BIBLIOMETRIC ANALYSIS AND SYSTEMATIC LITERATURE REVIEW: INTEGRATING PRODUCTION PLANNING AND FORECASTING IN MANUFACTURING AND REMANUFACTURING INDUSTRIES - Rianzah Munawaroh; Eni Noviani; Alfan Fakhrul Rozi; Puji Handayani; Nurika Restuningdiah (Yayasan Nuraini Ibrahim Mandiri, 2026)
In facing intense market competition and environmental sustainability demands, companies require accurate production planning and forecasting to avoid losses resulting from errors in demand predictions. This research aims to explore the relationship between production planning and forecasting with various analytical tools, as well as to map the methods and practices used by the manufacturing and remanufacturing industries. The method used is a combination of bibliometric analysis and systematic literature review (SLR). The bibliometric analysis, conducted using VOSviewer software, serves to quantitatively map publication trends, co-authorship networks, keyword co-occurrence, and thematic clusters within the existing literature, thereby revealing the intellectual structure of the field. The SLR complements this by providing a qualitative, step-by-step synthesis of the selected studies, ensuring a rigorous and reproducible evaluation of the evidence. Data were sourced from the Scopus and ScienceDirect databases covering the period 2021–2026. From an initial set of 214 articles, a screening process based on relevance, methodology, and alignment with the research questions was applied, The result in the selection of the 10 most relevant articles for in-depth analysis. The study found that no single forecasting method is best for all situations, its effectiveness depends on contexts such as demand volatility, product lifecycle stage, and sustainability constraints. Dynamic forecasting integrated with strategic planning is more resilient to external disruptions. The current research trend is shifting to hybrid models that combine machine learning with traditional statistical methods. The key challenge going forward is bridging the gap between state-of-the-art methods and industry realities, especially for SMEs that lack technical and financial resources. The solution needed is an adaptive framework that responds to disruptions in real time and balances predictive accuracy with practical application