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  • High Tech & Electronics Industry: Sustainability Industry Report - SCM | Supply Chain Resource Cooperative (SCRC) | North Carolina State University
    several developing countries as well as due the growth in cloud computing This increase in overall demand is certainly adding more risk to the sus tainability of this supply chain With no uniform environmental and labor laws governing this supply chain it is very critical for companies in a leadership position to take proactive measures to monitor their suppliers and ensure sustainability of their supply chains in the long run There are also new trends such as cloud computing that are changing the landscape of the computer industry Companies in the computer industry are setting up huge data centers across the world to support their cloud computing needs and this is putting increased focus on the energy consumption at these data centers Several companies have taken a leadership position in using renewable energy sources such as solar and wind to power their data centers as part of their sustainability initiatives We also came across several new initiatives that will be reshaping supply chains in the future One such initiative is the small business supplier connection portal that is being supported by several leading companies in the high tech and electronics sectors By using such collaborative platforms the efficiency of the supply chain can be improved across the board These kinds of initiatives will become critical to maintaining sustainable supply chains The hyper competition in the high tech and electronics industry sector coupled with a global supply chain and shortening product lifecycles is putting increasing pressure on many companies to maximize profits at all costs This can lead to some companies favoring profits over sustainability as described in the tragedy of the commons Hence to level the playing field for all and to provide com panies with added incentive to focus on sustainability initiatives we would recommend a minimum set of

    Original URL path: https://scm.ncsu.edu/scm-whitepapers/wp/high-tech-electronics-industry-sustainability-industry-report (2016-04-30)
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  • Global Chemical Industry Analysis: Sustainability Industry Report - SCM | Supply Chain Resource Cooperative (SCRC) | North Carolina State University
    over the next five years and thereafter While many of these companies have extensive sustain ability plans they are still lagging behind other industries The global chemical industry consists of a very diverse and complicated range of products In terms of revenue it is one of the world s largest markets The majority of consumer products that are used every day have at least some tie to the products of the chemical industry There are a few large players with a lot of smaller specialized companies Across the chemical industry expected worth is estimated to be around 3 trillion per year with relatively flat revenue growth in the EU and US The compound annual growth rate CAGR over the five years spanning 2006 2010 has been 5 At the low end Europe only grew at about 1 in 2010 with Asia representing the region with the largest CAGR at 10 in 2010 From the years 2010 2015 the chemicals market is expected to gain momentum and is forecasted to grow at a 8 compound annual growth rate The chemical market is recognized as being divided into five distinct segments base con sumer pharmaceutical specialty and fine and agriculture chemicals The base chemicals segment consists of the largest of the five with 41 of the total value A far second pharmaceuticals comprise of about 28 The remaining 31 is split between the remaining three segments The segmentation of the geography of the chemical industry overwhelmingly resides in the Asia Pacific region In 2010 the Asia Pacific accounted for 46 of the industry The remaining is split mostly between the Americas and Europe with 28 and 25 respectively Very little is found in the Middle East The majority of the raw materials or ingredients in the chemical industry are derived from oil

    Original URL path: https://scm.ncsu.edu/scm-whitepapers/wp/global-chemical-industry-analysis (2016-04-30)
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  • SCM White Papers - SCM | Supply Chain Resource Cooperative (SCRC) | North Carolina State University
    Ph D Clyde M Crider MBA Donavon Favre MA Tracy Freeman MBA Robert Handfield Ph D Jeffrey Stonebraker Ph D Don Warsing Ph D SCM Professionals SCM Research Resources SCM Pro Resources SCM Articles SCM White Papers SCM SCRC Director s Blog SCM Tutorials SCM Video Insights Library SCM Insights Polls SCM Topics SCM Research SCRC White Paper Library Latest White Papers Practice Summaries American Airlines Uses Should Cost Modeling to Assess the Uncertainty of Bids for Its Full Truckload Shipment Routes By Jeffrey Stonebraker Ph D We used decision analysis to develop a probabilistic model to help American Airlines assess the uncertainty of bid quotes for its full truckload FTL point to point freight shipments of maintenance equipment and in flight service items in the United States The model reduced the airline s risk of overpaying an FTL supplier Authors Poole College of Management Michael J Bailey John Snapp Subramani Yetur Jeffrey S Stonebraker Steven A Edwards American Airlines Aaron Davis Robert Cox Categories SCM Resources Professional Resources SCM Articles SCM Resources SCM Terms Supply Chain Management Basics SCM Basics Tariffs and Tax Primer NAICS Navigator SCM Blog Business Process Outsourcing Forecasting Healthcare Supply Management Supply Chain Analytics SCM

