ScrapEvents¤

Scrap quantity (weight or count) per shift, per reason and over time, and scrap cost per part or material, from a cumulative scrap signal (value_double by default).

ts_shape.events.production.scrap_tracking · Production guide · API reference

Example¤

import numpy as np
import pandas as pd
import ts_shape
from ts_shape.events.production.scrap_tracking import ScrapEvents

t = pd.date_range("2024-01-01 06:00", periods=960, freq="1min")
df = pd.concat([
    pd.DataFrame({"systime": t, "uuid": "line1:scrap_kg", "value_double": np.arange(960) * 0.05}),
    ts_shape.make_id_signal("line1:part_id", ["PN-100", "PN-200"], hold=480, freq="1min", start="2024-01-01 06:00"),
], ignore_index=True)

sc = ScrapEvents(df)
result = sc.scrap_by_shift(scrap_uuid="line1:scrap_kg")
print(result[["date", "shift", "scrap_quantity"]])
cost = sc.scrap_cost(scrap_uuid="line1:scrap_kg", part_id_uuid="line1:part_id",
                     material_costs={"PN-100": 3.20, "PN-200": 5.80})

Methods¤

Method	Returns
`scrap_by_shift(scrap_uuid)`	`date`, `shift`, `scrap_quantity`
`scrap_by_reason(scrap_uuid, reason_uuid)`	`reason`, `scrap_quantity`, `share_of_total_pct`
`scrap_cost(scrap_uuid, part_id_uuid, material_costs)`	per part: `scrap_quantity`, `cost_per_unit`, `total_cost`
`scrap_trend(scrap_uuid, window='1D')`	`period`, `scrap_quantity`

Notes¤

With handle_resets=True (default) a decreasing signal is a reset and the scrap after it still counts; every method adds a resets column. Increments are booked to the shift, reason or part of the reading that reports them, so groups add up to the signal total. handle_resets=False uses max(0, last - first) per group.
material_costs keys are the values of the part_id_uuid signal; the cost is per unit of the scrap signal (e.g. per kg).
Default shifts are 06-14, 14-22, 22-06; a night shift belongs to its start date (production_day=True).