Scaling Customer Experience using Generative AI Chatbot for Major Manufacturing Company
Business Problem
The business problem in this case study was the need for a unified platform that could handle all types of traffic and provide a comprehensive solution for managing shipment movements. The Freight Transportation company, needed a way to plan, execute, manage and communicate the movement of shipments in a more efficient and effective manner. The current system was lacking in several areas, including accurate projections, asset utilization, productivity and planning. This was causing a significant number of customer inquiries related to shipment status, which was affecting customer satisfaction and negatively impacting the company's operations.
Furthermore, Freight Transportation company was facing issues with identifying accountable failure locations within their operations. This was leading to operational inefficiencies and an inability to make data-driven decisions. Additionally, the company's marketing team was struggling to come up with effective pricing initiatives due to a lack of data and insights.
The company needed a big data solution that could provide them with a clear and accurate view of their shipment movements, as well as the ability to make data-driven decisions. They also needed a system that could provide an intuitive visualization of trip plans and consistent information flow to customer-facing tools, which would support improved customer service and satisfaction. Overall, Freight Transportation company needed a solution that would help improve the efficiency and effectiveness of their operations and provide them with the information they needed to make better business decisions.
Business Solution
The business solution for this case study was the implementation of a Freight Management system, an enterprise SaaS solution for scheduled merchandise and intermodal traffic. This system was designed to generate trip plans, adjust and communicate internally and to customers during execution, and provide tools to measure performance.
The Freight Management system is an automated shipment planning system that defines optimum routing based on shipment class, train planning and real-time execution. This allows for more accurate projections and improved asset utilization, productivity, and planning.
The system also provided point-of-failure data analysis and service measurement capabilities, which helped client identify accountable failure locations within their operations and make data-driven decisions. This helped to improve the overall efficiency and effectiveness of the company's operations.
Additionally, the system provided an intuitive visualization of trip plans and consistent information flow to customer-facing tools, which helped to reduce customer inquiries related to shipment status and improve customer service and satisfaction.
Furthermore, the system provided marketing team with the information they needed to come up with effective pricing initiatives.
In summary, The Freight Management system was a comprehensive solution that helped client improve the efficiency and effectiveness of their operations, make better business decisions, and provide better customer service. It was an automated shipment planning system that defined optimum routing based on shipment class, train planning and real-time execution and provided point-of-failure data analysis and service measurement capabilities.
Technical Solution
The technical solution for this case study involved several steps to ensure that the Freight Management system met the business requirements and provided a comprehensive solution for managing shipment movements.
The first step was gathering business requirements and creating technical design documents. This involved working with the business stakeholders to understand their needs and translate them into a technical design that would support the desired functionality.
Next, an Impact analysis document was created to assess the impact of the new system on the existing infrastructure and identify any potential issues that needed to be addressed.
The system was then developed using a Layered architecture that utilized Java, JavaEE, JSF, JMS, and Prime Faces. The user interfaces were developed using JSF, Prime Faces, JSON, HTML, and JavaScript to provide a user-friendly and intuitive experience.
The business layer was developed using Java, Java EE, JMS, and SOAP/REST web services to support the business logic and functionality of the system. The data layer was developed using EclipseLink, Oracle, and DB2 to provide a robust and scalable solution for storing and managing data.
The system was developed using a Test-Driven approach, with Junit and Mockito being used to create automated test cases. Additionally, manual testing was performed to ensure that the system met the business requirements and provided a high-quality solution.
Finally, the system was deployed using Jenkins for the automated CI/CD build process and Azure Cloud for production deployments. This provided a robust and scalable solution that could handle the demands of the business and support the growth of the company.
Technologies
The technology used in this case study is a combination of cloud computing and various programming languages and frameworks. The system was deployed on Azure Cloud, which provided a scalable and reliable platform for production deployments. The primary programming language used was JAVA, along with J2EE framework for building enterprise applications.
JAVA Server Faces (JSF) was used to build the user interface, which is based on HTML, JSON and Prime Faces. The system uses REST API and SOAP web services for communication, which are based on XML, XSD. The system also uses Spring Framework, which is a popular framework for building Java applications, and Oracle as the database management system. JMS and IBM MQ Server was used to handle messaging and queuing.
WebSphere Application Server was used as the server environment for deploying the application.
Customer Success Outcomes:
Improved shipment movement management: The Freight Management system provided a comprehensive solution for managing shipment movements, which helped the customer plan, execute, manage and communicate shipment movements in a more efficient and effective manner.
Increased accuracy and efficiency: The automated shipment planning system and real-time execution, provided more accurate projections and improved asset utilization, productivity, and planning.
Reduced customer inquiries: The intuitive visualization of trip plans and consistent information flow to customer-facing tools helped to reduce customer inquiries related to shipment status and improve customer service and satisfaction.
Improved operational efficiency: The system provided point-of-failure data analysis and service measurement capabilities, which helped the customer identify accountable failure locations within their operations and make data-driven decisions, resulting in improved operational efficiency.
Better data-driven decision-making: The system provided marketing team with the information they needed to come up with effective pricing initiatives, which helped the customer make better business decisions.
Scalable and reliable platform: The system was deployed on Azure Cloud, which provided a scalable and reliable platform for production deployments, ensuring that the system could handle the demands of the business and support the growth of the company.
Improved performance with the use of industry standard technologies: The system was built using industry standard technologies like JAVA, J2EE, JSF, HTML, JSON, REST API, Prime Faces, SOAP Web Services, XML, XSD, Spring, Oracle, JMS, WebSphere, IBM MQ Server, which ensured high performance and scalability of the system.
Overall, the customer was able to gain greater insights from their data, make better data-driven decisions, and scale their big data solution more easily, which helped them improve their operations and provide better customer service.
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