1,720,975 research outputs found
Research Trends and Outlooks in Assembly Line Balancing Problems
Full text linkThis paper presents the findings from the survey of articles published on the assembly line balancing problems (ALBPs) during 2014-2018. Before proceeding a comprehensive literature review, the ineffectiveness of the previous ALBP classification structures is discussed and a new classification scheme based on the layout configurations of assembly lines is subsequently proposed. The research trend in each layout of assembly lines is highlighted through the graphical presentations. The challenges in the ALBPs are also pinpointed as a technical guideline for future research works
Practical Implementation of Lean Management Techniques and Ergonomic Consideration to Improve Manual Assembly Process During the COVID-19 Crisis
Full text linkThis research focused on the cycle time reduction of the automotive audio monitor base frame assembly process in a small electronic parts manufacturing company through the application of the Lean and ergonomics approaches. In recent years, the company has faced the problem of not being able to assemble products to meet customers’ orders due to the need to lay off some skilled workers to survive during the COVID-19 crisis. This resulted in a severe loss in customer goodwill and confidence. The improvement process began with a review of the current assembly workflow as well as the detailed hand and arm movements of workers. Then, to determine the as-is operational procedure and average cycle time of the assembly process, a series of videos were recorded and playback in slow motion. Lean management techniques, such as 7 wastes and 5 Whys, were employed to identify the potential root causes of the problems. In addition, the ECRS (eliminate, combine, rearrange and simplify) techniques of Lean management in combination with the ergonomics principles were applied to modify the operational procedure and the postures and movements of the workers. The workspace and environmental conditions were also adjusted to enable more efficient workers’ operations. The result demonstrated that such an approach could help reduce the cycle time of the assembly process to achieve the predefined target
Facility Location Placement Optimisation for Bagged Cement Distribution During the COVID-19 Pandemic
Full text linkThis study was based on a challenge that one of Thailand's cement companies encountered in 2021 as a result of fierce competition in the Northeastern region and falling market share during the COVID-19 pandemic. Without a doubt, the management of supply chains played an essential role in this issue. As a result, this research addressed the company's distribution strategy by attempting to determine a new location for the distribution centre to achieve two conflicting objectives at the same time, namely, minimising total transportation cost and maximising service level (delivery lead time reduction). For the problem at hand, a linear programming model was developed. Once different options were identified, the pros and cons of each approach were evaluated, and then the distribution strategy was altered to meet the actual conditions. It was discovered that changing distribution centres in some locations was a successful strategy for shortening delivery lead times with an opportunity to achieve a 22% improvement in service level while still controlling transportation expenses prior to arriving at the end customers not beyond the target at 15% increased from the current strategy
Opportunities and Barriers to the Adoption of Blockchain-Based Games in an Online Gaming Company in Thailand
Full text linkThis research aims to explore potential opportunities and barriers related to the adoption of blockchain-based games in an online gaming company in Thailand. The identified opportunities are classified under the benefits framework proposed by Shang and Seddon [1], and the identified barriers are classified under the Technology-Organisation-Environment (TOE) framework. Based on the knowledge and experience of experts in the case company, all the opportunities and barriers are then assessed using the concept of Failure Mode and Effect Analysis (FMEA), which is further improved using the Analytic Hierarchy Process (AHP) by assigning a relative weight to each element of the FMEA before being used to find the priority number (PN). Next, the Pareto principle is applied to reveal the critical opportunities and barriers. As a result, a total of 21 critical opportunities are revealed and categorised into 5 dimensions: 4 operational opportunities; 3 managerial opportunities; 7 strategic opportunities; 4 infrastructure opportunities; and 3 organisational opportunities, and a total of 19 critical barriers are revealed and categorised into 3 dimensions: 7 technological barriers; 6 organisational barriers; and 6 environmental barriers. The TOWS matrix is then used to formulate possible strategies for the case company to exploit the opportunities and address the barriers to the adoption of blockchain-based games. As a result, a total of 7 SO, 12 ST, 5 WO, and 1 WT strategies are proposed. Based on the PNs and the interview with experts, a roadmap including short-, medium-, and long-term action plans is also developed to facilitate the adoption of blockchain-based games
Leveraging Partner Country Factors in Deep Learning for Thailand’s Forecasted Inflation Accuracy Enhancement
Full text linkThis paper focuses on improving the accuracy of headline inflation forecasts in Thailand. By evaluating the performance of deep learning models, time series forecasting models, and hybrid models in 1-, 3-, 6-, and 12-month advance forecast periods are investigated. In addition, the efficacy of including partner countries' inflation variables in the model is evaluated. There is a comparative analysis of various models, including ANN, RNN, LSTM, VAR, the hybrid model (VAR-ANN), and the BOTMM benchmark model of the Bank of Thailand. This study aimed to identify the most efficient model and demonstrate the impact of including partner countries' inflation on forecast accuracy. The results reveal that the hybrid model (VAR-ANN) consistently outperforms other models over several forecast periods, showing its superiority in capturing inflation trends. Specifically, the hybrid model (VAR-ANN) shows an average RMSE improvement of 50.36% over the BOTMM benchmark model from 2020 to 2022, with performance improvements of 52.94% in 2020, 56.56% in 2021, and 47.25% in 2022. In addition, the inclusion of partner countries' inflation significantly increases the accuracy of the predictions. These results are helpful for policymakers and practitioners working on inflation forecasts and emphasize the practical advantages of the hybrid model for enhancing prediction accuracy for Thailand's economic indicators
An Integration of Project Management Body of Knowledge and Project Management Information System to Improve On-time Deliverable of Liquefied Natural Gas Station Construction Projects
