Prescriptive analytics supports proactive optimization of what is best in the future, based on a variety of scenarios. The problems businesses face are often quite complex and potentially can be addressed by taking multiple courses of action. Predictive analytics helps model future events, while prescriptive analysis aims to show users how different actions will affect business performance and point them toward the optimal choice. As data-driven organizations continue to recognize that information can provide strategic competitive advantages, more will strive toward the prescriptive end of the analytics spectrum.
- Formulate optimizations of business outcomes by combining historical data, business rules, mathematical models, variables, constraints and machine-learning algorithms
- Use prescriptive analytics in scenarios where there are too many options, variables, constraints and data points for the human mind to efficiently evaluate without assistance from technology
- Use prescriptive analytics when experimenting in the real world would be prohibitively expensive or overly risky, or take too much time
- Run sophisticated analytical models and Monte Carlo simulations with known and randomized variables to recommend next steps, display if/then scenarios and gain a better understanding of the range of possible outcomes
- Apply prescriptive analytics to include pricing, inventory management, operational resource allocation, production planning, supply chain optimization, transportation and distribution planning, utility management, sales lead assignment, marketing mix optimization, and financial planning
- Use SAS, IBM, SAP, Tibco Software, MathWorks, Ayata, River Logic and KXEN (which is being acquired by SAP).
Our customers have been able to:
- Improve operations – eliminate inefficiencies and capture more value
- Manage resources more effectively – better utilize: capital, personnel, equipment, vehicles, facilities
- Mitigate risk – gain insight into how decisions can have business-wide impacts and hedge against data uncertainty
- Increase agility – dynamically generate plans and schedules to adapt to market conditions
- Improve customer satisfaction – achieve customer expectations for customization and speed