A. P. Redi, R. G. Widjaja, Iwan Agustono, M. Asrol, A. S. Budiman, F. Gunawan
This study reviews studies on a more viable battery for the energy storage system, the development of battery technology is towards a high capacity, low cost, and long battery lifespan. An accurate prediction of battery state, such as the state of charge, is important to help control the battery charging and discharging and extend the battery lifespan. Several reviews have provided an insightful summary regarding the development of methods to predict battery state for energy storage. This study provides a review that explores the application of machine learning to predict the battery state, including state of charge, state of health, and remaining useful life. Recent studies within this review shown that 64.7% researcher used Neural Network to do prediction with few studies do method combination to further overcome battery dynamic condition in real world application with less computational time and cost to enable integration with IoT technology. Furthermore, the opportunity to implement the energy storage system techniques to enable a smart, low-cost, self-sufficient implementation of the smart solar dryer for agricultural purposes is also elaborated
{"title":"A Review on The Application of Machine Learning To Predict The Battery State That Enables A Smart, Low-Cost, Self-Sufficient Drying And Storage System for Agricultural Purposes","authors":"A. P. Redi, R. G. Widjaja, Iwan Agustono, M. Asrol, A. S. Budiman, F. Gunawan","doi":"10.1145/3557738.3557846","DOIUrl":"https://doi.org/10.1145/3557738.3557846","url":null,"abstract":"This study reviews studies on a more viable battery for the energy storage system, the development of battery technology is towards a high capacity, low cost, and long battery lifespan. An accurate prediction of battery state, such as the state of charge, is important to help control the battery charging and discharging and extend the battery lifespan. Several reviews have provided an insightful summary regarding the development of methods to predict battery state for energy storage. This study provides a review that explores the application of machine learning to predict the battery state, including state of charge, state of health, and remaining useful life. Recent studies within this review shown that 64.7% researcher used Neural Network to do prediction with few studies do method combination to further overcome battery dynamic condition in real world application with less computational time and cost to enable integration with IoT technology. Furthermore, the opportunity to implement the energy storage system techniques to enable a smart, low-cost, self-sufficient implementation of the smart solar dryer for agricultural purposes is also elaborated","PeriodicalId":178760,"journal":{"name":"Proceedings of the 2022 International Conference on Engineering and Information Technology for Sustainable Industry","volume":"117 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124149279","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}
PT Ganding Toolsindo's problem is the replacement of the die which takes a long time, around 30 to 45 minutes per setup. The strategy that can be used to reduce setup time is to apply the Single Minute Exchange of Dies (SMED) method. This research aims to identify the cause of the high die replacement time on the SEYI SN2-300 Press Machine and take corrective steps to increase production effectiveness on the machine, as well as design jigs and fixtures that can make it easier for operators to replace the die. Results Based on data collection on setup dies activity on the SEYI SN2-300 Press Machine, all entered into the internal setup activity with a duration of 2144 seconds. Changes from an internal setup to an external setup have been made. Changing from internal setup to external setup can reduce downtime by 369 seconds. The design of the tools in the form of hanging rollers and roller die is expected to reduce the machine's internal setup time, where these tools can assist operators in moving old dies and installing new dies into the press machine. Based on the measurement of setup time after the implementation of the tools made, the results showed that there was a decrease in setup time of 331 seconds or 5.52 minutes..
