Predict-then-optimise

Predict-then-optimise is a two-stage decision framework: first, machine learning models predict uncertain parameters from historical data; second, these predictions serve as inputs for a deterministic optimization model to generate optimal decisions. This approach addresses the challenge of uncertainty in decision-making, a core component of ISO 22301 Business Continuity Management System (BCMS) requirements. Unlike traditional methods that use static-risk estimates, this framework allows for dynamic adaptation to changing environments. The method's effectiveness depends heavily on the accuracy of the prediction stage, as errors in prediction propagate through to the final decision. In the context of risk management, this means the predictive model must be rigorously validated against historical out-of-sample data before deployment to ensure it meets the reliability standards required for critical infrastructure and supply chain resilience.

In practice, the implementation follows three phases: Data-driven Prediction, Optimization-based Decision, and Continuous Feedback. For instance, a Taiwanese electronics manufacturer might use historical demand-supply data to predict component shortages using a Random Forest model. This prediction is then fed into an inventory optimization model to determine optimal safety stock levels. The company can be closely monitored against KPIs such as 'Service Level-at-Risk' and 'Forecast-Adjusted Cost-to-Serve.' A pilot implementation of this approach in a logistics firm resulted in a 20% reduction in stockouts and a 12% decrease in expedited shipping costs within six months. This aligns with the ISO 31000 risk treatment process, where options are evaluated based on their ability to mitigate the impact of identified risks while optimizing resource utilization.

Taiwan enterprises typically encounter three challenges: Data Silos, Technical Expertise Gaps, and Change Management Resistance. Data Silos occur when production, logistics, and finance data are not integrated, making it impossible to train accurate predictive models. The solution is to implement a centralized data-sharing platform compliant with GDPR and Taiwan's Personal Data Protection Act. Technical Expertise Gaps can be addressed by partnering with specialized consultants like Winners Consulting Services Co., Ltd. Change Management Resistance requires demonstrating ROI through pilot projects—for example, showing a 15% reduction in stock-out-related losses within the first quarter. The priority should be: 1. Data--centric infrastructure audit, 2. Pilot model deployment, 3. Full-scale integration and staff training.

Winners Consulting Services Co., Ltd. specializes in Predict-then-optimise for Taiwan enterprises, delivering compliant management systems within 90 days. Free consultation: https://winners.com.tw/contact