- Home
- /
- Naragain Phumchusri
Assoc. Prof. Naragain Phumchusri, Ph.D.
- 5th Floor of Engineering 4 Bldg., Room 504
- +66-2218-6822
- 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
2025
Chutima Binsriavanich, Naragain Phumchusri
An analysis of retail promotional pricing effectiveness using agent-based modeling Journal Article
In: Journal of Revenue and Pricing Management, vol. 24, no. 1, pp. 60 – 79, 2025, ISSN: 14766930, (Cited by: 1).
@article{Binsriavanich202560,
title = {An analysis of retail promotional pricing effectiveness using agent-based modeling},
author = {Chutima Binsriavanich and Naragain Phumchusri},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85211935209&doi=10.1057%2fs41272-024-00512-7&partnerID=40&md5=fb87bdd84c73aeee7fa60475fb557f63},
doi = {10.1057/s41272-024-00512-7},
issn = {14766930},
year = {2025},
date = {2025-01-01},
journal = {Journal of Revenue and Pricing Management},
volume = {24},
number = {1},
pages = {60 – 79},
publisher = {Palgrave Macmillan},
abstract = {In contemporary urban contexts, retail establishments have emerged as essential components of city life, engaging in fierce competition to capture consumer attention and augment their financial gains. Implementing a price promotion strategy is essential for efficaciously appealing customers. Nonetheless, the complex interaction between consumer preferences and product attributes, particularly in environments characterized by competitive product offerings, complicates the development of effective promotional strategies. This paper aims to present an agent-based simulation model for capturing the results of strategic approaches for retails offering competitive products, thereby sidestepping the need for empirical testing or data collection in real-world settings. In this model, consumer decision-making processes are initially influenced by two primary factors: the effectiveness of advertising and the impact of word-of-mouth communication. Subsequent decisions are then depending on the degree of price reduction encountered in-store. The simulation assesses various promotional tactics, examining the depth of price reductions, the frequency and timing of promotions, and the resultant impact on store profitability. The outcomes reveal that distinct strategies yield varying levels of effectiveness depending on the price elasticity of products. Moreover, non-overlapping promotional happenings yield superior profit margins as compared to concurrent promotions. The insights garnered from this study are anticipated to provide helpful guidance for future strategic planning for retails. © The Author(s), under exclusive licence to Springer Nature Limited 2024.},
note = {Cited by: 1},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
In contemporary urban contexts, retail establishments have emerged as essential components of city life, engaging in fierce competition to capture consumer attention and augment their financial gains. Implementing a price promotion strategy is essential for efficaciously appealing customers. Nonetheless, the complex interaction between consumer preferences and product attributes, particularly in environments characterized by competitive product offerings, complicates the development of effective promotional strategies. This paper aims to present an agent-based simulation model for capturing the results of strategic approaches for retails offering competitive products, thereby sidestepping the need for empirical testing or data collection in real-world settings. In this model, consumer decision-making processes are initially influenced by two primary factors: the effectiveness of advertising and the impact of word-of-mouth communication. Subsequent decisions are then depending on the degree of price reduction encountered in-store. The simulation assesses various promotional tactics, examining the depth of price reductions, the frequency and timing of promotions, and the resultant impact on store profitability. The outcomes reveal that distinct strategies yield varying levels of effectiveness depending on the price elasticity of products. Moreover, non-overlapping promotional happenings yield superior profit margins as compared to concurrent promotions. The insights garnered from this study are anticipated to provide helpful guidance for future strategic planning for retails. © The Author(s), under exclusive licence to Springer Nature Limited 2024.
Santi Wongkamphu, Naragain Phumchusri
Hybrid Approaches to Machine Learning for Improved Battery Sales Forecasting: A Case Study in Thailand Journal Article
In: Engineering Journal, vol. 29, no. 2, pp. 27 – 43, 2025, ISSN: 01258281, (Cited by: 0).
