For industrial companies, accurate short and medium-term production planning is crucial for resource allocation and maximum utilization of manufacturing capacities. If the efficiency of the production units is predicted reliably, the company can operate more economically due to predictability. Automotive companies usually monitor their efficiency and productivity using the Overall Equipment Effectiveness (OEE) as a standard Key Performance Indicator. This article presents a new approach in which the OEE value is predicted using different weights, target values and historical time data. The aim of this article is to determine the weight combination that allows for the most accurate prediction for three types of welding technologies. Firstly, a literature review demonstrates scientific relevance. Secondly, the proposed algorithm is described. In the third section, the prediction algorithm is presented through a case study. Several different weight combinations are applied and then compared using the Root Mean Square Error indicator. Last section concludes the paper. The presented algorithm can be easily and quickly applied in many cases of industrial environment.

The socio-economic history of aniseed in Turkey in the 20th century indicates the development of small peasantry, agricultural policies and agro-industry. Therefore, this study aims to analyse aniseed production in Turkey from the mid-1920s to the early 2000s in the triangle of state, peasant and economy. At this point, the TEKEL (State Monopoly) Administration, which creates added value to aniseed through market opportunities and raki production, has an important role in the sustainability of aniseed cultivation. Since aniseed is mostly produced by small agricultural enterprises, it has been identified as a product where farmer organisation is weak and production planning is not carried out. Since this situation causes difficulties in the marketing of the aniseed produced, it has been a part of political debates in the Grand National Assembly of Turkey. It is interesting to note that despite the technical advances in Turkish agriculture in the 20th century, anise production was labor intensive and remained primitive. In fact, despite the economic uncertainties caused by neoliberal agricultural policies after 1980, both the number of producers and anise production continued to increase. Despite these two contradictory situations, this study shows that TEKEL's demand for anise for raki production has ensured the sustainability of anise production in Turkey. In other words, TEKEL provided both sustainability in production, a market opportunity and value-added production for aniseed. In terms of methodology, this study approaches aniseed production from a more holistic perspective and touches upon both the daily lives of aniseed producers and political debates. The primary sources utilized in this study, such as the Presidential State Archive Documents, the minutes of the Grand National Assembly of Turkey and the national press, reflect the social, economic and political debates on anise production, while the Turkish Statistical Institute data show the changes in anise production by provinces over the years.

The interplay between learning effect and order acceptance in production planning

A hybrid neural network model for shale gas production prediction considering production plans and produced water

Intercity red meat trade exacerbates the spatial inequality of greenhouse gas emissions and health burdens in China.

This study aims to apply the Simplex Method to optimize production levels and maximize profits in Micro, Small, and Medium Enterprises (MSMEs) in Ogan Komering Ulu Regency. The research was conducted to address common challenges faced by MSMEs, such as limited raw materials, labor hours, and production costs, which often result in inefficient resource allocation. The Simplex Method, a mathematical optimization technique used in linear programming, was utilized to determine the optimal number of products to be produced under given constraints. Data were collected through field observations, structured interviews, and production records of selected MSMEs. The analysis revealed that applying the Simplex Method enabled business owners to increase production efficiency by up to 20% while maintaining the same level of resource usage. The study concludes that the Simplex Method can serve as an effective decision-making tool for MSMEs to optimize their production planning, reduce waste, and enhance profitability.

