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- นระเกณฑ์ พุ่มชูศรี
รศ. ดร.นระเกณฑ์ พุ่มชูศรี
- 5th Floor of Engineering 4 Bldg., Room 504
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- naragain.p@chula.ac.th
Overview
Dr. Naragain Phumchusri is an associate professor at the Department of Industrial Engineering, Chulalongkorn University, Thailand. She received her Ph.D. in Industrial Engineering from Georgia Institute of Technology, Atlanta, GA, USA in 2010. Her current research interests include stochastic models for revenue management, machine learning for demand forecasting, inventory optimization, warehouse & supply chain management, data analysis for tourism industry and promotion optimization in retails.
Education
Ph.D. in Industrial Engineering
Georgia Institute of Technology, United States, 2010
Master of Science in Industrial Engineering
Georgia Institute of Technology, United States, 2006
B.Eng. in Industrial Engineering
Chulalongkorn University, Thailand, 2004
Expertise
Statistics & Data Analysis
Publications
2024
Naragain Phumchusri, Nichakan Phupaichitkun
Sales prediction hybrid models for retails using promotional pricing strategy as a key demand driver Journal Article
In: Journal of Revenue and Pricing Management, vol. 23, no. 5, pp. 461 – 480, 2024, (Cited by: 5).
@article{Phumchusri2024461,
title = {Sales prediction hybrid models for retails using promotional pricing strategy as a key demand driver},
author = {Naragain Phumchusri and Nichakan Phupaichitkun},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189920531&doi=10.1057%2fs41272-024-00477-7&partnerID=40&md5=6daa0bdde1e4e129b142d701f3e1a5c2},
doi = {10.1057/s41272-024-00477-7},
year = {2024},
date = {2024-01-01},
journal = {Journal of Revenue and Pricing Management},
volume = {23},
number = {5},
pages = {461 – 480},
publisher = {Palgrave Macmillan},
abstract = {The implementation of promotional pricing strategies constitutes a key component within the realm of retail revenue management. Nonetheless, the accurate prediction of sales in the presence of price discounts proves challenging due to the influence of various factors that contribute to demand uncertainty and high fluctuations. This study aims to find the most suitable prediction models for retail product unit sales while comprehensively accounting for the complex impacts of contributing factors. The dataset, sourced from a case study of a retail company, spans the temporal interval from January 2020 to December 2022. The predictive models, encompassing linear regression, random forest, XGBoost, artificial neural networks, and hybrid machine-learning models, are systematically developed. Then, the identification of the most suitable model is facilitated through the computation and comparative analysis of the Mean Absolute Percentage Error, with due consideration given to the weighting by the respective product’s revenue, thereby offering a comprehensive assessment of overall performance. Additionally, different types of feature selection are experimented. Factors used in machine learning models are either using all the independent variables or using significant factors from the stepwise method, and either considering or not considering exogenous factors of other products in the same cluster grouped by category, subcategory, or K-means method. The result shows that the series hybrid model of random forest and XGBoost outperformed others. Considering factors affecting sales, it is found that the promotion period factor was the most important, followed by discount percentage and price factors. This research provides analytics framework for sales prediction for retails using promotional pricing as a key demand driver. © The Author(s), under exclusive licence to Springer Nature Limited 2024.},
note = {Cited by: 5},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The implementation of promotional pricing strategies constitutes a key component within the realm of retail revenue management. Nonetheless, the accurate prediction of sales in the presence of price discounts proves challenging due to the influence of various factors that contribute to demand uncertainty and high fluctuations. This study aims to find the most suitable prediction models for retail product unit sales while comprehensively accounting for the complex impacts of contributing factors. The dataset, sourced from a case study of a retail company, spans the temporal interval from January 2020 to December 2022. The predictive models, encompassing linear regression, random forest, XGBoost, artificial neural networks, and hybrid machine-learning models, are systematically developed. Then, the identification of the most suitable model is facilitated through the computation and comparative analysis of the Mean Absolute Percentage Error, with due consideration given to the weighting by the respective product’s revenue, thereby offering a comprehensive assessment of overall performance. Additionally, different types of feature selection are experimented. Factors used in machine learning models are either using all the independent variables or using significant factors from the stepwise method, and either considering or not considering exogenous factors of other products in the same cluster grouped by category, subcategory, or K-means method. The result shows that the series hybrid model of random forest and XGBoost outperformed others. Considering factors affecting sales, it is found that the promotion period factor was the most important, followed by discount percentage and price factors. This research provides analytics framework for sales prediction for retails using promotional pricing as a key demand driver. © The Author(s), under exclusive licence to Springer Nature Limited 2024.