    Original URL path: https://scm.ncsu.edu/scm-whitepapers/category/scm-resources (2016-04-30)
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  • Qualitative Methods :Measuring Forecast Accuracy : A Tutorial - SCM | Supply Chain Resource Cooperative (SCRC) | North Carolina State University
    Forecast Accuracy A Tutorial Published on Jan 25 2011 by Cecil Bozarth PhD Qualitative Methods Common Qualitative Forecasting Methods EXAMPLE Life Cycle analogy Analyzing the Life Cycle Data for the Previous Version Questions to Consider When Using the Life Cycle Analogy to Forecast for a New Product Common Qualitative Forecasting Methods Executive and outsider opinions Sales force composite This involves having product managers or sales reps developing individual forecasts and then adding them up Panel consensus Delphi method Both methods have experts work together to develop forecasts The Delphi method has experts develop forecasts individually then share their findings The process is repeated until a consensus emerges Life cycle analogy Used when the product is new The technique is based on the fact that many products have well defined life cycle stages Growth Maturity and Decline EXAMPLE Life Cycle Analogy A consumer products company is coming out with a new version of smoking cessation gum Sales history for the previous version is shown below Analyzing the Life Cycle Data for the Previous Version Due to competitive pressures and innovations the product has a definite life cycle Questions to Consider When Using the Life Cycle Analogy to Forecast for a New Product How long will each life cycle stage last What are we basing this on opinion survey etc In general will demand levels be higher or lower What are we basing this on opinion survey etc Key point Using life cycle data from a similar product provides a starting point and helps us focus on the right questions Categories SCM Tutorials Forecasting Read the Supply Chain Management Professional Newsletter Read the latest supply chain research articles and news as soon as we post them Privacy Policy Professional Resources SCM Articles SCM Resources SCM Terms Supply Chain Management Basics SCM Basics

    Original URL path: https://scm.ncsu.edu/scm-articles/article/qualitative-methods-measuring-forecast-accuracy-a-tutorial (2016-04-30)
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  • Multiple Regression: Approaches to Forecasting : A Tutorial - SCM | Supply Chain Resource Cooperative (SCRC) | North Carolina State University
    Insights Library SCM Insights Polls SCM Topics SCM Research SCRC Article Library Multiple Regression Approaches to Forecasting A Tutorial Multiple Regression Approaches to Forecasting A Tutorial Published on Jan 25 2011 by Cecil Bozarth PhD Multiple Regression Advanced techniques can be used when there is trend or seasonality or when other factors such as price discounts must be considered What is Multiple Regression Resulting Forecast Model Comparing Multiple Regression Model Results against Historic Demand What is Multiple Regression Analogous to single regression but allows us to have multiple predictor variables Y a b1 X1 b2 X2 b3 X3 Practically speaking there is a limit to the number of predictor variables you can have without violating some statistical rules In most cases 2 or 3 predictor variables should be plenty In this case we have 24 months of data In addition to an apparent upward trend we have price discount information and seasonality in the last two months of each year Let s develop a multiple regression forecast model that considers all these factors Resulting Forecast Model Demand 9117 08 275 41 Time Period 2586 31 Seasonal Bump 1 if seasonal bump is present 0 otherwise Comparing Multiple Regression Model Results against Historic Demand The multiple regression model does a decent job modeling past demand By plugging in the appropriate time period and seasonality value 0 or 1 we can use it to forecast future demands Categories SCM Tutorials Forecasting Read the Supply Chain Management Professional Newsletter Read the latest supply chain research articles and news as soon as we post them Privacy Policy Professional Resources SCM Articles SCM Resources SCM Terms Supply Chain Management Basics SCM Basics Tariffs and Tax Primer NAICS Navigator SCM Blog Business Process Outsourcing Forecasting Healthcare Supply Management Supply Chain Analytics SCM Tutorials CPFR Forecasting Inventory

    Original URL path: https://scm.ncsu.edu/scm-articles/article/multiple-regression-approaches-to-forecasting-a-tutorial (2016-04-30)
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  • Measuring Forecast Accuracy: Approaches to Forecasting : A Tutorial - SCM | Supply Chain Resource Cooperative (SCRC) | North Carolina State University
    Absolute Deviation MAD Tracking Signal Other Measures How Do We Measure Forecast Accuracy Used to measure Forecast model bias Absolute size of the forecast errors Can be used to Compare alternative forecasting models Identify forecast models that need adjustment management by exception Measures of Forecast Accuracy E rror A ctual demand F orecast OR e t A t F t Mean Forecast Error MFE For n time periods where we have actual demand and forecast values Ideal value 0 MFE 0 model tends to under forecast MFE 0 model tends to over forecast Mean Absolute Deviation MAD For n time periods where we have actual demand and forecast values While MFE is a measure of forecast model bias MAD indicates the absolute size of the errors Example Period Demand Forecast Error Absolute Error 3 11 13 5 2 5 2 5 4 9 13 4 0 4 0 5 10 10 0 0 0 6 8 9 5 1 5 1 5 7 14 9 5 0 5 0 8 12 11 1 0 1 0 n 6 observations Period Demand Forecast Error Absolute Error 3 11 13 5 2 5 2 5 4 9 13 4 0 4 0 5 10 10 0 0 0 6 8 9 5 1 5 1 5 7 14 9 5 0 5 0 8 12 11 1 0 1 0 2 14 MFE 2 6 0 33 MAD 14 6 2 33 Conclusion Model tends to slightly over forecast with an average absolute error of 2 33 units Tracking Signal Used to pinpoint forecasting models that need adjustment Rule of Thumb As long as the tracking signal is between 4 and 4 assume the model is working correctly Other Measures Categories SCM Tutorials Forecasting Read the Supply Chain Management Professional Newsletter Read