Micro, Small, and Medium Enterprises (MSMEs) play a crucial role in strengthening Indonesia’s regional economy and preserving cultural identity through creative industries. One of the prominent local cultural products is Tanjak Melayu, a traditional Malay headpiece produced in Rokan Hulu Regency, Riau. However, inconsistent market demand often leads to production inefficiencies and unstable marketing performance. This study aims to forecast the sales of Tanjak Melayu using the Trend Moment method integrated with seasonal adjustment analysis to support sustainable MSME marketing strategies. The dataset used consists of monthly sales records from January 2023 to December 2024, analyzed using quantitative forecasting techniques. The resulting trend equation, Y = 145.55 + 1.4087X, indicates an average monthly increase of 1.4 units in sales volume. Model validation produced a MAPE value of 3.78%—categorized as excellent accuracy—and an RMSE value of 9.08, reflecting a low prediction error compared to actual sales. The findings demonstrate that the Trend Moment method effectively captures both the upward sales trend and seasonal fluctuations, with demand peaks occurring in November and December. This research provides practical insights for MSME actors in optimizing production planning and marketing schedules, and theoretical contributions by highlighting the applicability of simple statistical forecasting for culture-based small enterprises toward sustainable economic development.

Relevance . The historians are very interested in Soviet industry history facts. A special attention is paid to management methods of Soviet production, the advanced activity standards introduction, the organization of employees and workers social culture at enterprises etc. Obviously, the work teams’ success of in USSR depended much on the skillful and effective enterprise administration management. So the agro biological industry experience analysis on various levels of its development could be very useful for market economics. It also could help to create agricultural and industry programs both in Russia and in some districts of it. The purpose of this study is to review documentary history sources of the largest USSR agro-biological industry enterprises, the Kursk Mallein-Tuberkuline Biofactory. Objectives is to restore Kursk Mallein-Tuberculin Biological Factory managers’ biography in 1938 – the first half of 1941. Methodology . As the author’s methodological base there were the objectivity and historicism principles. There was also used a historical-genetic, chronological ideographic (narrative) and retrospective methods among the special historical methods. I would like to point out an ideographic (narrative) method, this one helped to describe Biofactory individual events in its history, helped to explore those people biography that had made a significant contribution to its development. Results . A comprehensive study based on archival and printed sources allowed us to restore both Kursk Biofactory director Akim Fedorovich Kurkin biography and the enterprise development history during the Great Patriotic War. Conclusion : Industrial biological production first of all tuberculin and mallein in USSR was organized at Kursk biofactory and it had become a monopolist of its production by 1941. So, on the Great Patriotic War eve Kursk Biological Factory fulfilled the production plan thanks to Kurkin’s professional skills and enterprise team only.

Abstract With the continuous growth of personalized product demands and the upsurge in new customer orders, the arrival of new jobs and machine breakdowns due to overloading have emerged as common production disturbances, inevitably affecting the production plan. To maintain the stability of the scheduling for dynamic flexible job shops with machine breakdown and new job arrival, this article proposes a robust scheduling method that is designed with a flexible network structure and dual-action chained cooperative decision-making mechanism based on deep reinforcement learning (FD-DRL). First, a flexible neural network structure is innovatively constructed, which embeds the feature vector into operation nodes to design a dynamic production state extraction method with graph neural networks (GNN). Second, the dual-action chained cooperative decision-making mechanism is established for agents, who consider the new and remaining operations overall to maximize the utilization of machine idle time. Finally, through training and verification, the effectiveness and advancement of the proposed FD-DRL method are verified by comparing with heuristic/meta-heuristics and the static model of deep reinforcement learning (DRL).

Additive Manufacturing (AM) techniques, especially Fused Deposition Modelling (FDM), have generated much interest recently for their capabilities for manufacturing complex geometries using a variety of materials. In this work, a regression model has been developed for the FDM process performance enhancement and to control PETG processing. This study systematically analysed the effect of critical FDM parameters on key performance criteria such as printing time, dimensional deviation, and surface finish, including nozzle temperature, printing speed, and infill density. Experiments were carried out following a defined design of experiments to gather data which were then used to develop regression models for the prediction of printing results. A statistical treatment was done on the relationships among process variables with its impact on performance metrics. The predictive model developed showed a high level of accuracy, thus allowing for the identification of optimal levels for parameter settings conducive to PETG component efficiency, surface quality, and dimensional accuracy. Thus, the study acts as a practical guide for manufacturers willing to upgrade their additive manufacturing processes relating to process optimization, quality control, and production planning. By tying experimental inquiry with predictive modeling, this work delves deep into the dynamics of the FDM process and provides valuable insights for the mass use of PETG-based FDM in automotive, aerospace, biomedical, and other industry sectors.

