# Know what comes next, and plan for it · Anticipate · Dscvry > Anticipate turns what already happened into a forward view: failures, shortages, missed deadlines and recurring causes, each with how likely it is and when. Source: https://dscvryai.com/technology/anticipate/ # Know what comes next, and plan for it. Predict, forecast, simulate and plan from your own records, with how sure it is stated every time. What the AI can anticipate · How it works · Wherever the work is. Hosted Private cloud On-premise Edge Air-gapped ## Most surprises were in the data. Failures, shortages and missed deadlines usually announce themselves weeks ahead. The signs are spread across records nobody lines up, and by the time someone notices, it's too late to be cheap. ### Looking backwards. Reports say what happened last month, not what is about to happen. ### Single numbers. A forecast without a range hides how uncertain it is, so people either trust it too much or not at all. ### Patterns missed. The same cause behind different incidents goes unnoticed because each is handled alone. ## What the AI can anticipate. When it's likely to fail. ### Predict The chance of failure day by day, from the asset's own history and how its past failures began, set against your next planned stop. See it in equipment failure → https://dscvryai.com/use-cases/equipment-failure/ What you'll need, and when. ### Forecast Demand forecast from usage, plans and known events like shutdowns, so orders go in while there is still time. See it in stock shortages → https://dscvryai.com/use-cases/stock-shortages/ Try the change before you make it. ### Simulate A new shift pattern, price or supplier run against your own history first, so you see what it does to delivery, cost and workload before anything changes. See it in maintenance planning → https://dscvryai.com/use-cases/maintenance-planning/ A schedule that fits, and refits. ### Plan Jobs, people and parts fitted around your limits, such as crews, permits and shutdown windows, and rebuilt when a part runs late or a job overruns. See it in maintenance planning → https://dscvryai.com/use-cases/maintenance-planning/ The options, ranked, with the reasons. ### Recommend When something needs a decision, the choices are laid out in order with what each one costs and when it lands, so your team makes the call faster. See it in stock shortages → https://dscvryai.com/use-cases/stock-shortages/ ## Simple where it works. Honest about the range. Many forecasts are best made by simple statistics on good history. The AI uses more complex models only where they measurably do better, and always shows the range. Clean history Check Checked, structured records from the rest of the system. Baselines Rules & code Seasonality, trend and usage rates. Models Prediction Only where they beat the baseline on held-back data. Ranges Prediction How sure, not just how much. Warnings Operate Raised with enough time to act. ## Likely short before the delivery lands. Tank T-14 · antifreeze · level forecast. A forecast with its range. People decide what to do about it. **History.** 90 days of daily levels and 6 deliveries. **Plan.** Winter use starts in 9 days; last year's use rose 40% in the first fortnight. **Supply.** Next delivery confirmed for day 16. **Forecast.** Below minimum in about 11 days; likely between day 8 and day 15. Illustrative example. Site, tank and figures are fictional. ## Built to be trusted, carefully. A forecast is only useful if people know how far to trust it. - ### Always a range. Every forecast shows how sure it is, not just a single number. [Stock Shortages](https://dscvryai.com/use-cases/stock-shortages/) - ### Tested before it's used. Models are compared with simple baselines on data they haven't seen. [How Check works](https://dscvryai.com/technology/check/) - ### Explains the drivers. Each warning says which records and events moved the forecast. [How Understand works](https://dscvryai.com/technology/understand/) ## Where it shows up. Problems that are cheaper to see early. What fails next? ### Equipment failure Spot the patterns and warn what may fail next. See how → https://dscvryai.com/use-cases/equipment-failure/ What runs out before the next delivery? ### Stock shortages Anticipate shortages before they become a problem. See how → https://dscvryai.com/use-cases/stock-shortages/ What needs maintenance, and when? ### Maintenance planning Decide what should be maintained, and when. See how → https://dscvryai.com/use-cases/maintenance-planning/ What does this new rule change for us? ### Regulatory changes Map new rules to what the business must change. See how → https://dscvryai.com/use-cases/regulatory-changes/ ## Where it runs. The AI goes to where the work is, from hosted to air-gapped. ### A managed environment. Start with a hosted service when speed of adoption and managed operations are priorities. Agree data handling, access and service requirements as part of the engagement. Discuss hosted · ## The other capabilities. Each one is tested on your own work before you rely on it. ### Sense It sees, hears and reads the work as it happens. Explore Sense → https://dscvryai.com/technology/sense/ ### Understand From piles of paper to records you can use. Explore Understand → https://dscvryai.com/technology/understand/ ### Check Every line compared. Only the gaps reach you. Explore Check → https://dscvryai.com/technology/check/ ### Investigate Follow the evidence across systems. Find out what happened, and why. Explore Investigate → https://dscvryai.com/technology/investigate/ ### Operate The next step, taken. The reason, recorded. Explore Operate → https://dscvryai.com/technology/operate/ ## Bring us the work where wrong is expensive. Start with the operational problem. We’ll work out the data, models and agents it needs, and where it should run. [Talk to us →](https://dscvryai.com/contact/) **London**Discovery AI Limited · United Kingdom **Hyderabad**ReDscvry Technology Private Limited · India **Chennai**ReDscvry Technology Private Limited · India