Logistic and Operation Management Research (LOMR)

The Effect of Power Outages on Small- and Medium-sized Enterprises in South Africa

2024-11-08T18:50:23+07:00

The frequent power outages in South Africa have had a detrimental impact on the nation’s economy overall and on small and medium-sized enterprises in particular. The goal of this study is to determine how load shedding affects small- and medium-sized enterprises (SMEs) in South Africa. The research design pinpoints practical ways to reduce the negative effects of power outages on these companies. This study adopts a quantitative research approach. Fifty SME owners and managers from the Mahikeng local municipality were selected to represent the entire country using a descriptive single cross-sectional design and a non-probability convenience sample technique. The Statistical Package for the Social Sciences (SPSS), Version 27.0, was used to evaluate the collected data. The findings of the study indicate that load shedding has caused significant operational problems for South African small enterprises since 2008. As a result, to maintain output, many companies have had to make adaptations, including establishing backup power solutions and moving to manual labor. SMEs continue to face significant challenges because of the high cost of backup systems, the increase in criminal activity during blackouts, and the requirement for open and positive customer relations, despite efforts to mitigate these consequences. The present study underscores the criticality of SMEs’ investments in crime prevention strategies and dependable backup power systems in Mafikeng and throughout South Africa to mitigate the adverse impacts of ongoing load shedding on commercial operations and economic stability. It is underlined that maintaining client confidence and ensuring continued business performance despite these challenges requires effective customer relationship management.

Red-Green-Blue (RGB) Image Classification Using Deep Learning To Predict Sugarcane Crop Age

2024-11-08T18:37:56+07:00

Traditional sugarcane growth monitoring methods are time-consuming and error-prone. This study investigated the use of deep learning to automate and enhance the accuracy of sugarcane growth stage classification. The study develops deep learning-based system that leverages high-resolution drone imagery for precise sugarcane age classification, thereby enabling accurate identification of the growth stages. High-resolution drone images were captured at various stages of sugarcane growth and were stitched together to form a comprehensive dataset. Segmentation of isolated areas of interest for analysis. The ResNet-50 deep learning model, enhanced with an additional fully connected layer, was used to classify the growth stages. The model was trained on cropped image sections, and its performance was compared to other deep learning architectures, such as GoogLeNet and VGG, to evaluate its accuracy. The ResNet-50 model outperformed other architectures, achieving 91% accuracy in classifying growth stages, demonstrating its effectiveness in agricultural image analysis and its potential to advance precision agriculture. This study is the first to apply deep learning to sugarcane age classification using high-resolution drone imagery, and it sets a new benchmark for agricultural image analysis. The dataset containing drone images from specific sugarcane fields may limit the model’s generalizability to different regions and environmental conditions.

Assessing the Status and Challenges of Sole Proprietor Garment Businesses: Insights for Operational Enhancement

2024-11-17T23:40:26+07:00

A sole proprietorship is without a doubt, the simplest form of business organization. Garment business on the other hand is considered as one of the most popular ventures among women entrepreneurs in the Philippines. This study aimed to determine the status, issues and concerns of sole garment business in Taguig City to serve as basis for a proposal towards enhanced operations. It also attempted to determine the issues and concerns encountered in women entrepreneurs and their employees in the course of running the business. The descriptive method of research was used and it involved twelve (12) proprietors and fifty (50) employees as respondents. The data were analyzed and interpreted using the frequency, percentage, weighted mean and t-test. Results revealed that the respondents strongly agree on objective of the sole garment business, personnel, capital investments, operations requirements, wages and benefits, and supply of raw materials as vital factors for enhanced operations of sole garment business. The null hypothesis that there is no significant difference between the perceptions of the proprietors and employees on the status of sole garment business was accepted when it came to personnel, capital investments, operations requirements and wages and benefits. However, such null hypothesis was rejected when it came to objectives, raw materials supply, and equipment. As to issues and concerns encountered in sole garment business, the need for technologically advanced equipment came first, management style was ranked second, keeping up with fast fashion trend came in third while supply of raw materials was ranked fourth and inadequacy of business capitals came in the fifth rank.

