This document outlines an approach using CRISP-DM (Cross Industry Standard Process for Data Mining) to analyze food procurement prices across a supply chain. The goal is to determine if prices paid are optimal by identifying outliers, comparing categories and channels. Key steps include understanding the industry and data, preparing data by standardizing units of measure, modeling prices statistically, evaluating outliers and presenting findings to identify opportunities to negotiate better prices.

1. Price Story
Food Industry

2. Procurement
Price Performance

3. “
”
What gets measured, gets managed
Peter Drucker
Is the price right?

4. Are we buying right item at right price uniformly?
Statistical Question

5. Approach
• Cross Industry Standard Process for Data Mining (CRISP-DM)
• Business Understanding
• Data Understanding
• Data Preparation
• Modeling
• Evaluation
• Deployment

6. CRISP-DM : Business Understanding
Food Industry Features
• Many substitutes available
• Same item from different manufacturer
• De-Centralize buying
• Small local vendor like bakery
• Supplier base delivery
• Freight/Order size
• Multi channel sourcing – manufacturer item/supplier item 1:n
• Number of items
• Weekly price publication

7. CRISP-DM : Data Understanding
• Prices depends upon
• Quantity
• Item Size
• Item weight
• Pack size varies by quantity
• Variety of unit of measure
• Items can be classified into groups
• Meat, Dairy, Produce, …
• Dairy – Milk, Cheese, Yoghurt,…
• Milk – Fat Free, 2% Fat Free, …

8. CRISP-DM : Data Preparation
• Collect catalog price
• Identify prices with
• Ingredient
• Business segment
• Determine Uniform Unit of Measure
• Calculate unit level price

9. CRISP-DM : Modelling
Analysis Grouping
• Ingredient
• Item Classification
• Manufacturer Item
Statistical Analysis
• Data Analysis
• Determine central values
• Mean
• Median
• Determine Spread
• Range
• Standard Deviation
• Z- Score
• Data presentation
• Plot

10. CRISP-DM : Evaluation
• Determine data properties
• Outliers
• Range
• Anomaly
• Let data tell its story
• Graphs
• Excel

11. Deployment
CRISP-DM

12. Solution Architecture
Data Analysis Presentation

13. Price Outlier
For a ingredient compare prices for all
substitutes and identify outliers on 95%
confidence level

14. Category Comparison
Compare prices on item category basis
Like cheese price, eggs price

15. Channel Comparison
Compare prices for same item from
different distribution center