How manufacturers make the most of machine data

How manufacturers make the most of machine data

How manufacturers make the most of machine data

For many manufacturers, there is a disconnect between what goes on in their factories, including their engineering departments, and the core business processes supported by their ERP systems. It creates significant lag times for management to access, analyze and act on data from the manufacturing and development processes. Not having this data in real time could create problems with planning, inventory control, the supply chain or meeting customer expectations.

Barriers to incorporating data from machines on the shop floor into ERP are dropping. Much of the newer equipment is now internet-enabled, and some older machines can be adapted for connectivity. Companies like GE and Siemens are working to standardize platforms for machine-to machine communication. The leading ERP vendors have all taken advantage of this new connectivity to incorporate the machine data into relevant workflows.

So why aren’t all manufacturers connecting their shop floors to their ERP systems? The same old reasons for avoiding any significant technology project: cost, resistance to change and lack of understanding of the ROI.

Read Also:
Neo4j CEO: We're at 'a huge inflection point for graph databases'

Complexity can be a factor, too. Mike Lackey, global vice president of solutions management for SAP, gives the example of a company that has dozens of machines from multiple vendors. “The true value [of digital transformation] is tying all the machines together to see what they are producing, the cost structure, performance, and the quality of the output,” he says. “You can’t look at the data off the machines in silos.”

“Industrial digitization concerns two dimensions or core processes,” says Magnus Wilkerson, professor of production systems at Matardalen University in Sweden. “First, the order-to-delivery process, or operational process, integrates data across system layers and throughout the value chain. Critical activities are the integration of MOM/MES (manufacturing operations system/manufacturing execution system) layer into the architecture as well as the supply chain data integration. Second, the industrial digitization concerns the product and production development process. It integrates data across development platforms and stakeholders and enable virtual builds of new products and virtual verification of new processes.”

Read Also:
What CIOs need to know about containers

Those stakeholders might be people internal to the organization such as product managers, engineers or planners. They could also be external such as contract manufacturers, suppliers, or partners. Lackey spoke of an SAP customer, a large medical device manufacturer, that was designing and building a large and highly specialized piece of equipment used in cancer treatment.

 



Data Science Congress 2017

5
Jun
2017
Data Science Congress 2017

20% off with code 7wdata_DSC2017

Read Also:
The Data Warehousing Sanity Check

AI Paris

6
Jun
2017
AI Paris

20% off with code AIP17-7WDATA-20

Read Also:
4 Questions You Should Ask Yourself Before You Implement Any Big Data Strategy

Chief Data Officer Summit San Francisco

7
Jun
2017
Chief Data Officer Summit San Francisco

$200 off with code DATA200

Read Also:
Your In-Store Customers Want More Privacy

Customer Analytics Innovation Summit Chicago

7
Jun
2017
Customer Analytics Innovation Summit Chicago

$200 off with code DATA200

Read Also:
4 Questions You Should Ask Yourself Before You Implement Any Big Data Strategy
Read Also:
Big Data and Smart Data: Big Drivers for Smart Decision Making

HR & Workforce Analytics Innovation Summit 2017 London

12
Jun
2017
HR & Workforce Analytics Innovation Summit 2017 London

$200 off with code DATA200

Read Also:
The Data Warehousing Sanity Check

Leave a Reply

Your email address will not be published. Required fields are marked *