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.
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.
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.
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.
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.
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;
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.
The Business Case for Automation
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.