Whether Big Data or traditional data, analytics empowers system to utilize their data to drive adaptation decisions and actions impacting their system. These decisions can be strategic, tactical and also provide the capability to improve real time performance. Big data analytics in operations and supply chain is the extensive use of data to identify models that drive insight, decision and actions. Traditional data analytics empowers system to utilize their data to drive decisions and actions. There are four main levels of analytics, each one requiring different tools and capabilities:
Descriptive – what happened?
These are typically characterized as the mapping of current information such as the results of a census. Descriptive analytics can also provide opportunities to use real-time incoming data to drive immediate action as in a system cockpit shows status of essential variables versus their stated goals
Diagnostic – why did it happen?
This can be addressed using causal analysis. From a system content perspective a system is a group of interacting, interrelated, and interdependent elements that form a complex and unified whole that have purpose or function whereby the state of the system is the set of values held by its variables at any given time. System Dynamic provides a high level view of the system emphasizing the interactions between its constituent parts (cause effect feedback), as well as the impact of time on its dynamic behavior. Emergent properties are those properties of the whole, and only exist as a whole, not a properties of the component bits. Emerging properties have a fundamental impact on the system performance. Each organization will have unique emergent properties. Emergent phenomena are best understood by observing a ‘pattern resulting from the interactions’ among multiple elements in a system including aspects of the environment.
Predictive – what will happen?
Predictive analytics is typically about statistical analysis of historical data to forecast future activity. Demand planning for instance utilizes historical demand data combined with information about promotions or new product offerings, to come up with the expected demand for the next periods. Predictive analytics can also help companies transition from using Key Performance Indicators, which reflect what happened, to using Key Performance Predictors, which show what will most likely happen to manage their system/business.
Prescriptive – how to make it happen?
Using analytics in an effective way can allow better decisions with respect to adaptation in order to minimize risk and ensure some form of certainty.