Quantify Discount-Driven Waste Pressure

When the monthly executive pack or S&OP review asks how much margin was given up through discounting last month, the VP Supply Chain or supporting Finance and RevOps staff have to pull sales-line transactions, filter the right date range, total discount amounts, and reconcile the figure into slides or spreadsheets. In this workflow, the source is the sales transaction table `sales_dw.fact_sales`, which stores `sale_date` and `discount_amount` at the line level. Even for a simple monthly KPI, the work recurs every month and often has to be rechecked live in review meetings, which means management time is spent assembling a defensible number instead of interpreting whether discount pressure signals freshness, overstock, or waste risk.

Last Month Spoilage/Waste Proxy: Total Discount Amount on Sales Lines is a Decision Intelligence workflow designed for the VP Supply Chain. It places the execution-layer work in software: period-bound querying, discount aggregation, KPI assembly, and finance-ready formatting — about 40% of the role's cognitive labor, with 60% automation coverage of that layer in the candidate model. The VP gets back time for judgment on whether discounting is abnormal and for follow-on decisions on inventory, production, and supply policy.

See the Value Model

Interactive Demo

See the Decision Intelligence Workflow in Action

This workflow answers a narrow but operationally important question: what was the total discount amount applied on sales lines in a defined period. The cognitive task is not mathematical complexity; it is consistently retrieving the right transaction slice, applying the date window, summing the discount field, and presenting the result in a form that can be used in executive review without argument over source data.

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Designed for line-of-business leaders — specifically the VP Supply Chain responsible for freshness governance, executive KPI review, and waste reduction. This workflow handles the repetitive KPI assembly work so leadership attention stays on margin leakage interpretation and operating response. Why this matters: What line-of-business leaders actually need from Decision Intelligence →

Decision Intelligence Playground · Discount Leakage Monitoring for Supply Chain

KWF Runtime

Start with an example

See how this discount-pressure workflow would run in your environment.

In a working session, we will map your current monthly KPI assembly process, identify the transaction fields used to calculate discount leakage, and show the workflow running on your Databricks environment. We will also review where the output fits into your executive pack, finance discussion flow, and freshness or waste governance cadence.

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How It Works

What the Decision Intelligence Workflow Does

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.

Sum Discounts

Calculates the total discount amount on sales lines for the requested date range.

↑ click a step to explore the logic, query, and output

Step Detail

Business Logic

Query / Instructions

Sample Data Query Results

Step Business Logic

The workflow produces a single period-bound KPI value: total discount amount for the requested date range. That value can be inserted directly into a monthly executive pack and a finance-aligned margin leakage footnote or dashboard tile.

What this workflow does NOT do: It does not decide whether elevated discounting reflects inventory aging versus a commercial promotion, decide whether to throttle production for a product family, determine whether inventory should be rebalanced across locations, decide whether supplier lead-time or MOQ policy should be changed, or approve cold-chain or shelf-life investments.

Under the Hood

Data Warehouse Integration

The workflow runs directly against transactional sales data in the warehouse, using line-level discount amounts and sale dates to produce a period-specific KPI. That keeps the number tied to a single source of truth rather than ad hoc spreadsheet rollups used before executive meetings.

Although this candidate uses one fact table, the business question is cross-functional: the output supports supply chain, finance, and executive review discussions about freshness, overstock, waste pressure, and margin leakage.

Source system: Sales data warehouse · Domain: Sales and Supply Chain
Role in this workflow: This table provides the transaction-level sales lines used to calculate total discount amount for a defined period. In this workflow, it is the single source of truth for discount leakage measurement because it contains sale dates, order identifiers, channel context, and discount amounts on each line. When discount totals rise over a reporting window, the table supplies the evidence used in executive pack KPI reporting and follow-up investigation.

CREATE TABLE pura_vida_foods_dev.sales_dw.fact_sales (
  sale_id STRING NOT NULL,
  sale_date DATE NOT NULL,
  customer_id STRING NOT NULL,
  product_id STRING NOT NULL,
  order_id STRING NOT NULL,
  quantity DECIMAL(10,2) NOT NULL,
  unit_price DECIMAL(10,2) NOT NULL,
  unit_cost DECIMAL(10,2) NOT NULL,
  revenue DECIMAL(15,2) NOT NULL,
  cost DECIMAL(15,2) NOT NULL,
  profit DECIMAL(15,2) NOT NULL,
  profit_margin DECIMAL(8,4),
  discount_amount DECIMAL(10,2) DEFAULT 0.00,
  discount_percent DECIMAL(5,2) DEFAULT 0.00,
  sales_channel STRING,
  order_priority STRING,
  shipping_cost DECIMAL(10,2) DEFAULT 0.00,
  created_at TIMESTAMP,
  updated_at TIMESTAMP
);

SELECT
  ROUND(SUM(discount_amount), 2) AS total_discount_amount
FROM pura_vida_foods_dev.sales_dw.fact_sales
WHERE sale_date BETWEEN string">'{start_date}' AND string">'{end_date}';

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 40% Judgment 40% Strategic 20%

Execution Layer

40%

The execution layer retrieves the relevant sales lines for the requested period, sums discount amounts, and formats the result as a clean KPI for reporting.

Judgment Layer

40%

The VP Supply Chain still has to interpret whether the discount total is abnormal, what operational conditions likely caused it, and how to explain it in the business context.

Strategic Layer

20%

Longer-horizon choices such as inventory policy changes, lead-time adjustments, production strategy, and shelf-life or cold-chain investments remain management decisions.

Value Model

The Business Case for Automation

Time Recovered

Monthly KPI assembly

The workflow removes the recurring work of pulling and validating last-month discount totals for executive and S&OP reviews.

Annual Savings

$194,000 net benefit

The candidate's conservative value model estimates an 8.76x ROI after accounting for workflow cost.

Strategic Upside
Earlier Waste Signal
A faster discount proxy helps leadership identify potential freshness and overstock pressure earlier, before margin leakage turns into write-offs or reactive operating changes.

Kill Question: Without this workflow, the team answers the executive question by manually pulling transaction data, rebuilding the discount total for the month, and debating whether the number is complete enough to use as a waste-pressure proxy.

Primary Valuation Metric: Reduction in time-to-answer for 'how much did we discount last month?' during executive and S&OP reviews.

Next Step

Run your own value estimate for supply chain KPI assembly — or talk to us about this discount-pressure workflow for your VP Supply Chain team.

The Cognitive Labor Value Calculator models team size, role cost, and automation coverage to estimate the value of moving execution-layer work out of manual reporting. It takes under two minutes to complete.

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