What does Composite Analytics Really Mean?

Smart Manufacturing

Much has been written about capturing a digital representation of operations in what is commonly known as Smart Manufacturing. Smart Manufacturing aims to use advanced computer controls and data to provide insights into how to best optimize operations in a highly flexible and adaptive manner.   With Smart Manufacturing, the right information and right technology are available at the right time in the right form to the right people, powering smart decision-making within factories and across entire value chains.

Today’s Smart Manufacturing spans a wide variety of technologies and tools including connected devices and services, robotics, and big data analytics.  There is a heavy reliance on capturing machine data and data generated by manufacturing execution (shop floor control) systems. The labor or people part of Smart Manufacturing is typically captured by bar coding or entering work/shop order information into some type of enterprise resource planning (ERP) system. While some organizations use RFID, its high cost and spotty reliability has limited its acceptance.

What has been missing is a cost-effective and non-invasive manner to capture the human element – the most valuable component of any successful Smart Operation.  This is not hyperbole or a politically correct platitude. As manufacturing continues to strive towards automation, the people who remain, though smaller in numbers, will typically be more highly skilled and motivated. They are the ones with the deep domain expertise who have learned how to avoid potential landmines and to optimize processes – which may be different from the original as-designed processes.

 

Enter Composite Analytics

By adding new technologies to continuously, passively and unobtrusively sense people, it is now possible to build on machine information and materials information – to analyze how people interact with machines and materials in a process.

Sensor technologies can detect movement, sound, temperature, vibration, or electrical current. When combined with machine and material information, an exciting breakthrough in analysis emerges – composite analytics.  In some environments, visual learning and sensors can supplement or even replace machine data. 

A simple example of composite analytics involves layering information from the ERP system and big data from visual analytics and sensor data to understand actual production activities.  In an environment where operators are able to multi-task, it is close to impossible to use an ERP system to understand how the actual activities the worker performs correlate to the various open work orders. Composite analytics can help monitor operations in real time to identify process anomalies as they are happening, and  warn supervisors when costs or productivity are trending out of tolerance, allowing them to intervene before the opportunity window has closed.

 

Summary

By blending all the inputs from sensing people interacting with machines (equipment) to traditional materials (inventory) information, a true composite image emerges – an accurate digital mirror of your physical operations.  The power and breadth of composite analytics is just beginning to be understood, evidenced by the fact that each of our composite analytics projects have started small and then expanded as users grasped the potential to optimize their operations, improve efficiencies, and to limit risks. This is just the beginning of the creative ways operations folks will be using composite analytics.

Author:

Anthony Tarantino, PhD
Adjunct Professor, Santa Clara University – Operations and Finance
Six Sigma Master Black Belt, Certified Scrum Master, CPIM (APICS), CPM (ISM)
Senior Advisor to Atollogy

tony@atollogy.com