Green production management and supply chain management: demand forecasting with exponentially weighted moving averages adjusted for trend and seasonality
An accurate forecast of future demand is an absolute requirement for planning production without creating wasteful overages or shortages and hence constitutes a cornerstone of successful green engineering. This module provides a hands on introduction to Winter's model for time series forecastin...
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| Main Author: | |
|---|---|
| Format: | Electronic Video |
| Language: | English |
| Published: |
United States
IEEE
2011
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| Subjects: | |
| Online Access: | FHN01 TUM01 |
| Summary: | An accurate forecast of future demand is an absolute requirement for planning production without creating wasteful overages or shortages and hence constitutes a cornerstone of successful green engineering. This module provides a hands on introduction to Winter's model for time series forecasting from a user's perspective. It shows you step by step how to create a forecast based only on historical demand data. Winter's model involves quantifying and separating the trend and seasonality effects from historical data and projecting them into the future in order to make a forecast. One of the greatest issues with using Winter's model is the selection of values for the three smoothing constants. This module shows in a straight forward fashion how the selection values for the smoothing constants is the solution to an optimization problem. The appendix of the module shows step by step how someone with limited statistical and computer spreadsheet knowledge can obtain the optimal values of the smoothing constants using Excel's Solver add-in, and using Microsoft Excel, develop a working forecasting system |
| Item Description: | Description based on online resource; title from title screen (IEEE Xplore Digital Library, viewed November 17, 2020) |
| Physical Description: | 1 Online-Resource (1 Videodatei, 60 Minuten) color illustrations |
| ISBN: | 9781467306133 |
Staff View
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Record in the Search Index
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| isbn | 9781467306133 |
| language | English |
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| spelling | Swart, William Verfasser aut Green production management and supply chain management demand forecasting with exponentially weighted moving averages adjusted for trend and seasonality William Swart United States IEEE 2011 1 Online-Resource (1 Videodatei, 60 Minuten) color illustrations tdi rdacontent c rdamedia cr rdacarrier Description based on online resource; title from title screen (IEEE Xplore Digital Library, viewed November 17, 2020) An accurate forecast of future demand is an absolute requirement for planning production without creating wasteful overages or shortages and hence constitutes a cornerstone of successful green engineering. This module provides a hands on introduction to Winter's model for time series forecasting from a user's perspective. It shows you step by step how to create a forecast based only on historical demand data. Winter's model involves quantifying and separating the trend and seasonality effects from historical data and projecting them into the future in order to make a forecast. One of the greatest issues with using Winter's model is the selection of values for the three smoothing constants. This module shows in a straight forward fashion how the selection values for the smoothing constants is the solution to an optimization problem. The appendix of the module shows step by step how someone with limited statistical and computer spreadsheet knowledge can obtain the optimal values of the smoothing constants using Excel's Solver add-in, and using Microsoft Excel, develop a working forecasting system Green products Environmental management Production control Time-series analysis Planning Forecasting Supply chain management (DE-588)4017102-4 Film gnd-content |
| spellingShingle | Swart, William Green production management and supply chain management demand forecasting with exponentially weighted moving averages adjusted for trend and seasonality Green products Environmental management Production control Time-series analysis Planning Forecasting Supply chain management |
| subject_GND | (DE-588)4017102-4 |
| title | Green production management and supply chain management demand forecasting with exponentially weighted moving averages adjusted for trend and seasonality |
| title_auth | Green production management and supply chain management demand forecasting with exponentially weighted moving averages adjusted for trend and seasonality |
| title_exact_search | Green production management and supply chain management demand forecasting with exponentially weighted moving averages adjusted for trend and seasonality |
| title_exact_search_txtP | Green production management and supply chain management demand forecasting with exponentially weighted moving averages adjusted for trend and seasonality |
| title_full | Green production management and supply chain management demand forecasting with exponentially weighted moving averages adjusted for trend and seasonality William Swart |
| title_fullStr | Green production management and supply chain management demand forecasting with exponentially weighted moving averages adjusted for trend and seasonality William Swart |
| title_full_unstemmed | Green production management and supply chain management demand forecasting with exponentially weighted moving averages adjusted for trend and seasonality William Swart |
| title_short | Green production management and supply chain management |
| title_sort | green production management and supply chain management demand forecasting with exponentially weighted moving averages adjusted for trend and seasonality |
| title_sub | demand forecasting with exponentially weighted moving averages adjusted for trend and seasonality |
| topic | Green products Environmental management Production control Time-series analysis Planning Forecasting Supply chain management |
| topic_facet | Green products Environmental management Production control Time-series analysis Planning Forecasting Supply chain management Film |
| work_keys_str_mv | AT swartwilliam greenproductionmanagementandsupplychainmanagementdemandforecastingwithexponentiallyweightedmovingaveragesadjustedfortrendandseasonality |