@article{Wongkamphu202527,
title = {Hybrid Approaches to Machine Learning for Improved Battery Sales Forecasting: A Case Study in Thailand},
author = {Santi Wongkamphu and Naragain Phumchusri},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105000148099&doi=10.4186%2fej.2025.29.2.27&partnerID=40&md5=ddfdc08c3e9395cf76d0a3106ac47ba8},
doi = {10.4186/ej.2025.29.2.27},
issn = {01258281},
year = {2025},
date = {2025-01-01},
journal = {Engineering Journal},
volume = {29},
number = {2},
pages = {27 – 43},
publisher = {Chulalongkorn University, Faculty of Fine and Applied Arts},
abstract = {Battery sales forecasting is a critical component of demand planning in the automotive battery industry, directly influencing production, inventory management, and supply chain optimization. This study presents a comprehensive evaluation of traditional forecasting methods and machine learning techniques to predict monthly sales for a battery manufacturer in Thailand. Utilizing a dataset of monthly sales for the 10 best-selling products from January 2018 to December 2023, the research investigates the performance of traditional models such as Holt’s Linear Trend, Holt-Winters Seasonal, ARIMA, SARIMA, and SARIMAX. Advanced machine learning approaches, including Long Short-Term Memory (LSTM) networks and Artificial Neural Networks (ANN), are also explored. Additionally, hybrid models combining traditional and machine learning techniques are developed to leverage their respective strengths. The study integrates external factors such as economic indicators, industry-specific variables, and lagged data during feature selection to enhance predictive accuracy. Model performance is rigorously evaluated using Mean Absolute Percentage Error (MAPE). The results demonstrate that the hybrid ANN-LSTM model achieves the highest accuracy, with an average MAPE of 8.83%, significantly outperforming individual models, including the best-performing traditional model, ANN, at 9.43%. This research contributes to the field by providing a robust analytics framework that integrates traditional and advanced machine learning methodologies, offering actionable insights for battery sales forecasting and enhancing decision-making processes in the automotive industry. © 2025, Chulalongkorn University, Faculty of Fine and Applied Arts. All rights reserved.},
note = {Cited by: 0},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Battery sales forecasting is a critical component of demand planning in the automotive battery industry, directly influencing production, inventory management, and supply chain optimization. This study presents a comprehensive evaluation of traditional forecasting methods and machine learning techniques to predict monthly sales for a battery manufacturer in Thailand. Utilizing a dataset of monthly sales for the 10 best-selling products from January 2018 to December 2023, the research investigates the performance of traditional models such as Holt’s Linear Trend, Holt-Winters Seasonal, ARIMA, SARIMA, and SARIMAX. Advanced machine learning approaches, including Long Short-Term Memory (LSTM) networks and Artificial Neural Networks (ANN), are also explored. Additionally, hybrid models combining traditional and machine learning techniques are developed to leverage their respective strengths. The study integrates external factors such as economic indicators, industry-specific variables, and lagged data during feature selection to enhance predictive accuracy. Model performance is rigorously evaluated using Mean Absolute Percentage Error (MAPE). The results demonstrate that the hybrid ANN-LSTM model achieves the highest accuracy, with an average MAPE of 8.83%, significantly outperforming individual models, including the best-performing traditional model, ANN, at 9.43%. This research contributes to the field by providing a robust analytics framework that integrates traditional and advanced machine learning methodologies, offering actionable insights for battery sales forecasting and enhancing decision-making processes in the automotive industry. © 2025, Chulalongkorn University, Faculty of Fine and Applied Arts. All rights reserved.
Yanvaroj Pongsethpaisal, Naragain Phumchusri, Paveena Chaovalitwongse
Employing Neuroevolution of Augmenting Topologies (NEAT) in Linear Multi-Echelon Inventory Systems Journal Article
In: Engineering Journal, vol. 29, no. 3, pp. 11 – 26, 2025, ISSN: 01258281, (Cited by: 0).
