1 / 55

OPER3208-001 Supply Chain Management

OPER3208-001 Supply Chain Management. Spring 2007 Instructor: Prof. Setzler. Taylor, Chapter 10. Ch 10: Forecasting Demand (Taylor) . 1 st step in planning SC is demand forecasting

cairo-woods
Download Presentation

OPER3208-001 Supply Chain Management

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. OPER3208-001Supply Chain Management Spring 2007 Instructor: Prof. Setzler

  2. Taylor, Chapter 10

  3. Ch 10: Forecasting Demand (Taylor) • 1st step in planning SC is demand forecasting • For stable products with long sales histories, use standard models to identify trends and project into future (quantitative) • Group similar products together to improve the accuracy of your forecasts • For innovative products with no sales history, use subjective or judgmental techniques (qualitative)

  4. Ch 10: Forecasting Demand (Taylor) • Projecting Trends-Statistical models project future sales • For a product with a known sales history, the best guide to future sales is past performance • Using the techniques of time-series analysis • Apply standard formulas to • analyze a sales history • Extract info about recurring patterns • Use patterns to project sales into future • See upper panel in Figure 10.1 • Clearly a pattern in Figure 10.1 • There is an overall increase in sales from one year to the next, but sales appear to be flat within each year • The amount of variability from one month to the next appears constant over time

  5. Ch 10: Forecasting Demand (Taylor) • Projecting Trends-Statistical models project future sales • Time-series analysis of Figure 10.1 (upper panel) shown in lower panel reveals that demand actually varies in a systematic way over the course of each year, with higher sales in the Spring • Can predict expected sales for each month in the coming year (2003)

  6. Ch 10: Forecasting Demand (Taylor) • Projecting Trends-There are four components of demand • For a product with a flat sales curve, forecast is just the past month’s sales • For products that show a simple trend over time, may use moving average to project next month’s sales • If product’s history shows complex pattern (as seen in Figure 10.1) forecasting further into the future than next month you need to use the full model

  7. Ch 10: Forecasting Demand (Taylor) • Projecting Trends-There are four components of demand • The full model has four distinct components (see Figure 10.2) • The level component—average sales • The trend component—straight line reflecting the overall tendency for sales to increase or decrease • The seasonal component—the curve that captures the rise and fall in sales over the course of each year • The random component—all other variation in demand; has no systematic pattern • The 1st 3 are called systematic components because they behave consistently over time and can be predicted

  8. Ch 10: Forecasting Demand (Taylor) • Projecting Trends-There are four components of demand • When running a time-series analysis, the model 1st estimates these parameters by adjusting them to fit the historical data, then uses its estimates to project future sales

  9. Simple Moving Average Formula • The simple moving average model assumes an average is a good estimator of future behavior • The formula for the simple moving average is: Ft = Forecast for the coming period n = Number of periods to be averaged A t-1 = Actual occurrence in the past period for up to “n” periods

  10. Simple Moving Average Problem (1) Question: What are the 3-week and 6-week moving average forecasts for demand? Assume you only have 3 weeks and 6 weeks of actual demand data for the respective forecasts

  11. 11 Calculating the moving averages gives us: F4=(650+678+720)/3 = 682.67 F5=(678+720+785)/3 = 727.67 F6=(720+785+859)/3 = 788.00 • The McGraw-Hill Companies, Inc., 2004

  12. 12 F4=(650+678+720)/3 =682.67 F7=(650+678+720 +785+859+920)/6 =768.67 Calculating the moving averages gives us: • The McGraw-Hill Companies, Inc., 2004

  13. Simple Moving Average Problem (2) Data Question: What is the 3 week moving average forecast for this data? Assume you only have 3 weeks and 5 weeks of actual demand data for the respective forecasts

