Operations Fall 2015 Bruce Duggan Providence University College
Summary Forecasting is fundamental to any planning effort. In the short run, a forecast is needed to predict the requirements for materials, products, services, or other resources to respond to changes in demand. In the long run, forecasting is required as a basis for strategic changes, such as developing new markets, developing new products, or services, and expanding or creating new facilities.
Operations and SCM in Practice Starbucks Global Supply Chain Challenge: Largest coffeehouse company in the world (17,000 stores) In the 1990’s, a new store was opening every workday Created a single, global logistics system that drives supply chain planning from the demand forecast
Learning Objectives Understand role of forecasting as a basis for supply chain planning Classify: independent demand dependent demand Understand basic components of independent demand: average trend seasonal variation random variation Understand common qualitative forecasting techniques e.g.: Delphi method Know how to make time-series forecasts using moving averages exponential smoothing. Know how to measure forecast error
Demand Management Dependent demand is the demand for a product or service caused by the demand for other products or services Independent demand is the demand that cannot be derived directly from that of other products
A B(4) E(1)D(2) C(2) F(2)D(3) Demand Management Independent Demand: finished goods Dependent Demand: raw materials component parts sub-assemblies etc.
Types of Forecasts qualitative techniques subjective or judgmental based on estimates & opinions time-series analysis key idea: past demand data can be used to predict future demand causal forecasting key assumption: demand is related to some underlying factor or factors in the environment simulation models allow the forecaster to run through a range of assumptions about the condition of the forecast
Components of Demand average demand for a period of time trend seasonal variation cyclical variation random variation vs. autocorrelation
Components of Demand
Qualitative Techniques in Forecasting market research sales team estimates o (bottom up) executive estimate o (top down) panel consensus historical analogy Delphi method
Delphi Method 1.Choose the experts to participate representing a variety of knowledgeable people in different areas. 2.Through a questionnaire (or ), obtain 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 and distribute the final results to all participants.
Time Series Analysis options 1.simple moving average 2.weighted moving average 3.exponential smoothing which to choose depends on: time horizon to forecast data availability accuracy required size of forecasting budget availability of qualified personnel
Time Series Analysis
1. Simple Moving Average The simple moving average model assumes an average is a good estimator of future behavior. formula: F t = 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
1. Simple Moving Average Example
2. Weighted Moving Average Weighted moving average permits an unequal weighting on prior time periods. formula: F t = 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 w t = weight given to time period “t” (must total 1)
2. Weighted Moving Average Example monthsales ? periodweights t t t t F =.40(95) +.30(105) +.20(90) +.10(100) = 97.5
3. Exponential Smoothing Premise: The most recent observations might have the highest predictive value. Conclusion: Therefore, we should give more weight to the more recent time periods when forecasting.
3. Exponential Smoothing Formula F t = F t-1 + (A t-1 - F t-1 ) F t = Forecast for the coming period F t-1 = Forecast value in 1 past time period A t-1 = Actual occurrence in the past period α = Alpha smoothing constant
3. Exponential Smoothing Example 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 F 1 =D 1 LO5 monthsales ?
3. Exponential Smoothing Example Answer: The respective alphas colums denote the forecast values. Note that you can only forecast one time period into the future.
Y t = a + bx The simple linear regression model seeks to fit a line through various data over time Is the linear regression model x (Time) Y a Yt is the regressed forecast value or dependent variable in the model, a is the intercept value of the the regression line, and b is similar to the slope of the regression line. Linear Regression Analysis
Measurement of Error Mean Absolute Deviation (MAD) refers to the average forecast error using absolute values of the error of each past forecast. The ideal MAD is zero which would mean there is no forecasting error. The larger the MAD, the less the accurate the resulting model.
Measurement of Error Running Sum of Forecast Errors (RSFE) considers the nature of the error Tracking Signal a measure that indicates whether the forecast average is keeping pace with any genuine upward or downward changes in demand
Measurement of Error Tracking signal formula:
Learning Objectives Review 1. How does forecasting aid effective supply chain planning? 2. Why is forecasting not necessary for dependent demand items? 3. What are the four basic components of independent demand? 4. What are some qualitative forecasting techniques that can be used when no historical demand data is available? 5. What is the inherent assumption for moving average and exponential smoothing forecasts? 6. What is the purpose of measuring forecast error?
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