10/21/2013 1 Concepts and Techniques for Effective Forecast Management The Intricacies of Forecasting—Simplified Visual - 2 Introductions – Session Leader David F. Ross PhD, CFPIM, CSCP Senior Manager, Professional Development, APICS 35 years of industry, consulting, ERP, education, and professional development experience Teaching positions at NU Kellogg School of Management and Elmhurst College APICS Member since 1985 Published six books in supply chain management Meet your session leaders
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10/21/2013
1
Concepts and
Techniques for Effective
Forecast Management
The Intricacies of
Forecasting—Simplified
Visual - 2
Introductions – Session Leader � David F. Ross PhD, CFPIM, CSCP
� Senior Manager, Professional Development, APICS
� 35 years of industry, consulting, ERP, education, and professional development experience
� Teaching positions at NU Kellogg School of Management and Elmhurst College
� APICS Member since 1985
� Published six books in supply chain management
Meet your session leaders
10/21/2013
2
Visual - 3
Introductions – Session Leader � Bob Collins CFPIM, CIRM, CSCP
� Director, Professional Development, APICS (Staff position)
� 30 years of industry, consulting, ERP, education, and professional development experience
� Former APICS Instructor and volunteer –Chapter, District and APICS Board of Directors, APICS President (2003)
• 23 major principles of forecasting• Forecasting in the supply chain environment• Defining demand management and role of the demand
planner• Defining forecasting and the forecasting process• Review of qualitative forecasting techniques• Review of quantitative forecasting techniques• Performing forecast decomposition: trends and seasonal
items• Understanding associative (correlation) models• Reviewing the tools to chart forecast error• Detailing why forecasts fail
1. Supply chain management (SCM) refers to getting the right amount of the right product to where it is needed while managing productive resources levels to achieve maximum return on assets
1. Supply chain management (SCM) refers to getting the right amount of the right product to where it is needed while managing productive resources levels to achieve maximum return on assets
2. Demand management is the process of managing all independent demands for a company's product lines and effectively communicating these demands to the master scheduling and top management production functions
The process of combining statistical forecasting techniques and judgment to construct demand
estimates for products or services (both high and low volume; lumpy and continuous) across the
supply chain from the suppliers' raw materials to the consumer's needs. Items can be aggregated by product family, geographical location, product life cycle, and so forth, to determine an estimate of consumer demand for finished products, service
1. Supply chain management (SCM) refers to getting the right amount of the right product to where it is needed while managing productive resources levels to achieve maximum return on assets
2. Demand management is the process of managing all independent demands for a company's product lines and effectively communicating these demands to the master scheduling and top management production functions
3. Demand forecasting is the process of predicting future customer demand for a firm's goods and services
4. Sales and operational forecasting involves the input from marketing, sales, production, and financial plans to determine the disaggregated forecasts of product or service demand
4. Sales and operational forecasting involves the input from marketing, sales, production, and financial plans to determine the disaggregated forecasts of product or service demand
5. Demand forecasting is performed at different levels of detail incorporating dimensions of period, product, and customer/location
4. Sales and operational forecasting involves the input from marketing, sales, production, and financial plans to determine the disaggregated forecasts of product or service demand
5. Demand forecasting is performed at different levels of detail incorporating dimensions of period, product, and customer/location
6. Forecasting is a process that has as its objective the prediction of future events or conditions
4. Sales and operational forecasting involves the input from marketing, sales, production, and financial plans to determine the disaggregated forecasts of product or service demand
5. Demand forecasting is performed at different levels of detail incorporating dimensions of period, product, and customer/location
6. Forecasting is a process that has as its objective the prediction of future events or conditions
7. Effective forecasting starts with an comprehensive forecast design system
8. A forecasting technique is a systematic procedure for producing and analyzing forecasts
9. Qualitative methods are most commonly used in forecasting something about which the amount, type, and quality of historical data are limited
10. Quantitative methods are characterized by a rigorous data acquisition procedure along with an application of mathematical techniques. A method based on historical data will be no better than the quality of its data source