Full text linkThe objective of this study is to improve the liquefied natural gas station construction project to achieve on-time delivery. Diverse tools and techniques are integrated to make various interrelated activities in the project occur effectively as planned with less cost, suggested by the Project Management Body of Knowledge (PMBOK) guideline and the Project Management Information System (PMIS). To implement the PMIS along with the PMBOK, the project management software and Internet of Things (IoT) are utilized for real-time long-distance monitoring and control of the project. The proposed approach is implemented at a real demonstration project. The results reveal that the proposed approach is quite effective, which help increase the number of projects completed on schedule from 75% in the last year to 100% this year. Moreover, the implementation of the PMIS also results in substantial reductions in the employment allowance for routine site inspections and the travel expense for round-trip vehicles travelling from the company to the site
Real-Time Induction Motor Health Index Prediction in A Petrochemical Plant using Machine Learning
Full text linkThis paper presents real-time health prediction of induction motors (IMs) utilised in a petrochemical plant through the application of intelligent sensors and machine learning (ML) models. At present, maintenance engineers of the company implement time-based and condition-based maintenance techniques in periodically examining and diagnosing the health of IMs which results in sporadic breakdowns of IMs. Such breakdowns sometimes force the entire production process to stop for emergency maintenance resulting in a huge loss in the company’s revenue. Hence, top management decides to switch the operational practice to real-time predictive maintenance instead. Intelligent sensors are installed on IMs to collect necessary information related to their working statuses. ML exploits the real-time information received from intelligent sensors to flag abnormalities of mechanical or electrical components of IMs before potential failures are reached. Four ML models are investigated to evaluate which one is the best, i.e. Artificial Neural Network (ANN), Particle Swarm Optimization (PSO), Gradient Boosting Tree (GBT) and Random Forest (RF). Standard performance metrics are used to compare the relative effectiveness among different ML models including Precision, Recall, Accuracy, F1-score, and AUC-ROC curve. The results reveal that PSO not only obtains the highest average weighted Accuracy but also can differentiate the statuses (Class 0 – Class 3) of the IM more correctly than other counterpart models
Solving Many-Objective Car Sequencing Problems on Two-Sided Assembly Lines Using an Adaptive Differential Evolutionary Algorithm
Full text linkThe car sequencing problem (CSP) is addressed in this paper. The original environment of the CSP is modified to reflect real practices in the automotive industry by replacing the use of single-sided straight assembly lines with two-sided assembly lines. As a result, the problem becomes more complex caused by many additional constraints to be considered. Six objectives (i.e. many objectives) are optimised simultaneously including minimising the number of colour changes, minimising utility work, minimising total idle time, minimising the total number of ratio constraint violations and minimising total production rate variation. The algorithm namely adaptive multi-objective evolutionary algorithm based on decomposition hybridised with differential evolution algorithm (AMOEA/D-DE) is developed to tackle this problem. The performances in Pareto sense of AMOEA/D-DE are compared with COIN-E, MODE, MODE/D and MOEA/D. The results indicate that AMOEA/D-DE outperforms the others in terms of convergence-related metrics
Operational Process Improvement for Outpatient Services at a Private Medium-Sized Hospital
Full text linkThis study is dedicated to enhancing the efficiency and efficacy of a medium-sized community hospital's services, which have limited space and experience a high volume of visits from diverse patient types with distinct service processes. The hospital challenges meeting the waiting time Key Performance Indicator (KPI) primarily from internal factors. The research methodology involves a comprehensive approach, encompassing the collection of qualitative and quantitative data, interviews with hospital staff, on-site observations, and a detailed examination of processing times at each step within the outpatient department. Upon data analysis, the study identifies and categorises key issues within the current Outpatient Department (OPD). These issues are encapsulated in three main categories, i.e., the unavailability of doctors during critical periods, insufficient staff for document delivery, and ineffective communication. Addressing the imperative of minimising patient system dwell time, a key competitive objective in the healthcare sector, this article is dedicated to identifying and implementing tools within a Lean framework. Tools such as root cause analysis, Poka-Yoke, and visual control are identified and implemented to optimise outpatient operations. Using simulation software, quantitative data is utilised to simulate and evaluate the outpatient process. The simulation results underscore significant periods during which doctors are absent, and an imbalance in workforce distribution emerges as a bottleneck. From a Lean perspective, recommendations are formulated to address these issues, emphasising the need for schedule balancing and minimising batch size through a proposed document method. The efficacy of these recommendations is subsequently validated using the simulation models. Through a series of optimisations and experiments, the average time in the system of social security patients has demonstrated a noteworthy reduction from 1,999 seconds to 1,820 seconds, reflecting an 8% improvement
Demand Forecasting and Ordering Policy of Fast-Moving Consumer Goods with Promotional Sales in a Small Trading Firm
Full text linkThis research focuses on enhancing inventory management for fast-moving consumer goods (FMCGs) with promotional sales in a small trading company, particularly high-end items with fluctuating demand patterns. The analysis revealed that promotional campaigns led to an average demand increase of 60.44% for WM 85ML, and 161.76% for SW 85ML, highlighting the importance of including these variables in demand forecasting models. The research aims to determine an effective forecasting method for the company and develop an improved purchasing strategy. The methodology encompasses a comprehensive review of the existing system, problem investigation, solution proposal, and result analysis. Quantitative time-series forecasting methodologies specifically tailored to such luxury FMCGs were introduced including Exponential Smoothing and Holt-Winters’s Additive and Multiplicative forecasting. The application of these methods has led to a significant enhancement in forecast accuracy, with an approximate 90% improvement. The research's pivotal contribution is the development of a hybrid order policy named “Periodic Review with Safety Stocks and Reorder Point,” which merges a fixed-order quantity model with a fixed-time period model. This hybrid approach has practical implications for maintaining efficient inventory levels, enabling continuous promotional activities, and potentially reducing the company's inventory costs by approximately 30%
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