Portland State University Operations Management BA 339 Instructor: Scott Culbertson Jan 14th - Forecasting

Portland State University Varsity Subs Process Order Prep Sandwich Put in Oven Bake Toppings Counter Time Wrap & Deliver CT = 1 CT = 4CT = 1CT = 8 CT = 3CT = 7CT = 2 Q1

Portland State University Throughput? Process Order Prep Sandwich Put in Oven Bake Toppings Counter Time Wrap & Deliver CT = 1 CT = 4CT = 1CT = 8 CT = 3CT = 7CT = 2 Harry - Sam - Oven - Counter -

Portland State University Throughput? Process Order Prep Sandwich Put in Oven Bake Toppings Counter Time Wrap & Deliver CT = 1 CT = 4CT = 1CT = 8 CT = 3CT = 7CT = 2 Harry - 1/5 minutes or 12/hour Sam - 1/6 minutes or 10/hour - Constraint! Oven - 10/8 minutes or 75 hour Counter - Unlimited

Portland State University Varsity Subs How many subs in 4 hours? The easy answer is to look at the most constrained throughput of Sam (10 per hour). The answer then becomes 40 (4*10). However, the other logical conclusion is to say that the first 26 minutes of operation in the morning only yields 1 sub. Therefor, total time remaining would be 4*60 = 240 Minutes total 240 – 26 = 214 After 26 minutes, the rate is 10/hour. Therefor, they can produce 214/60*10 = subs (from the first 26 minutes) = On the test, either answer would be acceptable (40 or 36.66) as long as you state your assumptions.

Portland State University Dimensions of Strategic Competitive Advantage Innovator Operations Efficiency Customer Intimacy

Portland State University Dimensions of Operations Strategy & Competitive Advantage Time Price Quality Variety Competitive Advantage & Profit Means to best satisfy the customer

Portland State University Understand Key Relationships Understand Key Relationships Time Price Quality Variety Dimensions of Competitive AdvantageKey Elements of OPS Inventory Capacity Cycle Time

Portland State University How does Forecast Accuracy impact the key dimensions of Competitive Advantage?

Portland State University Financial impact: Inventory excursions create incremental cost and lost opportunity. Inventory’s Financial Impact Stockouts begin to occur before inventory hits zero. Lost sales occur until SC adjusts. Channel dries up, marketing momentum is lost. time Product Life Cycle Inventory Profile over Product Life Cycle Inventory Level - W.O.S. Initial Desired Inventory Level (ex: 4 WOS) Very high inventory. High carrying costs (component depreciation, capital cost). Very high inventory risk (time-to-consumption is high). A C B Excess inventory at end-of- life creates clearance discounts, price protection. Impacts pricing of next product. Impacts time-to-market of next product.

Portland State University Business goal: maximize total contribution margin over the life-cycle of a product. Cumulative Contribution

Portland State University Responsiveness of a supply chain is manifest in the height and duration of inventory excursions. Shorter supply chains are more robust to demand surprises. Supply Chain Response

Portland State University Shorter supply chains are more robust to demand surprises. Impact of Flow Time H.O.

Portland State University Learning Objectives After studying this chapter you should be able to:   Identify or Define :   Forecasting   Types of forecasts   Time horizons   Approaches to forecasts

Portland State University Learning Objectives - continued When you complete this chapter, you should be able to :  Understand and Use:  Moving averages  Exponential smoothing  Trend projections  Regression and correlation analysis  Measures of forecast accuracy

Portland State University What is Forecasting?   Process of predicting a future event   Underlying basis of all business decisions   Production   Inventory   Personnel   Facilities Sales will be $200 Million!

Portland State University  Short-range forecast  Up to 1 year; usually less than 3 months  Job scheduling, Procurement,worker assignments  Medium-range forecast  3 months to 3 years  Sales & production planning, budgeting  Long-range forecast  3+ years  New product planning, facility location Types of Forecasts by Time Horizon

Portland State University Influence of Product Life Cycle  Stages of introduction and growth require longer forecasts than maturity and decline  Forecasts useful in projecting  staffing levels,  inventory levels, and  factory capacity as product passes through life cycle stages

