Name
EE32: Deep Dive: Inventory, Purchasing Methods, PORG, and Forecasting -- Part 1 and Part 2
Date & Time
Monday, May 20, 2024, 8:00 AM - 5:00 PM
Description

EE32: Deep Dive: Inventory, Purchasing Methods, PORG, and Forecasting -- Part 1 and Part 2
* please note you must register for both parts of a two-part session.

Program Level: Beginner 

Don’t have a degree in purchasing or inventory control? Do you look at requirements reports, but don’t know how the numbers were calculated? If yes, then this course is for you. In this two-part course, we’ll dive into basic and advanced inventory theory. Help answer the questions of why Prophet 21 suggested that quantity to purchase, and how can I provide the highest level of customer service at the lowest overall cost. 

Note: This course includes Part 1 and Part 2. It is taught in two sessions. When you register, you are signed up for both sessions. 

Cost: There is an additional $700 fee to attend this session. This cost includes both sessions. 

Objectives: 

  • Understand the importance and objectives of inventory management 

  • Learn about the two fixed control and the two variable purchasing methods 

  • See how to calculate order point and order quantity for each purchasing method 

  • Understand basic forecasting and how to use the values in Item History 

  • Use PO Requirements Generation (PORG) to speed up and automate purchasing 

  • Gain an understanding of Advanced Demand Forecasting concepts and Demand Requirements Planning 

Audience: 

  • Purchasing Managers 

  • Inventory Managers and professionals 

Prerequisites: 

  • Basic Prophet 21 navigation 

  • Understanding system flow of orders being entered and creating demand for product 

  • Understanding of how purchase orders are created in Prophet 21 

Location Name
Governor's Chamber D
Full Address
Gaylord Opryland Resort & Convention Center
2800 Opryland Dr
Nashville, TN 37214
United States
Session Level
Advanced, Beginner
Platform 2024
Prophet 21
Industry
Distribution
Session Type 2024
Interactive Learning