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Jakey Blue - One of the best experts on this subject based on the ideXlab platform.

  • Performance Analysis of Demand Planning Approaches for Aggregating, Forecasting and Disaggregating Interrelated Demands
    International Journal of Production Economics, 2010
    Co-Authors: Argon Chen, Jakey Blue
    Abstract:

    A synchronized and responsive flow of materials, information, funds, processes and services is the goal of supply chain planning. Demand planning, which is the very first step of supply chain planning, determines the effectiveness of manufacturing and logistic operations in the chain. Propagation and magnification of the uncertainty of demand signals through the supply chain, referred to as the bullwhip effect, is the major cause of ineffective operation plans. Therefore, a flexible and robust supply chain Forecasting system is necessary for industrial planners to quickly respond to the volatile demand. Appropriate demand aggregation and Statistical Forecasting approaches are known to be effective in managing the demand variability. This paper uses the bivariate VAR(1) time series model as a study vehicle to investigate the effects of aggregating, Forecasting and disaggregating two interrelated demands. Through theoretical development and systematic analysis, guidelines are provided to select proper demand planning approaches. A very important finding of this research is that disaggregation of a forecasted aggregated demand should be employed when the aggregated demand is very predictable through its positive autocorrelation. Moreover, the large positive correlation between demands can enhance the predictability and thus result in more accurate forecasts when Statistical Forecasting methods are used.

  • Demand planning approaches to aggregating and Forecasting interrelated demands for safety stock and backup capacity planning
    International Journal of Production Research, 2007
    Co-Authors: Argon Chen, Jakey Blue
    Abstract:

    Results of demand planning serve as the basis of every planning activity in a demand–supply network and ultimately determine the effectiveness of manufacturing and logistic planning, such as capacity and safety stock planning, in the network. The uncertainty of demand signals that are propagated and magnified over the network becomes the crucial cause of ineffective operation plans. With the globalization of demand–supply networks and the desire for a more integrated operation plan, demand planning is now one of the greatest challenges facing manufacturers. To manage the demand variability, appropriate demand aggregation and Statistical Forecasting approaches are known to be effective. This paper will use the bivariate VAR(1) time-series model as a study vehicle to investigate the effects of aggregating two interrelated demands. It is shown that the aggregated time series of two VAR(1) times series is equivalent to the sum of two AR(1) time series. Through theoretical development, the paper further explor...

  • Demand Planning Approaches to Aggregating and Forecasting Interrelated Demands for Safety Stock and Backup Capacity Planning
    International Journal of Production Research, 2007
    Co-Authors: Argon Chen, Jakey Blue
    Abstract:

    Results of demand planning serve as the basis of every planning activity in a demand-supply network and ultimately determine the effectiveness of manufacturing and logistic planning, such as capacity and safety stock planning, in the network. The uncertainty of demand signals, that are propagated and magnified over the network, becomes the crucial cause of ineffective operation plans. With the globalization of demand-supply networks and the desire for a more integrated operation plan, demand planning is now one of greatest challenges facing manufacturers. To manage the demand variability, appropriate demand aggregation and Statistical Forecasting approaches are known to be effective. This paper will use the bivariate VAR(1) time series model as a study vehicle to investigate the effects of aggregating two interrelated demands. We show that the aggregated time series of two VAR(1) times series is equivalent to the sum of two AR(1) time series. Through theoretical development, we further explore the properties of the aggregated time series and provide guidelines for practitioners to determine proper aggregation and Forecasting approaches. A very important finding of our research is that demand aggregation is far more effective than Statistical Forecasting in operations planning for any two demands with low positive correlation or negative correlation.

Argon Chen - One of the best experts on this subject based on the ideXlab platform.

  • Performance Analysis of Demand Planning Approaches for Aggregating, Forecasting and Disaggregating Interrelated Demands
    International Journal of Production Economics, 2010
    Co-Authors: Argon Chen, Jakey Blue
    Abstract:

    A synchronized and responsive flow of materials, information, funds, processes and services is the goal of supply chain planning. Demand planning, which is the very first step of supply chain planning, determines the effectiveness of manufacturing and logistic operations in the chain. Propagation and magnification of the uncertainty of demand signals through the supply chain, referred to as the bullwhip effect, is the major cause of ineffective operation plans. Therefore, a flexible and robust supply chain Forecasting system is necessary for industrial planners to quickly respond to the volatile demand. Appropriate demand aggregation and Statistical Forecasting approaches are known to be effective in managing the demand variability. This paper uses the bivariate VAR(1) time series model as a study vehicle to investigate the effects of aggregating, Forecasting and disaggregating two interrelated demands. Through theoretical development and systematic analysis, guidelines are provided to select proper demand planning approaches. A very important finding of this research is that disaggregation of a forecasted aggregated demand should be employed when the aggregated demand is very predictable through its positive autocorrelation. Moreover, the large positive correlation between demands can enhance the predictability and thus result in more accurate forecasts when Statistical Forecasting methods are used.

