Operational Products Research Articles

Assessment of the relationship between body weight and body measurements among Morkaraman, Tushin and Awassi sheeps with different ages

This study aimed to identify morphological predictors of body weight in Morkaraman, Tushin, and Awassi sheep across different age groups, providing valuable insights for effective livestock management and breeding programs. Data were collected from 260 Morkaraman, 109 Tushin, and 96 Awassi sheep, with body measurements including body length (BL), wither height (WH), chest girth (CG), chest width (CW), chest depth (CD), rump width (RW), rump height (RH), ear length (EL), head length (HL), forehead width (FW), cannon forelimb circumference (CFC), and cannon hindlimb circumference (CHC). Pearson correlation analysis was conducted to assess relationships between body weight and these morphological traits. A multiple linear regression model was developed using the backward stepwise elimination method to estimate body weight based on the collected measurements. The results revealed that the Morkaraman breed and the 3-year-old age group had the highest average body weights. Among the morphological traits, CG (r = 0.702), RH (r = 0.694), BL (r = 0.678), and WH (r = 0.677) exhibited the strongest correlations with body weight. The final regression model identified CG, RW, RH, and HL as the most significant predictors, collectively explaining approximately 78.3% of the variation in body weight. These findings underscore the importance of selecting specific morphological traits for accurate body weight estimation in sheep. The results of this study have significant implications for sheep farming, particularly in enhancing breeding, management, and selection strategies. By focusing on key morphological predictors, livestock producers can improve decision-making processes, optimize resource allocation, and promote the overall productivity and sustainability of sheep farming operations.

An Analytical Study on Opportunities and Challenges towards Implementation of Industry 4.0 Tools for Manufacturing SMEs in Karnataka

In the era of Industry 4.0, there is a significant shift in manufacturing paradigms, offering substantial opportunities for operational efficiency, product quality, and supply chain integration. However, small and medium-sized enterprises (SMEs) in Karnataka face numerous challenges in adopting these advanced technologies. This study investigates the readiness and capacity of manufacturing SMEs in Karnataka to implement Industry 4.0 tools, focusing on financial capacity, workforce skills, infrastructure support, and comparative regional analysis. Data were collected from ten SMEs through structured questionnaires and discussions with officials, supplemented by secondary data from books, research papers, journals, annual reports, and websites. The data were analyzed using SPSS software, employing descriptive statistics, correlation analysis, and ANOVA. The findings reveal moderate to strong correlations between financial capacity, workforce skills, and infrastructure support, highlighting the interconnected nature of these factors. Despite uniform challenges across industries, Karnataka's SMEs demonstrate a high level of readiness and capacity for digital transformation, suggesting that broad-based initiatives could effectively support their adoption of Industry 4.0 technologies. The study provides actionable insights for policymakers and business leaders to facilitate the successful implementation of these advanced manufacturing tools.

Novel and cost-effective CNC tool condition monitoring through image processing techniques

CNC machining is an important part of the manufacturing industry. This paper introduces a novel and efficient approach for tool condition monitoring in CNC machine operations through the application of image processing techniques. By utilizing a consumer-grade camera capable of recording videos at 60 frames per second, the study demonstrates a cost-effective method to detect tool breakage and identify edge fractures. Basic image processing techniques, including frame extraction, background subtraction, thresholding, and morphological operations, are applied to analyze captured images and videos. This research not only offers a practical solution to enhance the efficiency and accuracy of CNC machine operations but also aligns with advancements in smart manufacturing and Industry 4.0. The findings will showcase the proposed system’s effectiveness in actual CNC machine environments, underscoring its potential to enhance maintenance strategies and operational productivity for small and medium-sized manufacturers without incurring high costs. Furthermore, it sets the stage for future investigations in this domain, indicating the possibility for enhancements through machine learning and an expanded application of these monitoring techniques.

