Production Management System Research Articles

Assessment of genetic diversity and population structure of U.S. Polypay sheep from breed origins to future genomic selection.

Knowledge of past and present genetic diversity within a breed is critical for the design and optimization of breeding programs as well as the development of strategies for the conservation of genetic resources. The Polypay sheep breed was developed at the U.S. Sheep Experiment Station (USSES) in 1968 with the goal of improving productivity in Western U.S. range flocks. It has since flourished in the more intensively managed production systems throughout the U.S. The genetic diversity of the breed has yet to be documented. Therefore, the primary objective of this study was to perform a comprehensive evaluation of the genetic diversity and population structure of U.S. Polypay sheep using both pedigree- and genomic-based methods. Pedigree data from 193 Polypay flocks participating in the National Sheep Improvement Program (NSIP) were combined with pedigree records from USSES (n = 162,997), tracing back to the breed's origin. A subset of these pedigreed sheep from 32 flocks born from 2011 to 2023 were genotyped with the GGP Ovine 50K BeadChip containing 51,867 single nucleotide polymorphisms (SNPs). Four subgroups were used for the pedigree-based analyses: 1) the current generation of animals born in 2020-2022 (n = 20,701), 2) the current generation with a minimum of four generations of known ancestors (n = 12,685), 3) only genotyped animals (n = 1,856), and 4) the sires of the current generation (n = 509). Pedigree-based inbreeding for the full population was 2.2%, with a rate of inbreeding of 0.22% per generation. Pedigree-based inbreeding, Wright's inbreeding, and genomic inbreeding based on runs of homozygosity were 2.9%, 1.3%, and 5.1%, respectively, for the genotyped population. The effective population size ranged from 41 to 249 for the pedigree-based methods and 118 for the genomic-based estimate. Expected and observed heterozygosity levels were 0.409 and 0.403, respectively. Population substructure was evident based on the fixation index (FST), principal component analysis, and model-based population structure. These analyses provided evidence of differentiation from the foundation flock (USSES). Overall, the Polypay breed exhibited substantial genetic diversity and the presence of a population substructure that provides a basis for the implementation of genomic selection in the breed.

Increasing Digital Transformation Readiness in Small and Medium Apparel Enterprises Through Maturity Model Evaluation

Small and Medium Enterprises (SMEs) play a pivotal role in the Indonesian economy, contributing significantly to Gross Domestic Product (GDP) and employment. However, the COVID-19 pandemic has diminished SMEs' GDP contribution. Digital transformation emerges as a crucial solution amidst these challenges. While many SMEs have adopted digitalization, they face obstacles such as product marketing, access to capital, raw material supplies, and effective application of digital technologies. Nevertheless, digitalization presents opportunities to enhance efficiency and expand global market access via the Internet in Indonesia. This study utilizes the Analytic Hierarchy Process (AHP) to formulate a digital transformation strategy, focusing specifically on optimizing internal business processes within apparel SMEs. Key findings from the AHP analysis highlight the critical importance of integrated production management systems, document scanning and digitization, customer data analysis, and basic digital skills training. These findings underpin strategic recommendations aimed at enhancing efficiency, productivity, and adaptability through technology adoption, employee training, and organizational restructuring. The strategy outlines short-term (6-12 months) and long-term (5 years) goals to guide SMEs toward achieving advanced digital transformation maturity. By prioritizing internal business processes, these initiatives aim to propel SMEs in the apparel sector towards greater success in the digital era.

Pathways toward Climate-Neutral Red Meat Production

Ruminant livestock industries can support the climate stabilization ambitions of the Paris Agreement through interventions that reduce GHG emissions (predominantly biogenic methane) and sequester carbon in landscapes. This study explored pathways for the Australian red meat industry (grazing, feedlot finishing, and domestic processing) to become climate neutral, whereby the radiative forcing (RF) footprint is plateaued and there is no additional forcing contribution. Emissions timeseries (CO2, N2O, CH4) were compiled for 1990 to 2020 and projected to 2030 under a business-as-usual scenario (including an 18% increase in sheep and 13% increase in beef cattle) and with a range of production system and vegetation management interventions. The RF footprint peaked in 2018 at 7.13 mW/m2 and decreased to 7.07 mW/m2 in 2020. With the future expansion of the herd/flock and under business-as-usual conditions, the RF footprint is projected to increase by 2.8% by 2030. However, with a combination of interventions, production has the potential to increase with a decreasing RF footprint, a condition that can be described as climate neutral. The Australian red meat industry has made an historical contribution to global RF increase. However, with ongoing RF management, it is possible to increase food production within climate-neutral limits.