{"title":"Implementation of Single Minute Exchange of Dies at PT Ganding Toolsindo","authors":"Indah Kurnia Mahasih Lianny, Sanurya Putri Purbaningrum, Edwin Sahrial Solih","doi":"10.1145/3557738.3557830","DOIUrl":"https://doi.org/10.1145/3557738.3557830","url":null,"abstract":"PT Ganding Toolsindo's problem is the replacement of the die which takes a long time, around 30 to 45 minutes per setup. The strategy that can be used to reduce setup time is to apply the Single Minute Exchange of Dies (SMED) method. This research aims to identify the cause of the high die replacement time on the SEYI SN2-300 Press Machine and take corrective steps to increase production effectiveness on the machine, as well as design jigs and fixtures that can make it easier for operators to replace the die. Results Based on data collection on setup dies activity on the SEYI SN2-300 Press Machine, all entered into the internal setup activity with a duration of 2144 seconds. Changes from an internal setup to an external setup have been made. Changing from internal setup to external setup can reduce downtime by 369 seconds. The design of the tools in the form of hanging rollers and roller die is expected to reduce the machine's internal setup time, where these tools can assist operators in moving old dies and installing new dies into the press machine. Based on the measurement of setup time after the implementation of the tools made, the results showed that there was a decrease in setup time of 331 seconds or 5.52 minutes..","PeriodicalId":178760,"journal":{"name":"Proceedings of the 2022 International Conference on Engineering and Information Technology for Sustainable Industry","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133780100","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}
The implementation of technology is used to support the Making Indonesia 4.0 Program in various industrial sectors in Indonesia to achieve optimization of company performance. This is in line with the Indonesian Government Program through the Ministry of Industry, namely Making Indonesia 4.0. In the Making Indonesia 4.0 Program, there are 7 leading manufacturing sectors. The automotive sector is the object of this research because automotive sector is a leading player in the export of Internal Combustion Engines (ICE) and Electrified Vehicles (EVs). The technology discussed in this research is to support the Making Indonesia 4.0 Program, namely the application of the Internet of Things (IoT) to automotive companies. This research aims to evaluate the critical success factor (CSF) of IoT implementation in automotive companies in Indonesia and obtain a relationship from each dimension that becomes an evaluation of the critical success factor (CSF) of IoT implementation in automotive companies in Indonesia. In this research, the validation stage was carried out by 5 panels of experts in Indonesia who have experience and knowledge in the field of IoT application in automotive companies. The results of the research using the DEMATEL method obtained 4 critical success factors (CSF) for IoT implementation in automotive companies in Indonesia which are dispatchers who are the top priority because they have a greater influence. The four dimensions are the Marketing Dimension, Finance Regulation, and Resources. While the other 4 dimensions, namely the Dimensions of Innovation and Ideas and Resources, Operations, People, and Management, Technology became the receiver as the last priority because it received greater influence. The results of this research can be used as a reference for the automotive industry and various other industries in conducting evaluations related to the critical success factor (CSF) of IoT implementation in companies to improve company performance.
{"title":"Evaluating Critical Success Factors for Implementation of Internet of Things (IoT) Using DEMATEL: A Case of Indonesian Automotive Company","authors":"I. M. Hakim, M. Singgih, I. Gunarta","doi":"10.1145/3557738.3557867","DOIUrl":"https://doi.org/10.1145/3557738.3557867","url":null,"abstract":"The implementation of technology is used to support the Making Indonesia 4.0 Program in various industrial sectors in Indonesia to achieve optimization of company performance. This is in line with the Indonesian Government Program through the Ministry of Industry, namely Making Indonesia 4.0. In the Making Indonesia 4.0 Program, there are 7 leading manufacturing sectors. The automotive sector is the object of this research because automotive sector is a leading player in the export of Internal Combustion Engines (ICE) and Electrified Vehicles (EVs). The technology discussed in this research is to support the Making Indonesia 4.0 Program, namely the application of the Internet of Things (IoT) to automotive companies. This research aims to evaluate the critical success factor (CSF) of IoT implementation in automotive companies in Indonesia and obtain a relationship from each dimension that becomes an evaluation of the critical success factor (CSF) of