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Price story

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Umesh Bajaj

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.

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Price Story Food Industry
Procurement Price Performance
What gets measured, gets managed Peter Drucker Is the price right?
Are we buying right item at right price uniformly? Statistical Question
Approach Cross Industry Standard Process for Data Mining (CRISP-DM) Business Understanding Data Understanding Data Preparation Modeling Evaluation Deployment
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
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,
CRISP-DM : Data Preparation Collect catalog price Identify prices with Ingredient Business segment Determine Uniform Unit of Measure Calculate unit level price
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
CRISP-DM : Evaluation Determine data properties Outliers Range Anomaly Let data tell its story Graphs Excel
Deployment CRISP-DM
Solution Architecture Data Analysis Presentation
Price Outlier For a ingredient compare prices for all substitutes and identify outliers on 95% confidence level
Category Comparison Compare prices on item category basis Like cheese price, eggs price
Channel Comparison Compare prices for same item from different distribution center

More Related Content

Price story

  • 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
  • 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