Production Management Research Articles

Role of IT on the Effectiveness of Logistics and Supply Chain Management Systems in the Pharmaceutical Sector in Saudi Arabia Vision 2030

The main objective of this study is to evaluate how IT might improve the efficacy and efficiency of supply chain management and logistics systems in the pharmaceutical sector because the pharmaceutical industry is experiencing a transformation by the lofty objectives outlined by Saudi Arabia Vision 2030. The study was conducted using 150 executives working in the Saudi Arabian pharmaceutical industry's logistics and supply chain division. An open-ended survey questions were used to determine how IT technology affects pharmaceutical logistics and supply chain using non-probability sampling, logistics managers' LinkedIn accounts were selected for the sample, irrespective of the organization's size, location, or gender. SPSS statistical tools were used to analyse the data. It was found that the integration of IT tools and systems has emerged as a crucial enabler in this process. By harnessing the power of data analytics, IoT (Internet of Things), and automation, pharmaceutical companies can gain real-time insights into their supply chains. This, in turn, allows them to develop agile responses to changing market dynamics and customer expectations. Such strategic decisions, driven by IT, enable businesses to adapt quickly, reorganize their operations, and offer value-added services tailored to the specific needs of the market. Moreover, the creative application of IT solutions facilitates efficient inventory management, demand forecasting, and tracking of pharmaceutical products from manufacturing facilities to end consumers results also show that companies may simply increase the pace of the supply chain with the aid of innovative chances in major choices and judgments.

Optimizing Productive Waqf Management to Achieve Sustainable Development Goal's (Study of the Indonesian Waqf Board (BWI) Representative of Lampung Province)

This research involves field research using a descriptive qualitative approach. The data collection techniques include observation, interviews, and documentation. Subsequently, data analysis was conducted through stages of data collection, data reduction, data display, and conclusion drawing. Data validity is ensured through source triangulation and technical triangulation. The results showed that the productive waqf managed by BWI Lampung Province is in the form of cash waqf. The management of cash waqf is carried out through three stages: collection, management, and distribution. BWI’s steps in optimizing productive waqf management involve improving BWI’s management, increasing public education about cash waqf, enhancing the use of digital technology, fostering cooperation with various stakeholders, and enhancing the competence of the nazir. Additionally, the contribution of BWI Lampung Province to the SDGs is evident in the achievement of three goals: no hunger, quality education, and decent work and economic growth. Keywords: productive Waqf, sustainable development goal’s, Indonesian Waqf Board

The Role of Waqaf and Zakat as Fiscal Policy Instruments in Indonesia

The role of zakat and waqf is huge in terms of helping the poor in Indonesia as they have many benefits if we implement them. The alternative and strategic solution offered by Islam is none other than a productive and creative zakat management (distribution and utilization) system. With the management as intended, it is hoped that it can empower the poor to become Aghniya (the rich) and make Mustahiq become muzakki. Likewise, waqf has many benefits, especially in terms of helping the poor to improve their welfare. Therefore, this study aims to examine the role of waqf and zakat as instruments of fiscal policy in Indonesia. The research was conducted using qualitative methods and the technique used by researchers was the library research method. The library method is research conducted by reading articles from journals as well as data from the Central Bureau of Statistics and the Indonesian Waqf Board using a literature review. The data used is time series data and the time of this research was between 2019 and 2022. Data sources were obtained from BAZNAS (National Amil Zakat Agency), BPS (Central Statistics Agency), and BWI (Indonesian Waqaf Agency). The results of the study show that waqf and zakat in a country’s macro-economy are very significant as fiscal policy instruments, positive impacts in various fields, especially economic and social in Indonesia, especially in increasing aggregate consumption to increase the country’s income level. Keywords: waqaf, zakat, fiscal policy

Leveraging Digital Transformation and ERP for Enhanced Operational Efficiency in Manufacturing Enterprises

