Google the term “advanced analytics” and you get back nearly 23 million results in less than a second.
Clearly, the use of advanced analytics is one of the hottest topics in the business press these days and is certainly top of mind among supply chain managers.
Yet, not everyone is in agreement as to just what the term means or how to deploy advanced analytics to maximum advantage.
At HP, the Strategic Planning and Modeling team has been utilizing advanced operational analytics for some 30 years to solve business problems requiring innovative approaches.
Over that time, the team has developed significant supply chain innovations such as postponement and award winning approaches to product design and product portfolio management.
Based on conversations we have with colleagues, business partners and customers at HP, three questions come up regularly – all of which this article will seek to address.
- What is the difference between advanced and commodity analytics?
- How do I drive innovation with advanced analytics?
- How do I set up an advanced analytics team and get started using it in my supply chain?
Advanced analytics vs. commodity analytics
So, what exactly is the difference between advanced analytics and commodity analytics? According to Bill Franks, author of “Taming The Big Data Tidal Wave,” the aim of commodity analytics is “to improve over where you’d end up without any model at all, a commodity modeling process stops when something good enough is found.”
Another definition of commodity analytics is “that which can be done with commonly available tools without any specialized knowledge of data analytics.”
The vast majority of what is being done in Excel spreadsheets throughout the analytics realm is commodity analytics.
Read more at Supply Chain & Big Data ÷ Analytics = Innovation
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