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AI-Driven Analysis of Key Determinants Shaping Integrated Business Planning: A Case Study of Haraz Dairy Company | ||
| Future Research on AI and IoT | ||
| مقاله 4، دوره 2، شماره 2، دی 2026، صفحه 50-60 اصل مقاله (1.17 M) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22080/frai.2026.30735.1039 | ||
| نویسندگان | ||
| hesamoddin motevalli1؛ hamidreza razavi* 2؛ saeid emam gholizadeh3؛ amirhosein azadnia4 | ||
| 1Assistant Professor, Faculty of Humanities and Social Sciences, Shomal University, Amol, Iran. | ||
| 2Associate Professor, Department of Management, Faculty of Humanities and Social Sciences, shomal University, Amol , Iran | ||
| 3Associate Professor, Department of Management, Faculty of Humanities and Social Sciences, Shomal University, Amol ,Iran | ||
| 4Associate Professor, National University of Ireland, Maynooth | ||
| تاریخ دریافت: 25 آذر 1404، تاریخ بازنگری: 11 شهریور 1405، تاریخ پذیرش: 13 شهریور 1405 | ||
| چکیده | ||
| Integrated Business Planning (IBP) has become a strategic requirement for modern organizations seeking improved coordination, operational efficiency, and competitive advantage. With the rapid development of artificial intelligence (AI), data-driven analytical approaches provide new opportunities for enhancing IBP by identifying key drivers and predicting performance outcomes. This study aims to examine the influential factors shaping integrated business planning in the Haraz Dairy Company using an AI-enhanced analytical framework. Data were collected through a structured questionnaire measuring fifteen indicators related to data management, process optimization, decision-support capability, and strategic alignment. After preprocessing and scaling, an unsupervised clustering method (k-means) was applied to categorize organizational conditions into two overall IBP readiness levels. Cluster labels were subsequently used as outcome variables for predictive modeling. Decision Tree and K-Nearest Neighbors (KNN) algorithms were implemented to determine the extent to which each factor contributes to IBP classification. Model performance was evaluated using ten-fold cross-validation and multiple metrics, including accuracy, precision, recall, F1-score, and confusion matrix analysis. The results indicated that process optimization, decision-support capability, and data quality features had the strongest influence on IBP readiness. Among the predictive models, the Decision Tree classifier provided superior interpretability with competitive accuracy. These findings highlight the value of AI-based modeling for diagnosing organizational readiness and guiding managerial interventions toward integrated planning. Practical recommendations for enhancing IBP maturity in the dairy industry are also discussed. | ||
| کلیدواژه‌ها | ||
| artificial intelligence؛ business؛ process optimization؛ data analysis | ||
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آمار تعداد مشاهده مقاله: 31 تعداد دریافت فایل اصل مقاله: 37 |
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