Literature Review : The Role of Digital Technology in Enhancing Entrepreneurship in the Era of Industry 4.0

This study examines the role of digital technology in enhancing entrepreneurship in the Era of industry 4.0 through a systematic literature analysis of various sources from 2013 to 2023. By adopting this methodological approach, the research elucidates the benefits, challenges, and solutions associated with the adoption of digital technology for business practitioners, particularly in the micro, small and medium enterprises (MSMEs). The analysis demonstrates that digital technology significantly enhances operational efficiency, productivity, and market reach, while also fostering innovation in business models. However, challenges such as resource limitations, a lack of digital literacy, and the digital divide persist as significant obstacles. Furthermore, the study identifies strategic solutions to mitigate these challenges, including digital literacy training, technology subsidies, and improvements to digital infrastructure. The limitations of this research include a constrained data range from 2013 to 2023 and insufficient exploration of practical dimensions and ethical issues pertaining to the adoption of digital technology. Future research is encouraged to investigate these topics in greater depth, particularly within localized contexts and their implications for business sustainability. These findings underscore the critical role of digital technology as a fundamental enabler for developing competitive and sustainable entrepreneurship in Era 4.0.

Classification of palm oil fruit ripeness based on AlexNet deep Convolutional Neural Network

The palm oil industry faces significant challenges in accurately classifying fruit ripeness, which is crucial for optimizing yield, quality, and profitability. Manual methods are slow and prone to errors, leading to inefficiencies and increased costs. Deep Learning, particularly the AlexNet architecture, has succeeded in image classification tasks and offers a promising solution. This study explores the implementation of AlexNet to improve the efficiency and accuracy of palm oil fruit maturity classification, thereby reducing costs and production time. We employed a dataset of 1500 images of palm oil fruits, meticulously categorized into three classes: raw, ripe, and rotten. The experimental setup involved training AlexNet and comparing its performance with a conventional Convolutional Neural Network (CNN). The results demonstrated that AlexNet significantly outperforms the traditional CNN, achieving a validation loss of 0.0261 and an accuracy of 0.9962, compared to the CNN's validation loss of 0.0377 and accuracy of 0.9925. Furthermore, AlexNet achieved superior precision, recall, and F-1 scores, each reaching 0.99, while the CNN scores were 0.98. These findings suggest that adopting AlexNet can enhance the palm oil industry's operational efficiency and product quality. The improved classification accuracy ensures that fruits are harvested at optimal ripeness, leading to better oil yield and quality. Reducing classification errors and manual labor can also lead to substantial cost savings and increased profitability. This study underscores the potential of advanced deep learning models like AlexNet in revolutionizing agricultural practices and improving industrial outcomes.

Sensitivity analysis of proppant transportation and settling in a hydraulic fracture

Hydraulic fracturing is the core technology for stimulating unconventional oil-gas reservoirs. The effective placement of proppant is directly linked to the success of the fracturing operation and reservoir productivity. In-depth investigations into the migration and settling behavior of proppant can provide a scientific foundation for optimizing fracturing parameters and fracture conductivity. Numerical simulations were employed to analyze the effects of many factors, such as the fracturing fluid velocity and viscosity, proppant size and density, and fracture surface roughness and injection position, on proppant migration within a planar fracture. Some interesting findings are: ① As the fracturing fluid velocity increases, the maximum height of the sand dune initially increases and then decreases, whereas the horizontal distance between the highest point of the sand dune and the entrance increases. ② Excessively high fracturing fluid viscosity results in a significant portion of the proppant remaining suspended within the fracturing fluid, thereby reducing the settling velocity and causing proppant to travel further. ③ Smaller proppant particles exhibit longer migration distances, making them more likely to travel further before settlement. ④ Proppant with lower densities exhibit superior transportability, with a larger proportion of the proppant remaining suspended in the fluid, resulting in more efficient fracture filling. ⑤ As the roughness of the fracture surface increases, the maximum height of the sand dune also increases, and fractures with rougher surfaces exhibit a greater sand dune area. ⑥ As the injection position decreases, the maximum height of the sand dune increases and then decreases.

Operational satellite cloud products need local adjustment – The Galapagos case of ecoclimatic cloud zonation

Operational satellite cloud products need local adjustment – The Galapagos case of ecoclimatic cloud zonation

Hybrid Numerical Weather Prediction: Downscaling GraphCast AI Forecasts for Downslope Windstorms

