Pub Date : 2023-06-30DOI: 10.24867/ijiem-2023-2-328
I. Rastgar, J. Rezaeian, I. Mahdavi, P. Fattahi
{"title":"A novel mathematical model for Integration of Energy-efficient Production Planning and Maintenance Scheduling","authors":"I. Rastgar, J. Rezaeian, I. Mahdavi, P. Fattahi","doi":"10.24867/ijiem-2023-2-328","DOIUrl":"https://doi.org/10.24867/ijiem-2023-2-328","url":null,"abstract":"","PeriodicalId":38526,"journal":{"name":"International Journal of Industrial Engineering and Management","volume":"1 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42416854","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-30DOI: 10.24867/ijiem-2023-2-327
Syeda Misbah Inayat, Syed Muhammad Rafay Zaidi, Husnain Ahmed, Danial Ahmed, Mehreen Kausar Azam, Zeeshan Ahmad Arfeen
{"title":"Risk Assessment and Mitigation Strategy of Large-Scale Solar Photovoltaic Systems in Pakistan","authors":"Syeda Misbah Inayat, Syed Muhammad Rafay Zaidi, Husnain Ahmed, Danial Ahmed, Mehreen Kausar Azam, Zeeshan Ahmad Arfeen","doi":"10.24867/ijiem-2023-2-327","DOIUrl":"https://doi.org/10.24867/ijiem-2023-2-327","url":null,"abstract":"","PeriodicalId":38526,"journal":{"name":"International Journal of Industrial Engineering and Management","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48480198","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-30DOI: 10.24867/ijiem-2023-2-326
Juliana Basulo Ribeiro, M. Amorim, L. Teixeira
{"title":"How To Accelerate Digital Transformation in Companies With Lean Philosophy? Contributions Based on a Practical Case","authors":"Juliana Basulo Ribeiro, M. Amorim, L. Teixeira","doi":"10.24867/ijiem-2023-2-326","DOIUrl":"https://doi.org/10.24867/ijiem-2023-2-326","url":null,"abstract":"","PeriodicalId":38526,"journal":{"name":"International Journal of Industrial Engineering and Management","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44741366","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-30DOI: 10.24867/ijiem-2023-2-331
Mirza Pasic, Ajdin Vatreš, I. Bijelonja, Mugdim Pasic
{"title":"Analysis of Development of Entrepreneurship Competences of Engineering Students Based on EntreComp Framework","authors":"Mirza Pasic, Ajdin Vatreš, I. Bijelonja, Mugdim Pasic","doi":"10.24867/ijiem-2023-2-331","DOIUrl":"https://doi.org/10.24867/ijiem-2023-2-331","url":null,"abstract":"","PeriodicalId":38526,"journal":{"name":"International Journal of Industrial Engineering and Management","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43880223","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-30DOI: 10.24867/ijiem-2023-2-329
Al Ansor Siahaan, M. Asrol
[1] D. Askenaizer and M. Watson Engineers, “Drinking Water Quality and Treatment,” 2001. [2] S. Sulistyani, A. Fillaeli, U. Negeri, and Y. K. Malang, “Uji kesadahan air tanah di daerah sekitar pantai kecamatan rembang propinsi jawa tengah,” 2012. [3] H. Elfil and A. Hannachi, “Reconsidering water scaling tendency assessment,” AIChE Journal, vol. 52, no. 10, pp. 3583–3591, Oct. 2006, doi: 10.1002/aic.10965. [4] A. Sharjeel, S. Anwar, A. Nasir, and H. Rashid, “Design, development and performance of optimum water softener,” Earth Sciences Pakistan, vol. 3, no. 