Prescriptive Analytics Prescriptive analytics is a form of data analytics that helps businesses make better and more informed decisions. Its goal is to help answer questions about what should be done to make something happen in the future.
Applications of Prescriptive Analytics Banking, Financial Services and Insurance (BFSI) Healthcare Online Learning Transportation and Travel Supply Chain and Logistics Manufacturing Marketing and Sales
Benefits of Prescriptive Analytics More Proactive Capturing Multiple Data Touchpoints and Formats Real-Time Insights Finding the Right Trade-off Maximum Use of Resources Gross Margin Management Enhanced Market Competition Analysis Removing Bottlenecks
Challenges with Prescriptive Analytics Difficult to Define a Fitness Function Human Bias in Model Complex Constraints
Prescriptive Modeling Prescriptive analytic models are designed to pull together data and operations to produce the roadmap that tells you what to do and how to do it right the first time.
Types of Prescriptive Modeling Linear Programming Integer Programming Non-Linear Optimisation Decision Analysis Case Studies Simulation Network modeling Project scheduling Dynamic programming Queuing models Decision support systems Heuristics Artificial Intelligence Expert systems Markov processes Decision tree analysis Game theory Goal programming Non linear programming Reliability analysis Genetic programming Data envelopment analysis
Non-Linear Optimisation
Applicability of Optimisation to the Business Problem Decision Space Definable Objective(s) Limitations Minimum Level of Complexity
Applications of Simulation Modelling Ecology Business Process Improvement Military Public Safety Airports Hospitals Ports Mining Amusement Parks Call Centers Supply Chains Manufacturing Telecommunications Criminal-justice system Emergency-response system Public Sector Customer Service