@article{Pongsethpaisal202511,
title = {Employing Neuroevolution of Augmenting Topologies (NEAT) in Linear Multi-Echelon Inventory Systems},
author = {Yanvaroj Pongsethpaisal and Naragain Phumchusri and Paveena Chaovalitwongse},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105002468604&doi=10.4186%2fej.2025.29.3.11&partnerID=40&md5=d7799e5a5f4fdc7c974b4d5f64f797af},
doi = {10.4186/ej.2025.29.3.11},
issn = {01258281},
year = {2025},
date = {2025-01-01},
journal = {Engineering Journal},
volume = {29},
number = {3},
pages = {11 – 26},
publisher = {Chulalongkorn University, Faculty of Fine and Applied Arts},
abstract = {Reinforcement learning has emerged as a leading algorithmic approach due to its successful applications across various domains. While many implementations favour the model-free approach for its aptitude for handling complex problems, its learning curve tends to be slower. Given the intricacies of the Linear Multi-Echelon Inventory System, a model-based approach might be more fitting, offering faster learning rates. This study seeks to integrate Neuroevolution of Augment Topologies (NEAT) – a hybrid of model-based reinforcement learning and evolutionary algorithms – into such an inventory system. Furthermore, the research delves into hyperparameter tuning, experimenting with seven specific hyperparameters to discern the most efficient combination and understand their inter-play. Benchmarking against the model-free Proximal Policy Optimisation (PPO) serves as a measure of NEAT’s effectiveness. Findings indicate that when optimally tuned, NEAT can slash total costs by 25.02% compared to PPO. Impressively, NEAT achieves this peak performance in a mere 1,000 generations, significantly outpacing PPO’s learning trajectory. © 2025, Chulalongkorn University, Faculty of Fine and Applied Arts. All rights reserved.},
note = {Cited by: 0},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Reinforcement learning has emerged as a leading algorithmic approach due to its successful applications across various domains. While many implementations favour the model-free approach for its aptitude for handling complex problems, its learning curve tends to be slower. Given the intricacies of the Linear Multi-Echelon Inventory System, a model-based approach might be more fitting, offering faster learning rates. This study seeks to integrate Neuroevolution of Augment Topologies (NEAT) – a hybrid of model-based reinforcement learning and evolutionary algorithms – into such an inventory system. Furthermore, the research delves into hyperparameter tuning, experimenting with seven specific hyperparameters to discern the most efficient combination and understand their inter-play. Benchmarking against the model-free Proximal Policy Optimisation (PPO) serves as a measure of NEAT’s effectiveness. Findings indicate that when optimally tuned, NEAT can slash total costs by 25.02% compared to PPO. Impressively, NEAT achieves this peak performance in a mere 1,000 generations, significantly outpacing PPO’s learning trajectory. © 2025, Chulalongkorn University, Faculty of Fine and Applied Arts. All rights reserved.
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, 2024, (Cited by: 1).
@article{Phumchusri2024,
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},
note = {Cited by: 1},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Thanaporn Kusomrosananan, Naragain Phumchusri
Inventory policy improvement with periodic review for perishable goods: A case study of a retail coffee shop in thailand Journal Article
In: Engineering Journal, vol. 28, no. 6, pp. 59 – 73, 2024, ISSN: 01258281, (Cited by: 1; All Open Access, Gold Open Access).
@article{Kusomrosananan202459,
title = {Inventory policy improvement with periodic review for perishable goods: A case study of a retail coffee shop in thailand},
author = {Thanaporn Kusomrosananan and Naragain Phumchusri},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85205096590&doi=10.4186%2fej.2024.28.6.59&partnerID=40&md5=171ab74c9a64b60013212ec4fea2de34},
doi = {10.4186/ej.2024.28.6.59},
issn = {01258281},
year = {2024},
date = {2024-01-01},
journal = {Engineering Journal},
volume = {28},
number = {6},
pages = {59 – 73},
publisher = {Chulalongkorn University, Faculty of Fine and Applied Arts},
abstract = {Inventory management is a fundamental component of successful retail operations. Effective techniques in retail inventory management are important in fulfilling customer demands, minimizing costs, and enhancing profitability for business in the competitive environment. This study aims to improve the inventory management strategy for perishable goods in a Thai coffee shop case study. The primary goals include minimizing occurrences of inventory surplus or shortage and indicating the most suitable inventory management approach for each stock-keeping unit (SKU). The most efficient inventory strategy is determined by evaluating the total inventory costs, composing of waste costs, potential loss costs, and holding costs. To this end, computational experiments are employed, deploying three varied periodic inventory policies per SKU. These policies differ in term of utilizing mean weekly demand, average daily demand, and modifying delivery schedules and frequencies. In addition to exploring various policies, the service level for each SKU is adjusted according to profit-cost ratio of each SKU to determine the most suitable service level corresponding to the most effective inventory management strategy. Following the experiments, an effective inventory policy for each SKU is determined. Results show that the new proposed policies can reduce costs by 60.74%, or about 256,922 Baht yearly, compared to the current policy. The new policy, based on daily demand and delivery adjustments, leads to smaller order, more frequent deliveries, allowing the perishable goods to be more refreshed. © 2024, Eng. J. All rights reserved.},
