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Demand Management and Forecasting
CHAPTER 10
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Learning Objectives After completing the chapter you will:
Understand the role of forecasting as a basis for supply chain planning Know how independent and dependent demand differs Know the components of independent demand-average, trend, seasonal and random variation Become familiar with common qualitative forecasting techniques such as the Delphi Method Learn how to make time-series forecasts using moving average, exponential smoothing, and regression See how the internet is used to improve forecasting
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Walmart’s Data Warehouse
Data warehouse with 35 terabytes data One of the largest The system tracks… Point of sale, inventory level, products in transit, market statistics, customer demographics, finance, returns, supplier performance Data are used for Analyzing trends Managing inventory Understanding customers
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Forecast Forecast Vital Basis of corporate long-run planning
Finance and accounting: basis for budgetary planning and cost control Marketing: relies on sales forecasting to plan new products Production and operations: use forecasts to make Periodic decisions Supplier selection, Process selection, Capacity planning, Facility layout Continual decisions Purchasing, Production planning, Scheduling, Inventory
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Forecast Perfect forecast? Virtually impossible.
Rather, it is important To establish the practice of continual review of forecasts To learn to live with inaccurate forecasts Should try to find and use the best forecasting method available, within reason
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Demand Management Purpose of DM Two basic sources of demand
To coordinate and control all sources of demand So the supply chain can be run efficiently and the product delivered on time Two basic sources of demand Dependent demand Demand for a product or service caused by the demand for other products or services Independent demand Demand that cannot be derived directly from demand of other products
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Demand Management
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Independent Demand About independent demand, the firm can
Take an active role to influence demand Apply pressure on its sales force Offer incentives Wage campaigns Cut prices Take a passive role and simply respond to demand If a firm is running at full capacity,… If a firm may be powerless to change demand, …
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Types of Forecasting Qualitative Time series analysis
Subjective, judgmental Based on estimates and opinions Time series analysis Past data Causal relationships Underlying factors Simulation
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Components of Demand Can be broken down into 6 components
Average demand for the period Trend Seasonal element Cyclical element Random variation Autocorrelation
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Components of Demand
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Components of Demand Cyclical factors Random variations
Difficult to determine Cyclical influence may come from Political elections, war, economic conditions, sociological pressures Random variations Caused by chance events Random = demand –(average, trend, seasonal, cyclical, and autocorrelative)
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Components of Demand Autocorrelation Trend Persistence of occurrence
Highly correlated with its own past values Random vs. Highly autocorrelative Random: may vary widely Highly autocorrelative: not expected to change very much Trend Exhibit 10.2
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Common Types of Trends
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Qualitative Techniques
1. Market Research 2. Panel Consensus 3. Historical Analogy 4. Delphi Method
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Market Research Hiring “MR” companies Used mostly for
Product research Looking for new product ideas Likes and dislikes about existing products Data collection methods Surveys Interviews
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Panel Consensus Idea Difficulty Executive judgment
A panel of people from a variety of positions can develop more reliable forecast than a narrow group Difficulty Lower levels are intimidated by higher levels Executive judgment
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Historical Analogy In trying to forecast a new product demand,
Existing or generic product is used Complementary products Substitutable products Competitive products Examples DVD player ~ DVD DVD player ~ VCR player Toaster ~ coffee pot
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Delphi Method To prevent Panel Consensus’s problem, Procedure
Conceals the identity of the individuals. Everyone has the same weight Procedure 1. Choose the experts to participate 2. Through a questionnaire (or ), obtain forecasts 3. Summarize the results and redistribute them along with appropriate new questions 4. Summarize again, refining forecast and conditions, and again develop new questions 5.Repeat step 4 if necessary. Distribute the final results
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Time Series Analysis Try to predict the future
Based on the past data Forecasting model should be choose depend on Time horizon to forecast Data availability Accuracy required Size of forecasting budget Availability of qualified personnel Firm’s degree of flexibility Consequence of a bad forecast
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Time Series Analysis
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Simple Moving Average
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Simple Moving Average
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Simple Moving Average Best period for the MA? Long period Short period
Random elements are smoothed Lagging trend Short period Closer following of trend
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Simple Moving Average
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Weighted Moving Average
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Weighted Moving Average
Choosing weights Experience, trial & error As a general rule, the most recent past is the most important indicator of what to expect in the future If the data are seasonal Weights should be established accordingly
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Weighted Moving Average
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Exponential Smoothing
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Exponential Smoothing
