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ศ. ดร.ปารเมศ ชุติมา
- 8th Floor of Engineering 4 Bldg., Room 811
- +66-2218-6847
- parames.c@chula.ac.th
Education
Ph.D. Manufacturing Engineering and Operations Management
University of Nottingham, England, 1995
M.Eng. Electrical Engineering
Chulalongkorn University, Thailand, 1989
M.Eng. Industrail Engineering & Management
Asian Institute of Technology, Thailand, 1988
B.Eng. Electrical Engineering (Honours Degree)
Chulalongkorn University, Thailand, 1986
Expertise
Engineering Management
Manufacturing & Service Systems
Publications
2024
Techin Arnonwattana, Parames Chutima
Leveraging Partner Country Factors in Deep Learning for Thailand’s Forecasted Inflation Accuracy Enhancement Journal Article
In: Engineering Journal, vol. 28, no. 6, pp. 37 – 58, 2024, ISSN: 01258281, (Cited by: 2; All Open Access, Gold Open Access).
@article{Arnonwattana202437,
title = {Leveraging Partner Country Factors in Deep Learning for Thailand’s Forecasted Inflation Accuracy Enhancement},
author = {Techin Arnonwattana and Parames Chutima},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85206901773&doi=10.4186%2fej.2024.28.6.37&partnerID=40&md5=1e8706d9dbe441cbd24dd6cf69f270f8},
doi = {10.4186/ej.2024.28.6.37},
issn = {01258281},
year = {2024},
date = {2024-01-01},
journal = {Engineering Journal},
volume = {28},
number = {6},
pages = {37 – 58},
publisher = {Chulalongkorn University, Faculty of Fine and Applied Arts},
abstract = {This 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. © 2024, Chulalongkorn University, Faculty of Fine and Applied Arts. All rights reserved.},
note = {Cited by: 2; All Open Access, Gold Open Access},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
This 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. © 2024, Chulalongkorn University, Faculty of Fine and Applied Arts. All rights reserved.
Narongchon Amapat, Parames Chutima
High-Density Polyethylene Film Price Forecast in Southeast Asia Market with Deep Learning Journal Article
In: Engineering Journal, vol. 28, no. 12, pp. 79 – 100, 2024, ISSN: 01258281, (Cited by: 0; All Open Access, Gold Open Access).
@article{Amapat202479,
title = {High-Density Polyethylene Film Price Forecast in Southeast Asia Market with Deep Learning},
author = {Narongchon Amapat and Parames Chutima},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85214918988&doi=10.4186%2fej.2024.28.12.79&partnerID=40&md5=f0cd7f0dd850424c8edca4e165c15e65},
doi = {10.4186/ej.2024.28.12.79},
issn = {01258281},
year = {2024},
date = {2024-01-01},
journal = {Engineering Journal},
volume = {28},
number = {12},
pages = {79 – 100},
publisher = {Chulalongkorn University, Faculty of Fine and Applied Arts},
abstract = {Neural networks (NN) have been used for over a decade to predict time series data, with various algorithms including linear and non-linear models. Forecasting and assessing polymer market prices are crucial for plastic resin producers due to the complexity and uncertainty of resource availability. This encompasses feedstock planning, raw material procurement, technological advancements for product transitions, sales planning, pricing strategies for commercialization, and investments driven by macroeconomic factors. Previous literature primarily utilized numerical data as input for deep learning models. This research contended that structured data by itself was inadequate for models to precisely predict outcomes in the volatile, uncertain, complex, and ambiguous (VUCA) environment. Three deep learning architectures, Long-Short Term Memory (LSTM), Encoder-Decoder, Temporal Convolutional Network and Recurrent Neuron Network (TCNRNN), were reviewed in this research to determine the most effective architecture for analysing structured data. Additionally, Natural Language Processing (NLP) was implemented in this research to gather market sentiment and enhance forecast accuracy. The study utilizes commodity market price announcements, economic indicators, and insight reports from reputable publishers. The study utilizes commodity market prices, economic indicators, and insightful reports. All information was obtained from a reputable publisher. The results were compared with the legacy model, which involved a human analyst and a linear regression model. Model performance was assessed using Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Symmetric Mean Absolute Percentage Error (SMAPE), and ANOVA. The linear regression forecast together with the human analyst model has an acceptable accuracy with a MAPE of 45.1%. Neural networks containing sentiment analyzers have been found to surpass the performance of human analysts and a linear regression model, with a MAPE of 17.1%. © 2024, Chulalongkorn University, Faculty of Fine and Applied Arts. All rights reserved.},