Micro and small business owners have to fight for their businesses to survive. In order to survive, micro and small business actors must first know the problems they face. The method used to find out the problems faced by conducting observations and also interviews with micro business actors. This research reveals the problems faced by micro and small business actors based on three company operating functions, namely Operations Management, Financial Management and Marketing Management. The results show that in the Operations section, micro and small business actors must improve production planning, 5S and layout arrangement. In the financial section, what must be addressed is financial recording, and in the marketing section, what must be addressed is product development and market expansion by penetrating the marketplace. Improvement of the problems faced is carried out by assisting MSEs by improving the company's operating functions

The large-scale integration of renewable energy has reduced power system flexibility and exacerbated supply–demand imbalances. In industrial parks, the combined variability of high energy-consuming industrial loads and photovoltaic (PV) generation further complicates the energy management challenge. Aiming to enhance the operational flexibility of industrial parks and mitigate supply–demand imbalances, this paper proposes a multi-time-scale stochastic energy management strategy that accounts for the uncertainty associated with PV generation. First, a conditional generative adversarial network (CGAN) is employed to generate the representative PV generation scenarios, thereby enabling the modeling of PV generation uncertainty within the optimal dispatch model. Considering the coupling mechanisms and control characteristics of various regulation resources within the industrial park, a multi-time-scale dispatch model is developed. In the day-ahead dispatch phase, the operational costs are minimized by optimizing the production plans of industrial loads. In contrast, in the intraday phase, the more flexible measures, such as adjusting the tap positions of arc furnaces and controlling the charge/discharge of energy storage systems, are employed to smooth power fluctuations within the park. A case study validated the effectiveness of the proposed approach, demonstrating a 7.56% reduction in power fluctuations and a 4.34% decrease in daily operating costs. These results highlight the significance of leveraging industrial loads in park-level systems to enhance cost efficiency and renewable energy integration.

Artificial intelligence (AI) currently enjoys significant preference and popularity among researchers, representing a highly sought-after research domain. It is envisaged that in the foreseeable future, numerous tasks traditionally executed by humans will be executed with greater efficiency, reliability and cost-effectiveness through the utilization of advanced AI techniques and applications. AI finds extensive application across various domains, including classification, prediction, generation and control. One notable application within the realm of production planning and control is demand forecasting. In this paper, the estimation of electricity energy demand is conducted by leveraging AI models, which involved the evaluation of weather data alongside various parameters. For this real-life application, a dataset sourced from Spain, obtained from an open data-sharing platform, is utilized as the primary input. Throughout the study, AI models such as Artificial Neural Networks (ANN), LightGBM and transformers are deployed to generate predictions. The findings generally indicated that all models demonstrated efficacy in predicting both increasing and decreasing values. Nonetheless, the LightGBM AI model emerged as the most competent demand forecasting model, boasting a Mean Absolute Percentage Error (MAPE) value of 8.76%.

Supply chain (SC) anomalies, such as inaccurate demand forecasts, inventory imbalances, and production delays, impede operational efficiency and financial performance in manufacturing plants, particularly in third-world contexts where data-driven forecasting methods remain underutilized. This study investigates SC anomalies within Fair Electronics, a key manufacturing partner and authorized distributor of Samsung products in Bangladesh. Through interviews with technicians, supervisors, and management, specific anomalies such as demand volatility, stockouts, and inefficiencies in resource allocation were identified, with a predominant issue being the reliance on experiential forecasting methods that often result in inaccurate demand predictions. Leveraging insights from an extensive literature review, this research introduces machine learning (ML)-based forecasting methodologies tailored to these challenges. Four ML models, including an autoregressive integrated moving average (ARIMA), extreme gradient boosting (XGBoost), long short-term memory (LSTM), and Prophet, were applied to diverse market segments of mobile products, with model evaluation based on metrics such as mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), and mean absolute percentage error (MAPE). XGBoost consistently emerged as the superior model in terms of forecasting accuracy and robustness. The study highlights the transformative potential of advanced ML techniques in enhancing demand forecasting within SCs, proposing a comprehensive framework that integrates these methods to optimize inventory management, production planning, and overall operational performance. This study bridges the gap between traditional and data-driven forecasting approaches, providing a robust evidence base for the adoption of ML in SCs operations, paving the way for enhanced decision-making, reduced inefficiencies, and improved financial outcomes in manufacturing environments similar to Fair Electronics. The findings also offer a roadmap for future research and practical applications in the evolving landscape of supply chain management (SCM).

