Demand Management and Forecasting Chapter 15 Demand Management and Forecasting

Learning Objectives Understand the role of forecasting as a basis for supply chain planning. Compare the differences between independent and dependent demand. Identify the basic components of independent demand: average, trend, seasonal, and random variation. Describe the common qualitative forecasting techniques such as the Delphi method and Collaborative Forecasting. Show how to make a time series forecast using regression, moving averages, and exponential smoothing. Use decomposition to forecast when trend and seasonality is present.

Demand Management Strategic forecasts: forecasts used to help set the strategy of how demand will be met Tactical forecasts: forecasted needed for how a firm operates processes on a day-to-day basis The purpose of demand management is to coordinate and control all sources of demand Two basic sources of demand Dependent demand: the demand for a product or service caused by the demand for other products or services Independent demand: the demand for a product or service that cannot be derived directly from that of other products LO 2

Demand Management Continued Not much a firm can do about dependent demand It is demand that must be met There is a lot a firm can do about independent demand Take an active role to influence demand Take a passive role and respond to demand LO 1

Types of Forecasts Four basic types Qualitative Time series analysis Causal relationships Simulation Time series analysis is based on the idea that data relating to past demand can be used to predict future demand Primary focus of this chapter LO 1 5

Average demand for a period of time Trend Seasonal element Components of Demand Average demand for a period of time Trend Seasonal element Cyclical elements Random variation Autocorrelation LO 3 7

Common Types of Trends LO 3

Short term: forecast under three months Time Series Analysis Short term: forecast under three months Tactical decisions Medium term: three months to two years Capturing seasonal effects Long term: forecast longer than two years Detecting general trends Identifying major turning points LO 5

A Guide to Selecting an Appropriate Forecasting Method LO 5

Pick Forecasting Model Based On Time horizon to forecast Data availability Accuracy required Size of forecasting budget Availability of qualified personnel LO 5 14

Linear Regression Analysis Regression: functional relationship between two or more correlated variables It is used to predict one variable given the other Y = a + bX Y is the value of the dependent variable a is the Y intercept b is the slope X is the independent variable Assumes data falls in a straight line LO 5

Example 15.1: The Data and Least Squares Regression Line LO 5

Example 15.1: Equations and Calculating Totals LO 5

Example 15.1: Calculating the Forecast LO 5

Decomposition of a Time Series Time series: chronologically ordered data that may contain one or more components of demand Decomposition: identifying and separating the time series data into these components Seasonal variation Additive: the seasonal amount is constant Multiplicative: the seasonal variation is a percentage of demand LO 6

Additive and Multiplicative Seasonal Variation Superimposed on Changing Trend LO 6

Example 15.3: The Data and Hand Fitting LO 6

Example 15.3: Computing Seasonal Factors and Computing Forecast LO 5

Decomposition Using Least Squares Regression Determine the seasonal factor Deseasonalize the original data Develop a least squares regression line for the deseasonalized data Project the regression line through the period of the forecast Create the final forecast by adjusting the regression line by the seasonal factor LO 6

Steps 1-3 Deseasonalized Demand LO 6

Steps 4 – 5 LO 6

Important to select the best period Simple Moving Average Useful when demand is neither growing nor declining rapidly and does not have seasonal characteristics Moving averages can be centered or used to predict the following period Important to select the best period Longer gives more smoothing Shorter reacts quicker to trends LO 5

Simple Moving Average Formula LO 5

Forecast Demand Based on a Three- and a Nine-Week Simple Moving Average LO 5

Weighted Moving Average 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 All the weights must sum to one LO 5

Experience and trial-and-error are the simplest ways Choosing Weights Experience and trial-and-error are the simplest ways Generally, the most recent past is the best indicator When data are seasonal, weights should be established accordingly LO 5

Exponential Smoothing Most used of all forecasting techniques Integral part of all computerized forecasting programs Widely used in retail and service Widely accepted because… Exponential models are surprisingly accurate Formulating an exponential model is relatively easy The user can understand how the model works Little computation is required to use the model Computer storage requirements are small Tests for accuracy are easy to compute LO 5

Exponential Smoothing Model LO 5 24

Exponential Smoothing Example (=0.20) LO 5 26

Trend Effects in Exponential Smoothing An trend in data causes the exponential forecast to always lag the actual data Can be corrected somewhat by adding in a trend adjustment To correct the trend, we need two smoothing constants Smoothing constant alpha () Trend smoothing constant delta (δ) LO 5

Exponential Forecasts versus Actual Demand over Time Showing the Forecast Lag LO 5

Trend Effects Equations LO 5

Bias errors: when a consistent mistake is made Forecast Error Bias errors: when a consistent mistake is made Random errors: errors that cannot be explained by the forecast model being used Measures of error Mean absolute deviation (MAD) Mean absolute percent error (MAPE) Tracking signal LO 5

The MAD Statistic to Determine Forecasting Error The ideal MAD is zero which would mean there is no forecasting error The larger the MAD, the less the accurate the resulting model LO 5 30

Tracking Signal The tracking signal (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 LO 5 33

Computing the MAD, the RSFE, and the TS from Forecast and Actual Data LO 5

Causal Relationship Forecasting Causal relationship forecasting: using independent variables other than time to predict future demand The independent variable must be a leading indicator Must find those occurrences that are really the causes LO 5

Qualitative Techniques in Forecasting Qualitative forecasting techniques take advantage of the knowledge of experts Most useful when the product is new or there is little experience with selling into a new region The following are samples of qualitative forecasting techniques Market research Panel consensus Historical analogy Delphi method LO 4

Web-Based Forecasting: (CPFR) 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 Objective is to exchange selected internal information to provide for a reliable, longer term future views of demand CPFR uses a cyclic and iterative approach to derive consensus forecasts LO 5 33

Web-Based Forecasting: Steps in CPFR Creation of a front-end partnership agreement Joint business planning Development of demand forecasts Sharing forecasts Inventory replenishment LO 5 33