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**Chapter 15 Demand Management & Forecasting**

Qualitative Forecasting Methods Simple & Weighted Moving Average Forecasts Exponential Smoothing Simple Linear Regression

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**Demand Management Independent Demand: Finished Goods Dependent Demand:**

B(4) C(2) D(2) E(1) D(3) F(2) Dependent Demand: Raw Materials, Component parts, Sub-assemblies, etc. A

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**Independent Demand: What a firm can do to manage it.**

Can take an active role to influence demand Can take a passive role and simply respond to demand

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**What Is Forecasting? Process of predicting a future event**

Underlying basis of all business decisions Production Inventory Personnel Facilities Sales will be $200 Million!

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**Types of Forecasts by Time Horizon**

Short-range forecast Job scheduling, worker assignments Medium-range forecast Sales & production planning, budgeting Long-range forecast New product planning, facility location

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**Types of Forecasts by Item Forecast**

Economic forecasts Address business cycle e.g., inflation rate, money supply etc. Technological forecasts Predict technological change Predict new product sales Demand forecasts Predict existing product sales

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**Types of Forecasts Qualitative (Judgmental) Quantitative**

Time Series Analysis Causal Relationships Simulation

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**Components of Demand Average demand for a period of time Trend**

Seasonal element Cyclical elements Random variation Autocorrelation

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**Finding Components of Demand**

Seasonal variation x Linear Trend x Sales 1 2 3 4 Year

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**B Cyclical Component Repeating up & down movements**

Usually 2-10 years duration Cycle Response B Mo., Qtr., Yr.

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Random Component Erratic, unsystematic, unpredictable ‘residual’ fluctuations © T/Maker Co.

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**Qualitative Methods Grass Roots Qualitative Methods Market Research**

Executive Judgment Grass Roots Qualitative Methods Market Research Historical analogy Delphi Method Panel Consensus

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Delphi Method l. Choose the experts to participate. There should be a variety of knowledgeable people in different areas. 2. Through a questionnaire (or ), obtain forecasts (and any premises or qualifications for the forecasts) from all participants. 3. Summarize the results and redistribute them to the participants along with appropriate new questions. 4. Summarize again, refining forecasts and conditions, and again develop new questions. 5. Repeat Step 4 if necessary. Distribute the final results to all participants.

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**Quantitative Forecasting Methods**

Time Series Causal Models Models Moving Exponential Trend Linear Regression Average Smoothing Projection

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Time Series Analysis Time series forecasting models try to predict the future based on past data. You can pick models based on: 1. Time horizon to forecast 2. Data availability 3. Accuracy required 4. Size of forecasting budget 5. Availability of qualified personnel

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**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

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Forecasting Example # 1 Weekly Video Rentals

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**Forecasting Video Rentals With Moving Averages**

Question: What are the 2-week and 4-week moving average forecasts for video rentals? Which forecast would you prefer?

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**Calculating the moving averages gives us:**

19 Calculating the moving averages gives us: The McGraw-Hill Companies, Inc., 2000

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**Which Forecast Would You Prefer?**

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Forecasting Example # 2 Quarterly Sales Data (Acme Tool Company)

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**Forecasting Quarterly Sales With Moving Averages**

Question: What are the 2-week and 4-week moving average forecasts for Quarterly Sales Which forecast would you prefer?

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**Calculating the moving averages gives us:**

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**Which Forecast Would You Prefer?**

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**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 weighted average is: wt = weight given to time period “t” occurrence. (Weights must add to one.)

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**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

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**Weighted Moving Average Problem (1) Solution**

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**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

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**Weighted Moving Average Problem (2) Solution**

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**Exponential Smoothing Model**

Ft = aAt-1 +(1- )Ft-1 Or, Equivalently Ft = Ft-1 + a(At-1 - Ft-1) a = smoothing constant Where, Ft = Forecast for period t At = Actual value in period t Note:

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**Exponential Smoothing Expansion**

Ft = At (1-)At (1- )2·At (1- )3At (1- )t-1·A0 Ft = Forecast value At = Actual value = Smoothing constant You may wish to discuss several points: - this is just a moving average wherein every point in included in the forecast, but the weights of the points continuously decrease as they extend further back in time. - the equation actually used to calculate the forecast is convenient for programming on the computer since it requires as data only the actual and forecast values from the previous time point. - we need a formal process and criteria for choosing the “best” smoothing constant.

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**Forecasting Weekly Video Rentals With Exponential Smoothing**

Question: Given the weekly video rental data, what are the exponential smoothing forecasts for periods 2-13 using a=0.10 and a=0.60? Assume F1=A1

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**Calculating the Exponential smoothing forecasts gives us:**

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**Which Forecast Would You Prefer?**

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**Forecasting Quarterly Sales for the Acme Tool Company With Exponential Smoothing**

Question: Given the quarterly sales data, what are the exponential smoothing forecasts for periods 2-13 using a=0.10 and a=0.60? Assume F1=A1

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**Calculating the Exponential smoothing forecasts gives us:**

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**Which Forecast Would You Prefer?**

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**Forecast Effects of Smoothing Constant **

Ft = At (1- )At (1- )2At Weights Prior Period 2 periods ago (1 - ) 3 periods ago (1 - )2 = = 0.10 = 0.90

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**The MAD Statistic to Determine Forecasting Error**

The ideal MAD is zero. The larger the MAD, the less the desirable the resulting model.

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Weekly Video Rentals

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**Quarterly Sales (Acme Tool Company)**

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**A Comparison of Exponential Smoothing Forecasts (Video Rentals)**

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**A Comparison of Exponential Smoothing Forecasts (Acme Tool)**

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**Tracking Signal Formula**

The TS is a measure that indicates whether the forecast average is keeping pace with any genuine upward or downward changes in demand. Depending on the number of MAD’s selected, the TS can be used like a quality control chart indicating when the model is generating too much error in its forecasts. The TS formula is:

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Calculating Tracking Signals for the Exponential Smoothing Forecasts From the Acme Tool Company Example

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**Tracking Signal Charts**

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**Linear Trend Projection**

Used for forecasting linear trend line Assumes relationship between response variable Y & time X is a linear function Estimated by least squares method Minimizes sum of squared errors

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**Linear Regression Model**

Y X a b i Error Regression line Observed value

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Correlation Answers ‘how strong is the linear relationship between 2 variables?’ Coefficient of correlation used Sample correlation coefficient denoted r Values range from -1 to +1 Measures degree of association Used mainly for understanding

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**Coefficient of Correlation Values**

Perfect Negative Correlation Perfect Positive Correlation No Correlation -1.0 -.5 +.5 +1.0 Increasing degree of negative correlation Increasing degree of positive correlation

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**Web-Based Forecasting: CPFR Defined**

Collaborative Planning, Forecasting, and Replenishment (CPFR) a Web-based tool used to coordinate demand forecasting, production and purchase planning, and inventory replenishment between supply chain trading partners. Used to integrate the multi-tier or n-Tier supply chain, including manufacturers, distributors and retailers. CPFR’s objective is to exchange selected internal information to provide for a reliable, longer term future views of demand in the supply chain. CPFR uses a cyclic and iterative approach to derive consensus forecasts. 33

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**Web-Based Forecasting: Steps in CPFR**

1. Creation of a front-end partnership agreement 2. Joint business planning 3. Development of demand forecasts 4. Sharing forecasts 5. Inventory replenishment 33

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