Tool Wear Monitoring in Hard Turning Using Sensor Fusion: An analytical approach

2024-11-17T19:23:13+07:00

At present, the life expectancy of tool has become a vital aspect in the manufacturing industries, especially where materials with high hardness have more importance. Nowadays hard steel has been widely used for manufacturing of commercial parts in military aircrafts, car systems and hydraulic tools, etc. Manufacturing industries, mainly concentrates on mass production of the products with precision and accuracy. In such cases the continuous machining may weaken the tool causing tool wear which ultimately affects the quality with production rate. To avoid such an unwanted scenario, different tool wear prediction techniques have been introduced which uses cutting force signals or average chip-tool interface temperature or surface roughness signals, etc. According to the literature, different techniques are available for the pre-judgment of the tool wear that shows variation in the accuracy of prediction. Sensor fusion technique can be employed to overcome this problem by combining data from different sensors intelligently to improve the process. Sensor fusion uses this combined data to correct deficiencies of individual sensors and predict the tool wear accurately which compensate for the sudden breakage of the tool in real life applications. In this paper, a new approach of tool wear prediction has been introduced to correlate different available sensor by using sensor fusion technique. Also, a mathematical approach has been derived for the sensor fusion purpose. The experimentation has been carried out using coated carbide inserts on 55 HRC hardened steel.

Advancing Sugarcane Farm Management Through NDVI-Based Color Mapping and Drone Imaging

2024-11-26T19:04:32+07:00

Precision agriculture has emerged as a key strategy for boosting crop productivity and optimizing resource use. This study leverages advanced imaging and machine learning to enhance the management of sugarcane farms. Using drones, high-resolution RGB images of sugarcane fields are captured and transformed into multispectral images through a Generative Adversarial Network (GAN), revealing critical spectral data for plant health assessment. The Normalized Difference Vegetation Index (NDVI) is derived from these multispectral images and serves as a vital measure of vegetation health. This NDVI data, combined with farmer-reported yield information, creates a comprehensive dataset linking NDVI values to actual crop yields. To predict sugarcane yield from NDVI values, we trained a feedforward neural network on this integrated dataset. The proposed method not only enhances prediction accuracy but also provides valuable insights into the connection between NDVI metrics and crop performance. The model was validated using individual field images, enabling precise yield predictions for different field sections. This study highlights the effectiveness of integrating drone imagery, machine learning, and remote sensing in precision agriculture. The combination of NDVI data with yield information provides a robust tool for optimizing sugarcane production, improving farm management decisions, and advancing agricultural sustainability.

Optimization of Work Posture at PT. XYZ: Analysis of the Rapid Entire Body Assessment (REBA) Method to Improve Health and Productivity

2024-11-08T18:23:16+07:00

The study at PT. Fenyen Agro Lestari identified poor work postures that affect worker health and productivity. Using the REBA method, the boiler department had the highest risk (score 11), followed by the maintenance (score 6) and Laboratory (score 4). Immediate ergonomic improvements are recommended to reduce musculoskeletal disorders (MSDs). The purpose of this research is to analyze the impact of poor working posture on workers' musculoskeletal health in various departments of PT. Fenyen Agro Lestari, Indonesia and recommend ergonomic improvements to reduce the risk of MSDs. The methodology involves REBA for postural assessment, collecting musculoskeletal complaints via questionnaires, and analyzing data to recommend ergonomic improvements. The findings revealed that poor postures cause significant musculoskeletal disorders, especially in the Boiler department, highlighting the need for ergonomic improvements. This study is novel in that it applies the Rapid Entire Body Assessment (REBA) method to a specific context within PT. Fenyen Agro Lestari, provides tailored ergonomic recommendations for multiple departments and addresses gaps in existing research on posture-related musculoskeletal disorders in the industry. The study’s limitations include a focus on only three departments, potential observer bias, and reliance on subjective questionnaire data, which may not fully capture all musculoskeletal issues or account for variations in individual worker experiences.