Simulació
Booking Tavascan
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- KPIs anualitzats
- Simulacions (0%, 20%, 50% & 100%)
- Stock conjunt
- Valors producció i vendes
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Resultats
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(click to download)
Doc: Sources
each line is a sold car
| week | week of the sale |
| chain | collection of countries that intervened in the chaned {country, arrival_port_week, index of the car on prod tab} |
| first_result_country | first car involved of the chain |
| first_result_index | index of production sheet assigned |
| wait_weeks | difference between the week of sale and the arrival_port_week of the chain's first car (the one delivered to the client). Wait_weeks of cars found in the stock are set to 0. |
| week_port | arrival week |
| chain_length | cars involved in the chain |
| delivery_week | max between week_port and week of sale |
sales_with_chains
Doc: Sources
prod
| index | identifier of the car (it is a different number from the production cars index) |
| week of arrival | week this new car will hit port arrival. We assume production on a week is unlimited. |
| country | country in charge of producing this car. The car is either a compensation car or a new sale (see country_propietary) |
| country_proprietary | country that received the car that has been produced |
| booking | index of the sale if any |
each line is a produced car, it takes into account both the cars in the planned production and the new ones due to chain algorithm,
no order bank is assumed.
We produce everything needed to deliver a client car in less than 22 weeks
Doc: Sources
new_cars
each line is a produced car. Subset of tab prod, any car that is not in the initial production file is listed here
| index | identifier of the car (it is a different number from the production cars index) |
| week of arrival | week this new car will hit port arrival. We assume production on a week is unlimited. |
| country | country in charge of producing this car. The car is either a compensation car or a new sale (see country_propietary) |
| country_proprietary | country that received the car that has been produced |
| booking | index of the sale if any |
Doc: KPIs
Photo at the end of each week. We compute the existing stock at a particular week by adding the cars arriving at port that have been produced and do not belong to a client (not sold)
stock
| prod | number of cars arrived at port this week |
| sold | number of cars arrived at port this week and already sold |
| stock_added | prod - sold |
| stock_agg | stock agg last week + stock added of the current week |
Doc: KPIs
wait_weeks
| period | |
| sum | sum of waiting delivery weeks of all the cars delivered in the period |
| mean | mean of waiting delivery weeks of all the cars delivered in the period |
| std | standard deviarion of waiting delivery weeks of all the cars delivered in the period |
| 25% | The 25% of cars are delivered before this number of weeks |
| 50% | The 50% of cars are delivered before this number of weeks |
| 75% | The 75% of cars are delivered before this number of weeks |
| 100% | The 100% of cars are delivered before this number of weeks |
Waiting delivery weeks: weeks waited by a client until delivery.
The mean of wait_weeks distributed by country
Doc: KPIs
sum of wait_weeks x country
The sum of wait_weeks distributed by country
mean of wait_weeks x country
Doc: KPIs
| stock | cars found in stock (waiting weeks = 0) |
| loaned | cars taken away from another country |
| found | cars already created and assigned (from the same country). Cars in transit. |
| new | cars from "new_cars" tab created with a final client from beggining of the pipeline (wait_weeks=22) |
| total | total cars delivered in the period |
source of productionSource type of the cars delivered in the period
Doc: KPIs x country
loaned from other countries
given away to other countries
Donor country of a delivered car
The loaned KPI from the source tab, by country
KPIs summary matrix
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stock
# of cars/week parked waiting to be sold
loaned
# of car sales from which source == loaned
waiting weeks
sum of weeks all customers have waited for their car
| 1.426.010 |
8%
|
5%
|
6.635K
| 16% |
5.570K
| 24% |
5.027K
| 24% |
4.997K
| 0% |
| 32% |
| 18% |
| 38% |
Resultats
13%
|
fi
seat_sim
By Bernat Esquirol