Purpose: The process of making the most of digital tools in the workplace to support an organization's success is known as "leveraging on technology." By automating manual tasks more quickly, modern ERP systems free up time and funds for other important projects. Data can be aggregated by contemporary ERP systems for simpler management, access, and analysis. Method: Systems for enterprise resource planning, or ERPs, have become a vital component of contemporary manufacturing processes. These integrated software solutions are essential for improving, streamlining, and controlling many aspects of manufacturing, including financial control, customer service, production and inventory management. Manufacturers can effectively manage vital resources like labor, equipment, and materials with the help of ERP systems. ERP helps eliminate waste, minimize production delays, and maximize resource utilization by offering real-time visibility into resource allocation and availability. Result and Conclusion: Manufacturing process synchronization and automation are made easier by ERP software. Work orders, production schedules, and quality control can all be managed by it, resulting in seamless and effective manufacturing processes. In manufacturing, keeping the proper amount of inventory on hand is a difficult task. ERP systems give producers insights into demand trends, order lead times, and reorder points, which helps them optimize inventory levels. In addition to lowering carrying costs and preventing stockouts, this minimizes superfluous inventory. Implication of Research: An organization's operational model is altered by digital transformation. This process involves systems, workflow, processes, and culture. Every level of an organization is impacted by this transition, which unifies data from different departments to collaborate more successfully. Originality/Value: Enhanced Efficiency: Businesses are able to allocate resources more effectively thanks to automation and streamlined processes, which also lower operational costs and increase productivity.

Performance of The Trigona,Sapiens Honey Agribusiness Developed By Communities Around The Laiwoi KPH Forest Area In Amonggedo District, Konawe Regency, Southeast Sulawesi Province

The Trigona sp bee is a type of biodiversity that has high economic value and is a leading commodity for Non-Timber Forest Products (HhBk), which is developed by the community around the KPH Laiwoi forest area, Amonggedo District, Konawe Regency. The community and government of Amonggedo Baru sub-district, Mataiwoi Village, Puasana Village, and Amonggedo District have formed farmer groups and Gapoktan to cultivate Trigona sp bees. as a sector of economic improvement for society. This community partnership activity was conducted in Mataiwoi Village and Amonggedo Baru Village, Amonggedo District, Konawe Regency. The method used in this survey research is the Agribusiness Technology Transfer Approach. Data was collected through observation and interview methods. Based on the community profile above, the problem of research on the performance of the Trigona Honey Agribusiness developed by the community around the KPH Laiwoi Forest area, Amonggedo District, Konawe Regency is presented in detail as follows: 1. The performance of the Trigona, sp Honey Agribusiness is determined by human factors, capital, processing tools and machines, honey management methods and technology, markets, and non-economic environmental factors carried out using a Technology Transfer approach to the Trigona—Sp Honey Bee Agribusiness subsystem. The community around the KPH Laiwoi Forest Area, Amonggedo District, Konawe Regency developed them. 2. Trigona, sp honey bee production management developed by the community is moving the colony from the primary habitat on a dead tree to an artificial habitat in artificial STUP boxes with a start-up of 20 boxes per group

Toward sustainable process industry based on knowledge graph: a case study of papermaking process fault diagnosis

Process industry suffers from production management in terms of efficiency promotion and waste reduction in large scale manufacturing due to poor organization of the intricate relational databases. In order to enhance the suitability of intelligent manufacturing systems in process industry, this study proposed an innovative top-down structure Knowledge Graph (KG) for process fault diagnosis, and papermaking was taken as a case study. The KG consists of a normalized seven-step-built ontology, which extracted instances of papermaking knowledge via Protégé software. The exported OWL file was imported into Neo4j software for visualization of the KG. The application in papermaking drying process for fault diagnosis shows that it can depict the material and energy flows throughout the process with a clearer relationship visualization than traditional measures. They also enable rationale search for faults and identification of their potential causes. The built KG efficiently manages the vast knowledge of the process, stores unstructured data, and promotes the intelligent development of process with high reusability and dynamicity that can rapidly import new production knowledge as well as flexibly self-updating.

