Main Purpose
IBM Decision Optimization provides a suite of tools and solutions to help businesses optimize complex decision-making processes. It combines techniques like mathematical programming and constraint programming to help organizations make data-driven decisions and improve operational efficiency.
Key Features
- Optimization Engines: IBM Decision Optimization utilizes industry-leading solution engines for mathematical programming and constraint programming.
- Modeling Assistant: The solution includes a modeling assistant that allows users to define goals and constraints for their optimization models using natural language interactions, without the need for coding.
- Integration with IBM Watson Studio: Decision Optimization can be integrated with IBM Watson Studio, enabling data science teams to leverage the power of prescriptive analytics and combine techniques like machine learning and optimization.
- Scalability: The solution is designed to handle large, real-world optimization problems with the speed required for interactive decision optimization applications.
- Deployment and Visualization: Decision Optimization allows users to easily operationalize their projects by deploying optimization models into production. It also provides powerful visualization features to test multiple scenarios and share graphical dashboards with business analysts.
Use Case
- Supply Chain Optimization: Decision Optimization can be used to optimize supply chain operations, including inventory management, production planning, and logistics optimization.
- Resource Allocation: It can help organizations optimize resource allocation, such as workforce scheduling, equipment utilization, and asset management.
- Financial Planning: Decision Optimization can assist in financial planning and portfolio optimization, helping organizations make informed investment decisions.
- Transportation and Logistics: It can optimize transportation and logistics operations, including route planning, vehicle scheduling, and load optimization.
