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Demand planning suite

In a nutshell

Just specifying the key column names, Shimoku will create a whole Demand planning Suite in less than 48 hours
shimoku.suite.demand_planning_suite(
data=data,
product='product_id',
date='date',
)
On the data
The minimum dataset for Shimoku to make predictions must contain
  • date column as timestamp
  • product_id as the product unique identifier
Nevertheless the more data you add the more will learn the algorithm, in particular, we suggest adding as much data as possible, being the ideal case the Shopify Order object

Updating

The first time you have to update at least 12 months of data. Nevertheless, after you post the historic dataset you can count on updated predictions just posting the daily lambdas by specifying how='append'
historic_dataframe = pd.read_csv('historic_orders.csv') # Orders from the last 12 months
shimoku.suite.demand_planning_suite(
data=historic_dataframe,
product='product_id',
date='date',
)
yesterday_df = pd.read_csv('yesterday_orders.csv') # orders from yesterday
shimoku.suite.demand_planning_suite(
data=yesterday_df,
product='product_id',
date='date',
# to tell Shimoku SDK that this is a piece of data
# to be concat to the historic one
how='append',
)
Full example
Including the Class instance call
api_key: str = getenv('API_TOKEN')
universe_id: str = getenv('UNIVERSE_ID')
business_id: str = getenv('BUSINESS_ID')
environment: str = getenv('ENVIRONMENT')
config = {
'access_token': api_key,
}
shimoku = shimoku.Client(
config=config,
universe_id=universe_id,
environment=environment,
)
shimoku.plt.set_business(business_id=business_id)
shimoku.suite.demand_planning_suite(
data=data,
product='product_id',
date='date',
)
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