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Optimizing Inventory Management for ACE Model 89 Electric Drill at Simkin Hardware

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Simkin Hardware faces a challenge with the sales of the ACE Model 89 Electric Drill, which have been low and variable despite the product's excellent quality and good per-unit profits. Rising customer complaints about stockouts are leading to lost sales as customers turn to competitors. Currently, drills are reordered in small quantities, which increases ordering costs. A thorough analysis using the EOQ model suggests optimal order quantities and reorder points to balance demand fulfillment, minimize stockouts, and reduce overall costs.

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Optimizing Inventory Management for ACE Model 89 Electric Drill at Simkin Hardware

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  1. Simkin Hardware

  2. Simkin Hardware Among the many products stocked by the Simkin Hardware Store is the ACE Model 89 Electric Drill. • Sales of the drill have been rather low and variable over the last year. • Even though the sales are not high, the quality of the drill is excellent and the per unit profits are good. • Simkin wants to offer a complete product line; so continued stocking of the ACE 89 is desirable.

  3. Simkin Hardware • There has been a rising number of customer complaints that the store is out of drills. Because customers usually have an urgent need for products when they come into the store, in the event of a stockout they go across the street to purchase their desired product from Simkin’s strongest competitor. • Lately, Art has ordered 10 drills at a time and reordered when 5 drills remain in stock. But he is very concerned about the high opportunity cost of the stock-outs as well as the rising customer complaints. Another concern is the increased cost of ordering when the ordering quantity is small.

  4. Daily Demand Frequency Probability Cumulative Probability Random Number 0 10 1 20 2 40 3 80 4 30 5 20 Totals 200 Demand profile

  5. Daily Demand Frequency Probability Cumulative Probability Random Number 0 10 10/200 = 0.05 1 20 20/200 = 0.10 2 40 0.20 3 80 0.40 4 30 0.15 5 20 0.10 Totals 200 1.00 Demand profile

  6. Daily Demand Frequency Probability Cumulative Probability Random Number 0 10 10/200 = 0.05 0.05 1 20 20/200 = 0.10 0.15 2 40 0.20 0.35 3 80 0.40 0.75 4 30 0.15 0.90 5 20 0.10 1.00 Totals 200 1.00 Demand profile

  7. Daily Demand Frequency Probability Cumulative Probability Random Number 0 10 10/200 = 0.05 0.05 01 – 05 1 20 20/200 = 0.10 0.15 06 – 15 2 40 0.20 0.35 16 – 35 3 80 0.40 0.75 36 – 75 4 30 0.15 0.90 76 – 90 5 20 0.10 1.00 91 – 00 Totals 200 1.00 Demand profile

  8. Lead Time Frequency Probability Cumulative Probability Random Number 1 8 2 20 3 12 Totals 40 Lead time profile

  9. Lead Time Frequency Probability Cumulative Probability Random Number 1 8 = 8/40 = 0.20 2 20 = 20/40 = 0.50 3 12 =12/40 = 0.30 Totals 40 Lead time profile

  10. Lead Time Frequency Probability Cumulative Probability Random Number 1 8 = 8/40 = 0.20 0.20 2 20 = 20/40 = 0.50 0.70 3 12 =12/40 = 0.30 1.00 Totals 40 Lead time profile

  11. Lead Time Frequency Probability Cumulative Probability Random Number 1 8 = 8/40 = 0.20 0.20 2 20 = 20/40 = 0.50 0.70 3 12 =12/40 = 0.30 1.00 Totals 40 Lead time profile Go to Excel

  12. Inventory Level AverageInventory (Q*/2) Optimal Order Quantity(Q*) Reorder Point (ROP) Time Lead Time EOQ Model When To Order

  13. What values for Q & R does the EOQ model suggest? • What is D – daily demand? • Find expected demand • What is L – lead time? • Find expected lead time • What is H? • 0.50 per drill per day • What is S? • $10 per order

  14. Lead Time Frequency Probability 1 8 0.20 2 20 0.50 3 12 0.30 Totals 40 Expected lead time Expected lead time = 0.20 * 1 + 0.50 * 2 + 0.30 * 3 = 2.1 days

  15. Daily Demand Frequency Probability 0 10 0.05 1 20 0.10 2 40 0.20 3 80 0.40 4 30 0.15 5 20 0.10 Totals 200 1.00 Expected demand Expected demand = 0.05 * 0 + 0.10 * 1 + 0.20 * 2 + 0.40 * 3 + 0.15 * 4 + 0.10 * 5 = 2.8 drills

  16. What values for Q & R does the EOQ model suggest?

  17. Simulate

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