What is prescriptive analytics?
Prescriptive analytics has become a hot spot in business applications and no organization should ignore it, but why?
In the first place, many factors influence in the way of success, however, a great management and strategic can allow us to have high efficiency and profitability in our business. Taking the best option at the right time is possible if this decision is supported by data.
Prescriptive analytics measure the effect of future decisions, because we know what will happen and why, and we can make the best decision. For example, if we would know purchase propensity for some products, Wouldn't you try to personalize the product offering and offer them products related to their consumption habits instead of flooding them with offers on products that you know in advance are not of interest to them, negatively impacting their experience as a consumer?, here is where prescriptive analytics help us.
It´s not magic, is data and a good analysis of it.
Some applications of descriptive analytics are:
- Minimize stock and maximize availability of products in all time.
Optimum automation and planning of campaigns seeking the best efficiency and results.
Planning of preventive maintenance that minimize the cost and maximize the level and quality of service.
Planning staff schedules to minimize labor costs and maximize the level of service quality. Taking into account employee satisfaction.
Prescriptive analytics is used to determinate the best solution from a range of options with the goal of optimizing operational efficiency.
This analytics is based in:
An exhaust research is done on the different options that we can use and the one that gives the best results to the company is chosen.
A prediction is made with different options to determinate the possible consequences of each of them.
STATISTICAL AND MATHEMATICAL TECHNIQUES:
Statistical and mathematical techniques are crucial in the development of prescriptive analytics.
The main difference between prescriptive analytics and predictive analytics is the call to action. While predictive analytics is decision-oriented, prescriptive analytics is action-oriented, is less theoretical and more practical. All of this has led to an increase in use.
Historically these decisions were made manually, a person made these analyzes and based on them made the best decision. Currently, the companies have so much information that doing these analyzes manually becomes almost impossible.
Because of this, automation in decision making is based on two fundamentals disciplines:
Business rules management systems: these are based on a set of know business rules used for decisions making. Los cuales se basan en un conjunto de reglas de negocio conocidas de antemano utilizadas para la toma de decisiones.
- Mathematical optimization: objective functions are taken as a reference that allow the company to differentiate a valid solution from another that is not. This is based on a set of techniques and algorithms that allow to represent the company context given all possible scenarios. Se toma como referencia funciones objetivo que permiten a la compañía distinguir una solución valida de otra que no lo es. Esto se basa en un conjunto de técnicas y algoritmos que permiten representar el contexto de la compañía teniendo en cuenta todos los escenarios posibles.