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6 results for “Demand forecast”
Evaluating demand forecasting models using multi-criteria decision-making approach
<p>The datasets added include the raw data, ANP weights calculations and TOPSIS ranking calculations for the demonstration case in the article titled: Evaluating demand forecasting models using multi-criteria decision-making approach.</p> <p>The files include a data explanation text file.</p>
MACHINE LEARNING APPROACHES FOR DEMAND FORECASTING: THE IMPACT OF CUSTOMER SATISFACTION ON PREDICTION ACCURACY
<p><span>This study investigates the effectiveness of various machine learning models in predicting product demand based on customer satisfaction data. Four models—Linear Regression, Random Forest, Gradient Boosting, and Support Vector Machine (SVM)—were evaluated using performance metrics, including Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R² score. The results indicate that Gradient Boosting achieved the highest accuracy, with an MAE of 2.56, MSE of 12.75, RMSE of 3.57, and R² score of 0.82, effectively capturing the complex, non-linear relationships inherent in customer satisfaction factors. Random Forest also demonstrated strong performance, while Linear Regression and SVM showed limitations in handling intricate datasets. These findings underscore the importance of utilizing advanced machine learning techniques for accurate demand forecasting, highlighting the critical role of customer satisfaction data in enhancing predictive capabilities. The insights gained from this research can guide organizations in optimizing inventory management and improving customer satisfaction in a rapidly evolving market.</span></p>
Railbelt 2050 Load, Electrification, and Behind-the-Meter Solar Hourly Load Demand for Aggressive and Moderate Electrification Forecasts
<p>The data in this file is comprised of hourly load demand data for the year of 2050 for Alaska's Railbelt transmission system from the aggressive and moderate load, electrification adoption, and behind-the-meter solar forcasts generated by the ACEP Railbelt Decarbonization Study. </p>
Dataset Used For Research On: Exploring Geographically Weighted Regression In Water Demand Forecasting For A Rapidly Developing City
<div> <p><span><span>Dataset Used For Research On: Exploring Geographically Weighted Regression In Water Demand Forecasting For A Rapidly Developing City</span></span></p> </div> <p> </p>
Service Level Anchoring in Demand Forecasting: The Moderating Impact of Retail Promotions and Product Perishability
<p>This dataset is used for the working paper "Service Level Anchoring in Demand Forecasting: The Moderating Impact of Retail Promotions and Product Perishability," authored by Fahimnia, Tan, and Tahirov. The data was collected during a laboratory experiment designed based on data from a real case in the fast-moving consumer goods (FMCG) industry. Each subject was assigned to one of the following treatment groups:</p> <ul> <li>T1 - forecasts were made for a nonperishable product (shelf life of 9 months), with no service level information.</li> <li>T2 - forecasts were made for a perishable product (shelf life 1 day), with no service level information.</li> <li>T3 - forecasts were made for a nonperishable product, with a high service level information.</li> <li>T4 - the forecasts were still for a nonperishable product, with a lower service level information.</li> <li>T5 - forecasts were made for a perishable product, with high service level information.</li> <li>T6 - forecasts were made for a perishable product, with low service level information.</li> </ul> <p>A total of 368 subjects prepared four forecasts each. For each forecast, a subject was provided with 30 weeks of sales data, including both normal and promotional weeks. The promotional weeks were highlighted as "Promo." The subjects were asked to provide their forecasts for week 31, basing their forecasts solely on historical data and potential sales promotions. Mean absolute percentage error (MAPE) was used to assess the accuracy of the forecasts. Percentage forecast bias was used to measure the deviation of adjusted forecasts from the normative benchmark forecast.</p> <p>The dataset includes four Excel files, two code script files, and a README file:<br><strong>1. Excel files 1 and 2:</strong><br><strong> "database_analysis.xlsx"</strong>: Contains average adjusted forecasts for each subject during both promotional and non-promotional periods, along with demographic information, calculated MAPE, forecast bias, service level, and product perishability. This file is used as input data in the "data_cleaning.R" script.<br><strong>"database_plot.xlsx"</strong>: This Excel file contains a compact and cleaned version of the data from the first file, excluding outliers, and was used to create visuals such as boxplots.<br><strong>2. Excel Files 3 and 4:</strong><br><strong>"Pool_1_Perishable.xlsx" </strong>and "<strong>Pool_2_Non_Perishable.xlsx"</strong>: Contain real datasets for perishable and non-perishable products used in the experiment.<br><strong>3. Code Script File:</strong><br><strong>"data_cleaning.R"</strong>: This script performs data pre-processing by cleaning and transforming the dataset.<br><strong>"analysis.R"</strong>: This script loads the cleaned data and performs statistical analysis, including ANOVA and hypothesis testing. </p>
Service Level Anchoring in Demand Forecasting: The Moderating Impact of Retail Promotions and Product Perishability
<p>This dataset is used for the working paper 'Service Level Anchoring in Demand Forecasting: The Moderating Impact of Retail Promotions and Product Perishability,' authored by Fahimnia, Tan, and Tahirov. The data was collected during a laboratory experiment designed based on data from a real case in the fast-moving consumer goods (FMCG) industry. Each subject was assigned to one of the following treatment groups:</p> <p> </p> <ul> <li>T1 (control group) - forecasts were made for a <strong>nonperishable</strong> product (shelf life of 9 months), with <strong>no service level</strong> information.</li> <li>T2 - forecasts were made for a <strong>nonperishable</strong> product, with a <strong>high service level </strong>information.</li> <li>T3 - the forecasts were still for a n<strong>onperishable</strong> product, with a<strong> lower service level </strong>information.</li> <li>T4 - forecasts were made for a <strong>perishable</strong> product, with <strong>high service level</strong> information.</li> <li>T5 - forecasts were made for a <strong>perishable</strong> product, with <strong>low service level</strong> information.</li> </ul> <p> </p> <p>A total of 313 subjects prepared four forecasts each. For each forecast, a subject was provided with 30 weeks of sales data, including both normal and promotional weeks. The promotional weeks were highlighted as 'Promo.' The subjects were asked to provide their forecasts for week 31, basing their forecasts solely on historical data and potential sales promotions. Mean absolute percentage error (MAPE) was used to assess the accuracy of the forecasts. Percentage forecast bias was used to measure the deviation of adjusted forecasts from the normative benchmark forecast.</p> <p>The new version of dataset includes three Excel files:</p> <ul> <li>Excel file 1 (“DataSet.xlsx”) – This file contains the average adjusted forecast for each subject during both the promotional and non-promotional periods, along with other data such as demographic information, calculated MAPE, forecast bias, service level, and product perishability.</li> <li>Excel file 2 and 3 (“Pool_1_Perishable” and “Pool_2_Non perishable”) - These files contain all the real datasets for perishable and non-perishable products used during the experiment.</li> </ul>
ScienceDex guides
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