2023
Naragain Phumchusri, Mookarin Thongoiam
Identifying target customer needs for a Master’s Degree Program in Industrial Engineering by conjoint analysis and Kano model Journal Article
In: Model Assisted Statistics and Applications, vol. 18, no. 2, pp. 135 – 147, 2023, (Cited by: 0).
@article{Phumchusri2023135,
title = {Identifying target customer needs for a Master's Degree Program in Industrial Engineering by conjoint analysis and Kano model},
author = {Naragain Phumchusri and Mookarin Thongoiam},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85168155346&doi=10.3233%2fMAS-221409&partnerID=40&md5=cb8b5bc7e8fc323df5818aae08266995},
doi = {10.3233/MAS-221409},
year = {2023},
date = {2023-01-01},
journal = {Model Assisted Statistics and Applications},
volume = {18},
number = {2},
pages = {135 – 147},
publisher = {IOS Press BV},
abstract = {Customer satisfaction has become a key factor in strategic work of many institutions towards the increasing competition regarding student recruitment. This paper presents a systematic approach to identify customer needs for a Master's Degree Program in Industrial Engineering based on target students' needs in the view of new product development. The approach consists of two methods: Choice-based conjoint analysis and Kano model. Conjoint analysis is used to explore important scores of each attribute of the program, i.e., specialist concentration, class period, research type, teaching language, teaching format, and tuition fee. Also, the popularity of levels in each attribute are identified. Latent class model is used to identify different clusters of target customers. The result indicates two different segments of different preferences. The heterogeneity of needs and preference is characterized mainly in levels of specialist concentration preference as well as other attributes such as tuition fee. Other attributes such as interdisciplinary, cooperate program, work experience requirement and group (with presence/absence option) are analyzed by Kano model to identify their categories, i.e., how important they are. This research contributes in the literature as a pioneer in applying these two methods to gain customer perception insights about new Master's curriculum development for education industry. © 2023-IOS Press. All rights reserved.},
note = {Cited by: 0},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Customer satisfaction has become a key factor in strategic work of many institutions towards the increasing competition regarding student recruitment. This paper presents a systematic approach to identify customer needs for a Master’s Degree Program in Industrial Engineering based on target students’ needs in the view of new product development. The approach consists of two methods: Choice-based conjoint analysis and Kano model. Conjoint analysis is used to explore important scores of each attribute of the program, i.e., specialist concentration, class period, research type, teaching language, teaching format, and tuition fee. Also, the popularity of levels in each attribute are identified. Latent class model is used to identify different clusters of target customers. The result indicates two different segments of different preferences. The heterogeneity of needs and preference is characterized mainly in levels of specialist concentration preference as well as other attributes such as tuition fee. Other attributes such as interdisciplinary, cooperate program, work experience requirement and group (with presence/absence option) are analyzed by Kano model to identify their categories, i.e., how important they are. This research contributes in the literature as a pioneer in applying these two methods to gain customer perception insights about new Master’s curriculum development for education industry. © 2023-IOS Press. All rights reserved.
Naragain Phumchusri, Warot Kosawanitchakarn, Sirawich Chawanapranee, Sirawish Srimook
Evaluating promotional pricing effectiveness using convenience store daily sales data Journal Article
In: Journal of Revenue and Pricing Management, vol. 22, no. 5, pp. 362 – 373, 2023, (Cited by: 6; All Open Access, Bronze Open Access, Green Open Access).
