Abstract
The reliance of Heineken Sedibeng Brewery on outdated manual tracking methods for returnable packaging inventory, such as spreadsheets and paper logs, has created systemic operational inefficiencies and severe shrinkage which refers to the unaccounted-loss of returnable packaging inventory (bottles and crates) between the recorded stock in manual logs and the actual physical inventory (with the highest figure recorded at –35.54%), severely hindering accurate, real-time demand forecasting. This research aimed to investigate the operational and financial impact of these manual processes and to propose a viable IoT- and predictive analytics-enabled framework to improve inventory management efficiency and reduce financial losses.
A mixed-methods approach was employed, involving process mapping, system data analysis, and an employee technological readiness questionnaire. This was followed by a Monte Carlo simulation, modeling 10,000 operational scenarios to quantitatively compare the costs and risks of the existing manual system against the proposed technological solution.
The diagnostic phase confirmed that systemic flaws, such as blind counting and invoice-based crediting, are the root cause of operational failures. The Monte Carlo simulation demonstrated a radical shift in performance under the proposed solution, projecting a 95% reduction in the mean inventory error variance and a 50% reduction in financial volatility compared to the manual system. Furthermore, the questionnaire confirmed strong implementation feasibility, with 70–80% of staff recognizing the value of IoT tools.
The findings provide robust, data-driven justification for technological investment, confirming that the IoT and predictive analytics framework is critical for mitigating financial risk and enhancing supply chain visibility. The study contributes to the literature by bridging the technological maturity gap for IoT applications within the beverage manufacturing sector.