1. 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 New Syllabus

2. Forecasting

3. How far into the future do you typically project when trying to forecast the health of your industry? ]less than 4 months3% ]4-6 months12% ]7-12 months28% ]> 12 months57% Forecasting Survey Fortune Council survey, Nov 2005

4.  Consumer price index 51%  Consumer Confidence index44%  Durable goods orders20%  Gross Domestic Product35%  Manufacturing and trade inventories and sales27%  Price of oil/barrel34%  Strength of US $46%  Unemployment rate53%  Interest rates/fed funds59% Indices to forecast health of industry Fortune Council survey, Nov 2005

5.  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 Forecasting Importance

6.  “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 Forecasting Importance

7.  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. Principles of Forecasting

8. 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 Important Factors to Improve Forecasting

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

10. 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 Forecasting

11. 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 Forecasting

12. 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. Forms of Forecast Movement Time (a) Trend Time (d) Trend with seasonal pattern Time (c) Seasonal pattern Time (b) Cycle Demand Demand Demand Demand Random 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. Simple Moving Average Jan120 Feb90 Mar100 Apr75 May110 June50 July75 Aug130 Sept110 Oct90 ORDERS MONTHPER MONTH

20. Jan120 Feb90 Mar100 Apr75 May110 June50 July75 Aug130 Sept110 Oct90 ORDERS MONTHPER MONTH MA nov = 3 = 90 + 110 + 130 3 = 110 orders for Nov Simple Moving Average D aug +D sep +D oct

21. Jan120– Feb90 – Mar100 – Apr75103.3 May11088.3 June5095.0 July7578.3 Aug13078.3 Sept11085.0 Oct90105.0 Nov –110.0 ORDERSTHREE-MONTH MONTHPER MONTHMOVING AVERAGE Simple Moving Average

22. Jan120– Feb90 – Mar100 – Apr75103.3 May11088.3 June5095.0 July7578.3 Aug13078.3 Sept11085.0 Oct90105.0 Nov –110.0 ORDERSTHREE-MONTH MONTHPER MONTHMOVING AVERAGE = 90 + 110 + 130 + 75 + 50 5 = 91 orders for Nov Simple Moving Average

23. Jan120– – Feb90 – – Mar100 – – Apr75103.3 – May11088.3 – June5095.099.0 July7578.385.0 Aug13078.382.0 Sept11085.088.0 Oct90105.095.0 Nov –110.091.0 ORDERSTHREE-MONTHFIVE-MONTH MONTHPER MONTHMOVING AVERAGEMOVING AVERAGE

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

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

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

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

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

29. F t +1 =  D t + (1 -  )F t where F t +1 =forecast for next period D t =actual demand for present period F t =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 Exponential Smoothing

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

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-TimeJust-In-Time and Lean Production Just-In-Time

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

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

36. ....  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 Lean Operations: Best Implementation is Toyota Production System

37. 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 Toyota’s waste elimination in Operations

38. Waste in Operations

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

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

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, standardize routings Waiting time Better scheduling, sufficient capacity Setup time Generally the biggest bottleneck

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

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

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

51. Kaizen Continuous improvement Continuous improvement Requires total employment involvement Requires total employment involvement Essence of JIT is willingness of workers to Essence of JIT is willingness of workers to Spot quality problems Spot quality problems Halt production when necessary Halt production when necessary Generate ideas for improvement Generate ideas for improvement Analyze problems Analyze problems Perform different functions Perform different functions

52. Goals of JIT 1.Reduced inventory - where? 2.Improved quality 3.Lower costs 4.Reduced space requirements 5.Shorter lead time 6.Increased productivity 7.Greater flexibility 8.Better relations with suppliers 9.Simplified scheduling and control activities 10.Increased capacity 11.Better use of human resources 12.More product variety 13.Continuous Process Improvement

53. Use JIT to finely tune an operating system Somewhat different in USA than Japan JIT is still evolving JIT as an inventory reduction program isn’t for everyone - JIT as a CPI program is! Some systems need Just-in- Case inventory JIT Implementation

54. Chapter 12 Inventory Management

57.  The average manufacturing organization spends 53.2% of every sales dollar on raw materials, components, and maintenance repair parts  Inventory Control – how many parts, pieces, components, raw materials and finished goods Why is Inventory Important to Operations Management?

58.  Accounting – zero inventory  Production – surplus inventory or “just in case” safety stocks  Marketing – full warehouses of finished product  Purchasing – caught in the middle trying to please 3 masters Inventory Conflict

59. Inventory Stock of items held to meet future demand Insurance against stock out Coverage for inefficiencies in systems Inventory management answers two questions How much to order When to order

60. Types of Inventory Raw materials Purchased parts and supplies In-process (partially completed) products Component parts Working capital Tools, machinery, and equipment Safety stock Just-in-case

61. Transportation Problems Poor Quality Inventory Accuracy Policies Training Inventory Hides Problems

62. 1.How much do we have now? 2.How much do we want? 3.What will be the output? 4.What input must we get?  Correctly answering the question about when to order is far more important than determining how much to order. Aggregate Inventory Management

63. Inventory Costs Carrying Cost Cost of holding an item in inventory As high as 25-35% of value Insurance, maintenance, physical inventory, pilferage, obsolete, damaged, lost Ordering Cost Cost of replenishing inventory Shortage Cost Temporary or permanent loss of sales when demand cannot be met

64. ABC Classification System Demand volume and value of items vary Classify inventory into 3 categories, typically on the basis of the dollar value to the firm PERCENTAGEPERCENTAGE CLASSOF UNITSOF DOLLARS A5 - 1570 - 80 B3015 C50 - 605 - 10

65.  Inventory controls  Security controls  Monetary constraints  Storage locations Why ABC?

66. Economic Order Quantity

67. Assumptions of Basic EOQ Model Demand is known with certainty and is constant over time No shortages are allowed Lead time for the receipt of orders is constant The order quantity is received all at once

68.  Customer specifies quantity  Production run is not limited by equipment constraints  Product shelf life is short  Tool/die life limits production runs  Raw material batches limit order quantity No reason to use EOQ if:

69. EOQ Formula EOQ = 2CoD2CoDCcCc2CoD2CoDCcCc C o = Ordering costs D = Annual Demand C c = Carrying Costs Cost per order can increase if size of orders decreases Most companies have no idea of actual carrying costs

70. When to Order Reorder Point is the level of inventory at which a new order is placed R = dL where d = demand rate per period L = lead time

71.  Fixed  Variable  Two Bin  Card  Judgmental  Projected shortfall Forms of Reorder Points

72.  Accurate Demand Forecast  Length of Lead Time  Size of order quantities  Service level Why Safety Stock

73.  Cyclic Inventory  Annual Inventory  Periodic Inventory  Sensitive Item Inventory Inventory Control

74. Vendor-Managed Inventory Not a new concept – same process used by bread deliveries to stores for decades Reduces need for warehousing Increased speed, reduced errors, and improved service Onus is on the supplier to keep the shelves full or assembly lines running variation of JIT Proctor&Gamble - Wal-Mart DLA – moving from a manager of supplies to a manager of suppliers Direct Vendor Deliveries – loss of visibility

75.  Defining stock-keeping units (SKUs)  Increase in number of SKUs – 15% over past 3 years  Dead inventory  Deals  Substitute items  Complementary items  Informal arrangements outside the distribution channel  Repair/replacement parts  Reverse logistics Inventory Management: Special Concerns

76.  No class until 28 Feb  Group presentations 7 Mar  Final Exam due 8 Mar What’s Next