  14. F4=(820+775+680)/3 =758.33 F6=(820+775+680 +655+620)/5 =710.00 Simple Moving Average Problem (2) Solution

  15. Weighted Moving Average Formula While the moving average formula implies an equal weight being placed on each value that is being averaged, the weighted moving average permits an unequal weighting on prior time periods The formula for the moving average is: wt = weight given to time period “t” occurrence (weights must add to one)

  16. Weighted Moving Average Problem (1) Data Question: Given the weekly demand and weights, what is the forecast for the 4th period or Week 4? Weights: t-1 .5 t-2 .3 t-3 .2 Note that the weights place more emphasis on the most recent data, that is time period “t-1” Note: t = 4 in this problem

  17. F4 = 0.5(720)+0.3(678)+0.2(650)=693.4 Weighted Moving Average Problem (1) Solution Weights: t-1 .5 t-2 .3 t-3 .2

  18. Weighted Moving Average Problem (2) Data Question: Given the weekly demand information and weights, what is the weighted moving average forecast of the 5th period or week? Weights: t-1 .7 t-2 .2 t-3 .1 What is t?

  19. F5 = (0.1)(755)+(0.2)(680)+(0.7)(655)= 672 Weighted Moving Average Problem (2) Solution Weights: t-1 .7 t-2 .2 t-3 .1

  20. Exponential Smoothing Model Ft = Ft-1 + a(At-1 - Ft-1) • Premise: The most recent observations might have the highest predictive value • Therefore, we should give more weight to the more recent time periods when forecasting

  21. Exponential Smoothing Problem (1) Data Question: Given the weekly demand data, what are the exponential smoothing forecasts for periods 2-10 using a=0.10 and a=0.60? Assume F1=D1

  22. F4 = 815.50 + 0.1(680 -815.50) = 801.95 F5 = 801.95 + 0.1(655 -801.95) = 787.26 F6 = 787.26 + 0.1(750-787.26) = 783.53 F7 = 783.53 + 0.1(802-783.53) = 785.38 F8 = 785.38+ 0.1(798-785.38) = 786.64 F8 = 786.64 + 0.1(689-786.64) = 776.88 F9 = 776.88 + 0.1(775-776.88) = 776.69 Ft = Ft-1 + a(At-1 - Ft-1) F2 = 820 + 0.1(820 -820) = 820 F3 = 820 + 0.1(775 -820) = 815.50

  23. Answer: The respective alphas columns denote the forecast values. Note that you can only forecast one time period into the future.

  24. Exponential Smoothing Problem (2) Data Question: What are the exponential smoothing forecasts for periods 2-5 using a = 0.5? Assume F1=D1

  25. F1=820+(0.5)(820-820)=820 F3=820+(0.5)(775-820)=797.75 Exponential Smoothing Problem (2) Solution

  26. Trend Effects in Exponential Smoothing Model • The equation to compute the forecast including trend (FIT) is

  27. Example 12.1: Forecast Including Trend (FIT) Assume an initial starting Ft of 100 units, a trend of 10 units, and alpha of .20, and a delta of .30. If actual demand turned out to be 115 rather than the forecast 100, calculate the forecast for the next period. If the actual turned out to be 120 instead of 121.3 then Ft+1 = 121.3 + .2(120-121.3) = 121.04 Tt+1 = 10.3 + .3(121.04 – 121.3) = 10.22 FITt+1 = 121.04 + 10.22 = 131.26 Adding the starting forecast and the trend: FITt-1 = Ft-1 + Tt-1 = 100 + 10 = 110 The actual At-1 is given as 115. Therefore, Ft = FITt-1 + α(At-1 – FITt-1) =110 + .2(115 – 110) = 111.0 Tt = Tt-1 + δ(Ft – FITt-1) = 10 + .3(111-110) = 10.3 FITt = Ft + Tt = 111.0 + 10.3 = 121.3

  28. Additive Seasonal Variation • Simply assumes that the seasonal amount is a constant no matter what the trend or average amount is Forecast including trend and seasonal = Trend + Seasonal Exhibit 12.15: Additive and Multiplicative Seasonal Variation Superimposed on Changing Trend

