252 research outputs found
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Inventory Control Model of Beef for Rendang Products
Full text linkThe definition and function of inventory management reveal the importance of modern industry in formulating policies that regulate the supply of raw materials, semi-finished products, and finished products. Unfortunately, most companies fail to consider the characteristics of their raw materials when determining inventory strategies. Raw materials that are perishable are those that consider their service life and storage time. Inventory management plays a crucial role in supply chain management, especially for perishable raw materials, such as food products. PT X, a food business in Padang, experienced difficulty in meeting the demand for rendang products due to a lack of raw materials. Therefore, this study aims to develop an inventory management model that takes into account the perishable raw materials' expiration time. The model development consists of three stages: model development design, inventory model formulation, and model testing. The proposed model resulted in a storage time interval of five days and an optimal order quantity of 34 kg of meat with a safety stock of 14 kg. Implementing this model led to lower total inventory costs for PT X than the actual conditions of the company. The total inventory cost obtained using this model is Rp279,797,822. This study emphasizes the importance of considering the characteristics of raw materials in determining inventory strategies to optimize inventory management effectively and efficiently. The study's findings can serve as a reference for other food businesses encountering similar inventory management challenges in the perishable food industry
Application of the Total Productive Maintenance to Increase the Overall Value of Equipment Effectiveness on Ventilator Machines
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Technical Evaluation and Financial Analysis of a Retrofitting Investment Project for Production Machinery in a Cement Plant
Full text linkIn today's rapidly evolving industrial landscape, businesses are increasingly challenged to strike a balance between enhancing productivity and maintaining product quality. Company X, a renowned cement manufacturer in Indonesia, relies heavily on four key raw materials, among which clay is particularly crucial for the raw mix. Recent trends have shown a decrease in the Al2O3 composition of clay, necessitating adjustments in clay capacity to uphold quality standards. A thorough technical evaluation of the plant highlighted that a significant number of critical machines, totaling 17, were operating with mechanical availability below the desired threshold. Additionally, a utility analysis pinpointed a shortfall in meeting the required clay tonnage, leading to the identification of machines that would benefit from retrofitting. The financial implications of this initiative were substantial, with the initial investment for the upgrades and subsequent operational costs in the first year being considerable. Yet, this expenditure was offset by a notable profit in the first year post-retrofitting. Key financial metrics further underscored the project's viability: a highly favorable Net Present Value (NPV), an impressive Internal Rate of Return (IRR), a rapid Payback Period (PP), and a significant Profitability Index (PI). These parameters, derived from an exhaustive analysis, clearly support the strategic decision to invest in retrofitting the production machinery at Company X's cement plant, illustrating the project's feasibility and the prospective benefits of this investment
Dynamic Scoring and Costing in the Orienteering Problem: A Model Based on Length of Stay
Full text linkIn today's travel and tourism landscape, the role of travel agents has become increasingly complex as they are challenged to explore a variety of potential destinations. More specifically, the complicated task of planning itineraries that truly satisfy travellers puts travel agents in a crucial role, increasing the complexity of itinerary planning. This complexity is compounded not only by the multitude of possible destinations, but also by non-negotiable constraints such as cost and time. To address these challenges, the orienteering problem represents a fundamental mathematical model that provides a theoretical basis for understanding the nuanced difficulties faced by travel agents.This study ventures into a novel iteration of the orienteering problem, with a particular focus on optimizing travel satisfaction based on length of stay. A notable aspect of this variant is the inclusion of time and cost constraints in the route determination process. Using an integer programming model, the satisfaction scores for each location are described by a diminishing returns function linked to length of stay, while the costs associated with each location follow a linear function influenced by the same parameter. The application of this model is in a hypothetical scenario with 32 nodes, with the calculations facilitated by the FilMINT solver. A sensitivity analysis examines time and cost constraints and shows their decisive influence on the optimization of travel routes. The results of this research contribute significantly to a strategic framework and provide travel agencies with the opportunity to create itineraries that not only meet practical limits but, more importantly, increase traveller satisfaction
The Effect of Sex Differences and Experience of Using Virtual Reality on Presence
Full text linkPresence greatly affects user experience and comfort when using virtual reality (VR). Presence is often associated with personal factors such as sex differences and experience using the instrument. There is a research gap related to presence judging by several studies, so it is an interesting topic for further study. This research aims to identify the effect of sex differences and experience using VR on presence. This study used two approaches namely subjective indicators by employing an Igroup Presence Questionnaire (IPQ) and objective indicators in the form of heart rate (HR) and task scores. The study made use of Kruskal-Wallis and MANOVA to determine whether there is an effect of sex differences and experience in using VR on presence. This study found that the sex variable affects a person's presence when playing VR, especially spatial score on the IPQ test, where women have a higher marginal means value than men. Another finding is that the experience of playing VR affects the delta heart rate, with the result that someone with no experience using VR is higher than those who have used VR before
Interrelationship Performance Indicators Model of Agile Supply Chain Management in Palm Oil Industry
