Understanding Prescriptive Analytics
If you’ve heard about (Business) Analytics or Advanced Analytics, then you’ve probably encountered analytics terms such as ‘Descriptive’, ‘Predictive’ and ‘Prescriptive’. PreWarp is considered to be a Prescriptive Analytics technology – we offer you a way to get recommended actions during a decision making process using optimisation modelling that is working under the hood of a user-friendly interface.
Unlike Descriptive (focused on reporting with basic trend or pattern recognition) or Predictive Analytics (focused on predicting the future with forecasting techniques), Prescriptive Analytics determines ways in which business processes should evolve or be modified. This is crucial to make data and business rules actionable instead of only providing insight into your data’s behaviour.
PreWarp enables you to define your business logic though a business modelling process while defining the input as well as the required output, variable decisions, and objective(s). Using advanced mathematical programming optimisation engines, AIMMS provides recommendations against a (combined) objective such as maximal revenue, minimal cost, optimal service etc. We do this in such a way that regular business users can analyse various options (scenarios) through a highly interactive user interface.
Those who are using advanced analytics are winning
Gartner research (2018) has found that the use of advanced analytics in the supply chain is improving forecast accuracy, cycle time reduction, capacity availability and contributing to cost reduction.
What you should consider
Embracing Prescriptive Analytics requires your (extended) team to be enthused about its benefits and ready to make the leap towards data-driven decisions. Moreover, the recommendations provided by a Prescriptive Analytics tool need support from the organisation – as they can sometimes appear to be counter-intuitive. Ensure your technology supports, and not hinders, this process. With PreWarp you have the ability to do rapid prototyping and immediately start working with end users that do not necessarily need to know the intrinsic qualities of analytics, but understand the results.