Accurate early prediction of final body weight (BW) is essential for optimized feeding strategies and slaughter planning in beef cattle production. This study evaluated the performance of three machine learning models (k-nearest neighbors, Random Forest, eXtreme Gradient Boosting), and one deep learning model [long short-term memory (LSTM)] to forecast the final BW of Hanwoo steers at various time points prior to slaughter. A total of 196 Hanwoo steers (7 to 31 months of age) from a commercial farm were utilized. Input data included monthly BW and feed nutrient intake (crude protein, ether extract, neutral detergent fiber, total digestible nutrients) across three growth stages. Six input configurations (I1-I6) were designed to predict the final BW at 17, 13, 9, 6, 3, and 1 month(s) before slaughter, with a target age of 31 months. The machine and deep learning models were assessed by five-fold cross-validation (training set) and a test set and evaluated via the coefficient of determination (R²) and root mean squared error (RMSE). Among the tested models, the LSTM achieved the highest prediction accuracy across all the configurations. The performance of the LSTM improved as the prediction point approached the target slaughter age: I1 (R² = 0.60, RMSE = 52.80), I2 (0.72, 45.40), I3 (0.76, 40.92), I4 (0.83, 35.84), I5 (0.90, 33.12), and I6 (0.97, 22.62). These results demonstrated that LSTM effectively captured temporal dependencies in sequential data, enabling more accurate BW forecasting under commercial conditions. While I6 achieved the highest prediction accuracy, the 3-6 month predictions (I4 and I5) demonstrated reasonably high accuracy, which could provide a practical timeframe for farm-level management and planning. This approach could be utilized in evidence-based decision-making in Hanwoo production by providing reliable predictions well ahead of slaughter.

Pareto‐optimal scheduling for just‐in‐time production systems with energy consideration

Petroleum and natural gas are among the most critical energy sources in contemporary societies, still impossible to replace with recoverable resources. They are projected to play a pivotal role in addressing the global energy demands in the near future. Achieving energy security for the present turbulent times is of utmost importance. The discovery and development of new hydrocarbon deposits, along with increasing productivity from existing fields. The majority of onshore oil and gas fields in Bulgaria are in a mature to final stage of exploitation, thus emphasizing the need for innovative approaches and modern methods for outlining perspective exploration territories. Some of the economic oil fields are still in production (Tyulenovo, Dolni and Gorni Dabnik, Dolni Lukovit - Staroseltsi and Burdarski Geran) and their recoverable potential remains to be fully tapped. Conversely, a number of other fields have been classified as depleted or with minimal remaining reserves, which seriously raises the question of their future (e.g., Devetaki, Pisarovo, Aglen and Deventsi). These "depleted" deposits are of significant interest due to the possibilities to reassess and apply modern technologies for optimization and increasing the yield from already exhausted fields. Therefore, the primary goals are enhancing the recovery factor to prolong the operational lifespan of existing brownfields and reassessing the hydrocarbon perspective areas in Northern Bulgaria. Moreover, a significant set of geological, geophysical and technical data concerning hydrocarbon accumulations is available for reassessment. This extensive data base provides a robust foundation for contemporary characterization and evaluation of natural reservoirs in the case of Devetaki gas condensate field and overall evaluation of several perspective adjacent areas (Bohot, Gradina, Kriva Bara, Bazovets, Tarnak and etc.). It also facilitates quantitative estimations of resource and reserve volumes within these reservoirs as well as delineation of future exploration territories. The integration of software platforms with modern geoscience concepts offers a cost-effective tool for economic growth. This study highlights the need for realistic geological models and production plans to enhance recovery from mature oil and gas fields in Bulgaria. Reassessment of the promising areas where hydrocarbons are present will also provide a new in-depth view on the future oil and gas sustainable exploration.

