Explain Daily Route Changes
When a delivery plan changes before dispatch, the last-mile route planner has to explain what moved, why it changed, and what each driver should do next. This workflow compiles route changes, attributes likely operational causes, and produces a driver-ready narrative for a specific plan and date.
Planner judgment stays intact; the workflow removes the execution work of collecting changes, matching causes, and formatting instructions. The estimated impact is $27,300 per year in hard savings from compressing this work by 50%.
See the Decision Intelligence Workflow in Action
This workflow handles a compound communication task that planners usually do manually under time pressure. It identifies which stops changed from the original plan, links those changes to observable operating factors, and assembles a plain-language explanation that a driver can act on immediately. The result is a repeatable narrative instead of one-off calls, texts, and ad hoc notes.
What the Decision Intelligence Workflow Does
This workflow handles a compound communication task that planners usually do manually under time pressure. It identifies which stops changed from the original plan, links those changes to observable operating factors, and assembles a plain-language explanation that a driver can act on immediately. The result is a repeatable narrative instead of one-off calls, texts, and ad hoc notes.
Click any step below to see the business logic, data query, and sample output for that step of the workflow.
The workflow produces a concise driver-facing route adjustment summary for a given plan and date. That output can be delivered in the driver mobile app and surfaced as an annotated update in the daily route planning dashboard.
Data Warehouse Integration
The workflow depends on joining route plan data with stop-level operating signals for the same service date. It retrieves planned versus adjusted sequence and assignment changes, then connects those changes to measurable factors so the narrative is traceable to actual operating data.
That means the explanation is built across route planning data and stop-level factor data, not from a single operational table.
CREATE TABLE retail_dev.last_mile_ops.route_plans (
plan_id VARCHAR,
stop_id VARCHAR,
plan_date DATE,
driver_id VARCHAR,
planned_sequence INTEGER,
adjusted_sequence INTEGER,
status VARCHAR
);
SELECT
plan_id,
plan_date,
stop_id,
driver_id,
planned_sequence,
adjusted_sequence,
status,
CASE
WHEN planned_sequence <> adjusted_sequence THEN 1
WHEN status <> string">'On Time' THEN 1
ELSE 0
END AS is_changed
FROM retail_dev.last_mile_ops.route_plans
WHERE plan_id = string">'{plan_id}'
AND plan_date = string">'{plan_date}';
CREATE TABLE retail_dev.last_mile_ops.stop_factors (
stop_id VARCHAR,
factor_date DATE,
traffic_index INTEGER,
customer_available BOOLEAN
);
SELECT
stop_id,
factor_date AS plan_date,
traffic_index,
customer_available
FROM retail_dev.last_mile_ops.stop_factors
WHERE factor_date = string">'{plan_date}'
AND stop_id IN (
SELECT stop_id
FROM retail_dev.last_mile_ops.route_plans
WHERE plan_id = string">'{plan_id}'
AND plan_date = string">'{plan_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 spent by planners on daily route plan explanations per driver.
Next Step
Run your own value estimate for last-mile route planning — or talk to us about this driver communication workflow.
The Cognitive Labor Value Calculator models team size, role cost, and automation coverage to estimate the time and cost tied to manual route-change explanations. It takes under two minutes to complete.