    Original URL path: https://scm.ncsu.edu/scm-articles/article/measuring-forecast-accuracy-approaches-to-forecasting-a-tutorial (2016-04-30)
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  • Single Regression: Approaches to Forecasting : A Tutorial - SCM | Supply Chain Resource Cooperative (SCRC) | North Carolina State University
    the single regression model EXAMPLE 16 Months of Demand History There is a clear upward trend but also some randomness Forecasted demand 188 55 69 43 Time Period Notice how well the regression line fits the historical data BUT we aren t interested in forecasting the past Forecasts for May 05 and June 05 May 188 55 69 43 17 1368 86 June 188 55 69 43 18 1438 29 The regression forecasts suggest an upward trend of about 69 units a month These forecasts can be used as is or as a starting point for more qualitative analysis EXAMPLE Building a Regression Model to Handle Trend and Seasonality Quarter Period Demand Winter 04 1 80 Spring 2 240 Summer 3 300 Fall 4 440 Winter 05 5 400 Spring 6 720 Summer 7 700 Fall 8 880 Regression picks up the trend but not seasonality effects Calculating seasonal index Winter Quarter Actual Forecast for Winter quarters Winter 04 80 90 0 89 Winter 05 400 524 3 0 76 Average of these two 83 Interpretation For Winter quarters actual demand has been on average 83 of the unadjusted forecast Seasonally adjusted forecast model For Winter quarter 18 57 108 57 Period 83 Or more generally 18 57 108 57 Period Seasonal Index Seasonally adjusted forecasts Comparison of adjusted regression model to historical demand Single regression and causal forecast models Time series assume that demand is a function of time This is not always true Examples Demand as a function of advertising dollars spent Demand as a function of population Demand as a function of other factors ex flu outbreak Regression analysis can be used in these situations as well We simply need to identify the x and y values EXAMPLE Causal Modeling Month Price per unit Demand 1 1

    Original URL path: https://scm.ncsu.edu/scm-articles/article/single-regression-approaches-to-forecasting-a-tutorial (2016-04-30)
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  • Double Exponential Smoothing: Approaches to Forecasting : A Tutorial - SCM | Supply Chain Resource Cooperative (SCRC) | North Carolina State University
    D SCM Professionals SCM Research Resources SCM Pro Resources SCM Articles SCM White Papers SCM SCRC Director s Blog SCM Tutorials SCM Video Insights Library SCM Insights Polls SCM Topics SCM Research SCRC Article Library Double Exponential Smoothing Approaches to Forecasting A Tutorial Double Exponential Smoothing Approaches to Forecasting A Tutorial Published on Jan 25 2011 by Cecil Bozarth PhD Double Exponential Smoothing What Is Double Exponential Smoothing Time Series with Trend Double Exponential Smoothing What Is Double Exponential Smoothing like regular exponential smoothing except includes a component to pick up trends Time Series with Trend Double Exponential Smoothing Formula Ft Unadjusted forecast before trend Tt Estimated trend AFt Trend adjusted forecast Ft a At 1 1 a Ft 1 Tt 1 Tt b At 1 Ft 1 1 b Tt 1 AFt Ft Tt To start we assume no trend and set our initial forecast to Period 1 demand We then calculate our forecast for Period 2 But Period 2 demand turns out to be 20 What is the trend estimate for Period 3 By Period 4 the model is starting to pick up on the trend And after a few periods the model locks on to the correct trend value Of course this example is simplified to make the numbers clearer Categories SCM Tutorials Forecasting Read the Supply Chain Management Professional Newsletter Read the latest supply chain research articles and news as soon as we post them Privacy Policy Professional Resources SCM Articles SCM Resources SCM Terms Supply Chain Management Basics SCM Basics Tariffs and Tax Primer NAICS Navigator SCM Blog Business Process Outsourcing Forecasting Healthcare Supply Management Supply Chain Analytics SCM Tutorials CPFR Forecasting Inventory Management Procurement SCM Features Hot Topics Lessons Learned Facts Figures SC Security SCM Topics Inventory Management Supply Chain Procurement Process Six Sigma

    Original URL path: https://scm.ncsu.edu/scm-articles/article/double-exponential-smoothing-approaches-to-forecasting-a-tutorial (2016-04-30)
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