100. Synthesis: Management and welfare in alternative poultry production systems: Contributions of the PPILOW European project

100. Synthesis: Management and welfare in alternative poultry production systems: Contributions of the PPILOW European project

Influence of cover crops on soil aggregate stability, size distribution and related factors in a no-till field

Soil aggregation plays an essential role in maintaining soil structure. A three-year field experiment was undertaken to explore the effects of cover crops on soil aggregate stability indices (mean weight diameter, water-stable aggregates greater than 0.25 mm, and soil erodibility factor), size distribution and their correlations with nine soil physical and chemical properties (soil bulk density, pH, organic carbon, total nitrogen, C/N, calcium, phosphorus, potassium, and cation exchange capacity) at 0–5 and 5–10 cm depths in a Marietta silt loam soil in a no-till field, Northeastern Mississippi. The cover crop treatments included elbon rye (Secale cereale L.), daikon radish (Raphanus sativus ssp. acanthiformis), Austrian winter field peas (Lathyrus hirsutus), and their mixture. The samples were wet-sieved to five size classes of aggregates (> 2, 2–1, 1–0.5, 0.5–0.25, and < 0.25 mm). Results showed that the < 0.25 mm aggregates dominated (50.95–78.02 %) soil aggregate size distribution at 0–5 and 5–10 cm depths. The rye plot soil displayed the greatest values for mean weight diameter (0.58 mm) in the two soil depths. Soil water-stable aggregates greater than 0.25 mm value under peas was significantly higher by 68.61 % compared to the no cover crop treatment at 0–5 cm depth, while soil erodibility factor did not change significantly. Mean weight diameter, water-stable aggregates greater than 0.25 mm, soil erodibility factor, 1–0.5 mm aggregates, and < 0.25 mm aggregates were significantly correlated with soil organic carbon, pH, bulk density, phosphorus, and potassium. The dominant factors driving changes in three aggregate stability indices and size fractions (excluding 2–1 mm aggregates) were bulk density and pH. Furthermore, high pH restricted soil aggregate stability and size distribution. This study demonstrated the positive impact of cover crops, specifically peas and rye, on soil aggregate stability and size distribution. This study will provide valuable information for future research on soil stabilization in crop production management systems.

A quick response data collection and management system for digital twin production line based on cloud-fog-edge computing collaboration

Background A production line is the basic unit of smart factories and smart manufacturing. However, owing to the development of the industrial Internet of Things, sensors, and other technologies, more data are being collected, leading to a data explosion, and the heterogeneous nature of multiple sources makes it difficult to manage data in a unified manner. Methods A production line data collection, storage, and management system based on cloud-fog-edge computing collaboration and a digital twin was designed. Multi-source heterogeneous data were collected and transmitted based on the OPC UA, and an information model of the production line was established. Modules for data mapping, publishing, and receiving were developed to achieve unified data collection and transmission. The data storage and management platform was constructed by front-end and back-end separation technologies. Results The developed data collection and management system was verified for functionality and performance on a digital twin production line. Functional tests show that the system has the functions of data acquisition and transmission, device addition and viewing, device data querying and downloading, data and model visualization, and user rights setting. The average time for edge data collection and transmission is 183.6ms. The average response time of the cloud for fog requests is less than 1s. This shows that the system can satisfy the real-time requirements of a digital twin production line. Conclusions The proposed system is real-time and stable, providing support for big data and virtual-reality interaction in digital twins.

ПЕРСПЕКТИВЫ ВНЕДРЕНИЯ СИСТЕМЫ «СЕЛЬХОЗРЕЦИКЛИНГ» В АПК РОССИИ

The analysis of the state of this type of activity carried out in the paper showed the feasibility of forming a national system for the disposal of decommissioned products. To date, the process of recycling of material-intensive products, including agricultural machinery, mostly takes place spontaneously, causing environmental and economic damage. (Research purpose) The research purpose is outlining the possibilities of developing the "Agricultural recycling" system in the agro-industrial complex of Russia, including agricultural machinery, livestock and other types of waste. (Materials and methods) It was determined that one of the most important conditions for the creation of the "Agricultural Recycling" system, in addition to financial aspects (recycling fee), is the active participation in the project of manufacturers of agricultural machinery, their dealers and specialized enterprises in the engineering sector of the agro-industrial complex, which will intensify the process of deploying production facilities for the collection and processing of recycled agricultural machinery and its components. (Results and discussion) It was noted that at present the creation of a recycling system is hindered not so much by the technology of various types of recycling works, as by the lack of a concept for creating a unified waste management system for production and consumption, in which the waste disposal system should take its place along with other industry systems. No less important factors hindering the creation of a Unified Waste Management System as a whole and a Unified Equipment Recycling System are the issues of organizing waste disposal processes, interaction between participants in the recycling system, regulatory, legislative and financial support for the functioning of this system. (Conclusions) The paper identifies the main factors influencing the possibility of developing the "Agricultural recycling" system in the Russian agro-industrial complex in the next two to three years.

