Posted By
Catherine Gutierrez Director of Inside Sales Parsec Automation Corporation Anaheim, CA

How to be Data-Centric in the Age of Big Data: 3 Steps

PostedTuesday, December 19,2017 at 12:01 PM

How to be Data-Centric in the Age of Big Data: 3 Steps

As many manufacturers begin to embrace Big Data, they are realizing it represents both a big opportunity and a big shift.

Big Data will allow an organization to set factory performance goals and track progress in real time; support Lean and other initiatives to improve quality, reduce costs, and increase responsiveness; and improve planning by connecting to ERP, batch, and other systems. Being prepared for this shift is essential to being successful in capturing the right data at the right time.

 Step 1: Commit to placing data at the heart of your organization

Placing data at the heart of an organization is the foundational step in the process to becoming data-centric. According to IBM, there are four types of data: Volume, Variety, Velocity, and Veracity[1]. Volume is ensuring you have a sufficient amount of data (not a problem for manufacturers). Variety means the different types of data you are collecting like counts, measurements, binary, or nominal. Velocity is the amount of data needed to paint a complete picture. Finally, Veracity is the certainty around the collection of your data. Automating collection can increase all four V’s, especially certainty. This is done by building systems to capture, store, and secure the data.

Step 2: Build systems to capture, store, and structure data, regardless of source

With data at the center of your organization, it’s time to talk about capture and processing. Thousands of sensors sending data each millisecond through a smart manufacturing infrastructure can overwhelm even the most robust network. Careful planning and clever data collection methods can help reduce network traffic. For instance, collecting Event-Based, Aggregate, or Packaged data can reduce data volumes significantly. Once you have decided on how you are going to capture and structure your data it’s time to analyze this data.

 Step 3: Add Data Analysis Skills and Tools

This is where the rubber meets the road. To make the most of becoming data-centric you need to ensure the foundation is set. This means collecting good data. Once you have verified the quality of that data you can start the path of analyzing it and using it impact the future of your organization. To do this means focusing on technologies that can give you the upper hand.

 MES should sit at the heart of every data-centric operation.

In today’s competitive market it is about doing more with less. Process Improvement initiatives aim to gain a competitive advantage by collecting, analyzing, and correlating data to determine root causes of process bottlenecks and inefficiencies.

The right MES is required in today's complex business environment of continuous change because it enables business agility by accommodating unforeseen changes and automating the information flow.  When you are selecting an MES, be sure to look for some key features.

First, the MES should run on proven technology, such as Microsoft SQL server. It should also be an integrated platform where new features can be activated without requiring an additional server.

Lastly, it should come with preconfigured functionality to help you get up and running quickly for many of the more popular applications, such as OEE, SPC, and e-records. There are various disciplines in manufacturing that need to come together at the same time, and a higher automation level is required for the separate systems to function efficiently.

The right type of MES can help you automate the data-collection and information flow, turning process improvement concepts and practices into decision support tools directly tied to the overall goals and objectives of the business.

 

Catherine Gutuerrez is the Director Channel Marketing at Parsec, a Certified member of the Control System Integrators Association. See Parsec's profile on CSIA's Industrial Automation Exchange. 

 [1] Source: IBM Big Data & Analytics Hub (http://www.ibmbigdatahub.com/infographic/four-vs-big-data)

Filed Under Information Management Manufacturing Information Systems Data collection, management and reporting Data Logging Software Autocoding Coding & Data Management