Supply chain and logistics

Data mapping

Entity

item (SKU)

Group

warehouse or plant

Profile

generic; configure time windows with verticals.register

Reference implementation

from datetime import datetime, timedelta
from vertiq import plugins, rules
from vertiq.mapping import Mapping, apply, repair
from vertiq.tasks import predict_event, predict_value

# rows: the source export as a list of dicts, e.g. list(csv.DictReader(open("export.csv")))
mapping = Mapping.parse({"entity": "Articulo", "time": "Movement Date", "type": "Movement Type", "value": "Quantity", "group": "Plant", "attrs": ["Batch", "Supplier"], "date_format": "%Y-%m-%d"})
m, notes = repair(mapping, rows)      # validates column names, value types and date format against the rows
ds, problems = apply(m, rows)         # rows that fail to load are reported, not imputed

@plugins.feature("stock")
def receipts_90(entity, before, cut):   # receives only events recorded before the cutoff
    return {"gr_90": sum(1 for e in before if e.type == "GR"
                         and e.at >= cut - timedelta(days=90))}

task = predict_event(ds, target="GI", window_days=14, pack="stock")
print(task.status, task.report())     # "trained" only if the model outperformed the baseline on held-out entities
if task.status == "trained":
    print(task.score("SKU-1678"))  # probability with contributing factors

size = predict_value(ds, target="GI", window_days=30, pack="stock")
print(size.status, size.report())

flags = rules.run(ds, [
    rules.repeat(2, type="return"),   # two returns of the same item within two days
])
print(flags["summary"])

This code is executed by the test suite (tests/test_guides.py) against generated records with these column names.