Modelling and analysis of production management system using vacation queueing theoretic approach

This paper explores a queueing model in a production management context, featuring periods of working vacations and Bernoulli vacation. When there are no pending orders, the manufacturing unit transits into a maintenance phase, also termed as working vacation, during which production continues, albeit at a slower rate. This diminished productivity could result in longer lead times and potential customer dissatisfaction. Depending on the influx of demand, the maintenance phase could be interrupted, propelling the unit back to full operational capacity. Conversely, if no orders are received during the maintenance phase, the unit has the choice of switching to standby mode in anticipation of incoming orders, or initiating an extended break. We utilize mathematical techniques such as continued fractions, Modified Bessel functions, and Laplace transforms to precisely compute the transient state probabilities of this model. To further illustrate the impact of these operational dynamics on production management, we present corroborative numerical examples.

A simulation-optimization approach for economic assessment of an imperfect serial production management under uncertainty

ABSTRACT 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.

Influence of successive cycles of water stress on tomato production at fruiting stage

Progressive reduction of water supply was imposed during early period (S1) and late period (S2) interceded by a recovery, with a moderate stress for first case and un intensive stress for second case, at fruiting stage of tomato (Lycopersicon esculentum Mill. cv. Rio Grand). The influence of this reduction on fruit production, shoot growth, water content (WC), relative water content (RWC), leaf area (LA), specific leaf weight (SLW), total soluble solids of fruit (T.S.S.) and maturity percentage of fruit were investigated. Repetition of water stress (S1+S2) was obviously reducing the shoot growth, fresh yield, WC, LA and maturity percentage, whereas that was not achieved in dry weight of fruits (FDW), RWC, SLW and T.S.S. Since, a significant difference was founded between the effects of S2 cycle and when that has been preceded by another one S1+S2 on accumulation of dry matter of fruit. Two successive cycles of water stress were doubled of FDW by two times of that registered in control. These results indicate that, during fruiting stage, the effect of gradually exposure of a moderate water stress period, followed by recovery, thereafter by another gradually exposure of un intensive water stress period, contributed to better accumulation of dry matter in fruit by an osmotic adjusting therefore improving water use efficiency, compared to the effect of only one high intensive of water stress applied during the second period. Repetition cycle of water stress may be useful to develop management systems for a reasonable production of tomato and thus reducing the needs of irrigation water in regions facing deficit of water supplies.

Planning of Production Capacities of Precast Construction Plants under Conditions of Changes in Demand for Products

The main criterion for a feasibility study of the production efficiency of precast construction plants, analysis of internal production reserves and optimal production volume is the production capacity indicator. Thus, the study of various Factors influencing the production capacity indicators is an important task. The results of research of the influence of the number of storeys and the set of block sections in the configuration of industrial residential buildings on changes in the ratio of product types in the range of the production programme of plants are presented in the paper. Fluctuations in the ratio of product types during the construction of different types of houses have been established. Based on the obtained results, the dependence of the production capacity plants on fluctuations in the ratio of product types has been constructed. It has been determined that the main reason for the revealed facts of a decrease in production capacity is the uneven loading of production lines with fluctuations in the ratio of product types in the range of production programs of precast construction plants. Taking into account the known facts of the dependence of production capacity on the concrete capacity of block sections of houses, the results of a comparison of the degree of influence of concrete capacity on the production capacity and the ratio of the main types of products in the range of production programs of plants are presented in the paper. It has been established that a key influence on the indicators of production capacity is exerted by changes in the ratio of product types. On the basis of the conducted research, optimal reserves of production capacity have been determined with a high share of different types of houses in the production programs of the plants. Taking into account the established indicators of production capacity reduction, their optimal reservation is within 20–25 % of the design capacity of the plants, which should serve as a reference point when planning the functioning of the production management system under the conditions of changes in demand for products.

