Improving Inventory Costs with EOQ

1. Mark Mastrangeli John Knezevic Improving Inventory Costs with EOQ

2. Agenda: • Problem Overview • Data Description & Data Sample • Assumptions(carrying, holding costs) • Pareto Analysis (ABC Classification) • EOQ Models • Sample Savings • Other Recommendations

3. Overview • Objectives Of Inventory Management • Minimize Inventory Costs • Maximize Customer Service • In order to Minimize Inventory Cost • Find Economic Order Quantity that minimizes: • Annual Ordering Cost + Annual Carrying Cost • Annual Purchasing Cost is not included because there is nothing you can do about it.

4. Data Description • Given 2.9 million line historical data file • Focusing on a single store’s A class inventory Items we calculated: • Current Method Annual Inventory Cost • EOQ Annual Inventory Cost • ~3000 Item Master Inventory spreadsheet • Eliminated Many Items to narrow inventory

5. Historical Data Sample

6. Relevant Data • Item No • Location • Item Description • Item Price • Average Cost • Max Order • Lead Time

7. Assumptions • 350 Day Working Year • $10.00 Cost per Order • 7% IRR Carrying Cost • Max Order Quantity could be changed

8. Pareto Analysis (ABC Classification) • Pareto Principle • 80% of a nation’s wealth, owned by 20% of population • Apply concept to inventory • A - account for 10-20% of items, 60-80% value • B - account for 20-40% of items, 15-30% value • C - account for 50-60% of items, 5-10% value • Focused exclusively on A items

9. The Basic EOQ Model • Can be used in planning the purchases of inventory items for retailers • General Assumptions: • Uses Constant Annual Demand vs. Fluctuating Periodic • Item’s purchase cost is independent of the quantity ordered and irrelevant in calculating annual inventory cost • Lead time is a certainty

10. Our Analysis: • Assuming, • Max Order Quantity has been changed. • $10.00 Cost per Order • 7% IRR • 350 Working Days per year • Calculated Cost of Actual known orders • Averaging Total Units Ordered over Duration and known number of orders • Calculated EOQ Model for all 27 Class ‘A’ Items

11. Our Analysis

12. Our Analysis:

13. Our Analysis:

14. Sample Savings • For All 27 Class ‘A’ Items • Current Total Cost of Inventory under given assumptions: $3,315.91 • EOQ Total Cost of Inventory: $1348.05 • Savings over current method: $1967.86 • Average Savings per Item: $72.88 • ~Annual Savings for All 1124 stores: • 1124 x $1967.86 = $2,211,874.64 • If Pareto Principle Holds $2,211,874.64 = 60% Value • Then 100% = $ 3,686,461.69

15. Other Findings: • For 3227 Orders, 516 Items in the history for Greenville, Dallas Center Avg Units per order = 2.7 • Under EOQ for 27 A Items 67 Orders per year • Under Current Method 307 Orders per year • 80% Reduction • 80% Reduction of 3227 = 645 Orders

16. Recommendations: • Maintaining Inventory Accuracy • Avoid stock outs at all cost • DC does not have item to send • Recommended levels of accuracy • A items ± 0.2% • B items ± 1% • C items ± 5% • Cycle Counting • Each month 1/12th of company items are counted • Consider Changing Max Order Quantity and Using EOQ