MISEshows the real model backtest, forecasting curve, table operation and the manager dashboard.

Hospitality intelligence

Hospitality intelligence / forecasting

MISE

A verifiable hospitality proof that translates eighteen months of reservations, POS, tables, weather, calendar context and personnel needs into an executable daily plan.

Interactive proof of concept

The case in 30 seconds

MISE

Problem: A verifiable hospitality proof that translates eighteen months of reservations, POS, tables, weather, calendar context and personnel needs into an executable daily plan.
Solution: A verifiable hospitality-proof with eighteen months of data, physical table plan, personnel planning and out-of-sample backtest.
Role and focus: Forecasting, personnel planning
Status: Interactive proof of concept
Result: Dataset, grid, backtest

Live lab / try it yourself

Enjoy a catering day

Choose a scenario and see how reservations, weather, tables, staff and turnover combine to create an actionable daily plan.

OpenMISE View the story

01 / InputDay, demand and capacityYou can change or choose this yourself

02 / SystemForecasting and operational planningForecasting · Operational data · Scenario engine

03 / EvidenceA verifiable work planThis makes the operation visible

QuestionHow is today going?

DecisionPeople,MISE-en-place, terrace

DatesLocal and context sensitive

TrustSources visible

Brand vision / preparation as a decision system

Everything is ready before the service starts

MISErefers directly toMISE-en-place: the catering language for good preparation. The logo connects table, timing and central decision without the maritime association of the previous work name.

Forecasting on the floor

From forecast to work plan

The outcome is not “82% pressure”.MISEsays when the crowds will come, where guests are likely to want to sit, which preparations require extra time and which signals are uncertain.

Real restaurant terrace with tables and chairs under a striped awning.

Internal signals

Reservations, cash register and planning

Historical covers, turnover per hour, no-shows, popular dishes, available employees and existing preparation.

Local context

Weather, roadwork and events

Not the national forecast, but the situation surrounding this case: permits, closures, market, concert and terrace temperature.

Market signals

Competition without false security

Social Deal, reviews and tourism data are given a source quality, refresh date and local relevance, not a blind weight.

Play through the day

Four scenarios, four different workplaces

Click on a scenario. The forecast not only changes the expected turnover, but also the concrete preparation and the certainty with whichMISEadvises.

Expected covers per hourBandwidth and deployment time

Scale up the team

Current model result184 coversBandwidth 163 to 205

Substantiated trust83%3 of 4 sources active

Reservations determine the basis; weather and local context drive terrace, peak and preparation.

Covers184+22% vs normal

Peak13:10terrace 76%

Extra starts2 × 60m09:30 patio crew

09:30

Cushions, umbrellas and outdoor station

11:00

Extra cold drinksMISE-en-place

12:30

Second runner to terrace

16:00

Reforecast with current covers

Covers132terrace uncertain

Rain14:05± 25 minutes

OccupancyCompact1 late shift postponeable

10:00

Open completely inside

12:00

Limited use of terrace

13:35

Pillows in signal

15:00

Replenish hot drinks

Covers211peak shifts

Passers-by+1.4k125m radius

MenuQuickshorten the lunch menu

09:00

Extra children's set and high chairs

10:30

Grab-and-go station ready

11:40

First peak before reservations

14:30

Recalibrate stock and waiting time

Covers119−18% forecast

Walk-ins−31%car traffic

Team−1 shiftstandby at 4:00 PM

08:30

Publish accessibility message

10:00

Make the cycle route visible

12:00

Do not release additional purchases

16:00

Reassess reservations

Human control Scenario not yet applied to planning, purchasing or team.

Design boundary: the numbers are demonstration dates. In a pilot, source quality, local influence and margin of error are calibrated per restaurant; a manager always holds the final personnel and purchasing decisions. Open the full 18-month data and personnel proof.

Data quality

Not every source deserves the same trust

Each input is given a separate score for topicality, local relevance, historical predictive value and availability.

A / High

Own operational data

Reservations and checkout are close to the decision and are calibrated per hour and type of day.

B / Context

Public local signals

Weather, permits and traffic are strong when time, distance and impact are clear.

C / Indicative

Market and competition data

Reviews and deals are signals, not truth. They are only used if there is demonstrable local coherence.

Prepare the terrace first

Weather and preparation determine the opening.

Lunch peak

Reservations and passers-by control the floor.

Reforecast

Current covers replace old assumptions.

Evening plan

Team and stock follow the latest context.

Independent concept case with demonstration data. Photography: Bayram Yalçın and Seher Doğan via Pexels. The restaurant is fictional; brand and interface are designed for this portfolio case.

The flagship series