Model for Predictive Procurement Planning to Sustain Operational Uptime
DOI:
https://doi.org/10.32628/IJSRSSH242772Keywords:
Predictive procurement, operational uptime, supply chain optimization, demand forecasting, inventory management, risk mitigation, service-level reliabilityAbstract
Ensuring operational uptime is a critical challenge for organizations operating in asset-intensive and high-reliability environments. Traditional procurement methods, often reactive and manually driven, frequently fail to anticipate supply disruptions, resulting in unplanned downtime, production delays, and increased operational costs. This study proposes a Model for Predictive Procurement Planning to Sustain Operational Uptime, which leverages data-driven insights, predictive analytics, and automated workflows to optimize procurement processes and maintain continuous operational readiness. The model integrates demand forecasting, inventory optimization, supplier performance monitoring, and risk assessment into a cohesive framework, enabling proactive procurement decisions aligned with operational priorities. The framework incorporates real-time data capture from enterprise resource planning (ERP) systems, supply chain management (SCM) platforms, and Internet of Things (IoT) devices, providing visibility into asset status, consumption patterns, and inventory levels. Predictive analytics techniques are applied to forecast material requirements, anticipate potential disruptions, and evaluate supplier reliability. Automated workflows and exception-handling protocols ensure timely replenishment, facilitate contingency planning, and minimize the impact of delays or shortages. Performance dashboards provide decision-makers with actionable insights for resource allocation, supplier management, and operational risk mitigation, supporting evidence-based decision-making and continuous improvement. By adopting this predictive procurement approach, organizations can enhance operational efficiency, reduce unplanned downtime, optimize inventory costs, and strengthen supplier reliability. The model is adaptable across diverse sectors, including manufacturing, energy, healthcare, and public-sector operations, and is particularly relevant for emerging and resource-constrained markets where supply chain disruptions have significant operational consequences. This presents a structured conceptual model that bridges predictive analytics, procurement planning, and operational resilience, offering a foundation for practical implementation and empirical validation. The model emphasizes proactive management, data-driven decision support, and cross-functional integration as essential enablers of sustained operational uptime.
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