  29. Multiplicative Seasonal Variation Forecast including trend and seasonal = Trend * Seasonal factor • The trend is multiplied by seasonal factors Exhibit 12.15: Additive and Multiplicative Seasonal Variation Superimposed on Changing Trend • Seasonal Factor (or Index) • The amount of correction needed in a time series to adjust for the season of the year See Example 12.3 and Example 12.4 on page 487 and 488, respectively

  30. Example 12.4: Computing Trend and Seasonal Factor from a Hand-Fit Straight Line Exhibit 12.17: Computing a Seasonal Factor from the Actual Data and Trend Line

  31. Exhibit 12.16 Example 12.4: Computing Trend and Seasonal Factor from a Hand-Fit Straight Line Given the Regression Equation: FITSt = Trend * Seasonal I—2000 FITS9 = [170 +55(9)]1.25 = 831 II—2000 FITS10 = [170 +55(10)]0.78 = 562 III—2000 FITS11 = [170 +55(11)]0.69 = 535 Iv—2000 FITS12 = [170 +55(12)]1.25 = 1,038 Tt = 170 + 55t Knowing the Seasonal Factors, we can compute the 2000 forecast including trend and seasonal factors (FITS) as follows:

  32. Decomposition Using Least Squares Regression • Error Range • When a straight line is fitted through data points and then used for forecasting, errors can come from two sources • Usual errors similar to the standard deviation of any set of data • Errors that arise because the line is wrong • Error range widens the further into the future you forecast

  33. Ch 10: Forecasting Demand (Taylor) • Projecting Trends-Confidence intervals show expected variations • Random components can’t be predicted • Model does estimate the magnitude of the components and projects it forward as well • Most forecasting tools draw confidence intervals on the forecast plot (see Figure 10.3) • The likelihood of actual demand being within the range indicated by the two bars is 90% • Only a 10% probability that it will fall either above or below the interval bars

  34. Ch 10: Forecasting Demand (Taylor) • Projecting Trends-Forecasting has a limited range • The accuracy of the forecast falls off dramatically as you look further out (see Figure 10.3)

  35. Ch 10: Forecasting Demand (Taylor) • Projecting Trends- Dynamic forecasting constantly updates values • You can increase the accuracy of forecasts by updating them continuously • Technique known as dynamic forecasting • Different from static forecasting, in which forecast was generated and then used as is through forecast horizon • Now, forecasting is fully automated • Most companies use dynamic forecasting

  36. Ch 10: Forecasting Demand (Taylor) • Projecting Trends-Forecasting confers a competitive advantage • Business advantage of forecasting is that it eliminates predictable variability from future demand • Allows you to plan production more precisely • Consider two firms (A and B) trying to predict the same flow of demand over a year • Most of the variability is due to a patter of increasing sales combined with seasonality (see Figure 10.3)

  37. Ch 10: Forecasting Demand (Taylor) • Projecting Trends-Forecasting confers a competitive advantage • Company A doesn’t use forecasting • Expensive proposition • Requires increased safety stock and reserves production capacity • Company B uses forecasting to eliminate known sources of variability • Gets by with very little safety stock and no reserve capacity • Gives a substantial financial advantage over Company A

  38. Ch 10: Forecasting Demand (Taylor) • Aggregating Demand – Companies rarely forecast individual products • In practice you would generate forecasts for individual products only in special situations • The cost of generating separate forecasts for thousands of different products would be prohibitive • The standard procedure is to group similar products together when making forecasts • Technique called aggregation • Aggregate forecasts are more reliable because they are based on larger samples of customer behavior

  39. Ch 10: Forecasting Demand (Taylor) • Aggregating Demand – Forecasts improve with larger samples • Small number of samples to generate predictions about a larger population risk sampling error • Picking a sample that doesn’t happen to represent the population as a whole • One of the basic laws of statistics is that the likelihood of sampling error goes down as the sample size goes up • The same with forecasting demand