Needed data The most recent forecast The actual demand that occurred for that forecast period Smoothing constant alpha (α) Level of smoothing Speed of reaction
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Exponential Smoothing
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Exponential Smoothing
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Exponential Smoothing
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Exponential Smoothing
Forecast lag
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Trend Effects in Exponential Smoothing
Equations for “forecast including trend (FIT)”
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Trend Effects in Exponential Smoothing
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Forecast Errors Error Forecast error
Difference between the forecast value and what actually occurred Residual Forecast error Sources of error Measurement of error
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Sources of Error Errors can come from a variety of sources
One common source Projecting past trends into the future Errors can be classified as … Bias Occur when a consistent mistake is made Random Those that cannot be explained by the forecast model being used
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Measurement of Error Standard error Mean squared error (or variance)
Mean absolute deviation (MAD)
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MAD
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MAD Problem Solution Month Sales Forecast Abs Error 1 220 n/a 2 250 255 5 3 210 205 4 300 320 20 325 315 10 40 Note that by itself, the MAD only lets us know the mean error in a set of forecasts 32
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Tracking Signal Tracking signal (TS)
Measurement that indicates whether the forecast average is keeping pace with any genuine upward or downward changes in demand Number of MAD
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Tracking Signal
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Exhibit 10.8
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Exhibit 10.9
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Simple Linear Regression Model
Y The simple linear regression model seeks to fit a line through various data over time a x (Time) Yt = a + bx Is the linear regression model 35
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Simple Linear Regression Formulas for Calculating “a” and “b”
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Simple Linear Regression Problem Data
Question: Given the data below, what is the simple linear regression model that can be used to predict sales in future weeks? 37
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Answer: First, using the linear regression formulas, we can compute “a” and “b”
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The resulting regression model is:
Yt = x Now if we plot the regression generated forecasts against the actual sales we obtain the following chart: 180 Period 135 140 145 150 155 160 165 170 175 1 2 3 4 5 Sales Forecast 39
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Example 10.2 Standard error of estimate
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Web-Based Forecasting -CPFR
Collaborative Planning Forecasting and Replenishment (CPFR) A Web-based tool Coordinate demand forecasting, production and purchase planning and inventory replenishment between supply chain trading partners.
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CPFR
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CPFR Objective Approach (cyclic and iterative)
Exchange selected internal information on a shared Web server In order to provide for reliable, longer-term future views of demand in the supply chain Approach (cyclic and iterative) 1. Creation of a front-end partnership agreement 2. Joint business planning 3. Development of demand forecasts 4. Sharing forecasts 5. Inventory replenishment
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Summary Demand Management Types of Forecasting Components of Demand
Qualitative Techniques Time Series Analysis CPFR
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Question Bowl Which of the following is a classification of a basic type of forecasting? Transportation method Simulation Linear programming All of the above None of the above 7
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Question Bowl Which of the following is an example of a “Qualitative” type of forecasting technique or model? Grass roots Market research Panel consensus All of the above None of the above 7
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Question Bowl Which of the following is an example of a “Time Series Analysis” type of forecasting technique or model? Simulation Exponential smoothing Panel consensus All of the above None of the above 7
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Question Bowl Which of the following is a reason why a firm should choose a particular forecasting model? Time horizon to forecast Data availability Accuracy required Size of forecasting budget All of the above 7
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Question Bowl Which of the following are ways to choose weights in a Weighted Moving Average forecasting model? Cost Experience Trial and error Only b and c above None of the above 7
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Question Bowl Which of the following are reasons why the Exponential Smoothing model has been a well accepted forecasting methodology? It is accurate It is easy to use Computer storage requirements are small All of the above None of the above 7
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Question Bowl The value for alpha or α must be between which of the following when used in an Exponential Smoothing model? 1 to 10 1 to 2 0 to 1 -1 to 1 Any number at all 7
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Question Bowl Which of the following are sources of error in forecasts? Bias Random Employing the wrong trend line All of the above None of the above 7
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Question Bowl Which of the following would be the “best” MAD values in an analysis of the accuracy of a forecasting model? 1000 100 10 1 7
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Question Bowl If a Least Squares model is: Y=25+5x, and x is equal to 10, what is the forecast value using this model? 100 75 50 25 None of the above 7
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Question Bowl Which of the following are examples of seasonal variation? Additive Least squares Standard error of the estimate Decomposition None of the above 7
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End of Chapter 10
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