An Optimized System for Automating Customer Orders and Follow-Ups at ABB
Samarajeewa, Sachintha (2025)
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-2025060621135
https://urn.fi/URN:NBN:fi:amk-2025060621135
Tiivistelmä
This thesis discusses the development of an optimized system for automating customer orders and follow-ups at ABB. The key objectives are to design an automated system for tracking sales orders and prioritizing follow-ups, store the data in a safe and common database, and increase decision-making using AI. The designed system provides big data sorting and processing capabilities, certifying compatibility with Excel-based functions and AI integration models.
The new system uses Microsoft Excel for the first stage data processing and formatting the final output, and SQL Server for building organized databases to store and process data. Moreover, an AI framework such as TensorFlow is useful to predict outcomes based on previous sales data, thereby increasing decision-making purposes.
By combining these technologies, the system aims to optimize the operations, enhance efficiency, and give meaningful results for business planning. The automated tracking system of sales orders and highlighting follow-ups confirms timely customer feedback and sales optimization. Secure database management secures sensitive organizational information, while AI-driven predictive decisions offer a cutting edge in business forecasting and strategic decision-making.
This study contributes to the field of business automation and database management by providing the practical application of data sorting, database management, and AI applications in a business environment. The results illustrate the capability of AI integrations in a cooperative environment and improve growth.
The new system uses Microsoft Excel for the first stage data processing and formatting the final output, and SQL Server for building organized databases to store and process data. Moreover, an AI framework such as TensorFlow is useful to predict outcomes based on previous sales data, thereby increasing decision-making purposes.
By combining these technologies, the system aims to optimize the operations, enhance efficiency, and give meaningful results for business planning. The automated tracking system of sales orders and highlighting follow-ups confirms timely customer feedback and sales optimization. Secure database management secures sensitive organizational information, while AI-driven predictive decisions offer a cutting edge in business forecasting and strategic decision-making.
This study contributes to the field of business automation and database management by providing the practical application of data sorting, database management, and AI applications in a business environment. The results illustrate the capability of AI integrations in a cooperative environment and improve growth.