Portland State University Strategy and Issues During a Product’s Life IntroductionGrowth Maturity Decline Standardization Less rapid product changes - more minor changes Optimum capacity Increasing stability of process Long production runs Product improvement and cost cutting Little product differentiation Cost minimization Over capacity in the industry Prune line to eliminate items not returning good margin Reduce capacity Forecasting critical Product and process reliability Competitive product improvements and options Increase capacity Shift toward product focused Enhance distribution Product design and development critical Frequent product and process design changes Short production runs High production costs Limited models Attention to quality Best period to increase market share R&D product engineering critical Practical to change price or quality image Strengthen niche Cost control critical Poor time to change image, price, or quality Competitive costs become critical Defend market position OM Strategy/Issues Company Strategy/Issues HDTV CD-ROM Color copiers Drive-thru restaurants Fax machines Station wagons Sales 3 1/2” Floppy disks Internet

Portland State University Product Demand Charted over 4 Years with Trend and Seasonality Year 1 Year 2 Year 3 Year 4 Seasonal peaksTrend component Actual demand line Average demand over four years Demand for product or service Random variation

Portland State University Realities of Forecasting  Forecasts are seldom perfect  Most forecasting methods assume that there is some underlying stability in the system  Both product family and aggregated product forecasts are more accurate than individual product forecasts

Portland State University Forecasting Approaches   Used when situation is ‘stable’ & historical data exist   Existing products   Current technology   Involves mathematical techniques   e.g., forecasting sales of color televisions Quantitative Methods   Used when situation is vague & little data exist   New products   New technology   Involves intuition, experience   e.g., forecasting sales on Internet Qualitative Methods

Portland State University Overview of Qualitative Methods  Jury of executive opinion  Pool opinions of high-level executives, sometimes augment by statistical models  Sales force composite  Estimates from individual salespersons are reviewed for reasonableness, then aggregated  Delphi method  Panel of experts, queried iteratively  Consumer Market Survey  Ask the customer

Portland State University Quantitative Forecasting Methods (Non-Naive) Quantitative Forecasting Linear Regression Associative Models Exponential Smoothing Moving Average Time Series Models Trend Projection

Portland State University  Persistent, overall upward or downward pattern  Due to population, technology etc.  Several years duration Mo., Qtr., Yr. Response © T/Maker Co. Trend Component

Portland State University  Regular pattern of up & down fluctuations  Due to weather, customs etc.  Occurs within 1 year Mo., Qtr. Response Summer © T/Maker Co. Seasonal Component

Portland State University  Repeating up & down movements  Due to interactions of factors influencing economy  Usually 2-10 years duration Mo., Qtr., Yr. Response Cycle  Cyclical Component

Portland State University  Erratic, unsystematic, ‘residual’ fluctuations  Due to random variation or unforeseen events  Union strike  Tornado  Short duration & nonrepeating © T/Maker Co. Random Component

Portland State University Naive Approach   Assumes demand in next period is the same as demand in most recent period   e.g., If May sales were 48, then June sales will be 48   Sometimes cost effective & efficient   Responds very quickly © 1995 Corel Corp.

Portland State University  MA is a series of arithmetic means  Used if little or no trend  Used often for smoothing  Provides overall impression of data over time  Equation MA n n  Demand in Previous Periods Periods Moving Average Method

Portland State University You’re manager of a museum store that sells historical replicas. You want to forecast sales (000) for 1998 using a 3-period moving average © 1995 Corel Corp. Moving Average Example

Portland State University Moving Average Solution

Portland State University Moving Average Solution

Portland State University Moving Average Solution

Portland State University Year Sales Actual Forecast Moving Average Graph

Portland State University  Used when trend is present  Older data usually less important  Weights based on intuition  Often lay between 0 & 1, & sum to 1.0  Equation WMA = Σ(Weight for period n) (Demand in period n) ΣWeights Weighted Moving Average Method

Portland State University Actual Demand, Moving Average, Weighted Moving Average Actual sales Moving average Weighted moving average

Portland State University

 Increasing n makes forecast less sensitive to changes  Do not forecast trend well © T/Maker Co. Disadvantages of Moving Average Methods

Portland State University  Form of weighted moving average  Weights decline exponentially  Most recent data weighted most  Requires smoothing constant (  )  Ranges from 0 to 1  Subjectively chosen Exponential Smoothing Method

Portland State University  F t =  A t  (1-  ) A t  (1-  ) 2 ·A t  (1-  ) 3 A t  (1-  ) t- 1 ·A 0  F t = Forecast value  A t = Actual value   = Smoothing constant  F t = F t -1 +  ( A t -1 - F t -1 )  Use for computing forecast Exponential Smoothing Equations