Full text linkThis paper tries to model agile supply chain management performance indicators in the palm oil industry. The interpretative Structural Modeling (ISM) method is used to find the relationship between these indicators. The ISM stages begin with identifying indicators, compiling contextual relationships, compiling reachability matrices, compiling level partitions, compiling digraphs, and compiling ISM models. Then MICMAC analysis is used to group each of these indicators into four categories based on their driving power and dependence power. In this study, 16 hands of agile supply chain management in the palm oil industry were obtained, of which the four-level ISM model could be constructed. Two indicators are at level 4, six hands are at level 3, three indicators are at level 2, and five indicators are at level 1. Meanwhile, through MICMAC analysis, five indicators are found in the independent indicators category, six hands are in the linkage indicator category, four indicators are included in the dependent indicator category, and one indicator is in the autonomous indicator category. This research can be used by managers in the palm oil industry who want to increase agility in their supply chain. In general, indicators at level 4 can affect indicators at level 3, and so on. So that management can start fixing the indicators at level 4 first. In addition, indicators that have a driving power value in MICMAC analysis can be prioritized to improve their performance
AVOA and ALO Algorithm for Energy-Efficient No-Idle Permutation Flow Shop Scheduling Problem: A Comparison Study
Full text linkGlobal energy consumption is a pressing issue and is predicted to continue increasing between 2010 and 2040. Among the various sectors, the industrial sector, particularly manufacturing, is the main driver of this increase. To effectively address this growing problem and support energy conservation efforts, reducing idle time on production-related machines is critical. The No-Idle Permutation Flow Shop Problem (NIPFSP) and, indirectly, the need to reduce energy consumption in manufacturing processes are the driving forces behind this study. The African Vultures Optimization Algorithm (AVOA) and the Ant Lion Optimizer (ALO) are two novel meta-heuristic algorithms designed to achieve this goal. The effectiveness of both AVOA and ALO was rigorously evaluated across three distinct scenarios: small, medium, and large. Statistical analysis, in the form of independent sample t-tests, was employed to compare the performance of these algorithms. We found that, while both algorithms yielded similar results in the small case, AVOA demonstrated a superior capability in optimizing the NIPFSP in the medium and large cases and, consequently, in curbing energy consumption. This implies that AVOA offers a more promising approach to addressing energy consumption concerns in the manufacturing sector, particularly in scenarios involving medium- to large-scale production processes. The implementation of such innovative meta-heuristic algorithms holds the potential to significantly contribute to global energy conservation efforts while enhancing the efficiency of industrial operations
Optimization of Gear Manufacturing for Quality and Productivity
Full text linkMulti-objective optimization in manufacturing can effectively be solved using Multicriteria decision-making (MCDM) techniques. This paper presents the implementation methodology of the Fuzzy-MOORA hybrid technique for multi-objective optimization in laser machining of stainless-steel gears. Further, simultaneous optimization of gear quality and process productivity have been reported. Four important laser parameters, i.e., laser power, cutting speed, focal position, and gas pressure, have varied during twenty-nine experiments to machine gears by a laser process. The quality of miniature gear was measured in terms of average surface roughness, mean roughness depth, and dimensional deviation. The productivity of the laser machining process was estimated via material removal rate. An optimum set of laser machining parameters obtained after Fuzzy-MOORA optimization is laser power 2000 W, cutting speed 3 m/min, focal position -2.5 mm, and gas pressure 16 bar. This work encourages researchers and scholars to make further attempts using such MCDM techniques to develop intelligent processes in industrial and manufacturing engineering
Physiological Signals as Predictors of Mental Workload: Evaluating Single Classifier and Ensemble Learning Models
Full text linkWith a growing emphasis on cognitive processing in occupational tasks and the prevalence of wearable sensing devices, understanding and managing mental workload has broad implications for safety, efficiency, and well-being. This study aims to develop machine learning (ML) models for predicting mental workload using Heart Rate Variability (HRV) as a representation of the Autonomic Nervous System (ANS) physiological signals. A laboratory experiment, involving 34 participants, was conducted to collect datasets. All participants were measured during baseline, two cognitive tests, and recovery, which were further separated into binary classes (rest vs workload). A comprehensive evaluation was conducted on several ML algorithms, including both single (Support Vector Machine/SVM and Naïve Bayes) and ensemble learning (Gradient Boost and AdaBoost) classifiers and incorporating selected features and validation approaches. The findings indicate that most HRV features differ significantly during periods of mental workload compared to rest phases. The SVM classifier with knowledge domain selection and leave-one-out cross-validation technique is the best model (68.385). These findings highlight the potential to predict mental workload through interpretable features and individualized approaches even with a relatively simple model. The study contributes not only to the creation of a new dataset for specific populations (such as Indonesia) but also to the potential implications for maintaining human cognitive capabilities. It represents a further step toward the development of a mental workload recognition system, with the potential to improve decision-making where cognitive readiness is limited and human error is increased
Order Allocation Model Considering Transportation Alternatives and Lateral Transhipment
Full text linkIntense competition among companies encourages them to provide the best quality of products in competitive price. It is important for company to manage supply chain properly in order to achieve that. Selecting the best reliable supplier is the key to reduce purchasing cost, increase customer satisfaction and improve the competitive ability. In this study, we develop an order allocation model in multi echelon environment which includes supplier, manufacturer, and retailer. We consider transportation alternatives for the shipment from supplier to manufacturer and also the shipment from manufacturer to retailer. This model allows lateral transshipment between retailers. A Mixed Integer Linear Programming (MILP) is used to model the system. Sensitivity analysis is conducted at the end of the research. The result shows that the retailer demand, lead time, material variable price are sensitive to the objective function while the transportation costs from supplier to manufacturer, from manufacturer to retailers, and between retailers are not sensitive to the objective function. Retailer demand parameter is also sensitive to all decision variables. The transportation cost from supplier to manufacturer, material prices, and lead time are sensitive to the order allocation from manufacturer to supplier, while transportation cost from manufacturer to retailers and transportation cost between retailers are sensitive to the allocation of product sent from the manufacturer to retailers and the allocation of product sent between retailers