Revenue
What should each outlet expect to sell over the next seven days, and how does that compare with the current target?
Explore →A useful first conversation is specific: what the team decides, when it decides, which outlet or item grain matters, and which records are available. Openfluid can then assess whether a forecast, operating rule or data change is required.
What should each outlet expect to sell over the next seven days, and how does that compare with the current target?
Explore →How many portions of the top 20 items should each outlet prepare tomorrow?
Explore →Which ingredients need purchase or transfer before the next reliable supplier receipt?
Explore →Which items repeatedly show excess preparation, and can actual outcomes be measured?
Explore →Agree one decision, user, horizon and expected output. We will not assume every module belongs in the first pilot.
Profile the files for coverage, grain, identifiers, timestamp rules and material gaps. Sensitive fields not needed for forecasting can be excluded.
Document what can be forecast now, what requires additional data and which baseline will be used for comparison.
Define implementation work, subscription, evaluation period, success measures, responsibilities and limitations.
No. A representative export is usually more useful for discovery because it reveals the actual fields and history before integration work begins.
Yes. Restaurant demand forecasting generally does not require guest names or direct identifiers. Retain only fields required for the agreed decision.
Confidentiality requirements can be discussed before non-public production data is shared. This prototype itself does not constitute an NDA.
Email product@openfluid.ai directly, or use the form above. Both reach the same inbox.
Send the decision, outlet count and available source exports to product@openfluid.ai.