@article{Phumchusri2023362,
title = {Evaluating promotional pricing effectiveness using convenience store daily sales data},
author = {Naragain Phumchusri and Warot Kosawanitchakarn and Sirawich Chawanapranee and Sirawish Srimook},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85142424328&doi=10.1057%2fs41272-022-00415-5&partnerID=40&md5=b40430900fce62f31ac942fea426c532},
doi = {10.1057/s41272-022-00415-5},
year = {2023},
date = {2023-01-01},
journal = {Journal of Revenue and Pricing Management},
volume = {22},
number = {5},
pages = {362 – 373},
publisher = {Palgrave Macmillan},
abstract = {One of the activities that can grab customers attention and rise sales for convenience stores is promotional pricing strategy. Our study aims to examine the effects of promotional pricing and other factors on sales. Six categories of products with 286 SKUs are explored. Four models are compared, and the results show that autoregressive-distributed lag model provides the lowest mean absolute percentage error (MAPE). This model can also capture the interaction between promotion and non-promotion products. Price elasticity of each product is found to be different, and it results different optimal prices for the maximum profit. Moreover, factors like holidays, the beginning of the month, or weekend, can uplift sales at a specific time. Unlike previous literature, this paper focuses on daily sales and related recent factors such as the number of COVID-19 cases. The methodology presented in this research provides guidelines for retailers to measure their pricing strategy and can be managerial insights for other retailers’ future strategy. © 2022, The Author(s), under exclusive licence to Springer Nature Limited.},
note = {Cited by: 6; All Open Access, Bronze Open Access, Green Open Access},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
One of the activities that can grab customers attention and rise sales for convenience stores is promotional pricing strategy. Our study aims to examine the effects of promotional pricing and other factors on sales. Six categories of products with 286 SKUs are explored. Four models are compared, and the results show that autoregressive-distributed lag model provides the lowest mean absolute percentage error (MAPE). This model can also capture the interaction between promotion and non-promotion products. Price elasticity of each product is found to be different, and it results different optimal prices for the maximum profit. Moreover, factors like holidays, the beginning of the month, or weekend, can uplift sales at a specific time. Unlike previous literature, this paper focuses on daily sales and related recent factors such as the number of COVID-19 cases. The methodology presented in this research provides guidelines for retailers to measure their pricing strategy and can be managerial insights for other retailers’ future strategy. © 2022, The Author(s), under exclusive licence to Springer Nature Limited.
Tirawat Jarukajornjinda, Naragain Phumchusri
Association for Computing Machinery, 2023, (Cited by: 0).
@conference{Jarukajornjinda2023179,
title = {Time Series and Machine Learning methods for Demand Forecasting: A case study of machine seller in prefabricated concrete industry},
author = {Tirawat Jarukajornjinda and Naragain Phumchusri},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85171144902&doi=10.1145%2f3603955.3604012&partnerID=40&md5=dd04258a7020aae6520613e00d8b6061},
doi = {10.1145/3603955.3604012},
year = {2023},
date = {2023-01-01},
journal = {ACM International Conference Proceeding Series},
pages = {179 – 184},
publisher = {Association for Computing Machinery},
abstract = {In this research, Time series and Machine learning methods are conducted to find a suitable monthly demand forecasting models and parameters for a medium-scaled machine seller company in Thailand, who is currently using Simple Moving Average as the current method. Historical demand data are collected for a half decade and used as a training set and cross validation set for different forecasting models (Exponential Smoothing, Autoregressive Integrated Moving Average and Long-Short term memory). The history of demand indicates that the patterns are composed of many types of time series components, including stationary, trend and seasonality. The performance of models is measured by Relative Total Absolute Error (RTAE) because some months contain zero value of demand. The results show that Long-Short term memory is the most suitable method as compared to others. © 2023 ACM.},
note = {Cited by: 0},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
In this research, Time series and Machine learning methods are conducted to find a suitable monthly demand forecasting models and parameters for a medium-scaled machine seller company in Thailand, who is currently using Simple Moving Average as the current method. Historical demand data are collected for a half decade and used as a training set and cross validation set for different forecasting models (Exponential Smoothing, Autoregressive Integrated Moving Average and Long-Short term memory). The history of demand indicates that the patterns are composed of many types of time series components, including stationary, trend and seasonality. The performance of models is measured by Relative Total Absolute Error (RTAE) because some months contain zero value of demand. The results show that Long-Short term memory is the most suitable method as compared to others. © 2023 ACM.
Naragain Phumchusri, Phongsatorn Amornvetchayakul
Machine learning models for predicting customer churn: a case study in a software-as-a-service inventory management company Journal Article
In: International Journal of Business Intelligence and Data Mining, vol. 24, no. 1, pp. 74 – 106, 2023, (Cited by: 7; All Open Access, Bronze Open Access).
