Predictive analytics agent using BigQuery ML and Vertex AI AutoML — forecasting demand, predicting equipment failures, and automating response actions before problems occur. Reduce downtime and improve planning accuracy simultaneously.
We implement a production-grade predictive analytics agent on Google Cloud — building Vertex AI AutoML models for demand forecasting, training BigQuery ML anomaly detection on your IoT sensor data, and connecting model outputs to automated work order creation and inventory adjustment workflows. Looker dashboards give planners real-time visibility into forecasts and maintenance predictions.
Equipment failures caught only after breakdown result in unplanned downtime, emergency repair costs, and production losses that dwarf the cost of predictive prevention.
Inaccurate demand forecasting leads simultaneously to overstock (tying up capital) and stockouts (losing sales) — inefficiency that compounds through the supply chain.
Manual forecasting processes involving spreadsheets, email, and offline collaboration take weeks to complete — leaving organisations planning on stale data.
Vertex AI AutoML models produce demand forecasts with calibrated confidence intervals — giving planners not just a number but a reliable range for inventory decisions.
IoT sensor data feeds BigQuery ML models that predict equipment failure probability — enabling maintenance to be scheduled at optimal times before failures occur.
Real-time anomaly detection flags unusual sensor patterns — vibration, temperature, pressure — before they escalate to failures, enabling pre-emptive intervention.
When predictive models exceed maintenance thresholds, work orders are created automatically in your CMMS or ERP — with priority, parts list, and estimated labour hours.
All forecasts include uncertainty bounds, enabling risk-adjusted planning — planners know which forecasts to rely on and where to carry additional safety stock.
The model ingests sensor data via Cloud IoT or Pub/Sub from any connected equipment — vibration sensors, temperature probes, pressure transducers, current monitors, and run-time counters. Historical maintenance records and failure logs from your CMMS are used for model training. We work with whatever sensor infrastructure you have in place and can advise on sensor gaps that would improve model accuracy.
Vertex AI AutoML performs well with 12–24 months of historical demand data. Shorter history reduces forecast accuracy, particularly for seasonal patterns. Where historical data is limited, we use transfer learning from analogous product categories or apply Bayesian priors based on industry demand profiles. The model improves continuously as more data accumulates.
Yes. Vertex AI AutoML supports cold-start forecasting for new products using feature similarity to existing products — matching on attributes like category, price point, seasonality, and channel. New product forecasts carry wider confidence intervals that narrow as actual demand data accumulates. We configure the cold-start approach based on your product catalogue structure.
We implement the CMMS integration as part of the engagement. We support standard integrations with IBM Maximo, SAP PM, Infor EAM, UpKeep, and Fiix. Custom integrations are implemented via API for any CMMS with a documented REST API. Work orders are created with configurable approval gates — low-priority maintenance may be created automatically, while high-cost interventions require approval before creation.
Stop reacting to failures and forecast misses. We implement production-ready predictive analytics in 4–6 weeks.