Luận Văn AN ENGINEERING APPROACH TO LOGISTICS AND PRODUCT MARKET FLOW USING MODIFIED PROGRESSIVE EVENT EXPONE

An engineering approach to logistics and forecasting of product market flow using modified progressive event exponential smoothing

This study aims at developing a forecasting model, which is based on both stochastic and judgmental forecasting techniques, of corporate sales performance. The basis for this study stems from the need for the following criteria for an integrated judgmental and statistical model: (1) The use of real rather than artificial data when developing and evaluating models; (2) The need for reliable adjustment for unusual future anticipated events; (3) The need for formulating and filtering experts inputs through justification and critical review of their judgmental estimates; (4) The need to use structured judgment as inputs to models; (5) The need to use evidence on each method's accuracy to allow accurate adjustments of the weights on the component forecasts. In view of the above criteria, this study primarily aims at developing a realistic judgmental and statistical model that can reveal the true obstacles associated with structuring judgmental inputs and assist in overcoming their impacts. The true challenge facing this goal lies in four aspects: (1) The process of transforming judgmental information into data; (2) Filtering the data down to the relevant and critical forecasting-associated data; (3) Structuring useful judgmental data into a model-ready format; (4) Integrating judgmental and past actual data to formulate a reliable forecasting model. In this study, new concepts are introduced in the process of structuring a reliable database for judgmental forecasting. These include: Budget Analysis (BA) and Product Style Cluster Analysis (PSCA). BA is a comprehensive structured judgmental forecasting system that is capable of transforming contextual information into future sales estimates in quantitative form. The purpose of the PSCA component is to track changes in product market flow, perform correspondence analysis between product substitutes, focus on enhancing current markets, and seek new markets that have high sales potential. Another important aspect of product style analysis is improving the judgmental forecasting capability of sales agents when presented with sales time series that are in both tabular and graphical displays. Finally, we propose an integrated model, called Modified Progressive Event Exponential Smoothing (MPEES), which modifies the exponential smoothing technique (ES) by adjusting the level component of its forecasts using the outcome of Budget Analysis (BU) after being adjusted for bias (AdBU) using the Budget Efficiency Coefficients (BE). (Abstract shortened by UMI.)
[TABLE="class: citation"]
[TR]
[TH]Format:[/TH]
[TD]Dissertation[/TD]
[/TR]
[TR]
[TH]Author(s):[/TH]
[TD]AbdElRehim, Ahmed Samy[/TD]
[/TR]
[TR]
[TH]Published:[/TH]
[TD]2004[/TD]
[/TR]
[TR]
[TH]Language:[/TH]
[TD]English

[/TD]
[/TR]
[/TABLE]
TABLE OF CONTENTS
1. INTRODUCTION . 1
2. REVIEW OF LITERATURE 11
2.1. BASIC CONCEPTS OF FORECASTING . 12
2.2. EXPONENTIAL SMOOTHING . 21
2.2.]. Applications of damped-trend seasonal exponential smoothing . 28
2.3. FORECASTING ACCURACY . 32
2.3.]. Mean Square Error (MSE) 32
2.3.2. Percentage Error (PE) and Mean Absolute Percentage Error (MAPE) 33
2.4. ADAPTIVE EXPONENTIAL SMOOTHING 34
2.5. J UDGMENTAL TIME-SERIES FORECASTING 37
2.5.1. Judgmental versus Quantitative forecasting methods . 37
2.5.1.1 Series/task characteristics 39
2.5.1.2. Judge/environmental characteristics 46
2.5.2. Integrating judgmental and quantitative forecasting 49
2.5.2.1. Model building . 50
2.5.2.2. Combination of objective and subjective forecasts 53
2.5.2.3. J udgmental adjustment of objective forecasts 56
2.5.2.4. Judgmental decomposition . 60
2.6. CLOSING REMARKS . 64
3. DEVELOPING AN APPLIED INTEGRATED STATISTICAL AND J UDGMENTAL
FORECASTING MODEL 69
3.1. INTRODUCTION 69
3.2. J UDGMENTAL UNANTICIPATED PROGRESSIVE EVENTS 71
3.3. BUDGET ANALYSIS 75
3 .4. INTEGRATING STATISTICAL AND JUDGMENTAL FORECAST 84
3 .5. HYPOTHETICAL APPLICATIONS OF THE INTEGRATED MODEL 87
3.5.]. First Scenario: (Significant Bias) . 88
3.5.2. Second Scenario: (Minimal Bias) . 92
3.5.3. Discussion of Results 95
4. APPLICATION DEVELOPMENT 99
4.1. DATABASE STRUCTURE . 100
4.2. BUDGET ANALYSIS 105
4.3. PRODUCT STYLE CLUSTER ANALYSIS . 111
4.4. FORECASTING ANALYSIS . 1 15
4.4.1. Data Specification . 115
4.4.2. Data Procurement . 117
4.4.3. Data Preparation 118
4.5. APPLICATION RESULTS USING REAL DATA . 120
4.6. DISCUSSION AND ANALYSIS 137
5. CONCLUSION AND FUTURE WORK . 141
REFERENCES 147