@article{Phumchusri202374,
title = {Machine learning models for predicting customer churn: a case study in a software-as-a-service inventory management company},
author = {Naragain Phumchusri and Phongsatorn Amornvetchayakul},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85179136907&doi=10.1504%2fIJBIDM.2024.135146&partnerID=40&md5=b272ae9f13b5d4f6f9e4a52771032a00},
doi = {10.1504/IJBIDM.2024.135146},
year = {2023},
date = {2023-01-01},
journal = {International Journal of Business Intelligence and Data Mining},
volume = {24},
number = {1},
pages = {74 – 106},
publisher = {Inderscience Publishers},
abstract = {Software-as-a-service (SaaS) is a software-licensing model, which allows access to software on a subscription basis using external servers. This article proposes customer churn prediction models for a SaaS inventory management company in Thailand. The main focus of this work is seeking the most suitable customer churn prediction model for this case-study SaaS inventory management company which is currently having a high churn rate issue. This paper explores four machine learning algorithms, which are logistic regression, support vector machine, decision tree (DT) and random forest. The results show that the optimised DT model is capable of outperforming other classification models toward recall scorer with validated testing scores of 94.4% of recall and 88.2% of F1-score. Moreover, feature importance scores are investigated for practical insights to identify features that are significantly related to churn behaviour. Therefore, the findings can help the case-study company indicate customers who are going to churn more precisely and enhance the effectiveness of managerial decisions and effective marketing movement. Copyright © 2024 Inderscience Enterprises Ltd.},
note = {Cited by: 7; All Open Access, Bronze Open Access},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Software-as-a-service (SaaS) is a software-licensing model, which allows access to software on a subscription basis using external servers. This article proposes customer churn prediction models for a SaaS inventory management company in Thailand. The main focus of this work is seeking the most suitable customer churn prediction model for this case-study SaaS inventory management company which is currently having a high churn rate issue. This paper explores four machine learning algorithms, which are logistic regression, support vector machine, decision tree (DT) and random forest. The results show that the optimised DT model is capable of outperforming other classification models toward recall scorer with validated testing scores of 94.4% of recall and 88.2% of F1-score. Moreover, feature importance scores are investigated for practical insights to identify features that are significantly related to churn behaviour. Therefore, the findings can help the case-study company indicate customers who are going to churn more precisely and enhance the effectiveness of managerial decisions and effective marketing movement. Copyright © 2024 Inderscience Enterprises Ltd.
Naragain Phumchusri, Poonnawit Suwatanapongched
Forecasting hotel daily room demand with transformed data using time series methods Journal Article
In: Journal of Revenue and Pricing Management, vol. 22, no. 1, pp. 44 – 56, 2023, (Cited by: 16).
@article{Phumchusri202344,
title = {Forecasting hotel daily room demand with transformed data using time series methods},
author = {Naragain Phumchusri and Poonnawit Suwatanapongched},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120064461&doi=10.1057%2fs41272-021-00363-6&partnerID=40&md5=237e8156dd6c9bc76403536cce5ad675},
doi = {10.1057/s41272-021-00363-6},
year = {2023},
date = {2023-01-01},
journal = {Journal of Revenue and Pricing Management},
volume = {22},
number = {1},
pages = {44 – 56},
publisher = {Palgrave Macmillan},
abstract = {Increasing tourism and economic growth have led to a remarkably increase in demand and competition for hotel business in Thailand. This paper aims to explore the benefits of data transforming to help forecast hotel daily room demand for a case-study hotel. The proposed results can be a forecasting framework for other hotels with similar demand patterns. The case-study hotel is a local 4-star, 97-room hotel in Thailand. The recorded data of daily room demand from 2016 to 2019 are used. For room demand forecasting, two different datasets of daily demand are used, i.e., pre-processed data and transformed data by smoothing technique. Different time series forecasting models are performed: (1) Same day last year, (2) Holt–Winters, (3) Seasonal Autoregressive integrated moving average (SARIMA), and (4) Box–Jenkins Box–Cox transformation trigonometric ARMA errors trend and multiple seasonal patterns. We compared the accuracy of each model in forecasting of room demand with the actual room occupancies in 2019. The model accuracy is evaluated using mean absolute error (MAE), mean absolute percentage error (MAPE), and median absolute percentage error (MdAPE). It is found that SARIMA using transformed dataset provided the best accuracy (MAE, 6.18; MAPE, 25.04%; MdAPE, 13.64%) and best fit with plots for 2-week forecast horizon of room demand data. This paper introduces the use of transformed dataset to increase the performance of SARIMA model, as compared to the pre-processed data. To our knowledge, unlike other research, this paper proposed the method of data pre-processing and data smoothing to deal with the high variation in room demand data. © 2021, The Author(s), under exclusive licence to Springer Nature Limited.},