IoT implementation in automotive companies in Indonesia. In this research, the validation stage was carried out by 5 panels of experts in Indonesia who have experience and knowledge in the field of IoT application in automotive companies. The results of the research using the DEMATEL method obtained 4 critical success factors (CSF) for IoT implementation in automotive companies in Indonesia which are dispatchers who are the top priority because they have a greater influence. The four dimensions are the Marketing Dimension, Finance Regulation, and Resources. While the other 4 dimensions, namely the Dimensions of Innovation and Ideas and Resources, Operations, People, and Management, Technology became the receiver as the last priority because it received greater influence. The results of this research can be used as a reference for the automotive industry and various other industries in conducting evaluations related to the critical success factor (CSF) of IoT implementation in companies to improve company performance.","PeriodicalId":178760,"journal":{"name":"Proceedings of the 2022 International Conference on Engineering and Information Technology for Sustainable Industry","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130667361","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}
B. Prihartono, Anies Sayyidatun Nisa, Hasrini Sari
The pool-to-pool shuttle service business, both for tourist and business purposes, has continued to grow in recent years owing to the aggressive competition among shuttle services which forces shuttle services to better understand their customers’ loyalty. This study seeks to develop a customer loyalty model by considering several factors that influence loyalty such as customer satisfaction, loyalty programs, corporate image, service recovery, perceived safety, price, and service quality. In addition, the effect of differences in the purpose of using the shuttle service was tested on the customer loyalty model. The sample of 384 respondents who have used shuttle services in the last 12 months were used in this study. Sample were divided into two groups, customer for business/college/school purpose and for vacations purpose. Data analysis is performed using multigroup SEM analysis. The SEM results of both of the group show that the absolute fit of the model is adequate judging by the RMSEA value of 0,062 and 0,063 respectively for each group. CFI and TLI which were used as the incremental fit also reach the value of more than 0,9 for both groups which indicate a good incremental fit. Furthermore, from the results showed that there was a significant moderating effect of the difference in the purpose of using the shuttle service on the customer loyalty model. In the group with business/college/school goals, safety perception has no effect on customer satisfaction and company image has no effect on customer loyalty. Customer satisfaction in this group is proven to influence customer loyalty, but the relationship is not stronger than the group with the purpose of using the shuttle service for vacations. In the group with vacation destinations, the perception of safety influences customer satisfaction, but company image has no effect on customer loyalty. Customer satisfaction in this group is proven to influence customer loyalty with a stronger tendency compared to the other group.
{"title":"Customer Loyalty Model Development of Pool-To-Pool Shuttle Service","authors":"B. Prihartono, Anies Sayyidatun Nisa, Hasrini Sari","doi":"10.1145/3557738.3557834","DOIUrl":"https://doi.org/10.1145/3557738.3557834","url":null,"abstract":"The pool-to-pool shuttle service business, both for tourist and business purposes, has continued to grow in recent years owing to the aggressive competition among shuttle services which forces shuttle services to better understand their customers’ loyalty. This study seeks to develop a customer loyalty model by considering several factors that influence loyalty such as customer satisfaction, loyalty programs, corporate image, service recovery, perceived safety, price, and service quality. In addition, the effect of differences in the purpose of using the shuttle service was tested on the customer loyalty model. The sample of 384 respondents who have used shuttle services in the last 12 months were used in this study. Sample were divided into two groups, customer for business/college/school purpose and for vacations purpose. Data analysis is performed using multigroup SEM analysis. The SEM results of both of the group show that the absolute fit of the model is adequate judging by the RMSEA value of 0,062 and 0,063 respectively for each group. CFI and TLI which were used as the incremental fit also reach the value of more than 0,9 for both groups which indicate a good incremental fit. Furthermore, from the results showed that there was a significant moderating effect of the difference in the purpose of using the shuttle service on the customer loyalty model. In the group with business/college/school goals, safety perception