Power-Carbon Information Management System Based on Machine Learning

With the deepening reform of the power market and carbon market, great progress has been made in informatization. Power information may be stored in many scattered places, and it is difficult to share data between different departments or systems. This leads to fragmentation and redundancy of information and makes information exchange difficult. Blockchain can improve the reliability of Power-Carbon Management System (briefly described as PCMS for convenience) data processing. PCMS informatization has become the basis for improving the quality and efficiency of project management and maximizing the environmental and economic benefits of the project. Because the power information management system can effectively control the flow of information and resource allocation. Due to the requirement of low-carbon and stable power production, PCMS attaches great importance to the application and implementation of information in power management, but does not attach enough importance to the informatization of power production management. Therefore, this paper analyzed the current situation, characteristics and existing problems of PCMS through machine learning algorithm, then constructed the design principles, and finally proposed the optimization path of PCMS according to the principles. The information collection ability and system control ability of the optimized PCMS were better than the original PCMS. The information collection ability was 14.2% higher than the original, and the system control ability was 9.8% higher than the original. In general, both blockchain and machine learning can improve the data reliability of PCMS.

The JTR just-in-time Production System and its Impact on Achieving Profits in Industrial Companies

This paper examines the JTR (Just-in-Time Replenishment) production system and its profound implications for industrial companies in enhancing profitability. JTR, an evolution of the traditional Just-in-Time (JIT) system, emphasizes not only the timely delivery of components but also the synchronization of production with demand fluctuations. The research delves into the core principles of JTR, its implementation strategies, and its effects on various facets of industrial operations. Through a comprehensive analysis of case studies and empirical data, this paper highlights the pivotal role of JTR in reducing inventory costs, minimizing waste, enhancing production efficiency, and ultimately elevating overall profitability for industrial enterprises. Furthermore, the study explores the challenges associated with adopting JTR and proposes strategies to overcome them, emphasizing the importance of organizational readiness, supplier collaboration, and technological integration. By shedding light on the transformative potential of JTR, this research contributes to a deeper understanding of contemporary production management paradigms and provides valuable insights for businesses aiming to thrive in today's dynamic market environment.

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.

The Role of Product Management and Market Research Analysis for Business Development in Selected Pharmaceutical Companies

This research paper delves into the pivotal role of product management and market research analysis in the context of pharmaceutical companies. It explores how these strategic tools contribute to business development amidst the intricate landscape of the pharmaceutical industry, characterized by rapid scientific advancements, evolving market dynamics, and stringent regulatory requirements. The paper emphasizes the evolution of product management within the pharmaceutical sector, highlighting its transition from a focus on research and development to encompassing comprehensive lifecycle management and commercialization strategies. Through a review of literature and data analysis, the paper examines various facets such as patient-centric innovation, regulatory compliance, digital transformation, competitive landscape analysis, and cross-functional collaboration. The study aims to shed light on the significance of integrating product management and market research analysis for driving sustainable business growth, enhancing competitiveness, and delivering impactful healthcare solutions in pharmaceutical companies.

Law, Technology, and Our Governance Dilemma

This article highlights a dilemma that we face when we turn to new tools that promise to improve on law’s imperfect governance. On the one hand, our discontent with law’s governance is both broad and deep, and much of it is rooted in the human nature of the legal enterprise. Yet, we remain attached to the essentially human nature of law’s governance. On the other hand, we recognise the potential benefits in technological governance but not without some displacement of the human element. Caught on the horns of this dilemma, we attempt to limit the loss of the human element by insisting that governance must be compatible with human rights or human dignity, or, more directly, that governance must limit the applications of technology so that they remain human-centric. Given a demand for human-centric applications of technologies, we consider how far humans might, and should, go in deploying new tools with a view to improving law’s imperfect governance. Should these tools be limited to assisting humans? Or, might they replace humans? Or might we even govern by technological management of places, products, and processes so that reliance on both humans and rules is reduced? It is concluded that, in all spheres of governance and in all human communities, the one thing that is essential is that the applications of new technologies are controlled so that they do not undermine the generic conditions which are presupposed by viable groups of human agents.