Abstract Preemptively managing electrical circuits during periods of elevated wildfire risk, such as downslope windstorms in complex terrain, necessitates accurate, high-resolution forecasts days in advance. Currently available high-resolution operational guidance is limited with respect to the forecasting period and/or spatial detail it can provide. As a consequence, many public utilities support their own NWP systems, primarily WRF-based, with initial conditions and boundary forcings supplied by operational global models such as the GFS. This study demonstrates pairing Google’s artificial intelligence (AI)-based GraphCast (GC) model, which operates on a 0.25° grid and produces global forecasts every 6 h, with a “last mile” WRF (WRF-LM) downscaling framework to refine GC’s spatial and temporal resolution for utility forecasting needs. We illustrate the potential of this system through a case study involving the December 2021 Marshall Fire windstorm in the Boulder, Colorado, area. Our results suggest that GC-initialized WRF forecasts are competitive with those guided by NCEP and ECMWF operational products, offering a promising hybrid approach for utility weather forecasting. Significance Statement Downslope windstorms are difficult to forecast with small errors potentially affecting the timing, magnitude, and location of concerning winds. Due to the elevated wildfire danger caused by these windstorms, accurate forecasts with lead times greater than 48 h are often needed to allow utilities time to prepare for the de-energization of transmission lines to reduce ignition risk. This paper examines methods of downscaling coarse synoptic-scale forecasts using the downslope windstorm associated with the 2021 Marshall Fire as our case study. A novel aspect of this work is the incorporation of machine learning weather prediction models and downscaling their forecasts using a high-resolution numerical model.

Enhanced efficiency and quality in wind turbine gearbox assembly: a new parallel assembly sequence planning (PASP) model

ABSTRACT This paper presents two major advances in parallel assembly sequence planning (PASP) for complex systems, specifically wind turbine gearboxes. The proposed time-cost-quality PASP hybrid model (PASP-TCQ) aims to enhance efficiency, reduce costs, and improve quality by aligning optimisation parameters with real-world demands. This model is designed to optimise complex assembly processes by addressing constraints on time, cost, and quality effectively. Additionally, we introduce a particle swarm-bacteria foraging optimisation (PSBFO) algorithm that integrates the global search capability of particle swarm optimisation (PSO) with the local optimisation strengths of bacteria foraging optimisation (BFO). Integrating PSBFO into PASP-TCQ achieves significant improvements: a 17% reduction in assembly time to 100 hours, a 10% cost reduction to $94,500, and a quality index improvement to 0.93. Statistical tests, including analysis of variance (ANOVA) and tukey’s honest significant difference (HSD), confirmed the PSBFO’s superiority, with significant gains of 8.44 units over BFO and 13.02 units over PSO in objective function values (p < 0.05). Extensive simulations on a 10 MW wind turbine gearbox validate the effectiveness of the PASP-TCQ and PSBFO, demonstrating their potential to enhance efficiency and productivity in industrial assembly operations.

Comparative Analysis of the Influence of Powder Factor and Energy Factor in Blast Design

Blast design is critical to blasting operations' cost efficiency and productivity. A key factor in estimating the cost efficiency of a blast design is the powder factor, which measures the volume of rocks fragmented by a unit explosive. Debate on charge density's influence on blast productivity has led to assumptions that the energy generated by explosives should be considered rather than the mass. Thus, this study aims to compare the influence of energy and powder factors on blast design and determine the most appropriate approach to blast planning. Data obtained from existing quarries were the burden, drilled-hole diameter, spacing, drilled-hole depth, mass of explosives, stemming, number of holes blasted, and the uniaxial compressive strength. These data were used to estimate the power and energy factors. Multivariable regression analysis was used to predict burden using drilled-hole diameter, uniaxial compressive strength energy factor, and powder factor. The results show that the burden prediction model using energy factor has a coefficient of determinants (R2) value of 0.8741, a standard error of 0.24, and a significance factor of 3.47E-09., while the prediction involving powder factor value is 0.8781, the standard error is 0.24, and the significance factor is 2.52E-09. The study concluded that the powder and energy factors influence blast design similarly. However, in this study, the use of powder factor is recommended because of its ease of estimation.

Economic supply chain analysis of Hilsa Fish Landing Centers (Maach Ghats) in Bangladesh: Operational insights and strategies