has no effect on customer satisfaction and company image has no effect on customer loyalty. Customer satisfaction in this group is proven to influence customer loyalty, but the relationship is not stronger than the group with the purpose of using the shuttle service for vacations. In the group with vacation destinations, the perception of safety influences customer satisfaction, but company image has no effect on customer loyalty. Customer satisfaction in this group is proven to influence customer loyalty with a stronger tendency compared to the other group.","PeriodicalId":178760,"journal":{"name":"Proceedings of the 2022 International Conference on Engineering and Information Technology for Sustainable Industry","volume":"11 10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116816643","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}
One of the processes to minimize downtime unit is the availability of good spare parts. In this study, we will discuss how the process of developing reorder quantity procedures in order to get the availability of spare parts is quite good. in this study, the desired goal is the result of the researcher at 90%. The DMAIC method is used in this study to improve the existing processes in the reordering point. Periodic review with demand fluctuations, vendors holding stock, and inspection programs is an implementation carried out in the inventory process in order to get the availability of slow-moving spare parts, fast-moving spare parts, and spare parts that are monitored for damage. The results obtained are reordering quantity using Inspection which contributes 13% and casting using holding stock vendors based on customer brainstorming contributed 38% while the forgetfulness made by the distributor with periodic review modeling system contributed 43% so the accuracy of the prediction in January was 94% this support is greater than 2021, which is 88%, so there is an increase of 6% and a reduction in the delay time for repairs from 2021, the average order delay is 5.5 days to an order delay of 3.9 days.
{"title":"IMPLEMENTING PERIODIC REVIEW – VARIABLE ORDER QUANTITY SYSTEM IN INVENTORY MANAGEMENT: A CASE STUDY HEAVY EQUIPMENT COMPANIES, KUTAI BARAT","authors":"Anggi Febrianto, T. D. Sofianti, G. Baskoro","doi":"10.1145/3557738.3557740","DOIUrl":"https://doi.org/10.1145/3557738.3557740","url":null,"abstract":"One of the processes to minimize downtime unit is the availability of good spare parts. In this study, we will discuss how the process of developing reorder quantity procedures in order to get the availability of spare parts is quite good. in this study, the desired goal is the result of the researcher at 90%. The DMAIC method is used in this study to improve the existing processes in the reordering point. Periodic review with demand fluctuations, vendors holding stock, and inspection programs is an implementation carried out in the inventory process in order to get the availability of slow-moving spare parts, fast-moving spare parts, and spare parts that are monitored for damage. The results obtained are reordering quantity using Inspection which contributes 13% and casting using holding stock vendors based on customer brainstorming contributed 38% while the forgetfulness made by the distributor with periodic review modeling system contributed 43% so the accuracy of the prediction in January was 94% this support is greater than 2021, which is 88%, so there is an increase of 6% and a reduction in the delay time for repairs from 2021, the average order delay is 5.5 days to an order delay of 3.9 days.","PeriodicalId":178760,"journal":{"name":"Proceedings of the 2022 International Conference on Engineering and Information Technology for Sustainable Industry","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133124523","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}
COVID-19 pandemic has changed the economic weather and business performance in multiple streams. The uncertainty condition caused by the pandemic needs to be carefully taken care by all companies and organizations due to rapid consumer trend shifting and volatile market condition. The sales and marketing strategy needs to be carefully taken during organizational decision-making process to avoid further loss. PT XYZ as one of the leading consumer goods in beauty industry experiences the same condition and challenge reflected by down-trend in the organization KPI. This research aims to introduce and provide predictive data analytics tools for enhancing sales forecast by comparing Random Forest and Neural Network as part of machine learning methods also Vector Autoregression (VAR) as conventional statistical forecasting methodology. As the result of this research, neural network returns better evaluation for skin care and Vector Autoregression for makeup category. Meanwhile data visualization is found necessary to provide additional factual information, includes the external factor, to support knowledge management for better rational decision-making process.