note = {Cited by: 16},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Increasing tourism and economic growth have led to a remarkably increase in demand and competition for hotel business in Thailand. This paper aims to explore the benefits of data transforming to help forecast hotel daily room demand for a case-study hotel. The proposed results can be a forecasting framework for other hotels with similar demand patterns. The case-study hotel is a local 4-star, 97-room hotel in Thailand. The recorded data of daily room demand from 2016 to 2019 are used. For room demand forecasting, two different datasets of daily demand are used, i.e., pre-processed data and transformed data by smoothing technique. Different time series forecasting models are performed: (1) Same day last year, (2) Holt–Winters, (3) Seasonal Autoregressive integrated moving average (SARIMA), and (4) Box–Jenkins Box–Cox transformation trigonometric ARMA errors trend and multiple seasonal patterns. We compared the accuracy of each model in forecasting of room demand with the actual room occupancies in 2019. The model accuracy is evaluated using mean absolute error (MAE), mean absolute percentage error (MAPE), and median absolute percentage error (MdAPE). It is found that SARIMA using transformed dataset provided the best accuracy (MAE, 6.18; MAPE, 25.04%; MdAPE, 13.64%) and best fit with plots for 2-week forecast horizon of room demand data. This paper introduces the use of transformed dataset to increase the performance of SARIMA model, as compared to the pre-processed data. To our knowledge, unlike other research, this paper proposed the method of data pre-processing and data smoothing to deal with the high variation in room demand data. © 2021, The Author(s), under exclusive licence to Springer Nature Limited.
2022
Chayakorn Auppakorn, Naragain Phumchusri
Daily Sales Forecasting for Variable-Priced Items in Retail Business Conference
Association for Computing Machinery, 2022, (Cited by: 6).
@conference{Auppakorn202280,
title = {Daily Sales Forecasting for Variable-Priced Items in Retail Business},
author = {Chayakorn Auppakorn and Naragain Phumchusri},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135075392&doi=10.1145%2f3535782.3535794&partnerID=40&md5=974de8dd16273688f16d0a765a855b96},
doi = {10.1145/3535782.3535794},
year = {2022},
date = {2022-01-01},
journal = {ACM International Conference Proceeding Series},
pages = {80 – 86},
publisher = {Association for Computing Machinery},
abstract = {Modern retail business manages products from various sources to serve consumers. To be able to respond to customers' needs, accurate sales forecasting is essential to prepare appropriate levels of stocks. This research aims to find methods and features to forecast the daily sales for a case study retail store chain having many categories of products with varied promotion prices. Three main models are considered: (1) Time series forecasting, i.e., TBATS model, (2) explanatory forecasting method, i.e., multiple linear regression, and (3) machine learning model, i.e., XGBoost. Different types of dependent and independent variables transformation in Stepwise regression are also considered in order to find the most accurate results. Since there were records of the number of Covid-19 cases in Thailand from 2020 and there was also government's welfare money policy during Covid-19 crisis, this paper attempts to find how these variables affects retail sales. Weighted absolute percentage error (WAPE) is used to compare the accuracy among different models. © 2022 ACM.},
note = {Cited by: 6},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Modern retail business manages products from various sources to serve consumers. To be able to respond to customers’ needs, accurate sales forecasting is essential to prepare appropriate levels of stocks. This research aims to find methods and features to forecast the daily sales for a case study retail store chain having many categories of products with varied promotion prices. Three main models are considered: (1) Time series forecasting, i.e., TBATS model, (2) explanatory forecasting method, i.e., multiple linear regression, and (3) machine learning model, i.e., XGBoost. Different types of dependent and independent variables transformation in Stepwise regression are also considered in order to find the most accurate results. Since there were records of the number of Covid-19 cases in Thailand from 2020 and there was also government’s welfare money policy during Covid-19 crisis, this paper attempts to find how these variables affects retail sales. Weighted absolute percentage error (WAPE) is used to compare the accuracy among different models. © 2022 ACM.
