Big Data Analytics for Demand Forecasting in the Mushroom Supply Chain

Nicholas Renaldo; Kristy Veronica; Achmad Tavip Junaedi; Suhardjo Suhardjo; Amries Rusli Tanjung; Sri Indrastuti; Wilda Susanti; Jaswar Koto; Sulaiman Musa; Nabila Wahid

doi:10.61230/luxury.v4i1.138

Authors

Nicholas Renaldo Institut Bisnis dan Teknologi Pelita Indonesia, Indonesia
Kristy Veronica Institut Bisnis dan Teknologi Pelita Indonesia, Indonesia
Achmad Tavip Junaedi Institut Bisnis dan Teknologi Pelita Indonesia, Indonesia
Suhardjo Suhardjo Institut Bisnis dan Teknologi Pelita Indonesia, Indonesia
Amries Rusli Tanjung Institut Bisnis dan Teknologi Pelita Indonesia, Indonesia
Sri Indrastuti Universitas Islam Riau, Indonesia
Wilda Susanti Institut Bisnis dan Teknologi Pelita Indonesia, Indonesia
Jaswar Koto Ocean and Aerospace Research Institute, Japan
Sulaiman Musa Kano State Polytechnic, Nigeria
Nabila Wahid American International University – Bangladesh, Bangladesh

DOI:

https://doi.org/10.61230/luxury.v4i1.138

Keywords:

Big Data Analytics, Demand Forecasting, Mushroom Supply Chain, Supply Chain Performance, Perishable Agribusiness, Digital Transformation

Abstract

The mushroom industry plays an increasingly important role in the agri-food sector due to rising demand for nutritious, functional, and sustainable food products. However, the mushroom supply chain faces significant challenges related to perishability, short shelf life, and demand uncertainty, which often result in inventory losses and inefficiencies. This study examines the role of big data analytics capability in enhancing demand forecasting accuracy and its impact on supply chain performance within the mushroom industry. Using a quantitative explanatory research design, data were collected through a structured questionnaire survey of mushroom supply chain actors, including producers, processors, distributors, and retailers. The data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results reveal that big data analytics capability has a significant positive effect on demand forecasting accuracy and supply chain performance. Furthermore, demand forecasting accuracy partially mediates the relationship between big data analytics capability and supply chain performance. These findings highlight the strategic importance of data-driven forecasting in managing demand uncertainty and improving operational efficiency in perishable agribusiness supply chains. This study contributes to the literature by extending big data analytics and demand forecasting research to the mushroom industry, providing both theoretical insights and practical implications for enhancing supply chain sustainability and competitiveness.

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