note = {Cited by: 0; All Open Access, Gold Open Access},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Neural networks (NN) have been used for over a decade to predict time series data, with various algorithms including linear and non-linear models. Forecasting and assessing polymer market prices are crucial for plastic resin producers due to the complexity and uncertainty of resource availability. This encompasses feedstock planning, raw material procurement, technological advancements for product transitions, sales planning, pricing strategies for commercialization, and investments driven by macroeconomic factors. Previous literature primarily utilized numerical data as input for deep learning models. This research contended that structured data by itself was inadequate for models to precisely predict outcomes in the volatile, uncertain, complex, and ambiguous (VUCA) environment. Three deep learning architectures, Long-Short Term Memory (LSTM), Encoder-Decoder, Temporal Convolutional Network and Recurrent Neuron Network (TCNRNN), were reviewed in this research to determine the most effective architecture for analysing structured data. Additionally, Natural Language Processing (NLP) was implemented in this research to gather market sentiment and enhance forecast accuracy. The study utilizes commodity market price announcements, economic indicators, and insight reports from reputable publishers. The study utilizes commodity market prices, economic indicators, and insightful reports. All information was obtained from a reputable publisher. The results were compared with the legacy model, which involved a human analyst and a linear regression model. Model performance was assessed using Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Symmetric Mean Absolute Percentage Error (SMAPE), and ANOVA. The linear regression forecast together with the human analyst model has an acceptable accuracy with a MAPE of 45.1%. Neural networks containing sentiment analyzers have been found to surpass the performance of human analysts and a linear regression model, with a MAPE of 17.1%. © 2024, Chulalongkorn University, Faculty of Fine and Applied Arts. All rights reserved.
Kanjanataj Chaowai, Parames Chutima
Demand Forecasting and Ordering Policy of Fast-Moving Consumer Goods with Promotional Sales in a Small Trading Firm Journal Article
In: Engineering Journal, vol. 28, no. 4, pp. 21 – 40, 2024, ISSN: 01258281, (Cited by: 1; All Open Access, Gold Open Access, Green Open Access).
@article{Chaowai202421,
title = {Demand Forecasting and Ordering Policy of Fast-Moving Consumer Goods with Promotional Sales in a Small Trading Firm},
author = {Kanjanataj Chaowai and Parames Chutima},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85193916907&doi=10.4186%2fej.2024.28.4.21&partnerID=40&md5=155659b5c4843b01f4291ab9c90eb98b},
doi = {10.4186/ej.2024.28.4.21},
issn = {01258281},
year = {2024},
date = {2024-01-01},
journal = {Engineering Journal},
volume = {28},
number = {4},
pages = {21 – 40},
publisher = {Chulalongkorn University, Faculty of Fine and Applied Arts},
abstract = {This 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%. © 2024, Chulalongkorn University, Faculty of Fine and Applied Arts. All rights reserved.},
note = {Cited by: 1; All Open Access, Gold Open Access, Green Open Access},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
This 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%. © 2024, Chulalongkorn University, Faculty of Fine and Applied Arts. All rights reserved.
Thanapatr Chitsrisakda, Parames Chutima, Arisara Jiamsanguanwong, Oran Kittithreerapronchai
Association for Computing Machinery, 2024, ISBN: 979-840071713-0, (Cited by: 1).