Portland State University You’re organizing a Kwanza meeting. You want to forecast attendance for 2000 using exponential smoothing (  =.10). The1995 forecast was © 1995 Corel Corp. Exponential Smoothing Example

Portland State University F t = F t -1 +  ·  ( A t -1 - F t -1 ) Time Actual Forecast, F t ( α =.10) (Given) NA Exponential Smoothing Example

Portland State University F t = F t -1 +  ·  ( A t -1 - F t -1 ) Time Actual Forecast, F t ( α =.10) (Given) ( NA Exponential Smoothing Example

Portland State University F t = F t -1 +  ·  ( A t -1 - F t -1 ) TimeActual Forecast,F t ( α =.10) (Given) ( NA Exponential Smoothing Example

Portland State University F t = F t -1 +  ·  ( A t -1 - F t -1 ) TimeActual Forecast,F t ( α =.10) (Given) ( ) NA Exponential Smoothing Example

Portland State University F t = F t -1 +  ·  ( A t -1 - F t -1 ) TimeActual Forecast,F t ( αααα =.10) (Given) ( ) = NA Exponential Smoothing Example

Portland State University F t = F t -1 +  ·  ( A t -1 - F t -1 ) TimeActual Forecast, F t ( α =.10) (Given) ( ) = ( ) = NA Exponential Smoothing Example

Portland State University F t = F t -1 +  ·  ( A t -1 - F t -1 ) TimeActual Forecast, F t ( α =.10) (Given) ( ) = ( ) = NA ( )= Exponential Smoothing Example

Portland State University F t = F t -1 +  ·  ( A t -1 - F t -1 ) TimeActual Forecast, F t ( α =.10) (Given) ( ) = ( ) = ( ) = ( ) = NA Exponential Smoothing Example

Portland State University F t = F t -1 +  ·  ( A t -1 - F t -1 ) TimeActual Forecast, F t ( α =.10) (Given) ( ) = ( ) = ( ) = ( ) = NA ( ) = Exponential Smoothing Example

Portland State University Year Sales Actual Forecast Exponential Smoothing Example

Portland State University F t =  A t  (1-  ) A t  (1-  ) 2 A t Forecast Effects of Smoothing Constant  Weights Prior Period  2 periods ago  (1 -  ) 3 periods ago  (1 -  ) 2 ==  = 0.10  = %

Portland State University F t =  A t  (1-  ) A t  (1-  ) 2 A t Weights Prior Period  2 periods ago  (1 -  ) 3 periods ago  (1 -  ) 2 ==  = 0.10  = % 9% Forecast Effects of Smoothing Constant 

Portland State University F t =  A t  (1-  ) A t  (1-  ) 2 A t Weights Prior Period  2 periods ago  (1 -  ) 3 periods ago  (1 -  ) 2 ==  = 0.10  = % 9% 8.1% Forecast Effects of Smoothing Constant 

Portland State University F t =  A t  (1-  ) A t  (1-  ) 2 A t Weights Prior Period  2 periods ago  (1 -  ) 3 periods ago  (1 -  ) 2 ==  = 0.10  = % 9% 8.1% 90% Forecast Effects of Smoothing Constant 

Portland State University F t =  A t  (1-  ) A t  (1-  ) 2 A t Weights Prior Period  2 periods ago  (1 -  ) 3 periods ago  (1 -  ) 2 ==  = 0.10  = % 9% 8.1% 90%9% Forecast Effects of Smoothing Constant 

Portland State University F t =  A t  (1-  ) A t  (1-  ) 2 A t Weights Prior Period  2 periods ago  (1 -  ) 3 periods ago  (1 -  ) 2 ==  = 0.10  = % 9% 8.1% 90%9%0.9% Spreadsheet Forecast Effects of Smoothing Constant 

Portland State University Choosing  Seek to minimize the Mean Absolute Deviation (MAD) If:Forecast error = demand - forecast Then:

Portland State University Exponential Smoothing with Trend Adjustment Forecast including trend (FIT t ) = exponentially smoothed forecast (F t ) + exponentially smoothed trend (T t )

Portland State University F t =  (Actual demand previous period) + (1-  )(Forecast last period+Trend estimate last period) F t =  (A t-1 ) + (1-  )(F t-1 + T t-1 ) or T t =  (Forecast this period - Forecast last period) + (1-  )(Trend estimate last period T t =  (F t - F t-1 ) + (1-  )T t-1 or Exponential Smoothing with Trend Adjustment