1, pp. 23–28, Jan. 2019, doi: 10.26480/esp.01.2019.23.28. [5] A. Sircar, K. Yadav, K. Rayavarapu, N. Bist, and H. Oza, “Application of machine learning and artificial intelligence in oil and gas industry,” Petroleum Research, vol. 6, no. 4. KeAi Publishing Communications Ltd., pp. 379–391, Dec. 01, 2021. doi: 10.1016/j. ptlrs.2021.05.009. [6] J. Jawad, A. H. Hawari, and S. Zaidi, “Modeling of forward osmosis process using artificial neural networks (ANN) to predict the permeate flux,” Desalination, vol. 484, Jun. 2020, doi: 10.1016/j.desal.2020.114427. [7] S. Singha, S. Pasupuleti, S. S. Singha, R. Singh, and S. Kumar, “Prediction of groundwater quality using efficient machine learning technique,” Chemosphere, vol. 276, Aug. 2021, doi: 10.1016/j.chemosphere.2021.130265. [8] A. Bannoud, “The electrochemical way of removing the hardness of water,” 1993. [9] A. Mahvi, N. Dariush, V. Forugh, and S. Nazmara, “Teawaste as An Adsorbent for Heavy Metal Removal from Industrial Wastewaters,” Am. J. Appl. Sci., vol. 2, Jan. 2005, doi: 10.3844/ajassp.2005.372.375. [10] C. C. Aggarwal, Neural Networks and Deep Learning. Springer International Publishing, 2018. doi: 10.1007/978-3-319-94463-0. [11] P. Goyal, S. Pandey, and K. Jain, “Unfolding Recurrent Neural Networks,” in Deep Learning for Natural Language Processing, Apress, 2018, pp. 119–168. doi: 10.1007/978-1-4842-3685-7_3. [12] Z. Zhao, W. Chen, X. Wu, P. C. Y. Chen, and J. Liu, “LSTM network: A deep learning approach for Short-term traffic forecast,” IET Intelligent Transport Systems, vol. 11, no. 2, pp. 68–75, Mar. 2017, doi: 10.1049/iet-its.2016.0208. [13] A. Saxena and T. R. Sukumar, “Predicting bitcoin price using lstm And Compare its predictability with arima model,” Int. J. Pure Appl. Math., vol. 119, no. 17, pp. 2591–2600, Feb. 2018, doi: 10.13140/RG.2.2.15847.57766. [14] N. K. Manaswi, “RNN and LSTM BT Deep Learning with Applications Using Python : Chatbots and Face, Object, and Speech Recognition With TensorFlow and Keras,” N. K. Manaswi, Ed. Berkeley, CA: Apress, 2018, pp. 115–126, doi: 10.1007/978-14842-3516-4_9. [15] A. Sherstinsky, “Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) network,” Phys. D Nonlinear Phenom., vol. 404, p. 132306, 2020, doi: 10.1016/j.physd.2019.132306. [16] H. Chung and K. S. Shin, “Genetic algorithm-optimized long short-term memory network for stock market prediction,” Sustainabili
[10] D. Askenaizer和M. Watson Engineers，“饮用水质量和处理”，2001。[10]李建军，刘建军，刘建军，“空气质量与空气质量的关系”，《中国环境科学》，2012。[10]李建军，“水结垢倾向评价的再思考”，《中国给水排水》，第2期。10, pp. 3583-3591, Oct. 2006, doi: 10.1002/aic.10965。[10]王晓明，王晓明，“水软化剂的设计、开发与性能研究”，《地球科学》第3卷，第2期。2019年1月，第23-28页，doi: 10.26480/esp.01.2019.23.28。[10] A. Sircar, K. Yadav, K. Rayavarapu, N. Bist, H. Oza，“机器学习和人工智能在石油和天然气工业中的应用”，石油研究，第6卷，第6期。4. 科爱出版传播有限公司，379-391页，2021年12月1日。doi: 10.1016 / j。ptlrs.2021.05.009。[10]王晓明，王晓明，王晓明，“基于人工神经网络(ANN)的海水正向渗透模型研究”，《海洋工程学报》，2014年6月，doi: 10.3969 / j.i ssn . 1006 - 1007。[10] S. Singha, S. Pasupuleti, S. S. Singha, R. Singh, S. Kumar，“基于高效机器学习技术的地下水水质预测”，环境科学，vol. 276, Aug. 2021, doi: 10.1016/j.c chemosphere.2021.130265。[10] A. Bannoud，“电化学方法去除水的硬度”，1993。[10]张晓明，张晓明，张晓明，“工业废水中重金属的吸附研究”，环境科学与技术，2011。j:。科学。