Tank Monitoring
Tank Refill Forecasting from Consumption History
Refill forecasting supports planning when consumption data is trustworthy.
Refill forecasting supports planning when consumption data is trustworthy. Present uncertainty and operational assumptions alongside the estimated date.
Prepare meaningful consumption data
Use validated volume, timestamps and known refill events. Exclude or label sensor faults, missing periods and maintenance transfers. A simple average that includes refilling can misrepresent consumption and produce a misleading estimate of remaining days.
Include lead time and reserve
Forecast when usable stock reaches the chosen reorder threshold, not necessarily physical empty. Include supplier lead time, delivery schedules and a justified reserve. Review different demand patterns for weekdays, shutdowns or production changes rather than assuming constant use.
Compare predictions with outcomes
Save each forecast and compare it with subsequent consumption. Report a range where variability is substantial and flag stale data. Let operators override known upcoming changes while retaining the original prediction for evaluation.
FAQ
Does forecasting require AI?
No. A transparent consumption model may be sufficient; use more complex methods only when evidence supports their benefit.
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