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New Syllabus

New Syllabus. Class 5: (Feb 7): Chap 11 (Inventory Management , Forecasting, Chapter 10 – Just in Time/Lean/TOC) Class 6: (Feb 14 ): Research for Presentations February 21 No Class

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New Syllabus

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  1. New Syllabus • Class 5: (Feb 7): Chap 11 (Inventory Management , Forecasting, Chapter 10 – Just in Time/Lean/TOC) • Class 6: (Feb 14): Research for Presentations • February 21 No Class • Class 7: (Feb 28) Supplemental Readings (Reverse Logistics – need “The Forklifts Have Nothing To Do!” Available in the Lewis and Clark Bookstore); Supply Chain Security, Take home final exam • Class 8: (Mar 7) Group presentations; Final Due

  2. Forecasting

  3. Forecasting Survey Fortune Council survey, Nov 2005 How far into the future do you typically project when trying to forecast the health of your industry? • less than 4 months 3% • 4-6 months 12% • 7-12 months 28% • > 12 months 57%

  4. Indices to forecast health of industry Fortune Council survey, Nov 2005 • Consumer price index 51% • Consumer Confidence index 44% • Durable goods orders 20% • Gross Domestic Product 35% • Manufacturing and trade inventories and sales 27% • Price of oil/barrel 34% • Strength of US $ 46% • Unemployment rate 53% • Interest rates/fed funds 59%

  5. Forecasting Importance • Improving customer demand forecasting and sharing the information downstream will allow more efficient scheduling and inventory management • Boeing, 1987: $2.6 billion write down due to “raw material shortages, internal and supplier parts shortages” Wall Street Journal, Oct 23, 1987

  6. Forecasting Importance • “Second Quarter sales at US Surgical Corporation decline 25%, resulting in a $22 mil loss…attributed to larger than anticipated inventories on shelves of hospitals.” US Surgical Quarterly, Jul 1993 • “IBM sells out new Aetna PC; shortage may cost millions in potential revenue.” Wall Street Journal, Oct 7, 1994

  7. Principles of Forecasting Forecasts are usually wrong every forecast should include an estimate of error Forecasts are more accurate for families or groups Forecasts are more accurate for nearer periods.

  8. Important Factors to Improve Forecasting • Record Data in the same terms as needed in the forecast – production data for production forecasts; time periods • Record circumstances related to the data • Record the demand separately for different customer groups

  9. Forecast Techniques • Extrinsic Techniques – projections based on indicators that relate to products – examples • Intrinsic – historical data used to forecast (most common)

  10. Forecasting Forecasting errors can increase the total cost of ownership for a product - inventory carrying costs - obsolete inventory - lack of sufficient inventory - quality of products due to accepting marginal products to prevent stockout

  11. Forecasting • Essential for smooth operations of business organizations • Estimates of the occurrence, timing, or magnitude of uncertain future events • Costs of forecasting: excess labor; excess materials; expediting costs; lost revenues

  12. Forecasting • Predicting future events • Usually demand behavior over a time frame • Qualitative methods • Based on subjective methods • Quantitative methods • Based on mathematical formulas

  13. Time Frame • Short-range to medium-range • Daily, weekly monthly forecasts of sales data • Up to 2 years into the future • Long-range • Strategic planning of goals, products, markets • Planning beyond 2 years into the future

  14. Demand Behavior • Trend • gradual, long-term up or down movement • Cycle • up & down movement repeating over long time frame • Seasonal pattern • periodic oscillation in demand which repeats • Random movements follow no pattern

  15. Demand Demand Random movement Time (a) Trend Time (b) Cycle Demand Demand Time (c) Seasonal pattern Time (d) Trend with seasonal pattern Forms of Forecast Movement

  16. Forecasting Methods • Time series • Regression or causal modeling • Qualitative methods • Management judgment, expertise, opinion • Use management, marketing, purchasing, engineering • Delphi method • Solicit forecasts from experts

  17. Time Series Methods • Statistical methods using historical data • Moving average • Exponential smoothing • Linear trend line • Assume patterns will repeat • Naive forecasts • Forecast = data from last period

  18. Moving Average • Average several periods of data • Dampen, smooth out changes • Use when demand is stable with no trend or seasonal pattern Sum of Demand In n Periods n

  19. ORDERS MONTH PER MONTH Jan 120 Feb 90 Mar 100 Apr 75 May 110 June 50 July 75 Aug 130 Sept 110 Oct 90 Simple Moving Average

  20. ORDERS MONTH PER MONTH Jan 120 Feb 90 Mar 100 Apr 75 May 110 June 50 July 75 Aug 130 Sept 110 Oct 90 3 90 + 110 + 130 3 = Simple Moving Average Daug+Dsep+Doct MAnov = = 110 orders for Nov

  21. ORDERS THREE-MONTH MONTH PER MONTH MOVING AVERAGE Jan 120 – Feb 90 – Mar 100 – Apr 75 103.3 May 110 88.3 June 50 95.0 July 75 78.3 Aug 130 78.3 Sept 110 85.0 Oct 90 105.0 Nov – 110.0 Simple Moving Average