， vol. 2, 2005年1月，doi: 10.3844/ ajasp .2005.372.375。[10] C. C. Aggarwal，神经网络与深度学习。b施普林格国际出版，2018。doi: 10.1007 / 978-3-319-94463-0。[10] P. Goyal, S. Pandey和K. Jain，“展开递归神经网络”，《自然语言处理的深度学习》，Apress, 2018，第119-168页。doi: 10.1007 / 978 - 1 - 4842 - 3685 - 7 - _3。[10]赵振宇，陈伟，吴晓霞，刘建军，“基于深度学习的LSTM网络短期交通预测方法”，智能交通系统，vol. 11, no. 1。2, pp. 68-75, 2017年3月，doi: 10.1049/ et-its.2016.0208。[10] A. Saxena和T. R. Sukumar，“使用lstm预测比特币价格并将其可预测性与arima模型进行比较”，Int。纯苹果。数学。，第119卷，第119号。17, pp. 2591 - 26,2018, doi: 10.13140/RG.2.2.15847.57766。N. K. Manaswi，“RNN和LSTM BT深度学习与使用Python的应用:Chatbots和Face, Object, and Speech Recognition with TensorFlow and Keras，”N. K. Manaswi, Ed. Berkeley, CA: Apress, 2018, pp. 115-126, doi: 10.1007/978-14842-3516-4_9。[10] A. Sherstinsky，“递归神经网络(RNN)和长短期记忆(LSTM)网络的基础”，物理学报。D非线性现象。中国科学，第404卷，第132306页，2020,doi: 10.1016/j.p yphys.2019.132306。[10]郑宏祥，“基于遗传算法优化的长短期记忆网络在股票市场预测中的应用”，vol. 10, no. 10。2018年10月10日，doi: 10.3390/su10103765。[10]李建军，李建军，“随机森林与逻辑回归的关系:一个大规模的基准实验”，《生物信息学》vol. 19, no. 1。2018年7月1日，doi: 10.1186/s12859-018-2264-5。[18] L. Breiman，《随机森林》，2001。[10]杨建军，杨建军，李建军，基于数据挖掘的商业分析:概念、技术和应用[j] .计算机科学与技术，2017。[10]林志刚，王志刚，王志刚，商业与经济的统计技术。麦格劳-希尔,2017年。水处理制药行业硬度预测机器学习模型的建立
{"title":"Development of a Machine Learning Model for Predicting Hardness in the Water Treatment Pharmaceutical Industry","authors":"Al Ansor Siahaan, M. Asrol","doi":"10.24867/ijiem-2023-2-329","DOIUrl":"https://doi.org/10.24867/ijiem-2023-2-329","url":null,"abstract":"[1] D. Askenaizer and M. Watson Engineers, “Drinking Water Quality and Treatment,” 2001. [2] S. Sulistyani, A. Fillaeli, U. Negeri, and Y. K. Malang, “Uji kesadahan air tanah di daerah sekitar pantai kecamatan rembang propinsi jawa tengah,” 2012. [3] H. Elfil and A. Hannachi, “Reconsidering water scaling tendency assessment,” AIChE Journal, vol. 52, no. 10, pp. 3583–3591, Oct. 2006, doi: 10.1002/aic.10965. [4] A. Sharjeel, S. Anwar, A. Nasir, and H. Rashid, “Design, development and performance of optimum water softener,” Earth Sciences Pakistan, vol. 3, no. 1, pp. 23–28, Jan. 2019, doi: 10.26480/esp.01.2019.23.28. [5] A. Sircar, K. Yadav, K. Rayavarapu, N. Bist, and H. Oza, “Application of machine learning and artificial intelligence in oil and gas industry,” Petroleum Research, vol. 6, no. 4. KeAi Publishing Communications Ltd., pp. 379–391, Dec. 01, 2021. doi: 10.1016/j. ptlrs.2021.05.009. [6] J. Jawad, A. H. Hawari, and S. Zaidi, “Modeling of forward osmosis process using artificial neural networks (ANN) to predict the permeate flux,” Desalination, vol. 484, Jun. 2020, doi: 10.1016/j.desal.2020.114427. [7] S. Singha, S. Pasupuleti, S. S. Singha, R. Singh, and S. Kumar, “Prediction of groundwater quality using efficient machine learning technique,” Chemosphere, vol. 276, Aug. 2021, doi: 10.1016/j.chemosphere.2021.130265. [8] A. Bannoud, “The electrochemical way of removing the hardness of water,” 1993. [9] A. Mahvi, N. Dariush, V. Forugh, and S. Nazmara, “Teawaste as An Adsorbent for Heavy Metal Removal from Industrial Wastewaters,” Am. J. Appl. Sci., vol. 2, Jan. 2005, doi: 10.3844/ajassp.2005.372.375. [10] C. C. Aggarwal, Neural Networks and Deep Learning. Springer International Publishing, 