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Pacific Steel Casting Co. IEOR 180 Senior Project

Pacific Steel Casting Co. IEOR 180 Senior Project . Zahin Ali Giftedy Herlim Steven Leonard Hac Phan Max Santana. Executive Summary. Company Background. Pacific Steel Casting Company produces a variety of steel parts and products

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Pacific Steel Casting Co. IEOR 180 Senior Project

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  1. Pacific Steel Casting Co. IEOR 180 Senior Project Zahin Ali GiftedyHerlim Steven Leonard HacPhan Max Santana

  2. Executive Summary

  3. Company Background • Pacific Steel Casting Company produces a variety of steel parts and products • Three production plants, each specialized in different weights: • 1. Shell Mold Plant • 2. Green Sand Plant • 3. Air Set Plant

  4. Production Process Metal is poured into casings made from a combination of molds and cores Molds and cores are created from predetermined patterns

  5. Problem

  6. Data Collection Machine Cycle Time Time needed to produce a “ready” product. Time Study: Three machines: 131, 132, 134. (80% of total production volume, 19 parts) Doesn’t include down time. 15-18 units per hour across all machines. Machine Cycle Time Time needed to produce a “ready” product. Time Study: Three machines: 131, 132, 134. (80% of total production volume, 19 parts) Doesn’t include down time. 15-18 units per hour across all machines.

  7. Data Collection Demand Schedule Order quantities. ( 19 Products, six-weeks period) Production Window: Two-weeks. Demand Schedule : Order quantities for six-weeks 3

  8. Simulation

  9. Model Assumptions Workers are not involved Pouring step occurs every 60 minutes Cores always available on hand Machine cycle time independent of product

  10. Model Assumptions • Downtime reduce machine cycle time Before : 10 molds per hour 15% DOWNTIME After : 8.5 molds per hour

  11. Model Assumptions • Before Downtime • After Downtime Processing time: 0.1 hour/unit with 20 % range 8-12 units/hour 15-18 units/hour

  12. Simplification • Not differentiate between mold and core production • Lack data from mold & core into ready product • Clean & Finish  single abstracted unit

  13. Scheduling Algorithm service in random order (SIRO) earliest due date (EDD) smallest quantity first (SQF) largest quantity first (LQF) daily breakdown scheduling algorithm (DBSA)

  14. DBSA Failure ! Schedule everything at the beginning of 2 weeks period Not really consider due date Not enough time , not enough capacity

  15. DBSA-n Week 1 Week 2 Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7 Day 8 Day 9 Day 10 A: 250 B:150 C:300 D:400 E: 200 Use DBSA-3 (n=3): Day I: Produce what is due on the next 3 days: Day 1, 2, 3 Day 2: Produce what is due on Day 2, 3, 4 3 1 2 A: 125 A: 125 B: 50 D:134 B: 50 B: 50 E: 66 C: 100 E: 67 C: 100 E: 67

  16. Results and Analysis

  17. Results and Analysis

  18. Results and Analysis

  19. Recommendations

  20. Recommendations • More fundamental problems persist • Data collection • Communication • Organizational structure

  21. Recommendations • Data collection • Current implementation is inconsistent • Makes it difficult to validate • Difficult determine source of problem

  22. Recommendations • Inter-departmental communications • Data shows that production control accepts orders beyond the capacity of the capacity • Lack of data makes it hard to communicate effectively

  23. Recommendations • Organizational structure • Production control should have the responsibility of scheduling productions • Allows for more complex scheduling algorithms • Better quote estimates

  24. Data Collection Machine Cycle Time     Time needed to produce a "ready" product from scratch. It includes time needed to produce cores, halves of a mold and assemble those as a " ready" product. There are 4 mold machines( 131, 132, 133, and 134) accounting for 80% of total production volume(19 parts) in plant 1. However, machine number 133 only produce B lines which constitutes a very small margin of production volume. Therefore, we discounted its importance in our analysis.              Machine cycle time data gathered by PSC shows that  production time of a "ready" product is consistent across machines. In one hour, each machine can produce 15 to 18 parts.  Demand Schedule      Demand schedule consist of order quantities needed by the due date and the due date itself. Demand schedule serves as an input to our scheduling algorithms. Originally, the demand schedule we acquired from PSC's  is in six-weeks period. Thus, we need to convert them into our production window which is in two-weeks period. Only then we can generate random demand schedule for 19 parts. In generating the random demand schedule, we weighted the order quantities actually observed by PSC's study. We understand that some products naturally have higher demand than the rest of the products.

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