The Chandar Khal Puran Bery Hilsa Fish Landing Center in Raipur Upazila, Laxmipur District, Bangladesh is vital to the local fishing economy, as revealed by a comprehensive economic supply chain analysis conducted from January to December 2019. This study, employing a mixed-methods approach, integrates qualitative and quantitative data gathered through focus group discussions, semi-structured interviews, direct observations, and surveys with key stakeholders including fishers, traders, and other fisheries-dependent professionals. The research uncovered significant infrastructural challenges at the landing center, including inadequate facilities, substandard hygiene conditions, and limited storage and preservation capabilities. Despite these obstacles, the center serves a vibrant fishing community of over 2,400 individuals, primarily engaged in hilsa fishing. Hilsa constitutes an impressive 90% of total fish landings, with peak season yields reaching up to 2.0 tons per day. The research cataloged nine types of fishing gear and three types of craft, with mechanized kusha boats predominating. Hilsa pricing and distribution are influenced by size, weight, and market dynamics, with distinct supply chains for local and distant markets. The study delves into the complex interactions among fishers, mahajons, arotdars, and paikers, highlighting their interdependent roles in credit flow, fish distribution, and profit-sharing. To address the identified challenges, including environmental concerns and market inequities, the study recommends constructing cold storage facilities, enhancing market infrastructure, and implementing policy reforms. These recommendations aim to improve operational efficiency, product quality, and sustainability, guiding stakeholders toward supporting the long-term viability of the hilsa supply chain while fostering sustainable practices and economic growth in the region.

Rebuilding the clinical and operational acute care pharmacist productivity model: Leveraging electronic health record data and analytics at an academic medical center.

In an effort to expedite the publication of articles, AJHP is posting manuscripts online as soon as possible after acceptance. Accepted manuscripts have been peer-reviewed and copyedited, but are posted online before technical formatting and author proofing. These manuscripts are not the final version of record and will be replaced with the final article (formatted per AJHP style and proofed by the authors) at a later time. Acute care pharmacy practice continues to evolve, and useful pharmacist productivity models should reflect contemporary practice. The purpose of this project was to rebuild an acute care pharmacist productivity model to accurately capture and categorize the variable workload of acute care generalists and specialists using electronic health record (EHR) data reports and analytic capabilities. The acute care pharmacist productivity model was rebuilt with 5 variable workload drivers, including order verification, medication preparation verification (product check), clinical scoring, pharmacist-documented progress notes, and pharmacy intensity score-weighted patient admissions and patient days. Reports from the EHR database were used to capture all workload drivers. For each variable workload driver, more granular categories included in the reports were assigned time standards based on the work's complexity. Work output data, represented as units of service, were mapped by the user's job title (ie, generalist or specialist) and by the inpatient department. The rebuilt pharmacist productivity model leverages EHR data and standard documentation of patient care activities to capture workload and reflect clinical practice. The EHR-generated reports enabled construction of a comprehensive and sustainable productivity model for acute care pharmacists without the need for additional manual documentation for productivity purposes.

REDUCING LOST TIME DUE TO SHIFT CHANGES FROM 7.00 HOURS TO 4.60 HOURS FOR THE PERIOD OF FEBRUARY – JULY 2024 TO INCREASE PRODUCTION ACHIEVEMENT AND OVERBURDEN PRODUCTIVITY OF PC2000 AT PT ANTAREJA MAHADA MAKMUR JOBSITE MIFA BERSAUDARA

The mining industry is facing the dual challenge of meeting the demand for raw materials while minimizing environmental impact and ensuring sustainable practices. As urbanization accelerates, the industry is increasingly adopting advanced technologies and methodologies to enhance efficiency and reduce waste. The purpose of the research is to analyze and implement strategies that minimize the downtime associated with shift changes in order to enhance overall production efficiency and productivity of the PC2000 machinery at the specified jobsite. The study aims to quantify the impact of reducing shift change time on production outcomes and overburden productivity, ultimately contributing to improved operational performance within the defined timeframe. The research contributes to the field of operational efficiency by providing empirical insights into the relationship between reduced shift change times and enhanced production performance in heavy machinery operations. The findings may serve as a valuable reference for similar industries aiming to optimize shift management and improve overall operational productivity, thus contributing to best practices in resource management and efficiency enhancement. Furthermore, comparative studies with other mining operations that have successfully implemented similar changes could provide a broader perspective on best practices and innovative strategies, contributing to the overall advancement of the mining industry.

