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With Gurobi, you can solve extremely complex business challenges, extremely quickly. But how does it work, exactly? This video shows you how.
With decision intelligence technology, you input the details about your complex, real-life business problem—and then the technology gets to work, exploring the trillions of ways you can achieve your goals, given your limitations.
Often in just seconds, you’ll receive your detailed action plan. And as soon as your business situation changes—or you want to explore possible what-if scenarios—just adjust your inputs and run it again.
| Input | Output |
| · Your business objectives (what you want to achieve)
· Your business constraints (the limitations you face) · Your decision variables (the questions you’re asking) |
A detailed set of decisions that optimize your business objectives, given your constraints. |
It’s how the world’s leading organizations make optimized decisions, at the speed of change.
Although there are countless ways to use decision intelligence technology, here are some real-world examples, with links to their stories:
Decision-making is growing increasingly complex—with multiple factors to consider and multiple stakeholder inputs and opinions. And this complexity puts business decision-makers in an extremely challenging situation.
For example, you likely need to achieve multiple, often-conflicting objectives, such as:
And you need to do so in the midst of:
Plus, you have many choices to make:
To top it off, shareholders increasingly expect decision-makers to explain and defend their decisions transparently—and back it all up with data.
It’s no wonder decision-making is so difficult.
You make decisions every day. And, of course, you work hard to make good decisions. But even good decisions can cost your organization millions in lost revenue and wasted time and resources.
That’s because there’s a huge difference between a good decision and an optimal decision.
When you make an optimal decision, you’ve considered every detail and every path and identified the best possible way forward—the one that will help your business achieve its goals, given its unique constraints. But that’s not something our human brains can do on their own, so it requires a special kind of technology: decision intelligence technology.
With decision intelligence technology, you input your goals and limitations and the questions you need to answer. Then the technology explores all of the trillions of ways you can achieve your goals, given your limitations. Finally, it outputs your best-possible action plan, with detailed answers to your specific questions.
By applying decision intelligence technology, businesses can make fast, confident, unbiased decisions—even in the midst of rapid change and global disruption. What’s more, they increase revenue, operational efficiency, and even customer and employee satisfaction.
Predictive analytics tools—like machine learning, statistical models, and simulations—seek to find patterns in data, in order to predict what might happen in the future. And as long as your present situation continues to look like your past, these historical insights can help inform your decisions.
But these tools stop there. They can’t tell you specifically what to do with those insights. As a result, most decision-makers are left to rely on “experience, ‘gut feeling,’ or opinions” when making decisions.
That’s why organizations are adding decision intelligence technology to their analytics toolkit.
By combining machine learning with decision intelligence technology, you can identify the optimal way to achieve your goals, given your predicted future.
For example, imagine you’re planning a trip. A machine learning model can predict what you may encounter along your journey (weather, traffic, engine trouble). But with decision intelligence technology, you can take those predictions into consideration, as well as your goals (fastest, cheapest, safest route) and constraints (time, budget, speed limits, roadside services), and identify the single best route you should take.
| Use machine learning to predict | Use decision intelligence to identify |
| A customer’s likelihood to buy more with targeted offers | Which discounts to offer, to which customers, to maximize profits |
| Supply chain issues, while there’s still time to remediate | The least costly way to reroute shipments |
| Who will launch a cyberattack, before it happens | Which investigators to assign to potential threats, based on investigator skill and potential damage |
| Experiments that are more likely to prove a hypothesis | Which experiments to pursue, based on talent, cost, and time |
| Imminent machine failure | When to shut down the production line for maintenance, so as to minimize costs and production disruptions |
| Price movements before the market does | How to allocate capital across all investment vehicles |
