e762ee4b68
Computes for the Manager At-Risk heatmap (Phase 4 tablet redesign). Non-stored — recomputed on /fp/manager/at_risk read; that endpoint caches its full payload for 60s so the cost is bounded. bottleneck_score = active_step_count * avg_wait_minutes avg_wait_minutes = rolling-7-day avg of (date_started - create_date) Work centres with high score show red in the heatmap — combination of queue length AND average wait time. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
116 lines
4.6 KiB
Python
116 lines
4.6 KiB
Python
# -*- coding: utf-8 -*-
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# Copyright 2026 Nexa Systems Inc.
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# License OPL-1 (Odoo Proprietary License v1.0)
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#
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# fp.work.centre — native plating work-centre model.
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#
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# Replaces mrp.workcenter for the plating flow. Plating work centres
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# are domain-specific (a tank line, a bake oven, a rack station — not
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# assembly cells). Each centre has a 'kind' that drives release-ready
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# validation on fp.job.step (e.g. wet_line -> bath+tank required).
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from odoo import fields, models
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class FpWorkCentre(models.Model):
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"""Routing station for a job step — replaces mrp.workcenter for
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plating after the Sub 11 MRP cutout.
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Each routing station has a `kind` (wet_line / bake / mask / rack /
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inspect / other) that drives release-ready validation on
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`fp.job.step` (e.g. wet_line requires bath+tank to be set before
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the step can start). Costable via `cost_per_hour`.
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Distinct from `fusion.plating.work.center` (Production Line),
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which is the physical shop-layout grouping that owns tanks.
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A Production Line typically contains many Routing Stations.
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"""
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_name = 'fp.work.centre'
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_description = 'Plating Routing Station'
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_order = 'sequence, code, name'
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name = fields.Char(string='Routing Station', required=True)
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code = fields.Char(required=True, help='Short code used on stickers and reports.')
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sequence = fields.Integer(default=10)
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facility_id = fields.Many2one(
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'fusion.plating.facility',
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string='Facility',
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)
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kind = fields.Selection(
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[
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('wet_line', 'Wet Line'),
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('bake', 'Bake Oven'),
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('mask', 'Masking'),
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('rack', 'Racking'),
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('inspect', 'Inspection'),
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('other', 'Other'),
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],
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required=True,
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default='other',
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)
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cost_per_hour = fields.Monetary(
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currency_field='currency_id',
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help='Used for fp.job.step cost rollups.',
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)
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currency_id = fields.Many2one(
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'res.currency',
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default=lambda self: self.env.company.currency_id,
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)
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default_bath_id = fields.Many2one('fusion.plating.bath')
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default_tank_id = fields.Many2one('fusion.plating.tank')
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# NOTE: `default_oven_id` from the spec/plan is omitted here — the
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# `fusion.plating.bake.oven` model lives in fusion_plating_shopfloor,
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# which the core module cannot depend on. The bridge module that
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# introduces fp.job/fp.job.step (Task 1.x) can re-introduce this
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# field via _inherit if/when the bake-oven coupling is needed.
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active = fields.Boolean(default=True)
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# Phase 4 tablet redesign — Manager At-Risk heatmap inputs.
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# Non-stored (recomputed on every read by /fp/manager/at_risk; the
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# endpoint caches the payload for 60s anyway so the cost is bounded).
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bottleneck_score = fields.Float(
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compute='_compute_bottleneck',
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string='Bottleneck Score',
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help='active_step_count * avg_wait_minutes (rolling 7-day). '
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'Drives the Manager At-Risk heatmap — work centres with '
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'high score have queue + wait pressure.',
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)
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avg_wait_minutes = fields.Float(
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compute='_compute_bottleneck',
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string='Avg Wait (min)',
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help='Average minutes that steps at this work centre waited in '
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'ready state before starting, over the last 7 days.',
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)
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def _compute_bottleneck(self):
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from datetime import timedelta
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Step = self.env['fp.job.step']
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now = fields.Datetime.now()
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seven_days_ago = now - timedelta(days=7)
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for wc in self:
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active_n = Step.search_count([
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('work_centre_id', '=', wc.id),
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('state', 'in', ('ready', 'in_progress')),
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])
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# Avg wait: recent steps where date_started is set; approximate
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# "ready since" as create_date when no explicit ready timestamp
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# is recorded. Bounded set (last 7 days) keeps the search cheap.
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recent = Step.search([
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('work_centre_id', '=', wc.id),
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('date_started', '>=', seven_days_ago),
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('date_started', '!=', False),
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])
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waits = []
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for s in recent:
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if s.create_date and s.date_started:
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waits.append(
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(s.date_started - s.create_date).total_seconds() / 60.0
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)
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avg = (sum(waits) / len(waits)) if waits else 0.0
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wc.avg_wait_minutes = avg
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wc.bottleneck_score = active_n * avg
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_sql_constraints = [
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('unique_code', 'UNIQUE(code)', 'Work centre code must be unique.'),
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]
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