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All Journal Jurnal Informatika Ekonomi Bisnis Jurnal Informatika Ekonomi Bisnis
Prisillia, Galuh
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Economics, Econometrics & Finance

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Comparative Analysis of Multicriteria Inventory Classification and Forecasing: A Case Study in PT XYZ Purwandaru, Dhanang; Ruldeviyani, Yova; Nugraheni, Sani; Prisillia, Galuh
Jurnal Informatika Ekonomi Bisnis Vol. 6, No. 4 (December 2024)
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/infeb.v6i4.1014

Abstract

One crucial aspect of supply chain management is inventory management. Inefficient inventory management can lead to various issues, such as product expiration, where a high number of items in the warehouse either have expired or are approaching expiration. This issue is experienced by a distribution SME in Indonesia, PT XYZ. Without such classifications, it becomes challenging to predict demand and manage stock levels efficiently. Therefore, the aim of this study is to classify inventory to identify the most important items to business and make a forecasting model of sales quantity to predict inventory replenishment using machine learning algorithms. To advance our research, we adopted the Cross Industry Standard Process for Data Mining (CRISP-DM) methodology. For inventory classification, we conducted a hybrid approach that combined TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) and ABC analysis (A: high-value items, B: medium-value items, and C: low-value items). The data employed in this study comprised secondary data, including purchase orders, sales orders, and stock movement records. The result reveals that 11 of the total 383 items under class A are important items for business. After obtaining labels from the ABC Analysis, we proceed to train models using KNN, SVC, and Random Forest for predicting inventory classification. Notably, the Random Forest model showcased remarkable performance and outperformed the rest of the models, achieving an accuracy of 99.21%. For inventory forecasting ARIMA displays a competitive performance with RMSE value 5.305 and MAE value 3.476, indicating a relatively accurate prediction with lower forecasting errors than two other models
Comparative Analysis of Multicriteria Inventory Classification and Forecasing: A Case Study in PT XYZ Purwandaru, Dhanang; Ruldeviyani, Yova; Nugraheni, Sani; Prisillia, Galuh
Jurnal Informatika Ekonomi Bisnis Vol. 6, No. 4 (December 2024)
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/infeb.v6i4.1014

Abstract

One crucial aspect of supply chain management is inventory management. Inefficient inventory management can lead to various issues, such as product expiration, where a high number of items in the warehouse either have expired or are approaching expiration. This issue is experienced by a distribution SME in Indonesia, PT XYZ. Without such classifications, it becomes challenging to predict demand and manage stock levels efficiently. Therefore, the aim of this study is to classify inventory to identify the most important items to business and make a forecasting model of sales quantity to predict inventory replenishment using machine learning algorithms. To advance our research, we adopted the Cross Industry Standard Process for Data Mining (CRISP-DM) methodology. For inventory classification, we conducted a hybrid approach that combined TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) and ABC analysis (A: high-value items, B: medium-value items, and C: low-value items). The data employed in this study comprised secondary data, including purchase orders, sales orders, and stock movement records. The result reveals that 11 of the total 383 items under class A are important items for business. After obtaining labels from the ABC Analysis, we proceed to train models using KNN, SVC, and Random Forest for predicting inventory classification. Notably, the Random Forest model showcased remarkable performance and outperformed the rest of the models, achieving an accuracy of 99.21%. For inventory forecasting ARIMA displays a competitive performance with RMSE value 5.305 and MAE value 3.476, indicating a relatively accurate prediction with lower forecasting errors than two other models
Determinants of Continued Use of Agile Methods: A Case Study of an E-Commerce Enabler in Indonesia Prisillia, Galuh; Raharjo , Teguh; Trisnawaty, Ni Wayan
Jurnal Informatika Ekonomi Bisnis Vol. 7, No. 1 (March 2025)
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/infeb.v7i1.1052

Abstract

Agile methodologies have gained popularity for their adaptability, faster time-to-market, and enhanced customer satisfaction. However, sustaining their use post-adoption poses challenges, including knowledge gaps, resistance to change, and communication issues. This study examines the determinants of continued use of Agile methods at PT XYZ, an e-commerce enabler in Indonesia. Using the Expectation Confirmation Model and Partial Least Squares Structural Equation Modeling, data from 61 IT staff members were analyzed to evaluate relationships between Confirmation, Perceived Usefulness, Satisfaction, and Continuance Intention. Results reveal that CO positively impacts PU and SA, while PU and SA significantly influence CI. Satisfaction emerges as a critical mediator between perceived value and continued use. These findings highlight the importance of aligning Agile practices with user expectations and perceived benefits to ensure sustained adoption. Practical implications include the need for comprehensive training, regular evaluations of team satisfaction, and organizational alignment with Agile principles.
Determinants of Continued Use of Agile Methods: A Case Study of an E-Commerce Enabler in Indonesia Prisillia, Galuh; Raharjo , Teguh; Trisnawaty, Ni Wayan
Jurnal Informatika Ekonomi Bisnis Vol. 7, No. 1 (March 2025)
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/infeb.v7i1.1052

Abstract

Agile methodologies have gained popularity for their adaptability, faster time-to-market, and enhanced customer satisfaction. However, sustaining their use post-adoption poses challenges, including knowledge gaps, resistance to change, and communication issues. This study examines the determinants of continued use of Agile methods at PT XYZ, an e-commerce enabler in Indonesia. Using the Expectation Confirmation Model and Partial Least Squares Structural Equation Modeling, data from 61 IT staff members were analyzed to evaluate relationships between Confirmation, Perceived Usefulness, Satisfaction, and Continuance Intention. Results reveal that CO positively impacts PU and SA, while PU and SA significantly influence CI. Satisfaction emerges as a critical mediator between perceived value and continued use. These findings highlight the importance of aligning Agile practices with user expectations and perceived benefits to ensure sustained adoption. Practical implications include the need for comprehensive training, regular evaluations of team satisfaction, and organizational alignment with Agile principles.
Co-Authors Nugraheni, Sani Purwandaru, Dhanang Raharjo , Teguh Trisnawaty, Ni Wayan Yova Ruldeviyani