note = {Cited by: 1; All Open Access, Gold Open Access},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Inventory management is a fundamental component of successful retail operations. Effective techniques in retail inventory management are important in fulfilling customer demands, minimizing costs, and enhancing profitability for business in the competitive environment. This study aims to improve the inventory management strategy for perishable goods in a Thai coffee shop case study. The primary goals include minimizing occurrences of inventory surplus or shortage and indicating the most suitable inventory management approach for each stock-keeping unit (SKU). The most efficient inventory strategy is determined by evaluating the total inventory costs, composing of waste costs, potential loss costs, and holding costs. To this end, computational experiments are employed, deploying three varied periodic inventory policies per SKU. These policies differ in term of utilizing mean weekly demand, average daily demand, and modifying delivery schedules and frequencies. In addition to exploring various policies, the service level for each SKU is adjusted according to profit-cost ratio of each SKU to determine the most suitable service level corresponding to the most effective inventory management strategy. Following the experiments, an effective inventory policy for each SKU is determined. Results show that the new proposed policies can reduce costs by 60.74%, or about 256,922 Baht yearly, compared to the current policy. The new policy, based on daily demand and delivery adjustments, leads to smaller order, more frequent deliveries, allowing the perishable goods to be more refreshed. © 2024, Eng. J. All rights reserved.
Naragain Phumchusri, Thiti Chewcharat, Supawish Kanokpongsakorn
Price promotion optimization model for multiperiod planning: a case study of beauty category products sold in a convenience store chain Journal Article
In: Journal of Revenue and Pricing Management, vol. 23, no. 2, pp. 164 – 178, 2024, ISSN: 14766930, (Cited by: 3).
@article{Phumchusri2024164,
title = {Price promotion optimization model for multiperiod planning: a case study of beauty category products sold in a convenience store chain},
author = {Naragain Phumchusri and Thiti Chewcharat and Supawish Kanokpongsakorn},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85168125852&doi=10.1057%2fs41272-023-00438-6&partnerID=40&md5=00ab18dc167d2f437dfe5892f31f43d4},
doi = {10.1057/s41272-023-00438-6},
issn = {14766930},
year = {2024},
date = {2024-01-01},
journal = {Journal of Revenue and Pricing Management},
volume = {23},
number = {2},
pages = {164 – 178},
publisher = {Palgrave Macmillan},
abstract = {Pricing strategy is vital in the retail sector as prices play an important role in driving revenues and profits. However, few studies have been conducted on retail promotion optimization, particularly amid the COVID-19 situation. This study aims to leverage statistical models to examine the effects of price promotion and other factors on sales during the COVID-19 period. In addition, an optimization model is proposed to maximize the profitability of a retail store through strategies for optimal promotional pricing. In this study, monthly sales data in four product categories with 245 stock keeping units from July 2020 to June 2022 from a case study convenience store chain were retrieved and preprocessed. Subsequently, statistical models, such as the autoregressive distributed lag model OWN and the autoregressive distributed lag model CROSS, were implemented to examine the effects of price, promotion and other factors on sales. In addition, factors such as price elasticity and cannibalization were extracted and analyzed from the demand models. An optimization model was built in accordance with the demand model to maximize the total profit of the retailer over a certain period by determining the strategy for optimal promotional pricing. Finally, sensitivity analyses were performed to explain the dynamics of the parameters involved in the optimization model. The methodology, results and insights from this research provide a preliminary framework to facilitate Thai retailers in optimizing their pricing strategies and achieving key business objectives. © The Author(s), under exclusive licence to Springer Nature Limited 2023.},
note = {Cited by: 3},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Pricing strategy is vital in the retail sector as prices play an important role in driving revenues and profits. However, few studies have been conducted on retail promotion optimization, particularly amid the COVID-19 situation. This study aims to leverage statistical models to examine the effects of price promotion and other factors on sales during the COVID-19 period. In addition, an optimization model is proposed to maximize the profitability of a retail store through strategies for optimal promotional pricing. In this study, monthly sales data in four product categories with 245 stock keeping units from July 2020 to June 2022 from a case study convenience store chain were retrieved and preprocessed. Subsequently, statistical models, such as the autoregressive distributed lag model OWN and the autoregressive distributed lag model CROSS, were implemented to examine the effects of price, promotion and other factors on sales. In addition, factors such as price elasticity and cannibalization were extracted and analyzed from the demand models. An optimization model was built in accordance with the demand model to maximize the total profit of the retailer over a certain period by determining the strategy for optimal promotional pricing. Finally, sensitivity analyses were performed to explain the dynamics of the parameters involved in the optimization model. The methodology, results and insights from this research provide a preliminary framework to facilitate Thai retailers in optimizing their pricing strategies and achieving key business objectives. © The Author(s), under exclusive licence to Springer Nature Limited 2023.