8. A forecasting technique is a systematic procedure for producing and analyzing forecasts
9. Qualitative methods are most commonly used in forecasting something about which the amount, type, and quality of historical data are limited
10. Quantitative methods are characterized by a rigorous data acquisition procedure along with an application of mathematical techniques. A method based on historical data will be no better than the quality of its data source
8. A forecasting technique is a systematic procedure for producing and analyzing forecasts
9. Qualitative methods are most commonly used in forecasting something about which the amount, type, and quality of historical data are limited
10. Quantitative methods are characterized by a rigorous data acquisition procedure along with an application of mathematical techniques. A method based on historical data will be no better than the quality of its data source
11. Forecasts are usually wrong
12. Forecasts are more accurate for aggregate groups
13. Time series analysis assists forecasters to isolate demand patterns occurring in the raw data
14. The utility of averages becomes problematic when time series data is affected by trend, seasonal, or cyclical patterns. Forecasters must then “decompose” the patterns into subpatterns to reveal how they impact the behavior of the series
13. Time series analysis assists forecasters to isolate demand patterns occurring in the raw data
14. The utility of averages becomes problematic when time series data is affected by trend, seasonal, or cyclical patterns. Forecasters must then “decompose” the patterns into subpatterns to reveal how they impact the behavior of the series
15. A trend is the basic tendency of a measured variable to grow or decline over a long period. The forecast extrapolation can be calculated as additive or a trend factor (percent)
3. Forecast calculationThe trend quantity is added to the base forecast to determine the trended forecast. The forecast is extrapolated into the future by adding the trend quantity to each future period’s trended forecast
16. Seasonality is a regularly recurring variation (timing and intensity) in a time series. Seasonal patterns are fluctuations that can recur over months, weeks, days, or even hours
16. Seasonality is a regularly recurring variation (timing and intensity) in a time series. Seasonal patterns are fluctuations that can recur over months, weeks, days, or even hours
17. Through associative (correlation) analysis, we measure the effects of mutual dependence in values of an item series
16. Seasonality is a regularly recurring variation (timing and intensity) in a time series. Seasonal patterns are fluctuations that can recur over months, weeks, days, or even hours
17. Through associative (correlation) analysis, we measure the effects of mutual dependence in values of an item series
18. An associative model with a single explanatory variable is called a simple regression model. Multiple regression refers to a model with one dependent and two or more explanatory variables
19. Forecasts are most useful when accompanied by a method for determining forecast error
20. Forecast error is a measure of forecast accuracy. Fitting error is a measure of model adequacy. It is important to distinguish between forecast errors and fitting errors
19. Forecasts are most useful when accompanied by a method for determining forecast error
20. Forecast error is a measure of forecast accuracy. Fitting error is a measure of model adequacy. It is important to distinguish between forecast errors and fitting errors
21. The use of multiple methods to arrive at the final forecast is highly recommended
19. Forecasts are most useful when accompanied by a method for determining forecast error
20. Forecast error is a measure of forecast accuracy. Fitting error is a measure of model adequacy. It is important to distinguish between forecast errors and fitting errors
21. The use of multiple methods to arrive at the final forecast is highly recommended
22. Create an integrated forecasting process that encourages communication, coordination, and collaboration among marketing sales, product management, production, distribution, finance, and forecasting organizations
Data Accuracy The data used for the forecast must be accurate, timely, complete, and easy to access
Unnecessary Items
Often forecasts are developed for items that should not be forecasted, for example dependent demand item usage
Lack of Management
Control
Forecasters must be diligent in monitoring the forecast to ascertain the degree of error, when the forecast should be altered, and what parameters should be used to guide forecast adjustment
23. The philosophy of forecast places primary emphasis on the forecasting process rather than on the numbers. If the forecaster has meticulously followed a proper forecasting process, the end result will be as good a forecast as can be delivered
“As far as the laws of mathematics refer to reality, they are not certain, and as far as they are certain, they do not refer to reality”- Einstein