Tracking social-economic system nitrogen flow in China for emissions reduction and efficiency improvement

The increasing human demand have driven intensive reactive nitrogen (Nr) use, leading to high emissions and low efficiency in entire social-economic system. Herein, we accomplished a social-economic Nr flow model to trace the multi-sectorial Nr use, efficiency, and emissions in China from 1980 to 2020. The results showed that Nr use intensity increased 3.1-fold within four decades, with the decreasing proportions of Nr recovery from 32.7 % to 16.8 %. The Nr use pathways of production, consumption and waste management stages showed low resources use efficiency in agriculture and industrial production, high degree of consumption loss, insufficient waste treatment characteristics currently. Nr emissions peaked in 2012 and declined 12.7 % to 2020 owing to the nitrogen oxides control and mitigating overuse of fertilizers, and will continuously decrease under strategies in 2040 (41.7 % compared with 2012), indicating the large potential for emission reduction and efficiency improvement in next few decades.

The use of generative AI in research: a production management case study from the aviation industry

The use of generative AI in research: a production management case study from the aviation industry

Blockchain-enabled platform-as-a-service for production management in off-site construction design using openBIM standards

Integrating building information modeling (BIM) with emerging blockchain technology has gained considerable attention in addressing design collaboration problems like poor data security, weak traceability, and low transparency in off-site construction. However, blockchain implementations in off-site construction design, especially with openBIM standards, currently lack standardized workflow management and methods for ensuring data completeness within the blockchain environment. To bridge these gaps, this paper presents a novel blockchain-enabled platform-as-a-service (PaaS), specifically tailored for off-site construction to enhance interoperability and collaborative design efficiency using openBIM standards. The proposed PaaS offers two significant intellectual contributions: (1) a data contract system that manages openBIM workflow standardization and (2) a data escrow system that ensures the completeness and integrity of openBIM data. The data contract system utilizes an innovative asynchronous smart contract algorithm to streamline workflow checks, while the data escrow system introduces a new blockchain-driven data-checking strategy to ensure openBIM completeness at the data level. Besides, a PaaS architecture is developed to clarify the interaction logic between these two systems. The PaaS is validated and evaluated in openBIM-based design collaborations from a real-world project, with results indicating improvements: a 38% time-saving in design issue coordination and a 32% reduction in design data delivery time. Furthermore, the platform received favorable feedback from project participants, validating the approach and confirming the PaaS as a valuable tool for enhancing design collaboration in off-site construction.

Remote Sensing-Based Monitoring of Cotton Growth and Its Response to Meteorological Factors

Cotton is an important economic crop and strategic resource. Monitoring its growth and analysing its response to meteorological factors are crucial for field management and yield estimation. This study selects the primary cotton-producing regions in northern Xinjiang as the study area. Firstly, using the Google Earth Engine cloud platform, the Cotton Mapping Index (CMI) was utilised to extract cotton planting areas from 2019 to 2023. Secondly, Sentinel-2A data were used to calculate the NDVI of cotton during the growing season and analyse its variation characteristics. Finally, correlation, lag, and partial correlation analyses were conducted between cotton NDVI and meteorological factors, including effective accumulated temperature, wind speed, precipitation, and solar shortwave radiation, to explore the response relationship. The results indicate the following: (1) The optimal classification threshold of CMI in the study area was determined to be 0.74, which was applied to extract cotton planting areas over the years. The overall classification accuracy achieved was 84.85%. The R2 value for the cotton area extracted by CMI compared to the cotton planting area in the statistical yearbook data is 0.98, with an average relative error of 16.84%. CMI’s classification use effectively distinguishes cotton from other major crops, such as wheat and corn, in the study area. Compared with different classification methods, CMI is more convenient and efficient for extracting cotton planting areas, contributing significantly to yield estimation and management. (2) We found that from 2019 to 2023, some fields were planted with cotton yearly. In order to prevent land degradation, a crop rotation system should be implemented, in which cotton rotates with other crops to reduce the rate of soil nutrient loss and achieve sustainable agricultural development. (3) NDVI can effectively monitor the spatiotemporal changes and regional variations in cotton growth. Sentinel-2 multi-spectral imagery possesses high spatial and temporal resolution, enabling effective monitoring of cotton growth, provision of cotton growth data for field managers, and application in cotton production management. Additionally, cotton yield estimation can be achieved by comparing the overall growth of cotton across different years. (4) Cotton NDVI exhibits a strong correlation with effective accumulated temperature and solar radiation, with the majority passing the significance test, suggesting a significant promotion effect on cotton growth by accumulated temperature and solar radiation. In cotton cultivation management, attention should be directed toward monitoring changes in accumulated temperature and solar radiation. Moreover, NDVI changes in response to solar radiation exhibit a certain lag. The correlation between NDVI and precipitation is low, likely attributed to local cotton cultivation primarily relying on drip irrigation. Cotton NDVI is negatively correlated with wind speed. Cotton planting should consider weather changes and take corresponding preventive management measures. The research results have significant reference value for monitoring cotton growth, disaster prevention, and sustainable agricultural development.

