Tech 147: Unit 3

1. Tech 147: Unit 3 Planning Modern Green Manufacturing Systems: Forecasting

2. On Planning for Future If a man take no thought about what is distant, he will find sorrow near at hand (Confucius)

3. Forecasting is the: • The process of estimation in unknown situations • The process of prediction • The practice of demand planning

4. Definitions Exponential smoothing Trend (e.g. seasonal) Forecast Prediction Growth analysis Qualitative forecast Horizon Quantitative forecast MRP Time-series forecast Lead time Regression forecast Model-forecast Moving average Kanban JIT Planning period Gross/net requirements Inventory item Scheduled receipts

5. Jacobs Et Al Chapter 2: Demand Management • Through demand management all potential demands on manufacturing capacity are collected and coordinated • This activity manages day-to-day interactions between customers and the company

6. Demand Management in the MPC System

7. Demand Management and the MPC Environment Forecasts of end items, spare parts, and other items in demand should be a part of the front-end modules of the MPC system

8. Demand Management Techniques • Aggregating and disaggregating forecasts • Make to stock demand management • Assemble-to-order demand management • Make-to-order demand management (engineer-to-order)

9. Managing Demand • Organizing for demand management • Monitoring the demand management system • Balancing supply and demand

10. Jacobs et al: Chapter 3 Forecasting

11. Forecasting Defined Forecasting is the process of making statements, estimation, or predictions about events or some variable of interest at some specified future date whose actual outcomes have not yet been observed.

12. Providing appropriate Forecast Information • Forecasting for strategic business planning • Forecasting for sales and operation planning • Forecasting for master production scheduling and control

13. Basic Forecasting Techniques • Causal/econometric methods • Time (trend) series • Judgmental methods • Other methods

14. Causal/Econometric Methods • Regression analysis using linear regression or non-linear regression • Autoregressive moving average (ARMA) • Autoregressive integrated moving average (ARIMA) • Econometrics

15. Regression analysis includes any techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables. • Regression analysis helps one understand how the typical value of the dependent variable changes when any one of the independent variables is varied, while the other independent variables are held fixed

16. Regression Analysis

17. Time series • Moving average • Basic exponential smoothing model • Trend enhancement of the basic exponential smoothing • Seasonal enhancement of the basic exponential smoothing • Extrapolation • Linear prediction • Trend estimation • Growth curve

18. Time Series

19. Forecasted Monthly Sales

20. Judgmental methods • Composite forecasts • Surveys • Delphi method • Scenario building • Technology forecasting • Forecast by analogy

21. Moving Average A moving average is a set of numbers, each of which is the average of the corresponding subset of a larger set of data points.

22. Moving Average Forecasting Example • For sales during six periods: • Period 1 = $10000 • Period 2 = $12000 • Period 3 = $9000 • Period 4 = $11000 • Period 5 = $9600 • Period 6 = $12100

23. Moving Average Example Sales for period 7 would be the average of the previous six periods: Or 10000+12000+9000+11000+9600+12100 = $10617

24. Trend enhancement of the basic exponential smoothing • Forecasting technique that uses a weighted moving average of past data as the basis for a forecast. • The procedure gives heaviest weight to more recent information and smaller weight to observations in the more distant past. • The reason for this is that the future may be more dependent upon the recent past than on the distant past. • The method is effective when there is random demand and no seasonal fluctuations in the data.

25. Trend enhancement of the basic exponential smoothing

26. Trend enhancement of the basic exponential smoothing New Forecast = Old Forecast + α (Actual - Old Forecast)

27. Trend enhancement of the basic exponential smoothing ESFt-1 + (actual demandt - ESFt-1)

28. Trend enhancement of the basic exponential smoothing • TEFt = Base valuet-1 + Trendt-1 • Base value = (actual demandt) +(1 – ) (Base valuet-1 + Trendt-1) • Trendt = (base valuet – base valuet-1) + (1 – )(Trendt-1) •  = Base value smoothing constant •  = trend smoothing constant • t = current time

29. Causal Forecasting Example A store manager determined that sale of electric generators increased by 10%, 30% and 50% respectively when there were categories 1, 2, and 3 storms in one state. Determine the following: • How many generators that would be sold when there was a category 3 storm if regular sale was 1500 generators. • Extra cash from the sale for the store if each generator costs $587.

30. Time Series Forecasting Example Determine the following: • The quantity of bicycles a company makes if each of their 17 facilities makes 257 bikes per day plus 2 extra bikes on top of its previous day’s production. Production period is schedule for 21 days. • The quantity it made on the 10th day if 5 of those days allowed for only 40% production. • A time series plot of the period’s production.

31. Other forecasting methods • Simulation • Prediction market • Probabilistic forecasting

32. Probabilistic Forecasting The probability of event A is the number of ways event A can occur divided by the total number of possible outcomes. It is expressed as: Requires knowledge of theorems of probability

33. Inventory Models • Economic order quantity (EOQ) • Computerized inventory control systems • Manual inventory system

34. EOQ • An inventory-related equation that determines the optimum order quantity that a company should hold in its inventory given a set cost of production, demand rate and other variables. • This is done to minimize variable inventory costs.  • The full equation is as follows:   Where :  S = Setup costs D = Demand rate P = Production cost I = Interestrate  

35. Obi, Chapter 3 Worker-Orienetd Values: Honesty, Self-Control, and Self-Respect

36. Honesty Truthfulness Sincerity Upright conduct Upright disposition Publishers Clearing House example

37. Ramifications of Workplace Honesty

38. Areas of Workplace Dishonesty

39. Self-Control • Self-control = Self-restraint • A worker’s ability to manage his or her own actions, desires, or motions. • Ability to hold back one’s self from some actions, desires or emotions that are most often experienced in the work place

40. Common Areas of Self-Control

41. Some Consequences of Lack of Self-Control in the Workplace

42. Self-Respect • Having proper regard for one’s own person, character, or reputation. • Due respect for oneself.

43. Some Areas of Self-Respect