IBM Decision Optimization

Solve planning and scheduling problems using optimization modeling tools and powerful CPLEX Optimizer and CP Optimizer solvers to make better business decisions

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9.0/10 (Expert Score) ★★★★★
Product is rated as #28 in category Data Science and Machine Learning Platforms
Ease of use
8.9
Support
9.1
Ease of Setup
9.2

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IBM Decision Optimization is a family of prescriptive analytics products that combines mathematical and AI techniques to help with business decision-making including operational, tactical and strategic planning and scheduling use cases. The solutions enable business decision-makers to choose the optimal course of action from millions of alternatives when faced with decisions that involve multiple variables, trade-off possibilities and complex constraints. The solution incorporates powerful optimization solvers namely CPLEX Optimizer and CP Optimizer to solve the breadth of optimization problems including mathematical and constraint programming and constraint-based scheduling models. Learn more about this portfolio here https://www.ibm.com/analytics/decision-optimization

IBM ILOG CPLEX Optimization Studio is one of the products within the IBM Decision Optimization portfolio. Organizations across industries are using IBM ILOG CPLEX Optimization Studio to drive operational efficiency and generate significant ROI by optimizing planning, scheduling, pricing and other business decisions. The offering provides users the flexibility to develop optimization models either using general programming language APIs like Python, Java, or using Optimization Programming Language (OPL). The powerful CPLEX optimization engines can deliver the power necessary to solve very large, real- world optimization problems at the speed required for today’s interactive decision optimization applications. Learn more about this product here – https://www.ibm.com/products/ilog-cplex-optimization-studio

IBM Decision Optimization is also included within Watson Studio Premium for Cloud Pak for Data to enable data science teams to capitalize on the power of prescriptive analytics and build innovative solutions using a combination of techniques like machine learning and optimization. Data science teams can easily demonstrate business value of optimization by leveraging tools like visual dashboards & modeling assistant to quickly build models, test/evaluate multiple scenarios, solve using powerful optimization engines and deploy the models easily .

IBM Decision Optimization solutions bring more than 30 years of experience in the field and is a proven optimization technology and organizations across industries are using IBM Decision Optimization solutions to run their mission-critical decision-making applications and have benefited by way of reduction in operating costs, increase in revenue and accelerated time to value.

IBM Decision Optimization
IBM Decision Optimization

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Customer Reviews

IBM Decision Optimization Reviews

Administrator in Building Materials

Advanced user of IBM Decision Optimization
★★★★★
Brillant design architecture

What do you like best?

We performed detailed and extensive PoC studies with different companies during our vendor selection process. Cplex was the only solver to be able to solve our detailed scheduling problem and IBM was much more professional. In addition to Cplex's power, we also very much appreciated the support and expertise of IBM l through the implementation of our project.

What do you dislike?

More help on analyzing infeasible problems and tuning would be nice.

Recommendations to others considering the product:

I strongly suggest others to test & analyze solvers as well as vendors thru PoCs even though that process takes considerable amount of time. And never forget about the future technical support.

What problems are you solving with the product? What benefits have you realized?

We solve different supply chain planning and scheduling problems including s&op and some specific blending problems. We are able to run many what-if scenarios automatically and ensure that we get a much better solution than manual rule-based approaches.

Review source: G2.com

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