Manlika Kiatthadasirikul, Paveena Chaovalitwongse, Naragain Phumchusri, Siravit Swangnop
Constraint Programming in Single Machine Scheduling for Minimizing Makespan with Multiple Constraints Journal Article
In: Engineering Journal, vol. 28, no. 11, pp. 81 – 97, 2024, ISSN: 01258281, (Cited by: 0; All Open Access, Gold Open Access).
@article{Kiatthadasirikul202481,
title = {Constraint Programming in Single Machine Scheduling for Minimizing Makespan with Multiple Constraints},
author = {Manlika Kiatthadasirikul and Paveena Chaovalitwongse and Naragain Phumchusri and Siravit Swangnop},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85211950460&doi=10.4186%2fej.2024.28.11.81&partnerID=40&md5=f790481bcdb8ce3f751e66de472af0c6},
doi = {10.4186/ej.2024.28.11.81},
issn = {01258281},
year = {2024},
date = {2024-01-01},
journal = {Engineering Journal},
volume = {28},
number = {11},
pages = {81 – 97},
publisher = {Chulalongkorn University, Faculty of Fine and Applied Arts},
abstract = {This study focuses on developing a scheduling model for sequencing a set of jobs with different release times in a single machine to meet non-similar due dates as well as to reduce total sequence-dependent setup time. A constraint programming (CP) model is proposed to solve the scheduling problem by minimizing makespan under multiple constraints, namely release times, sequence-dependent setup time, and due dates. The proposed constraint programming model is tested and compared with the baseline method derived from as-is scheduling of alloy wheels manufactures. The computational experiments show the proposed constraint programming model outperforms the baseline method in the average improvement in makespan and total setup time. For small-size problems, the proposed scheduling model were optimally solved in a short time, achieving the best average improvement in makespan of 4.8826% and the best average improvement in total setup time of 45.7924%. Despite increasing problem sizes, the proposed scheduling model's computational time deteriorates but continues to provide the best solutions, achieving the best average improvement in makespan of 7.4891% and the best average improvement in total setup time of 55.4033%. © 2024, Chulalongkorn University, Faculty of Fine and Applied Arts. All rights reserved.},
note = {Cited by: 0; All Open Access, Gold Open Access},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
This study focuses on developing a scheduling model for sequencing a set of jobs with different release times in a single machine to meet non-similar due dates as well as to reduce total sequence-dependent setup time. A constraint programming (CP) model is proposed to solve the scheduling problem by minimizing makespan under multiple constraints, namely release times, sequence-dependent setup time, and due dates. The proposed constraint programming model is tested and compared with the baseline method derived from as-is scheduling of alloy wheels manufactures. The computational experiments show the proposed constraint programming model outperforms the baseline method in the average improvement in makespan and total setup time. For small-size problems, the proposed scheduling model were optimally solved in a short time, achieving the best average improvement in makespan of 4.8826% and the best average improvement in total setup time of 45.7924%. Despite increasing problem sizes, the proposed scheduling model’s computational time deteriorates but continues to provide the best solutions, achieving the best average improvement in makespan of 7.4891% and the best average improvement in total setup time of 55.4033%. © 2024, Chulalongkorn University, Faculty of Fine and Applied Arts. All rights reserved.
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, ISSN: 14766930, (Cited by: 4).
@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},
issn = {14766930},
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: 4},
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, ISSN: 15741699, (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},
issn = {15741699},
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, ISSN: 14766930, (Cited by: 5; All Open Access, Bronze 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},
issn = {14766930},
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: 5; All Open Access, Bronze 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.