{"title":"Multivariate Sales Forecast Model Towards Trend Shifting During COVID-19 Pandemic: A Case Study in Global Beauty Industry","authors":"Chandra Hartanto, T. D. Sofianti, E. Budiarto","doi":"10.1145/3557738.3557850","DOIUrl":"https://doi.org/10.1145/3557738.3557850","url":null,"abstract":"COVID-19 pandemic has changed the economic weather and business performance in multiple streams. The uncertainty condition caused by the pandemic needs to be carefully taken care by all companies and organizations due to rapid consumer trend shifting and volatile market condition. The sales and marketing strategy needs to be carefully taken during organizational decision-making process to avoid further loss. PT XYZ as one of the leading consumer goods in beauty industry experiences the same condition and challenge reflected by down-trend in the organization KPI. This research aims to introduce and provide predictive data analytics tools for enhancing sales forecast by comparing Random Forest and Neural Network as part of machine learning methods also Vector Autoregression (VAR) as conventional statistical forecasting methodology. As the result of this research, neural network returns better evaluation for skin care and Vector Autoregression for makeup category. Meanwhile data visualization is found necessary to provide additional factual information, includes the external factor, to support knowledge management for better rational decision-making process.","PeriodicalId":178760,"journal":{"name":"Proceedings of the 2022 International Conference on Engineering and Information Technology for Sustainable Industry","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122423136","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}
The transportation sector is a significant contributor to global greenhouse gas (GHG) emissions. It is estimated that replacing fossil fuel-based vehicles with electric vehicles (EVs) powered by sustainable and renewable energy could contribute to approximately 21% of emission avoidance by 2050. To improve the efficiency of EV operation, various artificial intelligence (AI) technologies have been applied. Examples include charging system optimization, self-driving car technology, and traffic control technology. To understand the current readiness status of those technologies applications, a small database of AI use in EVs that is in practice globally is constructed. There are 23 locations of prototype projects identified. The projects are categorized by the AI type, developer type, size of operation, and readiness status. Readiness status is analysed with the Japan Technology Readiness Assessment (J-TRA) methodology. There are seven analysed parameters: 1) Market, 2) Technology development, 3) System Integration, 4) Sustainability Verification, 5) Safety, 6) Commercialization and 7) Cost and Risk. The results show that while there is a promising market, steady progress in technological development, and verified environmental benefits, more work is needed to ensure safety and integration with the current systems before the technology can reach higher readiness levels of commercialization, cost, and risk-coping mechanisms.
{"title":"Readiness Status of Artificial Intelligence Applications on Electric Vehicles: A mini global review and analysis using the J-TRA method","authors":"A. H. Pandyaswargo, M. Maghfiroh, H. Onoda","doi":"10.1145/3557738.3557848","DOIUrl":"https://doi.org/10.1145/3557738.3557848","url":null,"abstract":"The transportation sector is a significant contributor to global greenhouse gas (GHG) emissions. It is estimated that replacing fossil fuel-based vehicles with electric vehicles (EVs) powered by sustainable and renewable energy could contribute to approximately 21% of emission avoidance by 2050. To improve the efficiency of EV operation, various artificial intelligence (AI) technologies have been applied. Examples include charging system optimization, self-driving car technology, and traffic control technology. To understand the current readiness status of those technologies applications, a small database of AI use in EVs that is in practice globally is constructed. There are 23 locations of prototype projects identified. The projects are categorized by the AI type, developer type, size of operation, and readiness status. Readiness status is analysed with the Japan Technology Readiness Assessment (J-TRA) methodology. There are seven analysed parameters: 1) Market, 2) Technology development, 3) System Integration, 4) Sustainability Verification, 5) Safety, 6) Commercialization and 7) Cost and Risk. The results show that while there is a promising market, steady progress in technological development, and verified environmental benefits, more work is needed to ensure safety and integration with the current systems before the technology can reach higher readiness levels of commercialization, cost, and risk-coping mechanisms.","PeriodicalId":178760,"journal":{"name":"Proceedings of the 2022 International Conference on Engineering and Information Technology for Sustainable Industry","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129925166","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}
Ivander Ivander, K. Mahardini, Suryadiputra Liawatimena
PT. XYZ is a company focusing on producing herbal beverages in glass bottles. Inventory level is an important aspect and needs to be controlled by PT. XYZ because it has a strong relationship with the company's cash flow. At this moment, the average inventory level for the glass bottle of PT. XYZ is 32 days, while the target inventory level of the glass bottle must be achieved by PT. XYZ is 14 days. This research will focus on reducing the inventory level of glass bottles in PT. XYZ and ensure the supply sustainability in a high service level. Reduction of inventory level in PT. XYZ can be achieved by implementing the Internet of Things (IoT) and the Vendor Managed Inventory (VMI) method. VMI method implementation will focus on the stock availability with a low inventory level, and the implementation of the Internet of Things will make the inventory level information can be accessed in real-time and resolve this inventory problem. After improvement had been conducted, the service level (vendor performance) of glass bottles increased from 65% to 95%, and the inventory level reduced from 32 days of inventory (DOI) to 9 days.