Enhancing Waste-to-Energy Conversion Efficiency and Sustainability Through Advanced Artificial Intelligence Integration

Artificial intelligence (AI) has emerged as a pivotal tool in optimizing waste-to-energy conversion technology, addressing critical environmental issues while promoting sustainable energy sources. This study delves into the multifaceted role of AI in enhancing the efficiency and effectiveness of waste-to-energy processes. By leveraging AI, significant improvements can be achieved in automated waste sorting, process monitoring, and energy production forecasting. The integration of AI into these domains not only streamlines operations but also enhances the accuracy of data management, analysis, and processing. This results in a more efficient conversion of waste into energy, mitigating adverse environmental impacts and fostering sustainable energy practices. The research highlights the practical applications of AI in optimizing the entire waste-to-energy workflow, underscoring its potential to revolutionize this sector. Moreover, the study addresses the inherent challenges and discusses future prospects for AI implementation in waste-to-energy technologies. Through comprehensive analysis and case studies, the findings reveal that AI can significantly contribute to reducing environmental footprints and promoting a circular economy. This exploration provides valuable insights into how AI-driven innovations can lead to more sustainable and efficient waste management and energy production systems, paving the way for future advancements in this critical field.

Development and Validation of Avena Integrated Management (AIM): A Bioeconomic Decision Support Tool for Wild Oat Management in Australian Grain Production Systems

Abstract Wild oat is a long-standing weed problem in Australian grain cropping systems, potentially reducing the yield and quality of winter grain crops significantly. The effective management of wild oat requires an integrated approach of diverse control techniques that suit specific crops and cropping situations. This research aimed to construct and validate a bioeconomic model that enables the simulation and integration of weed control technologies for wild oat in grain production systems. The Avena spp. integrated management (AIM) model was developed with a simple interface to provide outputs of biological and economic data (crop yields, weed control costs, emerged weeds, weed seedbank, gross margins) on wild oat management data in a cropping rotation. Uniquely, the AIM was validated against real-world data on wild oat management in a wheat and sorghum cropping rotation, where the model was able to reproduce the patterns of wild oat population changes as influenced by weed control and agronomic practices. Correlation coefficients for 12 comparison scenarios ranged between 0.55 and 0.96. With accurate parameterization, AIM is thus able to make useful predictions on the effectiveness of individual and integrated weed management tactics for wild oat control in grain cropping systems.

OLE! Dairy model

The process of intensification of the dairy sector has been characterized in recent decades by the increase in milk production per hectare, the increase in livestock density, the inclusion of more concentrates in the diet, and the improvement of the genetic merit of dairy cows. The use of models has productive, environmental, and economic advantages. The objectives of the study were to describe a new model, “OLE! Dairy model”, to (a) simulate the biophysical performance of a pasture-based dairy production system; (b) evaluate the predictive capacity of the model with a set of statistical parameters, comparing its results with the biophysical performance of experimental studies of dairy farm systems, and (c) calibrate by adjusting the technical coefficient. The experimental design combines two feeding strategies with a different proportion of pasture in the diet and two animal genotypes. We make a description of the biophysical component and the calculations proposed in the “OLE! Dairy model”. Then a variety of parameters was calculated for model testing, including the Mean Squared Error, the Relative Prediction Error, the square root of the MSE, the Concordance Correlation Coefficient, and the Model Efficiency. The model presented a good predictive capacity for stocking rate and concentrate, pasture, and reserve intake. The predictive capacity of the model for individual production and area production improves after performing a rapid calibration, which allows for avoiding overestimations or underestimations that generate erroneous measurements in the planning and management of milk production systems, and can be adjusted to different conditions of production of the region.

Преимущества интеграции автоматизированных систем управления технологическим процессом угольных шахт с системами определения местоположения

Coal mining companies, especially those involved in underground operations, are currently at different stages on their way to digitalization and face a number of challenges, which are described in the article. One group of such problems is the difficulties concerned with the coal mine dispatching control. The paper shows how a properly selected approach, i.e. the integration of various automated process control systems with positioning systems, provides a solution to these problems and offers significant advantages both regarding the industrial safety and maximizing the economic effect. Examples of such integration are given for the conveyor transport control system, for the air and mine gas control systems as well as a CCTV systems with positioning systems within the Smart Mine® complex. An assessment of the current technical basis for the implementation of this approach is given, and the advantages of using a single platform for subsystems of the multi-functional safety system and production management systems are shown.

