Supply-chain alerts fail — prescriptive ops & playbooks to keep production

Large manufacturers and mid-to-large retail enterprises — roughly 200,000 potential customers in the target segment — routinely receive supply-chain alerts that fail to translate into reliable actions, producing avoidable production downtime and fulfillment shortfalls. Current monitoring and anomaly-detection tools identify problems but rarely provide the causal diagnosis or executable playbooks operators need under time pressure. The product would be a B2B SaaS combining real-time IoT and carrier telemetry with causal and counterfactual models to deliver ranked prescriptive playbooks and API-driven remediation actions, built to support a $160k ACV per account within a $32.0B addressable market. This is an opportune moment: enterprise demand for operational resilience is high (market score 92/100), AI techniques now allow prescriptive—not just predictive—guidance, and broader API/IoT adoption makes end-to-end root-cause analysis practical where it was not five years ago. Early pilots should be designed to measure conversion of alerts to resolution, mean time to recovery reduction, and clear production-days-saved ROI. Differentiation must rest on two practical pillars: trustworthy prescriptive logic (causal explanations and counterfactual validation) and low-friction integration into MES/ERP/TMS workflows so recommended playbooks are adopted or automated. Strengths are a large, urgent TAM and strong revenue potential (90/100), but challenges include messy legacy data, long enterprise sales cycles, and the hard work of proving causal recommendations across heterogeneous shop floors. A focused GTM—pilots with high-risk accounts, measurable KPIs, and partnerships with platform vendors—is the most realistic path to validate the model and scale.