def recommendations(analysed_orders: UncertainDemand, forecast: dict) -> dict:
""" Generate Recommendations for each SKU and the inventory Profile.
Args:
analysed_orders (UncertainDemand): UncertainDemand object of analysed orders.
forecast (dict): Output from a Forecast.
Returns:
dict: Returns recommendations for each sku and for the inventory profile.
Examples:
>>> from decimal import Decimal
>>> from supplychainpy.sample_data.config import ABS_FILE_PATH
>>> from supplychainpy.model_inventory import analyse
>>> from supplychainpy.model_inventory import recommendations
...
>>> analysed_order = analyse(file_path=ABS_FILE_PATH['COMPLETE_CSV_SM'],
... z_value=Decimal(1.28),
... reorder_cost=Decimal(5000),
... file_type="csv", length=12,currency='USD')
...
>>> holts_forecast = {analysis.sku_id: analysis.holts_trend_corrected_forecast for analysis in
... analyse(file_path=ABS_FILE_PATH['COMPLETE_CSV_SM'], z_value=Decimal(1.28),
... reorder_cost=Decimal(5000), file_type="csv",
... length=12,currency='USD')}
...
>>> recommend = recommendations(analysed_orders=analysed_order, forecast=holts_forecast)
"""
recommend = {'sku_recommendations': run_sku_recommendation(analysed_orders=analysed_orders, forecast=forecast),
'profile_recommendations': run_profile_recommendation(analysed_orders=analysed_orders, forecast=forecast),
}
return recommend
def recommendations(analysed_orders: UncertainDemand, forecast: dict) -> dict:
""" Generate Recommendations for each SKU and the inventory Profile.
Args:
analysed_orders (UncertainDemand): UncertainDemand object of analysed orders.
forecast (dict): Output from a Forecast.
Returns:
dict: Returns recommendations for each sku and for the inventory profile.
Examples:
>>> from decimal import Decimal
>>> from supplychainpy.sample_data.config import ABS_FILE_PATH
>>> from supplychainpy.model_inventory import analyse
>>> from supplychainpy.model_inventory import recommendations
...
>>> analysed_order = analyse(file_path=ABS_FILE_PATH['COMPLETE_CSV_SM'],
... z_value=Decimal(1.28),
... reorder_cost=Decimal(5000),
... file_type="csv", length=12,currency='USD')
...
>>> holts_forecast = {analysis.sku_id: analysis.holts_trend_corrected_forecast for analysis in
... analyse(file_path=ABS_FILE_PATH['COMPLETE_CSV_SM'], z_value=Decimal(1.28),
... reorder_cost=Decimal(5000), file_type="csv",
... length=12,currency='USD')}
...
>>> recommend = recommendations(analysed_orders=analysed_order, forecast=holts_forecast)
"""
recommend = {'sku_recommendations': run_sku_recommendation(analysed_orders=analysed_orders, forecast=forecast),
'profile_recommendations': run_profile_recommendation(analysed_orders=analysed_orders, forecast=forecast),
}
return recommend
def load_recommendations(summary, forecast, analysed_order):
recommend = run_sku_recommendation(analysed_orders=analysed_order, forecast=forecast)
for item in summary:
rec = Recommendations()
mk = db.session.query(MasterSkuList.id).filter(MasterSkuList.sku_id == item['sku']).first()
inva = db.session.query(InventoryAnalysis.id).filter(InventoryAnalysis.sku_id == mk.id).first()
rec.analysis_id = inva.id
reco = 'There are no recommendation at this time.' if recommend.get(item['sku'],
'There are no recommendation at this time.'
) == '' else recommend.get(item['sku'],
'None')
rec.statement = reco
db.session.add(rec)
db.session.commit()
def load_recommendations(summary, forecast, analysed_order):
recommend = run_sku_recommendation(analysed_orders=analysed_order,
forecast=forecast)
for item in summary:
rec = Recommendations()
mk = db.session.query(MasterSkuList.id).filter(
MasterSkuList.sku_id == item['sku']).first()
inva = db.session.query(InventoryAnalysis.id).filter(
InventoryAnalysis.sku_id == mk.id).first()
rec.analysis_id = inva.id
reco = 'There are no recommendation at this time.' if recommend.get(
item['sku'], 'There are no recommendation at this time.'
) == '' else recommend.get(item['sku'], 'None')
rec.statement = reco
db.session.add(rec)
db.session.commit()