IMPROVEMENT OF WAREHOUSE TECHNOLOGY IN INTERNATIONAL FREIGHT TRANSPORTATION

The article analyzes the existing methods of building cargo handling technologies in warehouses. The chosen topic is quite relevant, since the use of outdated warehouse infrastructure and the use of outdated mechanization equipment in most cases, the operation of warehousing facilities according to outdated transport and technological schemes in the processing of goods in warehouses; the absence or insufficient use of modern information systems for planning, organizing, controlling and accounting for warehouse operations; lead to significant financial losses. To address the issues related to the technological processing of goods in warehouses, the author proposes a methodology for developing and implementing a technology for determining the rational number of new highly maneuverable and energy-efficient machines. Modern requirements for warehouse operations necessitate the development and implementation of new innovative vehicles that will increase the productivity of warehouse operations by increasing the level of maneuverability and productivity of such vehicles. As a result of the calculations, it was determined that the application of the proposed approach to determining the rational number of new highly maneuverable and energy-efficient machines will reduce the cost of technological cargo handling. When handling the maximum amount of cargo arriving at the warehouse, the total cost of handling such cargo using the proposed mechanisms will be less than when using existing forklifts. When implementing the developed methodology for determining the rational number of mechanisms for performing loading and unloading operations during the technological processing of goods in warehouses, it is possible to obtain a significant economic effect. Under certain conditions of technological handling of goods in warehouses, it is advisable to use simulation modeling of the processes included in the technological schemes of cargo handling. In this case, it is necessary to take into account the external influence of various factors that can lead to significant disruptions in the warehouse.

Key Parameters of the Roof Cutting and Pressure Relief Technology in the Pre-Splitting Blasting of a Hard Roof in Guqiao Coal Mine

The phenomenon of crushing the support of the hard roof of a coal seam occurs occasionally during the coal mining process. However, making the hard roof fall is difficult due to its good integrity and high strength. A vast area of unsupported, suspended roof can easily form in the goaf, inducing the hidden dangers of rock burst and coal and gas outbursts. A deep-hole pre-splitting blasting technique is used to fracture the roof and relieve the pressure exerted by the rigid roof in order to improve the caving of the hard roof and protect the stability of the roadway, ensuring safe and effective operational production of the 1127 (1) working face in Guqiao Coal Mine. By collecting field samples, the mechanical properties of relevant rock formations are ascertained. Combining numerical simulation with theoretical computation, a roof cutting pressure-relief scheme with a roof cutting height of 13.5 m and a roof cutting angle of 20° is selected. This scheme can decrease the peak vertical stress on the roadway roof from 22.01 MPa to 13.63 MPa compared to when roof cutting is not performed. By ensuring the effectiveness of roof cutting for pressure relief, this scheme can optimize the actual construction workload to a minimum. The study’s conclusions provide insightful information and can be used as a guide for future research on related technical topics.

Assessing the Environmental Kuznets Curve in Agricultural Transport: The Case of Brazilian Soy

Abstract This research investigates the Environmental Kuznets Curve (EKC) hypothesis within Brazil’s soy transportation sector. It aims to determine if CO2 emissions from soy transport show an “Inverted-U” or “N” shaped relationship with income from 2002 to 2017. Using a mathematical optimization model and System GMM for analysis, the study finds an “N” shaped EKC, indicating emissions initially increase, decrease, and then rise again with income. This suggests long-term environmental damage from sector growth, highlighting the need for targeted measures to mitigate emissions and enhance operational productivity, such as investments in intermodal transport and road efficiency improvements.

The Impact of Using Digital Devices in Achieving Sustainable Economic Growth

Today, the world lives in the era of the digital revolution, witnessing a tremendous development in digital technology and communications. This great development has affected all aspects of economic and social life, and one of the aspects that it highlights is digital accounting and its impact on sustainable economic growth. Digital accounting is defined as the economic system that relies primarily on digital technology and electronic networks in its economic operations. Digital accounting includes the use of the Internet, information and communications technology, mobile applications, e-commerce, big data analytics, artificial intelligence, and many other technologies that enhance economic transactions and information exchange. The contribution of digital accounting to sustainable economic growth has several positive aspects. For example, digital accounting enhances innovation and leadership by providing a suitable environment for developing new businesses and discovering business opportunities. It also increases productivity and efficiency in economic operations by using digital technology and data analytics to improve operations and reduce costs. Thanks to the digital revolution, e-commerce is being enhanced and the market is expanding. Companies and individuals can now transact and trade online, opening doors to access global markets and expanding growth and competitive opportunities. The basis of our research is based on a basic hypothesis, which is that the expansion of digital accounting contributes to enhancing sustainable economic growth. The importance of the research lies in the importance of the study in adopting the problem of digital transformation and its effective role in supporting digital transformation and achieving sustainable economic growth. To prove the hypothesis of the research, it was divided into three main sections and several conclusions were reached, the most prominent of which is that the transition to digital accounting is a crucial element in achieving sustainable economic growth in the modern era and a number of recommendations, the most prominent of which is that the transition to digital accounting requires integration between technology, economy and society and cooperation between the public and private sectors.