  22. ORDERS THREE-MONTH MONTH PER MONTH MOVING AVERAGE Jan 120 – Feb 90 – Mar 100 – Apr 75 103.3 May 110 88.3 June 50 95.0 July 75 78.3 Aug 130 78.3 Sept 110 85.0 Oct 90 105.0 Nov – 110.0 90 + 110 + 130 + 75 + 50 5 = Simple Moving Average = 91 orders for Nov

  23. ORDERS THREE-MONTH FIVE-MONTH MONTH PER MONTH MOVING AVERAGE MOVING AVERAGE Jan 120 – – Feb 90 – – Mar 100 – – Apr 75 103.3 – May 110 88.3 – June 50 95.0 99.0 July 75 78.3 85.0 Aug 130 78.3 82.0 Sept 110 85.0 88.0 Oct 90 105.0 95.0 Nov – 110.0 91.0 Simple Moving Average

  24. Weighted Moving Average Adjusts moving average method to more closely reflect data fluctuations

  25. Wi Di WMAn = i = 1 where Wi = the weight for period i, between 0 and 100 percent  Wi= 1.00 Weighted Moving Average Adjusts moving average method to more closely reflect data fluctuations

  26. MONTH WEIGHT DATA August 17% 130 September 33% 110 October 50% 90 Weighted Moving Average Example

  27. MONTH WEIGHT DATA August 17% 130 September 33% 110 October 50% 90 November forecast 3 i = 1  WMA3 = Wi Di = (0.50)(90) + (0.33)(110) + (0.17)(130) = 103.4 orders Weighted Moving Average Example 3 Month = 110 5 month = 91

  28. Exponential Smoothing • Averaging method • Weights most recent data more strongly • Reacts more to recent changes • Widely used, accurate method

  29. Exponential Smoothing Ft +1 = Dt + (1 - )Ft where Ft +1 = forecast for next period Dt= actual demand for present period Ft= previously determined forecast for present period = weighting factor, smoothing constant • Averaging method • Weights most recent data more strongly • Reacts more to recent changes • Widely used, accurate method

  30. Forecast for Next Period 0 >  <= 1 Greater reaction to recent demand Lesser reaction to recent demand Forecast = (weighting factor)x(actual demand for period)+(1-weighting factor)x(previously determined forecast for present period)

  31. Forecast Accuracy • Find a method which minimizes error • Error = Actual - Forecast

  32. Forecast Control • Reasons for out-of-control forecasts • Change in trend • Appearance of cycle • Weather changes • Promotions • Competition • Politics

  33. Just-In-Time and Lean Production

  34. JIT In Services • Competition on speed & quality • Multifunctional department store workers • Work cells at fast-food restaurants • Just-in-time publishing for textbooks - on demand publishing a growing industry • Construction firms receiving material just as needed

  35. What is JIT ? • Producing only what is needed, when it is needed • A philosophy • An integrated management system • JIT’s mandate: Eliminate all waste

  36. Lean Operations: Best Implementation is Toyota Production System • TPS is a production management system that aims for the “ideal” through continuous improvement • Includes, but goes way beyond JIT. Pillars: • Synchronization • Reduce transfer batch sizes • Level load production • Pull production control systems (vs. push): Kanban • Quality at source • Layout: Cellular operations • Continuous Improvement (Kaizen): through visibility & empowerment ....

  37. Toyota’s waste elimination in Operations 1. Overproduction 2. Waiting 3. Inessential handling 4. Non-value adding processing 5. Inventory in excess of immediate needs 6. Inessential motion 7. Correction necessitated by defects

  38. Waste in Operations

  39. Waste in Operations

  40. Waste in Operations

  41. Flexible Resources • Multifunctional workers • General purpose machines • Study operators & improve operations

  42. The Push System Pre-planned issues of supplies/merchandise regardless of customer demand criteria Creates excess and shortages not efficient over the long run

  43. The Pull System • Material is pulled through the system when needed • Reversal of traditional push system where material is pushed according to a schedule • Forces cooperation • Prevent over and underproduction

  44. Kanban Production Control System • Kanban card indicates standard quantity of production • Derived from two-bin inventory system • Kanban maintains discipline of pull production • Production kanban authorizes production • Withdrawal kanban authorizes movement of goods

  45. A Sample Kanban

  46. Types of Kanbans • Bin Kanban - when bin is empty replenish • Kanban Square • Marked area designed to hold items • Signal Kanban • Triangular kanban used to signal production at the previous workstation • Material Kanban • Used to order material in advance of a process • Supplier Kanbans • Rotate between the factory and suppliers

  47. Components of Lead Time • Processing time • Reduce number of items or improve efficiency • Move time • Reduce distances, simplify movements, standardizeroutings • Waiting time • Better scheduling, sufficient capacity • Setup time • Generally the biggest bottleneck

  48. Common Techniques for Reducing Setup Time Preset Buttons/settings Quick fasteners Reduce tool requirements Locator pins Guides to prevent misalignment Standardization Easier movement

  49. Uniform Production • Results from smoothing production requirements • Kanban systems can handle +/- 10% demand changes • Smooths demand across planning horizon • Mixed-model assembly steadies component production

  50. Quality at the Source • Jidoka is authority to stop production line • Andon lights signal quality problems • Undercapacity scheduling allows for planning, problem solving & maintenance • Visual control makes problems visible • Poka-yoke prevents defects (mistake proof the system)

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