Mookarin Thongoiam, Naragain Phumchusri
Association for Computing Machinery, 2022, (Cited by: 1).
@conference{Thongoiam2022206,
title = {Identifying Customer Needs for a Master's Degree Program in Industrial Engineering: A Case Study from Prospective Students' Insights},
author = {Mookarin Thongoiam and Naragain Phumchusri},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135012306&doi=10.1145%2f3535782.3535810&partnerID=40&md5=219d10bb915377bf0084ced7db37518f},
doi = {10.1145/3535782.3535810},
year = {2022},
date = {2022-01-01},
journal = {ACM International Conference Proceeding Series},
pages = {206 – 213},
publisher = {Association for Computing Machinery},
abstract = {Customer satisfaction has become a key factor in strategic work of many institutions towards the increasing competition regarding student recruitment. This paper presents a systematic approach to identify customer needs for a Master's Degree Program in Industrial Engineering based on target students' needs in the view of new product development. The approach consists of two methods: Choice-based conjoint analysis and Kano's Model. Conjoint analysis is used to explore important scores of each attribute of the program, i.e., specialist concentration, teaching period, research type, program language, teaching format, and tuition fee. Also, the popularity of levels in each attribute are identified. Results from conjoint analysis indicate that the most preferred master curriculum is a program of Business Data Analytics concentration, English language, full-time, hybrid of online and onsite, independence study research type, and tuition fees of 63,500 Baht. Other attributes such as interdisciplinary, joint program, work experience requirement and group (with presence/absence option) are analyzed by Kano's model to identify their category. This research contributes in the literature as a pioneer in applying these two methods to gain customer perception insights about new Master's curriculum development for education industry. © 2022 ACM.},
note = {Cited by: 1},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Customer satisfaction has become a key factor in strategic work of many institutions towards the increasing competition regarding student recruitment. This paper presents a systematic approach to identify customer needs for a Master’s Degree Program in Industrial Engineering based on target students’ needs in the view of new product development. The approach consists of two methods: Choice-based conjoint analysis and Kano’s Model. Conjoint analysis is used to explore important scores of each attribute of the program, i.e., specialist concentration, teaching period, research type, program language, teaching format, and tuition fee. Also, the popularity of levels in each attribute are identified. Results from conjoint analysis indicate that the most preferred master curriculum is a program of Business Data Analytics concentration, English language, full-time, hybrid of online and onsite, independence study research type, and tuition fees of 63,500 Baht. Other attributes such as interdisciplinary, joint program, work experience requirement and group (with presence/absence option) are analyzed by Kano’s model to identify their category. This research contributes in the literature as a pioneer in applying these two methods to gain customer perception insights about new Master’s curriculum development for education industry. © 2022 ACM.
Pattraporn Jirapatsil, Naragain Phumchusri
Association for Computing Machinery, 2022, (Cited by: 3).
@conference{Jirapatsil202223,
title = {Market Basket Analysis for Fresh Products location improvement: A case study of E-Commerce Business Warehouse},
author = {Pattraporn Jirapatsil and Naragain Phumchusri},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135050284&doi=10.1145%2f3535782.3535786&partnerID=40&md5=c508846af3f162e5c4893e6dce815afc},
doi = {10.1145/3535782.3535786},
year = {2022},
date = {2022-01-01},
journal = {ACM International Conference Proceeding Series},
pages = {23 – 28},
publisher = {Association for Computing Machinery},
abstract = {Market Basket Analysis (MBA) uses the data mining technique as an analysis tool to understand association among many items. It is a useful tool for extracting information from large amount of data in many industrial areas, e.g., grocery, supermarkets, retailers, warehouse, mobile showroom, libraries, zoos, etc. The case-study company sells fresh products in E-commence business and currently has inefficient product location in warehouse, causing delays in picking process. Thus, the goal of this paper is to propose a market basket analysis method to gain insights from historical transactions, a set of recording data result in connections with sales-purchase activities, of the case-study company. Apiori algorithm is applied for association rules to analyze 2366 transactions data between July and December 2021. The results of data analysis are then utilized in rearranging products location in warehouse to reduce average picking distance per order. The results show that the average distance per order can be reduced by 54.4%. © 2022 ACM.},
note = {Cited by: 3},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Market Basket Analysis (MBA) uses the data mining technique as an analysis tool to understand association among many items. It is a useful tool for extracting information from large amount of data in many industrial areas, e.g., grocery, supermarkets, retailers, warehouse, mobile showroom, libraries, zoos, etc. The case-study company sells fresh products in E-commence business and currently has inefficient product location in warehouse, causing delays in picking process. Thus, the goal of this paper is to propose a market basket analysis method to gain insights from historical transactions, a set of recording data result in connections with sales-purchase activities, of the case-study company. Apiori algorithm is applied for association rules to analyze 2366 transactions data between July and December 2021. The results of data analysis are then utilized in rearranging products location in warehouse to reduce average picking distance per order. The results show that the average distance per order can be reduced by 54.4%. © 2022 ACM.