Economic Aspects of Zinc Oxide Fertilization in Yam (Dioscorea alata L.) in a Semi-Arid Region of Brazil

The management and improvement of yam productivity are associated with a good supply of essential nutrients for the growth and development of the crop that has economic viability. This research aimed to evaluate the economic feasibility of foliar fertilization with Zintrac® in two yam agricultural seasons (2022/2023 and 2023/2024). Therefore, two experiments were conducted at the Rafael Fernandes Experimental Farm, Mossoró, RN, Brazil. The experimental design was in a Latin square design with five treatments of doses of Zintrac® (0, 1, 2, 3, and 4 L ha−1) and five replications. Among the production costs of yams, labor and seed acquisition were the most significant. The highest profitability index was achieved with the dose of 1L Zintrac® ha−1 in the first season and second season, which corresponded to 78.97 and 57.86%. For the first season, increments were observed in all treatments that received zinc doses with increases of 48.70, 31.22, 14.30, and 15.93% for 1, 2, 3, and 4 L of Zintrac® ha−1 compared to the dose of 0 L ha−1. On the other hand, in the second season, there was an increase only in the dose of 1 L ha−1 of Zintrac®, which corresponded to 51.3% in the net yield (ha−1) of the dose of 0 L ha−1. Therefore, foliar zinc oxide fertilization was economically viable for the yam crop, obtaining higher economic indices at the dose of 1 L ha−1. The highest cost for growing yams is using a dose of 4 L ha−1 of Zintrac®, totaling USD 6977.59 (first season) and USD 6868.33 (second season)

Economic and financial viability of a pig farm in central semi-tropical Mexico: 2022–2026 prospective

To estimate the economic and financial viability of a pig farm in central sub-tropical Mexico within a 5-year planning horizon, a Monte Carlo simulation model was utilized. Net returns were projected using simulated values for the distribution of input and product processes, establishing 2021 as base scenario. A stochastic modelling approach was employed to determine the economic and financial outlook. The findings reveal a panorama of economic and financial viability. Net income increased by 555%, return on assets rose from 3.36% in 2022 to 11.34% in 2026, and the probability of decapitalization dropped from 58% to 13%, respectively in the aforesaid periods. Similarly, the probability of obtaining negative net income decreased from 40% in 2022 to 18% in 2026. The technological, productive, and economic management of the production unit allowed for a favorable scenario within the planning horizon. There is a growing interest in predicting the economic sectors worth investing in and supporting, considering their economic and development performance. This research offers both methodological and scientific evidence to demonstrate the feasibility of establishing a planning schedule and validating the suitability of the pork sector for public investment and support.

Recognition of Plastic Film in Terrain-Fragmented Areas Based on Drone Visible Light Images

