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Development of predictive sales model and ensuring data quality management

  • All the companies need to plan and budget for future. For planning they need sale forecasting so that accordingly they can manage their supply chain efficiently. Companies do have historical data which can be used for forecasting sale. However, the accuracy of the predictive model depends on the quality of data which is being fed to the model. Poor data quality may result in poor forecasting. Hence, there is need to work on data quality management and to formulate some generic approach for ensuring data quality. Besides, it is also required to detect abnormal sale from the past data, get the reason for those abnormal sale records and remove them from the data. Subsequently, cleaned data can be used to work on predictive modelling which will forecast sales with the most likelihood of near to accurate results. These historical data can be analyzed as a time series data by using as simple time series analysis as ARIMA or by using complicated neural network. Evaluation of these predictive models will help in making a decision of selecting a best fitted model for future forecasting. The thesis aims to work on data quality management of raw data and then analyze time series data to determine predictive model for forecasting. Besides, thesis also aims to understand how data is collected and how organization performs sales processes. This would not only facilitate in finding and bridging the gaps in the business processes but also in preparing the organization for the state-of-the-art technologies to enhance their business for future.

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Author:Richa Mishra
Advisor:Uwe Hack
Document Type:Master's Thesis
Year of Completion:2020
Granting Institution:Hochschule Furtwangen
Date of final exam:2020/02/29
Release Date:2020/03/09
Tag:ARIMA; Data quality management; Forecasting; Process re-engineering; Time series analysis
Degree Program:MBA - International Business Management
Functional area:Production, Operations and Supply Chain Management
Licence (German):License LogoUrheberrechtlich gesch├╝tzt