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Let’s Begin …

Let’s Begin …. Welcome to Week 10 Discussion Agenda for Tonight: Review of Exam Discussion Questions from Discussion Leaders Other Questions from Students. Exam Review (1a). Question 1 --

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Let’s Begin …

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  1. Let’s Begin … • Welcome to Week 10 Discussion • Agenda for Tonight: • Review of Exam • Discussion Questions from Discussion Leaders • Other Questions from Students 2000 John W. Nazemetz

  2. Exam Review (1a) • Question 1 -- • (25 pts.) Do CIM systems that employ Generative Computer Aided Process Planning benefit from the simultaneous use of Group Technology concepts? • Yes/No 2000 John W. Nazemetz

  3. Exam Review (1b) • Question 1 -- cont. • If so, how and/or under what conditions? (25 of 25 possible) • Similar shapes/designs -> similar routings • Operational Efficiencies (families, batching in families – recalculate families each period) • Reduction in part variation, increase volume of common parts • Reduction in tooling with reduction in parts variation, tooling cost with volume increase 2000 John W. Nazemetz

  4. Exam Review (1c) • Question 1 -- cont. • If not, why not (20/25 max available). • Generative CAPP, re-determines routing each time based on technical and economic feasibility so no permanent families established/establishable • BUT -- GT makes designs similar, so algorithmically derived routings are similar and other benefits are derivable. 2000 John W. Nazemetz

  5. Exam Review (2) • Question 2 -- • (25 pts.) Define and explain Quality Function Deployment. List and discuss its weak points. • Define and Explain (15 pts.) • Process to document and assess products with respect to an ordered and weighted set of customer requirements, technical specifications, the correlation of customer requirements and technical specifications, comparison to competitors, required quality (tolerances) of part, production methods. • Weaknesses (10 pts.) • Ability to gather customer requirements/competitor data • Ability to translate requirements to specifications • Ability to define correct weights/economics • Can make week analysis “look” stronger than it is 2000 John W. Nazemetz

  6. Exam Review (3) • Question 3 -- • Consider the various models proposed in the text and in lecture for developing part families and manufacturing cell groupings. List, briefly define, and evaluate three (3) models/methods for cell and/or family formation (i.e., explain the philosophy/objective of each algorithm/procedure, the steps of each algorithm/procedure, and discuss its strengths and shortcomings). (8 pts each plus 1) • Machine Component Group Analysis • Production Flow Analysis • Rank Order Clustering (King) • GT Coding and Pattern Recognition • Single-Linkage Cluster Analysis - Similarity Coefficients 2000 John W. Nazemetz

  7. Exam Review (4a) • Question 4a -- • (17 pts.) Consider Robots and Numerical Control Machines. Explain the similarities and differences between the two. • Similarities • Same basic technology, sensors • Similar programming • Similar function (move parts or tools in space) • Similar concepts – open/closed loop, pt to pt, continuous path, multi-axis, multiple tools/parts • Differences • Robots less precise, accumulate error while NC more precise, axis error is independent • Robot generally used for ergonomic and economic reasons, NC generally used for technological reasons 2000 John W. Nazemetz

  8. Exam Review (4b) • Question 4 -- • (8 pts.) Consider Robots and Numerical Control Machines. Briefly explain the role/use of each in FMS/Cellular Manufacturing under conditions of a) high product (design/shape) variability and low part volume (sales) as well as b) high part volume (sales) and low product (design/shape) variability. • Role – high product variability/low volume • Robot – allows flexibility via general purpose hand, teach or low-cost/time programming • NC – allows precision manufacturing via reprogramming • Role – low product variability/high volume • Robot – allows labor replacement, repetitive motions • NC – provides an alternative to custom, single purpose equip. 2000 John W. Nazemetz

  9. Exam Review (5a) • Question 5 -- • (25 pts.) Briefly define the life cycle and product concepts of Chris Vaughan. (15 pts.) • Life Cycle Concepts: • Design, Produce, Operate, Service, Dispose • All products have this life cycle • Iterative process (Less -> More detailed) • Applies across Supply Chain (all Levels) • Product Concepts: • Almost everything is a product • Products interact (Operate facility (product of “design and produce facility” is produces product 2000 John W. Nazemetz