Portland State University  F t = exponentially smoothed forecast of the data series in period t  T t = exponentially smoothed trend in period t  A t = actual demand in period t  = smoothing constant for the average  = smoothing constant for the trend Exponential Smoothing with Trend Adjustment

Portland State University Comparison of Forecasts Actual Demand Exponential smoothing Exponential smoothing + Trend

Portland State University Least Squares Deviation Time Values of Dependent Variable Actual observation Point on regression line

Portland State University Actual and the Regression Line Actual demand Y = X

Portland State University  Used for forecasting linear trend line  Assumes relationship between response variable, Y, and time, X, is a linear function  Estimated by least squares method  Minimizes sum of squared errors  i YabX i  Linear Trend Projection

Portland State University b > 0 b < 0 a a Y Time, X Linear Trend Projection

Portland State University Time Sales Sales vs. Time Scatter Diagram

Portland State University Least Squares Equations Equation: Slope: Y-Intercept: print

Portland State University You’re a marketing analyst for Hasbro Toys. You gather the following data: YearSales (Units) What is the trend equation? Linear Trend Projection Example

Portland State University You’re a marketing analyst for Hasbro Toys. Using coded years, you find Y i = X i. YearSales (Units) Forecast 2000 sales. ^ Linear Trend Projection Example Article

Portland State University Linear Regression Equations Equation: Slope: Y-Intercept:

Portland State University  Slope ( b )  Estimated Y changes by b for each 1 unit increase in X  If b = 2, then sales ( Y ) is expected to increase by 2 for each 1 unit increase in advertising ( X )  Y-intercept ( a )  Average value of Y when X = 0  If a = 4, then average sales ( Y ) is expected to be 4 when advertising ( X ) is 0 Interpretation of Coefficients

Portland State University Measures

Text uses symbol Y c Standard Error of the Estimate (standard deviation) What is the expected distribution around the regression line? sp

Portland State University  Answers: ‘ how strong is the linear relationship between the variables?’  Coefficient of correlation Sample correlation coefficient denoted r  Values range from -1 to +1  Measures degree of association  Used mainly for understanding Correlation

Portland State University Sample Coefficient of Correlation

Portland State University Perfect Positive Correlation Increasing degree of negative correlation Perfect Negative Correlation No Correlation Increasing degree of positive correlation Coefficient of Correlation Values

Portland State University Coefficient of Correlation and Regression Model

Portland State University  You want to achieve:  No pattern or direction in forecast error  Error = ( Y i - Y i ) = (Actual - Forecast)  Seen in plots of errors over time  Smallest forecast error  Mean square error (MSE)  Mean absolute deviation (MAD) Guidelines for Selecting Forecasting Model ^

Portland State University Time (Years) Error 0 0 Desired Pattern Time (Years) Error 0 Trend Not Fully Accounted for Pattern of Forecast Error

Portland State University  Mean Square Error (MSE)  Mean Absolute Deviation (MAD) Forecast Error Equations

Portland State University You’re a marketing analyst for Hasbro Toys. You’ve forecast sales with a linear model & exponential smoothing. Which model do you use? ActualLinear ModelExponential Smoothing YearSalesForecastForecast (.9) Selecting Forecasting Model Example

Portland State University Linear Model Evaluation

Portland State University Exponential Smoothing Model Evaluation

Portland State University Exponential Smoothing Model Evaluation Linear Model: MSE = Σ Error 2 / n = 1.10 / 5 =.220 MAD = Σ |Error| / n = 2.0 / 5 =.400 Exponential Smoothing Model: MSE = Σ Error 2 / n = 0.05 / 5 = 0.01 MAD = Σ |Error| / n = 0.3 / 5 = 0.06

Portland State University  Measures how well the forecast is predicting actual values  Ratio of running sum of forecast errors (RSFE) to mean absolute deviation (MAD)  Good tracking signal has low values  Should be within upper and lower control limits Tracking Signal

Portland State University Tracking Signal Running Sum Forecast Error

Portland State University Tracking Signal

Portland State University Tracking Signal

Portland State University Tracking Signal

Portland State University Tracking Signal

Portland State University Tracking Signal

Portland State University Tracking Signal

Portland State University Tracking Signal

Portland State University Tracking Signal

Portland State University Tracking Signal

Portland State University Tracking Signal

Portland State University Tracking Signal

Portland State University Tracking Signal

Portland State University Tracking Signal

Portland State University Plot of a Tracking Signal Time Lower control limit Upper control limit Signal exceeded limit Tracking signal Acceptable range MAD + 0 -