Author Archives: Gertjan de Lange
If you’ve read up on the latest topics in the field of data analysis, then you’ve probably encountered the term Prescriptive Analytics. Prescriptive Analytics is a type of Advanced Analytics that results in a recommended action. Unlike Descriptive (focused on reporting with basic trend or pattern recognition) or Predictive Analytics (focused on predicting the future with forecasting techniques), Prescriptive Analytics uses techniques like machine learning and mathematical modeling to help you improve decision making.
Prescriptive Analytics is at the cutting edge of data science and companies are increasingly interested in exploring its benefits. According to Gartner’s Forecast Snapshot, the Prescriptive Analytics software market will reach $1.1 billion in value by 2019. About 35% of companies are expected to adopt this type of analytics by 2020, but what exactly is driving adoption? AIMMS has been in the business for more than 25 years. Over time, we’ve identified 3 major reasons businesses feel compelled to adopt Prescriptive Analytics. Let’s explore them further.
My takeaways from Gartner’s Supply Chain Executive Conference 2016
Apps have come to define the way we work and live in many ways. We have cars with Apps, watches running Apps, TVs that operate with Apps…as the phrase goes “There’s an App for That.” There are Apps out there for just about anything, it’s hard to imagine a world without them. Why do we love Apps so much? We get the information we need upfront, they can be used anytime, anywhere, they help us connect, and they serve a specific purpose.
When it comes to Supply Chain Analytics, an “Apps approach” can have just as many benefits. Gartner’s Noha Tohamy offered a great presentation about this at their recent Supply Chain Executive Conference. But what exactly is a Supply Chain Analytics app? Noha defines it as “a solution developed in-house or by a service provider, targeted at a specific use case.” By taking an Apps approach, companies can build an app library with solutions. Think for instance of a Source Optimization app for your retail company – a custom solution that helps you understand how and when to source your supplies, foresee possible supplier issues and adapt your plans accordingly. Another example, this time in the oil industry, would be an oil blending app. Oil blending is not easy. Blending rules are always in development and are considered to be extremely proprietary. A custom Optimization or Prescriptive Analytics app can be adapted quickly as the rules change, it can empower people in your organization, be readily accessible for end users and help you create better blends.