of MBV was developed and a scientific problem was identified. [ABSTRACT FROM AUTHOR]

Paris polyphylla Smith var. Chinensis (Franch.) Hara is a medicinal plant that belongs to the Liliaceae family. Its main components are parissaponins, which have excellent medicinal effects such as anti-inflammatory, anti-tumor, etc. Improving the quality of parissaponins through artificial directional regulation has emerged as a practice to meet medical demand and is a new research hotspot. In this paper, P. polyphylla plants were treated with UVA, UVB, and UVC, and the contents of PolyPhyllin VI (PPVI) and PolyPhyllin VII (PPVII), saponin synthase (squalene synthase, SS; cycloartenol synthase, CAS; cytochrome P450, CYP450; and glycosyl transferases, GT) activity, MDA, and the photosynthetic pigment indexes were measured and analyzed. The results showed that PPVII content increased by 32.43% with UVC treatment after 4 h (3.43 mg/g), but the PPVI and PPVII contents in the other groups decreased compared with CK (control group) and they did not return to the original level after 4 h. SS, CAS, CYP450, and GT synthases were activated in varying degrees via UV treatment and increased, respectively, by 22.93%, 10.83%, 20.15%, and 25.98%. Among them, GT, as the last of the synthetases, had a shorter response time to UVB (30 min) and UVC (15 min); the difference was sensible compared with CK. Moreover, UV had a stressing effect and promoted the rapid accumulation of MDA content (increased 17.66%, 34.53%, and 9.65%) and carotenoid (increased 7.58, 5.60, and 7.76 times) within 4 h compared to CK. UVB and UVC radiation visibly improved chlorophyll a content (42.56% and 35.45%), but UVA did not, and the change in chlorophyll b content showed no overt statistical difference. In addition, PPVI and PPVII were negatively correlated with SS, CAS, carotenoids, and MDA (p < 0.05) and positively correlated with CYP450, GT, and chlorophyll a (p < 0.05). This study provides a theoretical basis for using UV light to regulate secondary metabolism in P. polyphylla, which is of great value for production management. [ABSTRACT FROM AUTHOR]