2018. doi: 10.1007/978-3-319-94463-0. [11] P. Goyal, S. Pandey, and K. Jain, “Unfolding Recurrent Neural Networks,” in Deep Learning for Natural Language Processing, Apress, 2018, pp. 119–168. doi: 10.1007/978-1-4842-3685-7_3. [12] Z. Zhao, W. Chen, X. Wu, P. C. Y. Chen, and J. Liu, “LSTM network: A deep learning approach for Short-term traffic forecast,” IET Intelligent Transport Systems, vol. 11, no. 2, pp. 68–75, Mar. 2017, doi: 10.1049/iet-its.2016.0208. [13] A. Saxena and T. R. Sukumar, “Predicting bitcoin price using lstm And Compare its predictability with arima model,” Int. J. Pure Appl. Math., vol. 119, no. 17, pp. 2591–2600, Feb. 2018, doi: 10.13140/RG.2.2.15847.57766. [14] N. K. Manaswi, “RNN and LSTM BT Deep Learning with Applications Using Python : Chatbots and Face, Object, and Speech Recognition With TensorFlow and Keras,” N. K. Manaswi, Ed. Berkeley, CA: Apress, 2018, pp. 115–126, doi: 10.1007/978-14842-3516-4_9. [15] A. Sherstinsky, “Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) network,” Phys. D Nonlinear Phenom., vol. 404, p. 132306, 2020, doi: 10.1016/j.physd.2019.132306. [16] H. Chung and K. S. Shin, “Genetic algorithm-optimized long short-term memory network for stock market prediction,” Sustainabili","PeriodicalId":38526,"journal":{"name":"International Journal of Industrial Engineering and Management","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47490424","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Purpose: This work aims to evaluate demand forecasting models to determine if using exogenous factors and machine learning techniques helps improve performance compared to univariate statistical models, allowing manufacturing companies to manage demand better.Design/methodology/approach: We implemented a multivariate Auto-Regressive Moving Average with eXogenous input (ARMAX) statistical model and a Neural Network-ARMAX (NN-ARMAX) hybrid model for forecasting. Later, we compared both to a standard univariate statistical model to forecast the demand for electrical products in a Colombian manufacturing company.Findings: The outcomes demonstrated that the NN-ARMAX model outperformed the other two. Indeed, demand management improved with the reduction of overstock and out-of-stock products.Research limitations/implications: The findings and conclusions in this work are limited to Colombian manufacturing companies that sell electrical products to the construction industry. Moreover, the experts from the company that provided us with the data also selected the external factors based on their own experiences, i.e., we might have disregarded potential factors.Practical implications: This work suggests that a model using neural networks and including exogenous variables can improve demand forecasting accuracy, promoting this approach in manufacturing companies dealing with demand planning issues.Originality/value: The findings in this work demonstrate the convenience of using the proposed hybrid model to improve demand forecasting accuracy and thus provide a reliable basis for its implementation in supply chain planning for the electrical/construction sector in Colombian manufacturing companies.