PT. XYZ是一家专注于生产玻璃瓶草药饮料的公司。库存水平是一个重要的方面，需要由PT. XYZ控制，因为它与公司的现金流有很强的关系。此时PT. XYZ的玻璃瓶平均库存水平为32天，而PT. XYZ必须达到的玻璃瓶目标库存水平为14天。本研究将侧重于减少PT. XYZ的玻璃瓶库存水平，并确保高服务水平的供应可持续性。降低PT. XYZ的库存水平可以通过实施物联网(IoT)和供应商管理库存(VMI)方法来实现。VMI方法的实施将重点关注低库存水平下的库存可用性，而物联网的实施将使库存水平信息可以实时访问并解决这一库存问题。改进后，玻璃瓶的服务水平(供应商绩效)从65%提高到95%，库存水平从32天的库存(DOI)降低到9天。
{"title":"Inventory Level Reduction with VMI and Internet of Things Method","authors":"Ivander Ivander, K. Mahardini, Suryadiputra Liawatimena","doi":"10.1145/3557738.3557873","DOIUrl":"https://doi.org/10.1145/3557738.3557873","url":null,"abstract":"PT. XYZ is a company focusing on producing herbal beverages in glass bottles. Inventory level is an important aspect and needs to be controlled by PT. XYZ because it has a strong relationship with the company's cash flow. At this moment, the average inventory level for the glass bottle of PT. XYZ is 32 days, while the target inventory level of the glass bottle must be achieved by PT. XYZ is 14 days. This research will focus on reducing the inventory level of glass bottles in PT. XYZ and ensure the supply sustainability in a high service level. Reduction of inventory level in PT. XYZ can be achieved by implementing the Internet of Things (IoT) and the Vendor Managed Inventory (VMI) method. VMI method implementation will focus on the stock availability with a low inventory level, and the implementation of the Internet of Things will make the inventory level information can be accessed in real-time and resolve this inventory problem. After improvement had been conducted, the service level (vendor performance) of glass bottles increased from 65% to 95%, and the inventory level reduced from 32 days of inventory (DOI) to 9 days.","PeriodicalId":178760,"journal":{"name":"Proceedings of the 2022 International Conference on Engineering and Information Technology for Sustainable Industry","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134029675","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}
The use of e-marketplace in Indonesia has been growing rapidly for the last decade. The practicality has been the main reason for customers to utilize their shopping activity through e-marketplace. This study aimed to identify and investigate the factors influencing customer purchase intention in the context of e-marketplace. Moreover, this study will be divided into two parts. First part is to present a theoretical perspective of literature in the context of customer purchase intention represented by S-O-R theory and Prospect Theory as basic theories to adopting the model. The second part discusses on a conceptual model, which proposed four antecedent factors (interface quality, information quality of online review, enjoyment, and trust) and one moderating variable: risk, that affect customers purchase intention. The result shows that the interface quality of e- marketplace, information quality of the online review, enjoyment and trust have positive effects towards customer purchase intention. Meanwhile, relationship between trust and purchase intention does not moderate by risk. This result also implied that customers start to consider the usefulness of interface quality and information quality of online reviews before making a purchase. This study suggests that e-marketplaces should place greater reinforcement on developing quality factors of interface and online review on their platform to promote a positive attitude and behavior of customer. Furthermore, this study will help managers to understand the main factors in the online shopping environment which drives customers purchase intention to increase e-marketplace productivity and efficiency.
