Decision-Making Systems in Smart Agriculture Based on Forecasting Supply Chain: A New Approach in the Business of Technopreneurship

Dedet Deperiky; Hadi Rafindo; Trio Candra Yoga

doi:10.54518/rh.5.5.2025.823

Authors

Dedet Deperiky Universitas Tamansiswa Padang, Indonesia
Hadi Rafindo Universitas Tamansiswa Padang, Indonesia
Trio Candra Yoga Universitas Tamansiswa Padang, Indonesia

DOI:

https://doi.org/10.54518/rh.5.5.2025.823

Keywords:

ARIMA, Decision-Making Systems, Interpretive Structural Modeling, Supply Chain Forecasting, Technopreneurship

Abstract

The agricultural sector contributes significantly to Indonesia’s economy 12.4% GDP, 29% workforce, yet faces persistent challenges including climate uncertainty, market fluctuations, and inefficient supply chains due to inadequate decision-making systems. This study aims to analyze the relationship between decision-making and agricultural supply chains integrated with technopreneurship, determine integrated strategies for improving efficiency and sustainability and develop plans to reduce crop failure risks and market demand uncertainty. A quantitative approach was employed using Structural Equation Modeling, Interpretive Structural Modeling and ARIMA forecasting methods with 300 farmer samples and 7 experts from Solok Regency, West Sumatra, during January-September 2025. SARIMAX models successfully predicted potato production and prices with high accuracy. ISM analysis identified hierarchical relationships among objectives, needs, constraints, activities, and actors, revealing seed independence and superior variety development as key drivers. Integrating forecasting-based decision-making systems with technopreneurship principles enhances agricultural supply chain efficiency, though data quality and model validation remain critical challenges.

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