  40. Ch 10: Forecasting Demand (Taylor) • Aggregating Demand – Aggregation is essential for reliability • Forecasts also aggregate demand across customer type, geographical region, and other factors • The fact that forecasts are based on the number of sales within each forecasting period means that sales histories are automatically aggregated across time

  41. Ch 10: Forecasting Demand (Taylor) • Aggregating Demand – Use Pareto Analysis to help group products • One important consideration for aggregating products into groups is the overall level of sales • A handful of products account for the majority of sales • This is known as the “80:20 rule” • States that 80% of sales come from 20% of the products • A more formal technique, called Pareto Analysis, uses three categories with a breakdown of 80% A products, 15% B, and 5% C. • Pareto Analysis reflects the classic 80:20 rule • Pareto Analysis also expresses the observation that ½ the products of a company usually account for 95% of the company’s sales (See Figure 10.5)

  42. Ch 10: Forecasting Demand (Taylor) • Aggregating Demand – Forecast fast-moving products separately • Invest much more effort in forecasting products in category A (the small number of products that account for the majority of revenues • Either forecast them individually, or by aggregating them into small groups • Demand for these products (A) is critical to the success of your company • You should aggregate the 50% of the products that account for only 5% of sales into large groups • Small contribution to sales and low data density

  43. Ch 10: Forecasting Demand (Taylor) • Aggregating Demand – Combine products with similar sales patterns • When combining products for aggregate forecasts, be careful not to mix products with different sales patterns • Don’t pool seasonal products with nonseasonal products because that would underestimate the effect of season on the seasonal goods • You shouldn’t combine seasonal products with different peaks • You could miss seasonal components altogether

  44. Ch 10: Forecasting Demand (Taylor) • Aggregating Demand – Group customers by region or by type • Aggregation across customers is usually done either by region or by type • Aggregating demand by region tends to group customers that exhibit the same seasonality, style, and fashion preferences • These variations usually have a strong regional component

  45. Ch 10: Forecasting Demand (Taylor) • Aggregating Demand – Group customers by region or by type • It also provides a head start on distribution planning because it groups expected demand according to its destination • Customer segments defined by such characteristics as demand volume, required customer service level, order frequency, and other buying habits can also be used to aggregate customer • This is another good place to apply Pareto Analysis • Often reveals that 80% of total sales comes from 20% of customer base • ½ of customer base accounts for only 5 % of sales

  46. Ch 10: Forecasting Demand (Taylor) • Aggregating Demand – Use percentages to make item forecasts • If you know a product normally accounts for 12% of the sales of a group in an aggregate forecast, then you just multiply that forecast by 12% to get back the item forecast • If aggregate forecasting is done correctly all items in the forecast should share the same demand pattern as a group

  47. Ch 10: Forecasting Demand (Taylor) • Analyzing the Future – Time-series analysis doesn’t fit every product • For example, new products • Have no sales history • If similar products exist, it may be possible to project sales by taking a percentage of an existing aggregate forecast • If not, then need other means of forecasting

  48. Ch 10: Forecasting Demand (Taylor) • Analyzing the Future – Forecasts often require cause-effect analysis • Unlike time-series analysis—it is much more art than science • Subjective or judgmental techniques

  49. Ch 10: Forecasting Demand (Taylor) • Analyzing the Future – Subjective techniques analyze extrinsic factors • The general approach—consider all the business influences that might affect future sales • Estimate their individual effects • Combine estimates into a prediction • Most influences are extrinsic factors, because they lie outside your immediate control • e.g., state of the economy, characteristics of the market, needs and wants of customers • Intrinsic factors • e.g., pricing, promotions, etc.

  50. Ch 10: Forecasting Demand (Taylor) • Analyzing the Future – Economic factors are readily incorporated • The major effect of general economic factors acts as a multiplier on sales • A robust, expanding economy • Generally increases sales • A weak economy • Generally decreases sales

More Related