Mathematical modelling of the process line of a multifunctional fuel gas preparation unit (MGPU) to improve its accident-free operation

The purpose of the research is a comprehensive study of the process line of a multifunctional fuel gas preparation unit (MGPU), creation of a mathematical model capable of predicting and controlling gas dryness in the process of MGPU operation, as well as determination of optimal parameters of the unit operation to improve accident-free operation and high productivity of the equipment using treated gas.Methods. Mathematical modeling with the use of multiple regression model for prediction of dew point temperature and its influence on accident-free operation of the plant was carried out. The adequacy of the model was confirmed by the coefficient of determination and Fisher's criterion. An analysis of the limiting factors for the accident-free operation of the MGPU, including temperature, pressure and gas composition, is also presented. The accuracy of numerical modeling of the trouble-free operation of the MUPG process line based on the developed model of multiple regression of gas dryness was estimated using paired correlation coefficients and elasticity coefficients. Results. In the course of the work, the accident-free operation of the process line of MGPU was modeled based on the developed multiple regression model of gas dryness. The following interpretation of the model parameters is possible: increasing factor X1 by 1 leads to an average decrease in Y by 0.279; increasing factor X2 by 1 leads to an average increase in Y by 0.46; increasing factor X3 by 1 leads to an average increase in Y by 0.000418; increasing factor X4 by 1 leads to an average increase in Y by 13.288; increasing factor X5 by 1 leads to an average decrease in Y by 13.337; increasing factor X6 by 1 leads to an average decrease in Y by 0. According to the maximum coefficient β2=0.384 we can conclude that the factor X2 has the greatest influence on the result Y. Statistical significance of the equation was tested using the coefficient of determination and Fisher's criterion. According to the assessment of the accuracy of numerical modeling of the trouble-free operation of the MUPG process line, based on the developed model of multiple regression of gas dryness, a strong linear relationship between X1 and Y, a low linear relationship between X2 and Y, a low linear relationship between X3 and Y, a moderate linear relationship between X4 and Y, a moderate linear relationship between X5 and Y, moderate linear relationship between X6 and Y.Conclusion. It was found that in the investigated situation: "in the possibility of prediction and control of gas dryness: justification of the hypothesis about the possibility of prediction of dew point temperature and its influence on accident-free operation" the parameters of the model are statistically significant.

Survive the economic downturn: Operating flexibility, productivity, and stock crash

AbstractOperating flexibility supports a firm's resilience strategy during challenging times by enabling them to promptly cut down operating costs associated with unproductive resources. We employ a real options model to formalize this insight. Our empirically grounding analytics motivate a firm‐level proxy for downscale operating flexibility (FLEX), which effectively captures the adjustment frictions across different contexts of firms' operations. Using U.S. data between 1961 and 2020, we show that operating flexibility mitigates the risk of stock price crashes, especially during periods of economic recession. Consistent with the loss‐curtailment mechanism, the operating flexibility effect is more pronounced for firms with lower productivity/profitability or higher operating leverage and is further amplified during longer and more severe recessions. Managers may avail themselves of our well‐tested empirical measure of operating flexibility to guide their efforts in building a more resilient operations structure.

Smart Buff Manager: A Co-Designed Mobile Application for Enhancing Buffalo Farm Management in Thailand

This study focuses on the co-design and development of a smartphone application, the "Smart Buff Manager," especially for smallholder buffalo farmers in Thailand. The application aims to meet the needs of buffalo farmers by providing an effective and user-friendly tool for record-keeping and farm management. We employed the ADDIE (Analysis, Design, Development, Implementation, and Evaluation) instructional design method to ensure the application addressed the practical needs and preferences of the farmers. The primary features of the app include record-keeping, health monitoring, and breeding management, focusing on various issues faced by smallholder farmers. The user-centered design method provides an intuitive interface that gathers feedback from users, resulting in increased satisfaction and a higher tendency to recommend apps. Implementation of the app significantly improved farm management by enhancing operational efficiency, productivity, and animal welfare. Users of the application reported much greater compliance with vaccination schedules than non-users, potentially improving herd health and long-term production. This case study confirms the potential of mobile technology to improve farm operations and decision-making while also highlighting the essential role of involving end-users in the design process to develop appropriate and effective digital farming systems.