@conference{Chitsrisakda2024371,
title = {Application of the NSGA-III algorithm to investigate the Cobot assembly line balancing problem with disabled workers},
author = {Thanapatr Chitsrisakda and Parames Chutima and Arisara Jiamsanguanwong and Oran Kittithreerapronchai},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85202770712&doi=10.1145%2f3664968.3666115&partnerID=40&md5=8941551992234dff90b0d0709492afcf},
doi = {10.1145/3664968.3666115},
isbn = {979-840071713-0},
year = {2024},
date = {2024-01-01},
journal = {ACM International Conference Proceeding Series},
pages = {371 – 379},
publisher = {Association for Computing Machinery},
abstract = {Analyzing the assembly line balancing problem with cobots on parallel adjacent assembly lines is the main objective of this research. On the assembly line, human workers and robots, or cobots, are permitted to work in parallel or independently. There are two categories of workers in the assembly lines: workers without disabilities and workers with disabilities. Workstations are assigned jobs, workers, and cobots in order to accomplish system-, human-, and robot-related goals. The well-known evolutionary methodology of the Non-dominated Sorting Genetic Algorithm (NSGA-III) is utilised to identify optimal solutions because the problem is NP-hard. Workers with disabilities exist, thus their effects on the system's objectives are examined. It has been shown that how workers with disabilities have a substantial effect on assembly line efficiency. © 2024 Owner/Author.},
note = {Cited by: 1},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Analyzing the assembly line balancing problem with cobots on parallel adjacent assembly lines is the main objective of this research. On the assembly line, human workers and robots, or cobots, are permitted to work in parallel or independently. There are two categories of workers in the assembly lines: workers without disabilities and workers with disabilities. Workstations are assigned jobs, workers, and cobots in order to accomplish system-, human-, and robot-related goals. The well-known evolutionary methodology of the Non-dominated Sorting Genetic Algorithm (NSGA-III) is utilised to identify optimal solutions because the problem is NP-hard. Workers with disabilities exist, thus their effects on the system’s objectives are examined. It has been shown that how workers with disabilities have a substantial effect on assembly line efficiency. © 2024 Owner/Author.
2023
Panasin Chungsawanant, Parames Chutima, Chaipong Pongpanich
Multi-criteria decision making and project portfolio management for transport infrastructure investment in Thailand Journal Article
In: International Journal of Process Management and Benchmarking, vol. 13, no. 1, pp. 47 – 72, 2023, ISSN: 14606739, (Cited by: 7).
@article{Chungsawanant202347,
title = {Multi-criteria decision making and project portfolio management for transport infrastructure investment in Thailand},
author = {Panasin Chungsawanant and Parames Chutima and Chaipong Pongpanich},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85147690154&doi=10.1504%2fIJPMB.2021.10038487&partnerID=40&md5=4e426bce1b6d20f2b4ac36ca17c32d45},
doi = {10.1504/IJPMB.2021.10038487},
issn = {14606739},
year = {2023},
date = {2023-01-01},
journal = {International Journal of Process Management and Benchmarking},
volume = {13},
number = {1},
pages = {47 – 72},
publisher = {Inderscience Publishers},
abstract = {Transport project selection is always critical in government planning due to limited resources and diverse demands. Project portfolio management (PPM) was proposed in this study to suggest an optimal set of projects under predetermined constraints. With multiple conflicting criteria, comprehensive steps of multi-criteria decision making (MCDM) procedures were proposed in this study. The process begins with a step of pre-screening the projects and selecting the criteria for project evaluation. Sequential steps of: 1) weight determination process using the proportion method; 2) MCDM process using preference ranking organisation method for enrichment evaluations (PROMETHEE) II; 3) constraint consideration process using PROMETHEE V, was proposed to evaluate and select an optimal project portfolio. The study showed that decision making could be successfully done for the transport infrastructure investment with minimal complex calculation steps and questionnaire input from experts. Copyright © 2023 Inderscience Enterprises Ltd.},
note = {Cited by: 7},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Transport project selection is always critical in government planning due to limited resources and diverse demands. Project portfolio management (PPM) was proposed in this study to suggest an optimal set of projects under predetermined constraints. With multiple conflicting criteria, comprehensive steps of multi-criteria decision making (MCDM) procedures were proposed in this study. The process begins with a step of pre-screening the projects and selecting the criteria for project evaluation. Sequential steps of: 1) weight determination process using the proportion method; 2) MCDM process using preference ranking organisation method for enrichment evaluations (PROMETHEE) II; 3) constraint consideration process using PROMETHEE V, was proposed to evaluate and select an optimal project portfolio. The study showed that decision making could be successfully done for the transport infrastructure investment with minimal complex calculation steps and questionnaire input from experts. Copyright © 2023 Inderscience Enterprises Ltd.
Chayut Pacharatham, Parames Chutima
Facility Location Placement Optimisation for Bagged Cement Distribution During the COVID-19 Pandemic Journal Article
In: Engineering Journal, vol. 27, no. 7, pp. 75 – 95, 2023, ISSN: 01258281, (Cited by: 1).