{"title":"Demand forecasting using a hybrid model based on artificial neural networks: A study case on electrical products","authors":"H. Quiñones, Oscar Rubiano, Wilfredo Alfonso","doi":"10.3926/jiem.3928","DOIUrl":"https://doi.org/10.3926/jiem.3928","url":null,"abstract":"Purpose: This work aims to evaluate demand forecasting models to determine if using exogenous factors and machine learning techniques helps improve performance compared to univariate statistical models, allowing manufacturing companies to manage demand better.Design/methodology/approach: We implemented a multivariate Auto-Regressive Moving Average with eXogenous input (ARMAX) statistical model and a Neural Network-ARMAX (NN-ARMAX) hybrid model for forecasting. Later, we compared both to a standard univariate statistical model to forecast the demand for electrical products in a Colombian manufacturing company.Findings: The outcomes demonstrated that the NN-ARMAX model outperformed the other two. Indeed, demand management improved with the reduction of overstock and out-of-stock products.Research limitations/implications: The findings and conclusions in this work are limited to Colombian manufacturing companies that sell electrical products to the construction industry. Moreover, the experts from the company that provided us with the data also selected the external factors based on their own experiences, i.e., we might have disregarded potential factors.Practical implications: This work suggests that a model using neural networks and including exogenous variables can improve demand forecasting accuracy, promoting this approach in manufacturing companies dealing with demand planning issues.Originality/value: The findings in this work demonstrate the convenience of using the proposed hybrid model to improve demand forecasting accuracy and thus provide a reliable basis for its implementation in supply chain planning for the electrical/construction sector in Colombian manufacturing companies. ","PeriodicalId":38526,"journal":{"name":"International Journal of Industrial Engineering and Management","volume":"64 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2023-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88247034","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Purpose: This study intends to uncover factors that can accelerate digital transformation in established companies. This study examines the relationship between generic culture, digital culture, digital literacy, attitudes to change and perceived performance in digital transformation.Design/methodology/approach: A cross-sectional survey was conducted using a questionnaire with 383 employees. The data were analyzed using Structural Equation Modeling (SEM).Findings: This study shows that digital culture, legacy culture, and digital literacy significantly influence employee attitudes towards digital transformation and perceived performance. Additionally, digital literacy mediates the relationship between digital culture and employee attitudes towards digital transformation. Furthermore, employee attitudes towards digital transformation significantly impact their perceived performance.Research limitations/implications: Generalizability may be necessary given the case study approach's small sample size. Hence, more research is required to collect more representative samples.Practical implications: This study contributes to literature by providing empirical evidence on the importance of digital culture, legacy culture, and digital literacy for successful attitudes towards digital transformation. The findings of this study can be used to develop strategies for organizations undergoing digital transformation. A well-defined business culture supporting digital transformation is critical. Organizations should encourage employees to adapt and become accustomed to an innovative environment to boost performance. Accelerating digital transformation can also be done by enhancing digital technology competence and refining employees' attitudes toward digital transformation in the internalization process.Originality/value: Most studies have neglected the dynamic role of corporate culture in accomplishing digital transformation in favour of focusing more on technology. This study thus intends to fill this gap by uncovering how corporate culture and the employees' readiness can drive digital transformation.