Optimal solving of a binary knapsack problem on a D-Wave quantum machine and its implementation in production systems

AbstractThe efficient management of complex production systems is a challenge in today’s logistics. In the field of intelligent and sustainable logistics, the optimization of production batches, especially in the context of a rapidly changing product range, requires fast and precise computational solutions. This paper explores the potential of quantum computers for solving these problems. Traditional computational methods are often limited when it comes to optimizing complex logistics systems. In response to these challenges, the paper proposes the use of a hybrid algorithm that combines quantum technologies with classical computational methods. Such integration allows the computational power of both types of technologies to be harnessed, leading to faster and more efficient identification of optimal solutions. In this work, we consider the knapsack problem, a classic NP-hard optimization problem that is commonly used to verify the effectiveness of new algorithm construction methods. The algorithm presented is based on the Branch and Bound method and aims to ensure solution optimality in the context of the non-determinism of quantum computers. Within the algorithm, computations are performed alternately on a classical processor and a quantum processor. In addition, the lower and upper bounds of the objective function are computed in constant time using the D-Wave quantum machine.

Scalable Compositional Digital Twin-Based Monitoring System for Production Management: Design and Development in an Experimental Open-Pit Mine

While digital twins (DTs) have recently gained prominence as a viable option for creating reliable asset representations, many existing frameworks and architectures in the literature involve the integration of different technologies and paradigms, including the Internet of Things (IoTs), data modeling, and machine learning (ML). This complexity requires the orchestration of these different technologies, often resulting in subsystems and composition frameworks that are difficult to seamlessly align. In this paper, we present a scalable compositional framework designed for the development of a DT-based production management system (PMS) with advanced production monitoring capabilities. The conducted approach used to design the compositional framework utilizes the Factory Design and Improvement (FDI) methodology. Furthermore, the validation of our proposed framework is illustrated through a case study conducted in a phosphate screening station within the context of the mining industry.

ТЕОРЕТИЧЕСКИЕ АСПЕКТЫ АВТОМАТИЗАЦИИ ПРОИЗВОДСТВЕННО-ХОЗЯЙСТВЕННОЙ ДЕЯТЕЛЬНОСТИ НА ПРЕДПРИЯТИЯХ ПОЛИГРАФИИ

Automation of production and economic activities at printing enterprises involves the use of advanced digital solutions or technological equipment with automatic configuration and process control functions. The key benefits of automation include speeding up order processing, increasing the accuracy of calculations, optimizing the utilization of process equipment, reducing operating costs, and increasing the efficiency of planning and managing production processes. Digital solutions enable monitoring of financial and operational flows, leading to increased productivity and profitability. The object of this research is production management systems, automated information systems, as well as methods and technologies used to optimize work processes at the stages of production of printed products. The purpose of the study is to summarize the theoretical foundations for improving the efficiency of production and economic activities in printing enterprises by using automation capabilities. Research methods: analysis of literature and scientific sources on existing theories and practices of automation in printing, graphic method, deduction and induction, systematization. The scientific novelty of the study lies in the generalization of modern approaches to automation of production, technological, economic and managerial aspects of the activities of printing enterprises, as well as in a brief overview of currently proposed digital solutions in this area.

Balancing protection and production in ocean conservation

AbstractWith the acceleration of the global biodiversity and climate crises, the need to protect and sustainably manage ocean resources has never been greater. However, the science needed to integrate ocean protection (through marine protected areas and OECMs) and sustainable production in the blue economy (particularly pelagic fisheries) remains underdeveloped and contested. The scientific divide and the knowledge gaps still remaining have created serious real-world challenges for practitioners seeking to reconcile protection and production approaches, and is hindering progress in achieving global conservation targets. Here, we identify the vital science necessary to bring together the “twin pillars” of protection and production, integrating mutually reinforcing meaningful protections at scale, while also driving management of production systems to internationally accepted sustainability standards. The research community must rapidly develop this new horizon of ocean science – particularly in pelagic ecosystems - to aid countries and practitioners in achieving global conservation and sustainable development targets.

Analysis of the truck haulage system in an underground mine using long-term log data

ABSTRACT This paper addresses the collection, analysis, and optimization of log data related to the truck haulage system in a mine. After selecting an underground mine in South Korea as the research area, the Mine Production Management System (MPMS) using Bluetooth beacons and tablet PCs was utilized to collect log data related to the haulage system over 18 months. Through the analysis of the collected log data, strategies to enhance the productivity and efficiency of the haulage system were derived. Weekly analysis of log data files was performed, identifying periods with abnormal truck cycle times and conducting an analysis of the underlying causes. It was revealed that the truck cycle time is influenced by the haulage system, equipment operation, and the log data collection system. To shorten truck cycle times and improve the productivity and efficiency of transportation systems, continuous collection and regular analysis of log data and dynamic system and equipment operation based on this are important. Analyzing log data collected over an extended period can help predict future trends in the haulage system and formulate measures and strategies in response. Therefore, it can serve as a valuable decision-making tool for establishing short-term, medium-term, and long-term production and operation plans.