@article{Pacharatham202375,
title = {Facility Location Placement Optimisation for Bagged Cement Distribution During the COVID-19 Pandemic},
author = {Chayut Pacharatham and Parames Chutima},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85167452261&doi=10.4186%2fej.2023.27.7.75&partnerID=40&md5=acc577e8e5baf523abbddb8294fadffe},
doi = {10.4186/ej.2023.27.7.75},
issn = {01258281},
year = {2023},
date = {2023-01-01},
journal = {Engineering Journal},
volume = {27},
number = {7},
pages = {75 – 95},
publisher = {Chulalongkorn University, Faculty of Fine and Applied Arts},
abstract = {This 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. © 2023, Chulalongkorn University, Faculty of Fine and Applied Arts. All rights reserved.},
note = {Cited by: 1},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
This 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. © 2023, Chulalongkorn University, Faculty of Fine and Applied Arts. All rights reserved.
Parames Chutima
Preface Conference
Institute of Electrical and Electronics Engineers Inc., 2023, ISBN: 979-835032428-0, (Cited by: 0; All Open Access, Bronze Open Access).
@conference{Chutima2023viii,
title = {Preface},
author = {Parames Chutima},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85179614912&doi=10.1109%2fIEAI59107.2023.00005&partnerID=40&md5=2a310e5ef0febf0f47099409cea212b4},
doi = {10.1109/IEAI59107.2023.00005},
isbn = {979-835032428-0},
year = {2023},
date = {2023-01-01},
journal = {Proceedings - 2023 4th International Conference on Industrial Engineering and Artificial Intelligence, IEAI 2023},
pages = {viii},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
note = {Cited by: 0; All Open Access, Bronze Open Access},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Alongkorn Polsuksiri, Parames Chutima
Simulation Analysis to Estimate Performances of Automated Parking System Conference
Association for Computing Machinery, 2023, ISBN: 979-840070838-1, (Cited by: 0).
@conference{Polsuksiri2023297,
title = {Simulation Analysis to Estimate Performances of Automated Parking System},
author = {Alongkorn Polsuksiri and Parames Chutima},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85171147329&doi=10.1145%2f3603955.3603972&partnerID=40&md5=f0cb7cd081bb5cc8df5b279138928d1a},
doi = {10.1145/3603955.3603972},
isbn = {979-840070838-1},
year = {2023},
date = {2023-01-01},
journal = {ACM International Conference Proceeding Series},
pages = {297 – 301},
publisher = {Association for Computing Machinery},
abstract = {Automated parking systems make it possible to alleviate the parking problem, improve effective investment in real estate, and reduce the area devoted to parking. Currently, there are several types of automated parking systems, depending on the manufacturer. This article focuses on the pallet type and robot type, and the main factors in the selection of an automatic parking system are cost and system performance. Generally, the performance of an automatic parking system is calculated based on the average processing time between the nearest and the farthest parking at only two positions, which results in low reliability. Therefore, the objective of this paper is to study and estimate the performance of automatic parking systems by comparing the pallet-type and robot-type systems through computer simulations using SIMIO software and analyzing the costs. The results show that the waiting time to entry of the robot type is at least 47% more efficient than that of the pallet type. However, the waiting time for retrieval of the pallet type is at least 16% more efficient than that of the robot type, and the total cost of the robot type is about 4% lower than that of the pallet type. © 2023 ACM.},
note = {Cited by: 0},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Automated parking systems make it possible to alleviate the parking problem, improve effective investment in real estate, and reduce the area devoted to parking. Currently, there are several types of automated parking systems, depending on the manufacturer. This article focuses on the pallet type and robot type, and the main factors in the selection of an automatic parking system are cost and system performance. Generally, the performance of an automatic parking system is calculated based on the average processing time between the nearest and the farthest parking at only two positions, which results in low reliability. Therefore, the objective of this paper is to study and estimate the performance of automatic parking systems by comparing the pallet-type and robot-type systems through computer simulations using SIMIO software and analyzing the costs. The results show that the waiting time to entry of the robot type is at least 47% more efficient than that of the pallet type. However, the waiting time for retrieval of the pallet type is at least 16% more efficient than that of the robot type, and the total cost of the robot type is about 4% lower than that of the pallet type. © 2023 ACM.
Yanisa Suraphan, Parames Chutima
Availability Improvement of Die Attach Machines Conference
Association for Computing Machinery, 2023, ISBN: 979-840070838-1, (Cited by: 0).