{"title":"An empirical study of emerging digital culture and digital attitudes in an established company","authors":"T. Fahmi, J. Tjakraatmadja, H. Ginting","doi":"10.3926/jiem.5976","DOIUrl":"https://doi.org/10.3926/jiem.5976","url":null,"abstract":"Purpose: This study intends to uncover factors that can accelerate digital transformation in established companies. This study examines the relationship between generic culture, digital culture, digital literacy, attitudes to change and perceived performance in digital transformation.Design/methodology/approach: A cross-sectional survey was conducted using a questionnaire with 383 employees. The data were analyzed using Structural Equation Modeling (SEM).Findings: This study shows that digital culture, legacy culture, and digital literacy significantly influence employee attitudes towards digital transformation and perceived performance. Additionally, digital literacy mediates the relationship between digital culture and employee attitudes towards digital transformation. Furthermore, employee attitudes towards digital transformation significantly impact their perceived performance.Research limitations/implications: Generalizability may be necessary given the case study approach's small sample size. Hence, more research is required to collect more representative samples.Practical implications: This study contributes to literature by providing empirical evidence on the importance of digital culture, legacy culture, and digital literacy for successful attitudes towards digital transformation. The findings of this study can be used to develop strategies for organizations undergoing digital transformation. A well-defined business culture supporting digital transformation is critical. Organizations should encourage employees to adapt and become accustomed to an innovative environment to boost performance. Accelerating digital transformation can also be done by enhancing digital technology competence and refining employees' attitudes toward digital transformation in the internalization process.Originality/value: Most studies have neglected the dynamic role of corporate culture in accomplishing digital transformation in favour of focusing more on technology. This study thus intends to fill this gap by uncovering how corporate culture and the employees' readiness can drive digital transformation. ","PeriodicalId":38526,"journal":{"name":"International Journal of Industrial Engineering and Management","volume":"8 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2023-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84594414","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-27DOI: 10.15282/ijim.17.2.2023.9456
Mohd Irman Ibrahim, Huei Ruey Ong, Azrul Zamir Mohd Idris, Md Maksudur Rahman Khan, Martin Gomes
This work elucidated the knowledge transfer method used by an international first tier advanced composite multinational company supplier in transferring the technical knowledge to a Malaysian advanced composite manufacturer. Effective route of knowledge transfer could significantly improve the performance of the company. The process of the transferring knowledge is an ongoing progression of learning, adjusting, and improving. An excellent knowledge transfer could benefit both the knowledge provider and receiver. A “Backward Engineering” investigation on the relationship between the factors during the knowledge transfer process is useful, to be an ideal reference for others. This paper exhibits the technological transfer executed by the aero-composite manufacturer. Method chosen in this study is focus group discussion involving the working committee of respective programme. It was found that a systematic two stages with multi-phases of knowledge transfer has been explored during flat-curvature structures aero-composite project. Tacit and explicit knowledge is important for transferring technical knowledge in industry context. It is noticed that a useful knowledge transfer mechanism has an impact on the performance of organization such as increases in productivity, profits, and growth.
{"title":"DISCOVERING THE KNOWLEDGE TRANSFERS FRAMEWORK ON AERO-COMPOSITE MANUFACTURER IN MALAYSIA","authors":"Mohd Irman Ibrahim, Huei Ruey Ong, Azrul Zamir Mohd Idris, Md Maksudur Rahman Khan, Martin Gomes","doi":"10.15282/ijim.17.2.2023.9456","DOIUrl":"https://doi.org/10.15282/ijim.17.2.2023.9456","url":null,"abstract":"This work elucidated the knowledge transfer method used by an international first tier advanced composite multinational company supplier in transferring the technical knowledge to a Malaysian advanced composite manufacturer. Effective route of knowledge transfer could significantly improve the performance of the company. The process of the transferring knowledge is an ongoing progression of learning, adjusting, and improving. An excellent knowledge transfer could benefit both the knowledge provider and receiver. A “Backward Engineering” investigation on the relationship between the factors during the knowledge transfer process is useful, to be an ideal reference for others. This paper exhibits the technological transfer executed by the aero-composite manufacturer. Method chosen in this study is focus group discussion involving the working committee of respective programme. It was found that a systematic two stages with multi-phases of knowledge transfer has been explored during flat-curvature structures aero-composite project. Tacit and explicit knowledge is important for transferring technical knowledge in industry context. It is noticed that a useful knowledge transfer mechanism has an impact on the performance of organization such as increases in productivity, profits, and growth.","PeriodicalId":38526,"journal":{"name":"International Journal of Industrial Engineering and Management","volume":"34 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2023-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82773632","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-27DOI: 10.15282/ijim.17.2.2023.9461
Akio Van James P. Montizo, Sairon Paul P. Mahinay, Vinsem B. Mindog, Joanna Lynn Mercado
The purpose of this study is to determine the preferred selected characteristics of shopping malls among Davaoeños during Covid-19. It specifically seeks to determine the level of preference among Davaoeños’ preferred shopping mall characteristics according to (1) service experience, (2) internal environment, (3) convenience, (4) utilitarian factors, (5) proximity, and (6) demonstration. This study also seeks to determine if there is a significant difference in the preference level for shopping malls when respondents are grouped according to their profiles. Quantitative research design was used, and simple random sampling was utilized with 100 respondents. As for the major findings, there is a significant difference in the shopping mall’s internal environment when respondents are grouped according to their age, occupation, and civil status.
