| The palm oil industry faces mounting pressure from tightening sustainability regulations, price volatility, and persistent productivity gaps between large estates and smallholders, repositioning data-driven decision-making as a strategic imperative. Although business analytics has been increasingly examined in adjacent agro-industrial domains, a dedicated synthesis for the palm oil sector remains absent. This study addresses that gap through a PRISMA-guided systematic literature review of peer-reviewed publications retrieved from the Scopus database. The review maps business analytics implementation across three dimensions: the type of analytics applied, the enabling technological components, and the value-chain stage at which analytics is deployed. Findings reveal that descriptive analytics dominates the literature, concentrated at the plantation level, while predictive and prescriptive analytics remain comparatively underdeveloped. Recurring constraints include data quality limitations, infrastructural deficits, and uneven adoption between large estates and smallholders. Six future research directions are derived from observed gaps, spanning precision management, remote sensing integration, decision support systems, predictive modelling under climate uncertainty, blockchain-based traceability, and socio-economic impact assessment. This study contributes a sector-specific evidence base for researchers, practitioners, and policymakers advancing analytics-driven approaches in the palm oil industry. |
