Toolkit

The tools below replicate the manual arithmetic you will perform in your weekly labs before scaling those calculations into real-life datasets. We provide these tools strictly for verification purposes. You are expected to check your own working against our expected baseline. Discovering a mathematical discrepancy between your local spreadsheet and this page is a problem you want to solve in Week 9, not upon receiving a graded Event Portfolio. The tools are sequenced to mirror the logic of the twelve-week curriculum: we begin by defining the basic economic unit of alcohol, introduce the regulatory constraints that distort its retail price, and finally model how to mathematically optimize for it under these logistical constraints.

Index calculator

One product in, its standard-drink count and ABV-to-dollar index out. The conversion is week 2's, applied exactly.

Product

Volume (mL)

Alcohol by volume (%)

Shelf price (AUD)

Load a worked example

Standard drinks: —
Index (drinks per dollar): —
Price per standard drink: —

View the same data as a table

This product's index against the catalogue average
Category	Index (standard drinks per dollar)
This product	2.285
Catalogue average	0.861

Tax regime comparator

One product, costed under the 29% ad valorem Wine Equalisation Tax and under a volumetric excise per litre of pure alcohol. Week 4 against week 6.

Product and rates

Volume (mL)

Alcohol by volume (%)

Wholesale value (AUD)

Excise rate (AUD per litre of pure alcohol)

Tax payable under each regime
Regime	Tax payable	Per standard drink
Ad valorem (WET)	—	—
Volumetric excise	—	—

View the same data as a table

Tax payable under each regime for the entered product
Category	Tax payable (AUD)
Ad valorem (WET)	$2.03
Volumetric excise	$43.68

Constrained selection

A fixed budget and a minimum number of distinct types, over a worked catalogue of 9 products. Greedy ranking against the dynamic-programming formulation.

Constraints

Budget (AUD)$200

Minimum distinct types3

Result under each method
Method	Standard drinks	Spend	Types
Greedy (top index first)	—	—	—
Dynamic programming	—	—	—

View the same data as a table

Greedy against dynamic-programming selection
Category	Standard drinks
Greedy	432.4
Dynamic programming	363.1

The objective function here is total standard drinks, because that is the quantity week 10's formulation maximises. It is a description of what the model does, not a recommendation about what to buy; week 11's externality framework is where the cost this objective does not price in is dealt with.

What these do not do

None of these tools reads a live price. Week 9 is where you build the thing that does, against a catalogue you chose in week 1, under the crawl-delay rules on the policies page. A figure typed into this page is a figure you are responsible for.

The toolchain behind them

To successfully complete the quantitative modeling in this course, students must become proficient with the following industry-standard toolkits:

Python (BeautifulSoup4 & Selenium): Essential for the automated extraction of supermarket inventories. Students will write scripts to bypass basic DOM obfuscation and compile raw pricing data from regional liquor retailers.
R & ggplot2: Used for statistical computing and generating visualizations. You will use R to calculate the price elasticity of discount spirits and map standard drink equivalencies across varying tax brackets.
QGIS (Quantum GIS): An open-source geographic information system used in Week 7 for retail spatial optimization. Students will map the floor plans of suburban liquor outlets to analyze the spatial ergonomics of tax-inefficient stock placement.
The ABV-Index Calculator (Internal Tool): A proprietary SlopU web widget provided in Week 1. This tool allows for rapid manual verification of your scraped data by calculating the baseline (Volume * ABV * 0.789) / Priceformula.