In order to meet the growing demand for food and achieve food security development goals, contemporary agriculture increasingly depends on plastic coverings such as agricultural plastic films. The remote sensing-based identification of these plastic films has gradually become a necessary tool for agricultural production management and soil pollution prevention. Addressing the challenges posed by the complex terrain and fragmented land parcels in karst mountainous regions, as well as the frequent presence of cloudy and foggy weather conditions, the extraction efficacy of mulching films is compromised. This study utilized a DJI Mavic 2 Pro UAV to capture visible light images in an area with complex terrain features such as peaks and valleys. A plastic film sample dataset was constructed, and the U-Net deep learning model parameters integrated into ArcGIS Pro were continuously modified and optimized to achieve precise plastic film identification. The results are as follows: (1) Sample quantity significantly affects recognition performance. When the sample size is 800, the accuracy of plastic film extraction notably improves, with area accuracy reaching 91%, a patch quantity accuracy of 96.38%, and an IOU and F1-score of 85.89% and 94.20%, respectively, compared to the precision achieved with a sample size of 300; (2) Different learning rates, batch sizes, and iteration numbers have a certain impact on the training effectiveness of the U-Net model. The most suitable model parameters improved the training effectiveness, with the highest training accuracy achieved at a learning rate of 0.001, a batch size of 10, and 25 iterations; (3) Comparative experiments with the Support Vector Machine (SVM) model validate the suitability of U-Net model parameters and sample datasets for precise identification in rugged terrains with fragmented spatial distribution, particularly in karst mountainous regions. This underscores the applicability of the U-Net model in recognizing plastic film coverings in karst mountainous regions, offering valuable insights for agricultural environmental health assessment and green planting management in farmlands.

ChatGPT as a Tool for Medical Education and Clinical Decision-Making on the Wards: Case Study.

Large language models (LLMs) are computational artificial intelligence systems with advanced natural language processing capabilities that have recently been popularized among health care students and educators due to their ability to provide real-time access to a vast amount of medical knowledge. The adoption of LLM technology into medical education and training has varied, and little empirical evidence exists to support its use in clinical teaching environments. The aim of the study is to identify and qualitatively evaluate potential use cases and limitations of LLM technology for real-time ward-based educational contexts. A brief, single-site exploratory evaluation of the publicly available ChatGPT-3.5 (OpenAI) was conducted by implementing the tool into the daily attending rounds of a general internal medicine inpatient service at a large urban academic medical center. ChatGPT was integrated into rounds via both structured and organic use, using the web-based "chatbot" style interface to interact with the LLM through conversational free-text and discrete queries. A qualitative approach using phenomenological inquiry was used to identify key insights related to the use of ChatGPT through analysis of ChatGPT conversation logs and associated shorthand notes from the clinical sessions. Identified use cases for ChatGPT integration included addressing medical knowledge gaps through discrete medical knowledge inquiries, building differential diagnoses and engaging dual-process thinking, challenging medical axioms, using cognitive aids to support acute care decision-making, and improving complex care management by facilitating conversations with subspecialties. Potential additional uses included engaging in difficult conversations with patients, exploring ethical challenges and general medical ethics teaching, personal continuing medical education resources, developing ward-based teaching tools, supporting and automating clinical documentation, and supporting productivity and task management. LLM biases, misinformation, ethics, and health equity were identified as areas of concern and potential limitations to clinical and training use. A code of conduct on ethical and appropriate use was also developed to guide team usage on the wards. Overall, ChatGPT offers a novel tool to enhance ward-based learning through rapid information querying, second-order content exploration, and engaged team discussion regarding generated responses. More research is needed to fully understand contexts for educational use, particularly regarding the risks and limitations of the tool in clinical settings and its impacts on trainee development.

Operational taxonomy of farmers' towards circular bioeconomy in regional level

The concept of the bioeconomy holds great promise for promoting green growth and competitiveness across Europe through the use of renewable biological resources, such as plant and animal biomass, while adhering to the principles of circularity. Despite its introduction by the European Commission in 2015, little effort has been made to define or describe what exactly a circular bioeconomy entails. In the case of Greece in particular, however, the survival of the region of Western Macedonia appears to be highly dependent on sustaining its agricultural activity by incorporating elements related to this crucial sector of the economy. In order for this agricultural transformation to be effective, bio-economic practices relevant to crop production and appropriate alternative management practices must be universally implemented at all levels. To achieve this, it was necessary to collect questionnaires from 412 farmers in the region for analysis purposes, and to classify their responses based on k-means cluster analysis, which later formed these systematic groups: modernists, early adopters and latecomers. Evaluation of these categories revealed a variety of factors, such as age and income, that significantly influenced their attitudes towards adopting bioeconomy farming practices and related circularity principles. Overall, understanding these challenges opens the door for policy decisions aimed at supporting the development of sustainable rural areas.