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All Journal Journal of Applied Business and Technology PROCEEDING OF INTERNATIONAL CONFERENCE ON BUSINESS MANAGEMENT AND ACCOUNTING Luxury: Landscape of Business Administration Interconnection: An Economic Perspective Horizon
Wahid, Nabila
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Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Social Sciences

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Smart Processing Machines and Business Efficiency in Goat Milk Agro-Enterprises Junaedi, Achmad Tavip; Panjaitan, Harry Patuan; Renaldo, Nicholas; Nyoto, Nyoto; Jahrizal, Jahrizal; Dalil, M; Koto, Jaswar; Musa, Sulaiman; Wahid, Nabila; Veronica, Kristy; Faruq, Umar
Luxury: Landscape of Business Administration Vol. 3 No. 2 (2025): Luxury: Landscape of Business Administration
Publisher : First Ciera Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61230/luxury.v3i2.137

Abstract

The increasing demand for functional and health-oriented dairy products has positioned goat milk agro-enterprises as a promising business sector, particularly in emerging economies. Despite this potential, many goat milk businesses face persistent challenges related to production inefficiency, high operational costs, and limited scalability. This study aims to examine the impact of smart processing machines on business efficiency in goat milk agro-enterprises. Using a quantitative approach, data were collected from small and medium-sized goat milk processing enterprises and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results reveal that smart processing machine adoption has a positive and significant effect on business efficiency, including cost efficiency, productivity, and operational effectiveness. The findings indicate that smart processing machines function not merely as technological tools but as strategic business resources that enhance operational performance and competitiveness. This study contributes to the business and agribusiness literature by providing empirical evidence at the production-machine level and highlighting the strategic value of smart manufacturing technologies in small-scale agro-enterprises. The findings offer practical insights for business owners, policymakers, and technology developers in promoting sustainable and efficient goat milk processing businesses.
Big Data Analytics for Demand Forecasting in the Mushroom Supply Chain Renaldo, Nicholas; Veronica, Kristy; Junaedi, Achmad Tavip; Suhardjo, Suhardjo; Tanjung, Amries Rusli; Indrastuti, Sri; Susanti, Wilda; Koto, Jaswar; Musa, Sulaiman; Wahid, Nabila
Luxury: Landscape of Business Administration Vol. 4 No. 1 (2026): Luxury: Landscape of Business Administration
Publisher : First Ciera Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61230/luxury.v4i1.138

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.
Co-Authors Achmad Tavip Junaedi Amries Rusli Tanjung Andi Andi Cecilia Cecilia, Cecilia Dalil, M Dodi Sofyan Arief, Dodi Sofyan Fadrul, Fadrul Gusrio Tendra Harry Patuan Panjaitan Hutahuruk, Marice Br Indrastuti, Sri Indri Yovita Jahrizal Koto, Jaswar Marlim, Yulvia Nora Musa, Sulaiman Nicholas Renaldo Nyoto Nyoto, Nyoto Purnama, Intan Rahman, Sarli Rozalia, Dwi Kirtapati Sally Sevendy, Tandy Suhardjo Suhardjo Suhardjo Surya Safari SD Umar Faruq Veronica, Kristy Wilda Susanti William William