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IoT big data could fine-tune lean manufacturing

From the shop floor to the warehouse and throughout the supply chain, the Internet of Things promises a pipeline of real-time data to help optimize operations through lean manufacturing techniques.

Of all the technologies associated with lean manufacturing, experts say machine-to-machine (M2M) communications -- what many now call the Internet of Things (IoT) -- stands to have the greatest impact, creating a pipeline of data that can be leveraged to fine-tune lean strategies and make adjustments on the fly.

Intelligent devices spread throughout the shop floor could capture data to help manufacturers keep tabs on equipment uptime and utilization rates, lean experts say, making it easier to maintain production at optimal levels. Similarly, IoT could play a role beyond the plant floor in the warehouse and throughout the supply chain, creating a real-time data feed that could be mined for insights to optimize operations on an on-going basis.

"By capturing larger volumes of data, we're going to see patterns we've not seen before to drive the next level of efficiency within the plant, the manufacturing processes, and across the whole supply chain," said Tony Winter, CTO at QAD Inc., a Santa Barbara, Calif.-based maker of ERP systems for manufacturers.

For example, smarter plant-floor equipment, such as a robot or conveyor line, could help companies optimize their asset management and maintenance strategies in a variety of ways. Not only could the equipment alert plant floor personnel to problems in real time, but the data feeds could be analyzed to uncover patterns that would allow technicians to predict potential failures or redeploy resources in a more optimal fashion, he explained.

"IoT takes the planning and execution into a real-time environment," said Josh Greenbaum, president of Enterprise Applications Consulting, a consultancy specializing in enterprise software that is based in Berkeley, Calif. "Everyone does preventive maintenance based on a mean-time-to-failure analysis, but IoT literally lets you listen to the hum of the turbines and know by frequency whether they are out of spec and need to be fixed now. That's a huge change in how companies are doing maintenance, which would have a significant impact on costs."

The data generated by devices connected via IoT can also be enlisted to bolster another methodology often used with lean manufacturing: Six Sigma, said John Denzel, chief executive officer of FlowVision LLC, a Dillon, Colo.-based consulting company specializing in lean manufacturing. "Now you can do Six Sigma with really big data," he explained. "What that buys you is process control and a lack of variation of product. You modernize processes in real time instead of inspecting machines at the end. Eventually, that helps you improve yields and reduce scrap."

IoT early days

While IoT data has lots of potential for advancing lean manufacturing, it is still early on, and neither the technology nor companies' internal processes are mature enough to take full advantage of a real-time big data stream. Evolving that infrastructure and creating the right set of business processes will be a big part of the challenge for manufacturers, Greenbaum said.

Specifically, manufacturers will need to establish a robust data infrastructure that works across the broader set of machines on the shop floor while breaking down protocol barriers so the machines can communicate effectively and in real time. They will also need to establish a bidirectional data flow so they are not only collecting information from IoT devices, but pushing control back to the machines to optimize their use, he said.

"It takes a certain level of technology infrastructure to support this, and a lot of manufacturers haven't yet made that leap," said Cindy Jutras, president of Mint Jutras, a technology and research advisory firm based in Windham, N.H. "Trying to do that with outdated technology is impossible."

Connectivity and a service-oriented architecture are prerequisites to modern IoT or M2M environments, Jutras said, as is in-memory processing or other technologies adept at handling high-volume, high-velocity data. "When you are dealing with huge volumes of data, you can bog down really quickly if you are using the old kinds of relational database structures," she said.

Equally important, lean experts say, is being able to interpret the data and have the management and organizational structure to facilitate taking action. "With IoT, we're creating the data that helps make the decisions, but creating the data for data's sake isn't doing anyone any good," said Steve Halliday, president of High Tech Aid, a consulting company specializing in manufacturing and the supply chain based in Gibsonia, Pa. "Just because you have lots of data doesn't make you efficient. You have to be able to handle the data and use it to do things that will help the company."

Next Steps

See a case study on lean manufacturing

Hear a podcast on applying lean techniques to quality control

Read a book chapter on implementing lean manufacturing

What is poka-yoke?

This was last published in September 2014

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