@conference{Suraphan2023239,
title = {Availability Improvement of Die Attach Machines},
author = {Yanisa Suraphan and Parames Chutima},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85171158738&doi=10.1145%2f3603955.3603989&partnerID=40&md5=e90b052b8d57f3490c07ca728cf1ea4e},
doi = {10.1145/3603955.3603989},
isbn = {979-840070838-1},
year = {2023},
date = {2023-01-01},
journal = {ACM International Conference Proceeding Series},
pages = {239 – 244},
publisher = {Association for Computing Machinery},
abstract = {The objective of this study is to enhance the overall performance of die-attach machines. Due to the importance of the die attach process in the manufacturing of integrated circuits, it was discovered that the installation process machine has the lowest utilization value. This was determined by collecting general data and analyzing the various processes involved in the manufacturing of integrated circuits. From the research on the availability rate, performance rate, and quality rate, it was found that the availability rate was the lowest which contributed to the low OEE of the installation process machine. Therefore, the purpose of this research was to improve setup time to raise the operating rate. According to the result of the research, the setup time for lead type can be reduced to 63 minutes and more workpieces can be produced, i.e., 21% of the original production volume. © 2023 ACM.},
note = {Cited by: 0},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
The objective of this study is to enhance the overall performance of die-attach machines. Due to the importance of the die attach process in the manufacturing of integrated circuits, it was discovered that the installation process machine has the lowest utilization value. This was determined by collecting general data and analyzing the various processes involved in the manufacturing of integrated circuits. From the research on the availability rate, performance rate, and quality rate, it was found that the availability rate was the lowest which contributed to the low OEE of the installation process machine. Therefore, the purpose of this research was to improve setup time to raise the operating rate. According to the result of the research, the setup time for lead type can be reduced to 63 minutes and more workpieces can be produced, i.e., 21% of the original production volume. © 2023 ACM.
Sasithorn Kamnon, Parames Chutima
Association for Computing Machinery, 2023, ISBN: 979-840070838-1, (Cited by: 1).
@conference{Kamnon2023144,
title = {Inventory Value Reduction by Improving Parts Purchasing Process in a Household Appliance Manufacturer},
author = {Sasithorn Kamnon and Parames Chutima},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85171151032&doi=10.1145%2f3603955.3604007&partnerID=40&md5=625596390ca8af9f6563542dab6cc921},
doi = {10.1145/3603955.3604007},
isbn = {979-840070838-1},
year = {2023},
date = {2023-01-01},
journal = {ACM International Conference Proceeding Series},
pages = {144 – 149},
publisher = {Association for Computing Machinery},
abstract = {This study was implemented in a Household Appliance Manufacturing Company to improve the part purchasing process, reduce inventory value by prioritizing products, planning products with analysis demand pattern from the previous demand records, planning to find the right order quantity, and controlling inventory stock. This is one of the most successful approaches to the part purchasing process improvement and cost reduction. In the current situation, some month has an inventory turnover ratio of less than 1. To improve this problem, Analysis demand pattern was used to set purchase policy and the ABC analysis was ordered of important products to improve. The ABC analysis prioritizes products group whose inventory value of more than 70% of the total inventory value is group A. Group A can be prioritized as A1 (accumulating a percentage of 50% of the total inventory value in Group A), and A2 (leftover items from A1). Group A1 was the first priority to improve. Having done all, the inventory value was decreased by the new part purchasing policy, which is the ROP technique and The Silver Meal method. From the target of reducing inventory value to 20.19 MB/month. As a result, group A1's inventory value of November 2022 was reduced to 36.58 MB from Group A1's inventory value of November 2021, which can achieve the target. © 2023 ACM.},
note = {Cited by: 1},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
This study was implemented in a Household Appliance Manufacturing Company to improve the part purchasing process, reduce inventory value by prioritizing products, planning products with analysis demand pattern from the previous demand records, planning to find the right order quantity, and controlling inventory stock. This is one of the most successful approaches to the part purchasing process improvement and cost reduction. In the current situation, some month has an inventory turnover ratio of less than 1. To improve this problem, Analysis demand pattern was used to set purchase policy and the ABC analysis was ordered of important products to improve. The ABC analysis prioritizes products group whose inventory value of more than 70% of the total inventory value is group A. Group A can be prioritized as A1 (accumulating a percentage of 50% of the total inventory value in Group A), and A2 (leftover items from A1). Group A1 was the first priority to improve. Having done all, the inventory value was decreased by the new part purchasing policy, which is the ROP technique and The Silver Meal method. From the target of reducing inventory value to 20.19 MB/month. As a result, group A1’s inventory value of November 2022 was reduced to 36.58 MB from Group A1’s inventory value of November 2021, which can achieve the target. © 2023 ACM.