  10. Exam Review (5b) • Question 5 -- • (25 pts.) Briefly define the life cycle and product concepts of Chris Vaughan. Comprehensively discuss how his definition/concepts apply/do not apply in the practice of Concurrent Engineering. (10 pts.) • Phases of life cycle define major inputs to concurrent engineering (design must consider production, operation, service and disposal of product) • Iteration of design • Design document(s) (product) – output/input of design facility, iterates from conceptual design to “final” design to redesign to re-redesign, … • Stages of design correspond to (design) supply chain 2000 John W. Nazemetz

  11. Discussion Questions (Jeff Short) 2000 John W. Nazemetz

  12. Discussion Questions (Jeff Short) • What is the difference between JIT, MRP, and Lean Manufacturing? • JIT – minimum (zero) inventory goal • Absorbs fluctuations/disruptions via excess capacity (labor and machines) • MRP – maximize utilization • Absorbs fluctuation by planning/scheduling • Lean Manufacturing – a manufacturing philosophy of efficiency which, when implemented, shortens the time between customer order and factory shipment by eliminating waste. "Uses less of everything compared with mass production” • Focus on efficiency/productivity of process 2000 John W. Nazemetz

  13. Discussion Questions (Jeff Short) • Chapter 10 is about MRP and Chapter 11 is concerned with JIT. These seem to be diametrically opposed views of production planning and control. What is the current state of research to resolve the philosophical disagreements and reach a more practical operating philosophy? • Diametrically opposed in theoretically pure form – • In practice, they blend – • Balance ability to predict and preplan fluctuating vs. constant stable system (production, customer, …) • No smooth/known demands (Seasonality, competitors) • No smooth production (machine/managerial breakdown) • No 100% availability (temporary demand exceeding capacity, rush orders) • Inventory to deal with unexpected • Research using each method criteria (utilization/inventory) 2000 John W. Nazemetz

  14. Discussion Questions (Jeff Short) • Can JIT exist in practice for a manufacturer with high variance in demand? • Yes, but if variance in demand is high, a large amount of excess production capability must be available to meet demand (on-time). As this cascades through a system, more interchangeable, cross trained workers needed with resultant higher labor costs. 2000 John W. Nazemetz

  15. Discussion Questions (Jeff Short) • I understood the Wagner and Whitin Algorithm produced this property: either the inventory carried to period t+1 from period t must be zero or the production quantity in period t+1 is zero. • Question 6 asks the student to begin with an inventory, obviously a violation of the W-W algorithm. I felt there were several ways to deal with this problem, all of which modified the algorithm. Which way would you suggest? • Reduce demand in first period(s) to reduce inventory to zero. • Is there a stochastic or fuzzy representation of the W-W algorithm?? • Yes, simulation/modified Newsboy problem • “goodness of solution” is determined by ability to predict period demands/distribution of demand. 2000 John W. Nazemetz

  16. Discussion Questions (Jeff Short) • Both W-W and Naidu & Singh treat the variable of h, holding costs, as a well-know entity. I have never worked for/with a company that could fix the value of h for any product under all storage conditions. How is h handled in your experience? • Via “Best Estimate” Usage • V = value of part/product ($) • h = V x (interest rate +inflation rate + insurance rate + spoilage rate + pilferage rate + management rate) • + Size x (cost of space) • All rates in ($/$inv), Cost of Space in $/unit volume • Can stratify inventory (Pareto-type Analysis) 2000 John W. Nazemetz

  17. Discussion Questions (Mike Blanton) 2000 John W. Nazemetz

  18. Discussion Questions (Mike Blanton) • Problem 10.7 was difficult to do. It followed example 10.4 which was continued from example 10.3. The examples seemed to leave information out, under the assumption that they were easy to follow. For me and I assume other students this was not the case. Could you please fill in the blanks. • I’ll try on the next slides. 2000 John W. Nazemetz

  19. Discussion Questions (Mike Blanton) • In example/problem, an LP model is solved. • For 10.7 – • One must first calculate the demand (dt) • In period one dt = 50 + 60 + 70 + 80 = 260 units • In common units Xt = 2(50+70) + (60+80) = 380hours • This will mean k = 1 hour/unit for 380 units • Perform similar calculations for other periods. • Then set up LP problem: 2000 John W. Nazemetz

  20. Discussion Questions (Mike Blanton) • Subject to • With Wo=240, k=1 • d1=380 • All 0 subscripts dropped • Non-negativity 2000 John W. Nazemetz

  21. Discussion Questions (Class) 2000 John W. Nazemetz

  22. Discussion Questions (Class) • Other Questions as raised 2000 John W. Nazemetz

  23. Discussion Session 10 • End of Class • Have a Good Week and I’ll see you next time! 2000 John W. Nazemetz

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