Rank Delivery Failure Causes
When a region shows a sudden spike in failed deliveries, the last-mile route planner has to explain what changed before the next operations review. This workflow detects the cluster, ranks the likely causes, and produces an evidence-backed narrative with recommended follow-up actions.
The planner still decides what to escalate, who to challenge, and which intervention fits the region; the workflow removes the cross-checking, evidence assembly, and write-up work. The estimated impact is 50% compression in time spent producing root-cause narratives per failure cluster event.
See the Decision Intelligence Workflow in Action
This workflow handles a compound operational analysis task: first confirming that a delivery failure pattern is materially outside normal regional performance, then testing the most relevant contributing signals, then turning that evidence into a usable explanation. The work is repetitive but time-sensitive because planners are expected to brief managers quickly and with evidence they can defend.
What the Decision Intelligence Workflow Does
This workflow handles a compound operational analysis task: first confirming that a delivery failure pattern is materially outside normal regional performance, then testing the most relevant contributing signals, then turning that evidence into a usable explanation. The work is repetitive but time-sensitive because planners are expected to brief managers quickly and with evidence they can defend.
Click any step below to see the business logic, data query, and sample output for that step of the workflow.
It produces a ranked root-cause narrative for a defined region and time window, including the size of the failure spike, the supporting evidence for each likely driver, and suggested areas for operational follow-up. The output can be used in regional ops briefings, carrier reviews, and incident post-mortems.
Data Warehouse Integration
The workflow depends on operational history plus adjacent signals that usually sit in separate reporting domains. It brings those records together so the planner does not have to manually reconcile whether the failure spike aligns with carrier degradation, route disruption, or customer availability issues.
The analysis is cross-domain by design, combining delivery status trends with carrier performance, road closure activity, and customer no-show behavior for the same region and date range.
CREATE TABLE retail_dev.last_mile_ops.delivery_status_daily (
delivery_id VARCHAR,
region_id VARCHAR,
delivery_date DATE,
status VARCHAR
);
WITH current_window AS (
SELECT
region_id,
COUNT(*) AS deliveries_current,
SUM(CASE WHEN status = string">'Failed' THEN 1 ELSE 0 END) AS failures_current
FROM retail_dev.last_mile_ops.delivery_status_daily
WHERE region_id = string">'{region_id}'
AND delivery_date BETWEEN string">'{start_date}' AND string">'{end_date}'
GROUP BY region_id
),
prior_window AS (
SELECT
region_id,
COUNT(*) AS deliveries_prior,
SUM(CASE WHEN status = string">'Failed' THEN 1 ELSE 0 END) AS failures_prior
FROM retail_dev.last_mile_ops.delivery_status_daily
WHERE region_id = string">'{region_id}'
AND delivery_date BETWEEN (CAST(string">'{start_date}' AS DATE) - {time_window_days}) AND (CAST(string">'{start_date}' AS DATE) - 1)
GROUP BY region_id
)
SELECT
c.region_id,
c.deliveries_current,
c.failures_current,
CASE WHEN c.deliveries_current = 0 THEN NULL ELSE CAST(c.failures_current AS DOUBLE) / c.deliveries_current END AS failure_rate_current,
p.deliveries_prior,
p.failures_prior,
CASE WHEN p.deliveries_prior = 0 THEN NULL ELSE CAST(p.failures_prior AS DOUBLE) / p.deliveries_prior END AS failure_rate_prior,
CASE
WHEN p.deliveries_prior = 0 OR p.failures_prior = 0 THEN NULL
ELSE CAST(c.failures_current AS DOUBLE) / p.failures_prior - 1
END AS failure_count_wow_change
FROM current_window c
LEFT JOIN prior_window p
ON c.region_id = p.region_id;CREATE TABLE retail_dev.last_mile_ops.carrier_performance_daily (
carrier_id VARCHAR,
region_id VARCHAR,
perf_date DATE,
on_time_rate DOUBLE
);
WITH daily_failures AS (
SELECT
region_id,
delivery_date AS d,
SUM(CASE WHEN status = string">'Failed' THEN 1 ELSE 0 END) AS failures
FROM retail_dev.last_mile_ops.delivery_status_daily
WHERE region_id = string">'{region_id}'
AND delivery_date BETWEEN string">'{start_date}' AND string">'{end_date}'
GROUP BY region_id, delivery_date
),
peak_day AS (
SELECT region_id, d AS peak_date, failures
FROM daily_failures
QUALIFY ROW_NUMBER() OVER (PARTITION BY region_id ORDER BY failures DESC, d DESC) = 1
)
SELECT
p.carrier_id,
p.region_id,
p.perf_date AS date,
p.on_time_rate
FROM retail_dev.last_mile_ops.carrier_performance_daily p
JOIN peak_day pk
ON p.region_id = pk.region_id
AND p.perf_date = pk.peak_date
ORDER BY p.on_time_rate ASC;CREATE TABLE retail_dev.last_mile_ops.road_closures_daily (
region_id VARCHAR,
closure_date DATE,
closure_count INTEGER
);
WITH daily_failures AS (
SELECT
region_id,
delivery_date AS d,
SUM(CASE WHEN status = string">'Failed' THEN 1 ELSE 0 END) AS failures
FROM retail_dev.last_mile_ops.delivery_status_daily
WHERE region_id = string">'{region_id}'
AND delivery_date BETWEEN string">'{start_date}' AND string">'{end_date}'
GROUP BY region_id, delivery_date
),
peak_day AS (
SELECT region_id, d AS peak_date, failures
FROM daily_failures
QUALIFY ROW_NUMBER() OVER (PARTITION BY region_id ORDER BY failures DESC, d DESC) = 1
)
SELECT
r.region_id,
r.closure_date AS date,
r.closure_count
FROM retail_dev.last_mile_ops.road_closures_daily r
JOIN peak_day pk
ON r.region_id = pk.region_id
AND r.closure_date = pk.peak_date;CREATE TABLE retail_dev.last_mile_ops.customer_availability_daily (
region_id VARCHAR,
availability_date DATE,
no_show_rate DOUBLE
);
WITH daily_failures AS (
SELECT
region_id,
delivery_date AS d,
SUM(CASE WHEN status = string">'Failed' THEN 1 ELSE 0 END) AS failures
FROM retail_dev.last_mile_ops.delivery_status_daily
WHERE region_id = string">'{region_id}'
AND delivery_date BETWEEN string">'{start_date}' AND string">'{end_date}'
GROUP BY region_id, delivery_date
),
peak_day AS (
SELECT region_id, d AS peak_date, failures
FROM daily_failures
QUALIFY ROW_NUMBER() OVER (PARTITION BY region_id ORDER BY failures DESC, d DESC) = 1
)
SELECT
a.region_id,
a.availability_date AS date,
a.no_show_rate
FROM retail_dev.last_mile_ops.customer_availability_daily a
JOIN peak_day pk
ON a.region_id = pk.region_id
AND a.availability_date = pk.peak_date;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: Reduction in average time to produce root-cause narratives per delivery failure cluster event.
Next Step
Run your own value estimate for delivery failure analysis — or talk to us about your last-mile route planning team.
The Cognitive Labor Value Calculator models team size, role cost, and automation coverage for this workflow so you can estimate labor impact quickly. It takes under two minutes to complete.