Naragain Phumchusri, Warot Kosawanitchakarn, Sirawish Srimook, Sirawich Chawanapranee
Agent-based Simulation for Convenient Store’s Promotion Strategy Selection Conference
vol. 2022-December, IEEE Computer Society, 2022, (Cited by: 1).
@conference{Phumchusri2022776,
title = {Agent-based Simulation for Convenient Store's Promotion Strategy Selection},
author = {Naragain Phumchusri and Warot Kosawanitchakarn and Sirawish Srimook and Sirawich Chawanapranee},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85146329719&doi=10.1109%2fIEEM55944.2022.9989902&partnerID=40&md5=02defe887f5e0e011fb518052ac68aa9},
doi = {10.1109/IEEM55944.2022.9989902},
year = {2022},
date = {2022-01-01},
journal = {IEEE International Conference on Industrial Engineering and Engineering Management},
volume = {2022-December},
pages = {776 – 780},
publisher = {IEEE Computer Society},
abstract = {Nowadays, convenience store becomes more important to urban life. It is also a competitive industry for retailers who would like to gain attention from customers and grow their profit. To achieve their goals, doing promotion is a way to go. However, promotion has two sides where the gain is also coming with the possibility of loss, so a thoughtful decision is concerned when doing the promotion. This study aims to develop agent-based simulation, an approach to deal with complex and high-dimension problems, to find how each strategy works on different price elasticities for strategic insights for the company's future planning. The agent-based simulation is able to reach many possibilities of combinations between strategy and price elasticity instead of testing or gathering data in the real world. To achieve our purpose, we vary strategies while fixing the price elasticity and see how customers react in each situation. The customer's decision is initially based two factors (advertisement effectiveness, and word of mouth), and then it is based on price reduction rate when they come to the store. The strategy is designed on two dimensions, percentage of price reduction and frequency of the promotion. The result shows that different strategies work on different price elasticity values where high price reduction rate strategy works well on high price elasticity, and vice versa. This study provides an insight about promotional strategy selection and future vision for a new method to approach complex problems. © 2022 IEEE.},
note = {Cited by: 1},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Nowadays, convenience store becomes more important to urban life. It is also a competitive industry for retailers who would like to gain attention from customers and grow their profit. To achieve their goals, doing promotion is a way to go. However, promotion has two sides where the gain is also coming with the possibility of loss, so a thoughtful decision is concerned when doing the promotion. This study aims to develop agent-based simulation, an approach to deal with complex and high-dimension problems, to find how each strategy works on different price elasticities for strategic insights for the company’s future planning. The agent-based simulation is able to reach many possibilities of combinations between strategy and price elasticity instead of testing or gathering data in the real world. To achieve our purpose, we vary strategies while fixing the price elasticity and see how customers react in each situation. The customer’s decision is initially based two factors (advertisement effectiveness, and word of mouth), and then it is based on price reduction rate when they come to the store. The strategy is designed on two dimensions, percentage of price reduction and frequency of the promotion. The result shows that different strategies work on different price elasticity values where high price reduction rate strategy works well on high price elasticity, and vice versa. This study provides an insight about promotional strategy selection and future vision for a new method to approach complex problems. © 2022 IEEE.