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Spot BOPIS Pickup Drift

When a district ops manager heads into the weekly ops call or an escalation review, the immediate question is whether one store's late pickups are isolated or part of a wider slowdown. This workflow compares a store's on-time pickup rate to its prior period and district median, then produces a variance table and directional summary.

The manager still decides whether the issue reflects process breakdown, staffing strain, or a temporary spike; the workflow removes the retrieval, calculation, and formatting work. The estimated impact is a 30.0% shift in role time from execution-layer work to higher-value decision making.

Interactive Demo

See the Decision Intelligence Workflow in Action

This workflow handles a recurring comparative analysis task: pulling BOPIS order counts for a store, calculating on-time pickup performance for the current and prior equal-length periods, and benchmarking that store against the district median. It applies the same materiality logic each time so weekly reviews and exception triage start from a consistent baseline instead of ad hoc spreadsheet work.

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Designed for line-of-business leaders — specifically the District Operations Manager responsible for omnichannel fulfillment execution across multiple stores. It focuses on the recurring pre-read and escalation work that consumes management time before any judgment is applied. Why this matters: What line-of-business leaders actually need from automation →
Decision Intelligence Playground · BOPIS Pickup Variance Review
KWF Runtime
Start with an example
See how this variance review would run in your retail operations environment.
In a working session, we will map the pickup-speed review to your district structure, service thresholds, and existing fulfillment metrics. We will also show the workflow running on your Google Cloud Platform environment with the output format your operations leaders would actually use in weekly review and escalation triage.
Schedule a GCP Session
How It Works

What the Decision Intelligence Workflow Does

This workflow handles a recurring comparative analysis task: pulling BOPIS order counts for a store, calculating on-time pickup performance for the current and prior equal-length periods, and benchmarking that store against the district median. It applies the same materiality logic each time so weekly reviews and exception triage start from a consistent baseline instead of ad hoc spreadsheet work.

Click any step below to see the business logic, data query, and sample output for that step of the workflow.

Pre-specified logic, not runtime guessing — Most AI agent frameworks work by figuring things out on the fly. These Decision Intelligence workflows work differently. The Knowledge Work Foundry analyzes the cognitive labor pattern before deployment and encodes the decision logic directly into the configuration — which tables to query, which thresholds define a breach, how signals are ranked, and what the output artifact should contain. That analysis happens once. By the time the workflow runs, there is nothing left to figure out.
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Store Rate
Calculates the focus store's on-time pickup rate for the requested period and the prior equal-length period.
↑ click a step to explore the logic, query, and output
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Step Detail

                      

                    

The workflow produces a directional statement and a compact variance table showing orders, on-time orders, on-time pickup percentage, change versus prior period, district median percentage, and change versus district median. That gives the manager a ready-to-use pre-read artifact for the ops call or escalation discussion.

What this workflow does NOT do: It does not decide whether to escalate the store to a formal vendor or process review, reallocate labor across stores, determine whether a decline is acceptable given local operating conditions, or set district-wide staffing and fulfillment standards. It also does not choose which store leaders to coach first or what corrective action plan to require.
Under the Hood

Data Warehouse Integration

The workflow reads directly from fulfillment order-event data in the warehouse and calculates a standardized operating metric without requiring a manager to rebuild the logic each week. That matters because this task is repetitive, threshold-driven, and sensitive to denominator effects that are easy to misread in manual analysis.

Even though the comparison is presented as a single operational view, it spans time-window logic, peer-group benchmarking, threshold evaluation, and executive-ready formatting across district and store-level fulfillment data.

Source system: Fulfillment order events summary  ·  Domain: Omnichannel Operations
Role in this workflow: This table supplies the order-level facts used to calculate BOPIS on-time readiness performance for both the focus store and its district peer set. It provides the volume, timing, and exception signals needed to determine whether a service issue is a local store execution problem or part of a broader district slowdown.
CREATE TABLE pura_vida_foods_dev.omni_mart.mart_bopis_order_fact (
  order_id STRING,
  store_id STRING,
  region_id STRING,
  order_date DATE,
  promised_ready_ts TIMESTAMP,
  actual_ready_ts TIMESTAMP,
  pickup_completed_ts TIMESTAMP,
  is_on_time_ready BOOLEAN,
  is_exception BOOLEAN
);