  • Demand planning approaches to aggregating and Forecasting interrelated demands for safety stock and backup capacity planning
    International Journal of Production Research, 2007
    Co-Authors: Argon Chen, Jakey Blue
    Abstract:

    Results of demand planning serve as the basis of every planning activity in a demand–supply network and ultimately determine the effectiveness of manufacturing and logistic planning, such as capacity and safety stock planning, in the network. The uncertainty of demand signals that are propagated and magnified over the network becomes the crucial cause of ineffective operation plans. With the globalization of demand–supply networks and the desire for a more integrated operation plan, demand planning is now one of the greatest challenges facing manufacturers. To manage the demand variability, appropriate demand aggregation and Statistical Forecasting approaches are known to be effective. This paper will use the bivariate VAR(1) time-series model as a study vehicle to investigate the effects of aggregating two interrelated demands. It is shown that the aggregated time series of two VAR(1) times series is equivalent to the sum of two AR(1) time series. Through theoretical development, the paper further explor...

  • Demand Planning Approaches to Aggregating and Forecasting Interrelated Demands for Safety Stock and Backup Capacity Planning
    International Journal of Production Research, 2007
    Co-Authors: Argon Chen, Jakey Blue
    Abstract:

    Results of demand planning serve as the basis of every planning activity in a demand-supply network and ultimately determine the effectiveness of manufacturing and logistic planning, such as capacity and safety stock planning, in the network. The uncertainty of demand signals, that are propagated and magnified over the network, becomes the crucial cause of ineffective operation plans. With the globalization of demand-supply networks and the desire for a more integrated operation plan, demand planning is now one of greatest challenges facing manufacturers. To manage the demand variability, appropriate demand aggregation and Statistical Forecasting approaches are known to be effective. This paper will use the bivariate VAR(1) time series model as a study vehicle to investigate the effects of aggregating two interrelated demands. We show that the aggregated time series of two VAR(1) times series is equivalent to the sum of two AR(1) time series. Through theoretical development, we further explore the properties of the aggregated time series and provide guidelines for practitioners to determine proper aggregation and Forecasting approaches. A very important finding of our research is that demand aggregation is far more effective than Statistical Forecasting in operations planning for any two demands with low positive correlation or negative correlation.

Tatiyana V Apanasovich - One of the best experts on this subject based on the ideXlab platform.

  • Statistical Forecasting of electric power restoration times in hurricanes and ice storms
    IEEE Transactions on Power Systems, 2007
    Co-Authors: Haibin Liu, Rachel A Davidson, Tatiyana V Apanasovich
    Abstract:

    This paper introduces a new method for estimating the time at which electric power will be restored after a major storm. The method was applied for hurricanes and ice storms for three major electric power companies on the East Coast. Using an unusually large dataset that includes the companies' experiences in six hurricanes and eight ice storms, accelerated failure time models were fitted and used to predict the duration of each probable outage in a storm. By aggregating those estimated outage durations and accounting for variable outage start times, restoration curves were then estimated for each county in the companies' service areas. The method can be applied as a storm approaches, before damage assessments are available from the field, thus helping to better inform customers and the public of expected post-storm power restoration times. Results of model applications using testing data suggest they have promising predictive ability.

Paul P Wang - One of the best experts on this subject based on the ideXlab platform.

  • intelligent system to support judgmental business Forecasting the case of estimating hotel room demand
    IEEE Transactions on Fuzzy Systems, 2000
    Co-Authors: Ben M Ghalia, Paul P Wang
    Abstract:

    Forecasting is an instrumental tool for strategic decision-making in any business activity. Good forecasts can reduce the uncertainty about the future and, hence, help managers make better decisions. Virtually all Statistical Forecasting techniques depend on the continuity of historical data and time series and may not predict a discontinuous change in the business environment. Often times, this discontinuity is known to managers who then must rely on their judgment to make forecast adjustments. We discuss the role of judgmental Forecasting and take the problem of estimating future hotel room demand as a practical business application. Next, we propose IS-JFK: an intelligent system to support judgmental Forecasting and knowledge of managers. To account for vagueness in the knowledge elicited from managers and the approximate nature of their reasoning, the system is built around fuzzy IF-THEN rules and uses fuzzy logic for decision inference. IS-JFK supports two methods for forecast adjustments: 1) a direct approach and 2) an approach based on fuzzy intervention analysis. Actual data from a hotel property are used in some case-scenario simulations to illustrate the merits of the intelligent support system.

Robert C Tournay - One of the best experts on this subject based on the ideXlab platform.

  • Long-Range Statistical Forecasting of Korean Summer Precipitation
    2008
    Co-Authors: Robert C Tournay
    Abstract:

    Abstract : We examined long-range Statistical Forecasting methods for Korean summer precipitation (KSP). We reviewed existing literature on the East Asian summer monsoon to develop a background on current KSP research and on the relationship of KSP to climate variations. Second, we explored interannual variability of KSP using composite and correlation analyses. We found that circulation anomalies in the spring prior to the monsoon in the tropical northwest Pacific alter sea surface temperatures (SST). These SST anomalies then persist into the following summer, leading to summer circulation anomalies that alter the flow into Korea and the precipitation on the seasonal scale. From this relationship, we developed a seasonal Forecasting index. Third, we looked at KSP on the intraseasonal scale, to develop Statistical forecast methods with five to thirty day leadtimes. We found that the Korean summer monsoon onset, break and withdrawal are positively correlated to the El Ni o / La Ni a state. We found that the Madden-Julian Oscillation (MJO), when conditioned with our seasonal index, showed skill in Forecasting with lead times out to 20 days. Last, we found that tropical cyclone activity in Korea is impacted by ENSO on the interannual scale, and MJO on the intraseasonal scale.