Abstract This paper studies integrated production and safety stock planning in high-tech low-volume manufacturing supply chains facing various complexities. Multiple end items assembled from a large number of materials, with intricate production processes and component commonality, result in a general-structure multi-echelon network subject to uncertain lead times and capacity constraints. Furthermore, extensive overall lead times necessitate planning based on forecasts of highly uncertain demand, which exhibits non-stationarity due to business cycles. We introduce a data-driven rolling horizon framework that combines a dynamic tactical production-inventory planning model, incorporating demand forecast and supply progress updates, and a strategic safety stock placement heuristic. A distinctive feature of our integer programming-based production planning model is the inclusion of safety stock replenishment decisions following a given safety stock policy, thereby acknowledging the crucial interplay between production and safety stock planning in capacitated systems. Our heuristic effectively exploits the production planning outcomes to derive efficient safety stock policies, trading off inventory investment and customer service. We provide a comparative study with planning practices at our industry partner, ASML. Our findings show that existing hedging, based on safety stocks derived from classical stochastic inventory models, results in service levels 13 percentage points below target due to limiting assumptions regarding demand uncertainty and capacity constraints. Moreover, we highlight the importance of an integrated approach towards multiple uncertainties to benefit from risk pooling, where current separated strategies require a 9% increase in inventory investment when additionally including lead time uncertainty, while our methodology maintains delivery performance with minimal extra cost.

A marketing strategy is a series of planned steps used by a company to effectively introduce and offer products or services to consumers. Yuki Mart has three marketing strategies to increase sales: pricing, product planning, and distribution systems. This study aims to analyze the extent to which marketing strategies can increase sales at Yuki Mart in Gunungsitoli City. This study uses qualitative research methods. The main focus of the study lies in three aspects of Yuki Mart's marketing strategy: pricing, product planning, and distribution systems. The research results show that Yuki Mart's marketing strategy is still not optimal in increasing sales. Yuki Mart needs to optimize its marketing strategy so that in the future, Yuki Mart can increase sales and improve its business.

Abstract A study conducted at the University of North Carolina Wilmington examined the growth of hatchery‐raised black sea bass (BSB), Centropristis striata , juveniles using recirculating aquaculture systems (RAS) technologies. Regression analysis was used to establish the relationship between fish weight and age. Subsequent growth curves were derived for hypothetical genetic generations (F1 and F2) assuming a 12.5% weight increase per generation. A model of a commercial‐scale RAS facility was developed to optimize stocking, tank transfer, and harvesting schedules for each generation. The study compared biomass, harvest frequency, annual yield, and tank space efficiency (TSE) under various scenarios. Results showed that a four‐stage production plan model consisting of three tanks during the final growout stage was most effective to produce F0 generation fish, yielding 35,389 lbs (16,052 kg) per cohort, 6.02 harvests, and 220,035 lbs (99,806 kg) total biomass annually, with a TSE of 89.29%. For F1 and F2 generations, similar production plan models resulted in increased biomass and TSE. Selective breeding led to an 11.8% increase in annual biomass yield for F1 fish and an additional 9.2% for F2 due to shorter inter‐harvest intervals and higher TSE. Iterative testing of stocking and harvesting strategies was determined to be crucial for optimizing harvest quantity and timing in RAS BSB growout facilities.