WITH bounds AS (
  SELECT
    string">'{start_date}'::DATE AS start_date,
    string">'{end_date}'::DATE AS end_date,
    ((string">'{end_date}'::DATE - string">'{start_date}'::DATE) + 1) AS period_days
),
store_period AS (
  SELECT
    f.store_id,
    f.region_id,
    CASE
      WHEN f.order_date BETWEEN (SELECT start_date FROM bounds) AND (SELECT end_date FROM bounds) THEN string">'current'
      WHEN f.order_date BETWEEN ((SELECT start_date FROM bounds) - (SELECT period_days FROM bounds))
                          AND ((SELECT end_date FROM bounds) - (SELECT period_days FROM bounds))
      THEN string">'prior'
      ELSE NULL
    END AS period_label,
    COUNT(*) AS orders,
    SUM(CASE WHEN f.is_on_time_ready THEN 1 ELSE 0 END) AS on_time_orders,
    SUM(CASE WHEN f.is_on_time_ready THEN 1 ELSE 0 END) * 1.0 / COUNT(*) AS on_time_rate
  FROM pura_vida_foods_dev.omni_mart.mart_bopis_order_fact f
  WHERE f.region_id = string">'{region_id}'
    AND (
      f.order_date BETWEEN (SELECT start_date FROM bounds) AND (SELECT end_date FROM bounds)
      OR f.order_date BETWEEN ((SELECT start_date FROM bounds) - (SELECT period_days FROM bounds))
                        AND ((SELECT end_date FROM bounds) - (SELECT period_days FROM bounds))
    )
  GROUP BY f.store_id, f.region_id, period_label
),
median_period AS (
  SELECT
    region_id,
    period_label,
    median(on_time_rate) AS district_median_on_time
  FROM store_period
  WHERE period_label IS NOT NULL
  GROUP BY region_id, period_label
),
focus AS (
  SELECT *
  FROM store_period
  WHERE store_id = string">'{store_id}'
    AND period_label IS NOT NULL
)
SELECT
  f.store_id,
  f.region_id,
  f.period_label,
  f.orders,
  f.on_time_orders,
  f.on_time_rate,
  m.district_median_on_time,
  (f.on_time_rate - m.district_median_on_time) AS delta_vs_median
FROM focus f
JOIN median_period m
  ON f.region_id = m.region_id
 AND f.period_label = m.period_label
ORDER BY f.period_label;
Cognitive Labor Analysis

Where the Work Sits in the Labor Stack

Not all cognitive labor is equally automatable. The KWF analysis breaks the workflow into three layers — execution, judgment, and strategic — and maps each step to the layer it belongs to. Execution-layer work is automatable. Judgment and strategic work stays with the manager.

Execution 50% Judgment 35% Strategic 15%
Execution Layer
50%
The execution layer retrieves order counts, computes on-time rates, compares current versus prior period and district median, applies materiality thresholds, and formats the variance output.
Judgment Layer
35%
The manager interprets whether the variance indicates a real execution problem, capacity strain, or a temporary fluctuation and decides what operational follow-up is warranted.
Strategic Layer
15%
District leadership retains decisions about staffing standards, escalation policy, process redesign, and broader omnichannel operating model changes.
Value Model

The Business Case for Automation

Time Recovered
30.0% role time shifted
Based on the value model, automation coverage moves about 30% of role effort out of recurring execution work and into judgment and oversight.
Annual Savings
$1.133M net benefit
The conservative planning model estimates approximately $1.163M in value uplift and about $1.133M in net annual benefit after automation cost.
Strategic Upside
Fewer BOPIS Escalations
The strategic upside is earlier detection of store-level pickup degradation, reducing repeated exceptions, customer dissatisfaction, and emergency operating responses.
Kill Question: Without this workflow, the district manager or an analyst manually pulls store-level BOPIS order counts, rebuilds current-versus-prior calculations, compares the store against district peers, and assembles a one-off variance view before each ops call or escalation. That means slower triage, inconsistent comparison logic, and more time spent proving whether the issue is isolated before discussing action.

Primary Valuation Metric: Decrease in BOPIS exception escalations per 1,000 orders after adopting the standardized variance review.

Next Step

Run your own value estimate for district fulfillment operations — or talk to us about your BOPIS review workflow.

The Cognitive Labor Value Calculator models team size, role cost, and automation coverage so you can estimate how much manager time is tied up in recurring execution work. It takes under two minutes to complete.