{"title":"DAVAOEÑOS’ PREFERRED CHARACTERISTICS OF SHOPPING MALLS DURING COVID-19","authors":"Akio Van James P. Montizo, Sairon Paul P. Mahinay, Vinsem B. Mindog, Joanna Lynn Mercado","doi":"10.15282/ijim.17.2.2023.9461","DOIUrl":"https://doi.org/10.15282/ijim.17.2.2023.9461","url":null,"abstract":"The purpose of this study is to determine the preferred selected characteristics of shopping malls among Davaoeños during Covid-19. It specifically seeks to determine the level of preference among Davaoeños’ preferred shopping mall characteristics according to (1) service experience, (2) internal environment, (3) convenience, (4) utilitarian factors, (5) proximity, and (6) demonstration. This study also seeks to determine if there is a significant difference in the preference level for shopping malls when respondents are grouped according to their profiles. Quantitative research design was used, and simple random sampling was utilized with 100 respondents. As for the major findings, there is a significant difference in the shopping mall’s internal environment when respondents are grouped according to their age, occupation, and civil status.","PeriodicalId":38526,"journal":{"name":"International Journal of Industrial Engineering and Management","volume":"86 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2023-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88294317","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-27DOI: 10.15282/ijim.17.2.2023.9037
N. Aripin, G. Nawanir, S. Hussain
Although lean has gained many accomplishments, 90% of the manufacturers that implemented lean failed to sustain the implementation, and these results have led academics to consider lean culture as a soft lean approach for successful lean implementation. This research is aimed to investigate the role of lean culture for a successful lean implementation. This survey-based was a cross-sectional study with 151 final respondents from discrete manufacturers in Malaysia. The samples were selected using a cluster sampling procedure from medium and large manufacturing companies registered with the Federation of Manufacturers Malaysia (FMM). The data was analyzed using SmartPLS 4.0 software. The result showed evidence that lean manufacturing implementation is positively impacted by lean culture. This study contributes to the body of knowledge and widens the bounds of the current literature, and offers insight to the lean practitioners on lean implementation techniques to strategize the roadmap and assure continuous execution by considering the role of lean culture.
{"title":"LEAN CULTURE FOR A SUCCESSFUL LEAN MANUFACTURING IMPLEMENTATION: AN EMPIRICAL EVIDENCE FROM MALAYSIAN MANUFACTURING INDUSTRY","authors":"N. Aripin, G. Nawanir, S. Hussain","doi":"10.15282/ijim.17.2.2023.9037","DOIUrl":"https://doi.org/10.15282/ijim.17.2.2023.9037","url":null,"abstract":"Although lean has gained many accomplishments, 90% of the manufacturers that implemented lean failed to sustain the implementation, and these results have led academics to consider lean culture as a soft lean approach for successful lean implementation. This research is aimed to investigate the role of lean culture for a successful lean implementation. This survey-based was a cross-sectional study with 151 final respondents from discrete manufacturers in Malaysia. The samples were selected using a cluster sampling procedure from medium and large manufacturing companies registered with the Federation of Manufacturers Malaysia (FMM). The data was analyzed using SmartPLS 4.0 software. The result showed evidence that lean manufacturing implementation is positively impacted by lean culture. This study contributes to the body of knowledge and widens the bounds of the current literature, and offers insight to the lean practitioners on lean implementation techniques to strategize the roadmap and assure continuous execution by considering the role of lean culture.","PeriodicalId":38526,"journal":{"name":"International Journal of Industrial Engineering and Management","volume":"2 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2023-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87071441","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：