{"title":"Is Interface Quality and Information Quality on Online Review Matters?","authors":"Vanesa Hana Budiarani, S. Nugroho","doi":"10.1145/3557738.3557832","DOIUrl":"https://doi.org/10.1145/3557738.3557832","url":null,"abstract":"The use of e-marketplace in Indonesia has been growing rapidly for the last decade. The practicality has been the main reason for customers to utilize their shopping activity through e-marketplace. This study aimed to identify and investigate the factors influencing customer purchase intention in the context of e-marketplace. Moreover, this study will be divided into two parts. First part is to present a theoretical perspective of literature in the context of customer purchase intention represented by S-O-R theory and Prospect Theory as basic theories to adopting the model. The second part discusses on a conceptual model, which proposed four antecedent factors (interface quality, information quality of online review, enjoyment, and trust) and one moderating variable: risk, that affect customers purchase intention. The result shows that the interface quality of e- marketplace, information quality of the online review, enjoyment and trust have positive effects towards customer purchase intention. Meanwhile, relationship between trust and purchase intention does not moderate by risk. This result also implied that customers start to consider the usefulness of interface quality and information quality of online reviews before making a purchase. This study suggests that e-marketplaces should place greater reinforcement on developing quality factors of interface and online review on their platform to promote a positive attitude and behavior of customer. Furthermore, this study will help managers to understand the main factors in the online shopping environment which drives customers purchase intention to increase e-marketplace productivity and efficiency.","PeriodicalId":178760,"journal":{"name":"Proceedings of the 2022 International Conference on Engineering and Information Technology for Sustainable Industry","volume":"131 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134102546","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}
L. Sanny, Grace Junita Siwy, Vinna Suhendi, I. Triana, Lea Simek, B. Kelana
This research aims to help company as SME in used cooking oil industry to discover and understand the internal and external conditions, explore their strengths and opportunities, and provide recommendations for the right export strategy to face competition with similar competitors to increase sales. This descriptive research uses primary and secondary data. Primary data is collected by interviewing key persons of the company and secondary data is collected from journals, articles and other relevant professional sources. The data analysis technique used in this research is 7S McKinsey analysis, PESTEL analysis, SWOT analysis, and TOWS Matrix. This study found that the strategy recommendation for SME in used cooking oil is market penetration through digital marketing.
{"title":"Sustainable Export Strategy of Used Cooking Oil SME in Indonesia","authors":"L. Sanny, Grace Junita Siwy, Vinna Suhendi, I. Triana, Lea Simek, B. Kelana","doi":"10.1145/3557738.3557871","DOIUrl":"https://doi.org/10.1145/3557738.3557871","url":null,"abstract":"This research aims to help company as SME in used cooking oil industry to discover and understand the internal and external conditions, explore their strengths and opportunities, and provide recommendations for the right export strategy to face competition with similar competitors to increase sales. This descriptive research uses primary and secondary data. Primary data is collected by interviewing key persons of the company and secondary data is collected from journals, articles and other relevant professional sources. The data analysis technique used in this research is 7S McKinsey analysis, PESTEL analysis, SWOT analysis, and TOWS Matrix. This study found that the strategy recommendation for SME in used cooking oil is market penetration through digital marketing.","PeriodicalId":178760,"journal":{"name":"Proceedings of the 2022 International Conference on Engineering and Information Technology for Sustainable Industry","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129398463","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}
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