Powercharge Lean Manufacturing with Internet of Things

Lean Six Sigma (LSS) principles in a mass production environment always remained a popular and proven method for continuous improvement, waste reduction and cost savings. LSS theories, such as Kanban, Kaizen, 5S, RCA (Root Cause Analysis), TPM (Total Productive Maintenance), FMEA (Failure mode and effect analysis), define and measure many processes in a factory. Traditionally, these theories were implemented with the help of policies, procedures coupled with manual data analysis on spreadsheets.

Lean Six Sigma (LSS) principles in a mass production environment always remained a popular and proven method for continuous improvement, waste reduction and cost savings. LSS theories, such as Kanban, Kaizen, 5S, RCA (Root Cause Analysis), TPM (Total Productive Maintenance), FMEA (Failure mode and effect analysis), define and measure many processes in a factory. Traditionally, these theories were implemented with the help of policies, procedures coupled with manual data analysis on spreadsheets.

With the invention of new sensors, connected devices, internet of things (IoT) and advanced data analytics, lean projects and process improvements are about to be supercharged for faster implementation, higher service levels, reduced downtime, increased efficiency and productivity. IoT are about to take lean implementations and factory operations in general to the next level of maturity for an optimized environment. It is also believed that this technological advancement (IoT coupled with data analytics) is about to drive the fourth industrial revolution for smart manufacturing.

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“It will make manufacturing more affordable in high wage countries [and be a] major threat to China” Detlef Zühlke, director of innovative factory systems at DFKI

What is IoT (Internet of Things)

The Internet of Things (IoT) is the network of physical objects or “things embedded with electronics, software, sensors, and network connectivity, which enables these objects to collect and exchange data [1]. Industry 4.0 and Industrial Internet are derived terms of IoT that are adapted for manufacturing environments.

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Supply Chains & Inventory Control:

Monitoring inventory levels real time can significantly improve supply chains by minimizing loss and waste and optimizing demand and capacity planning activities. JIT (Just in Time) gets a boost when there is immediate sharing of demand data throughout the supply chain. Currently this is achieved with the help of Kanban cards which could be replaced altogether with IoT.

iotinternet of things

Lean operational efficiencies can lead to expense reduction in a different manufacturing areas including:

Real-time Quality Control:

Real-time tracking of quality KPIs could be achieved for all parts and processes regardless of data volume and analytical complexity. Current quality procedures relying on random samples for inspections and tests will soon be expanded to an entire population.

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Predictive Maintenance: Performing predictive maintenance for machinery that monitors and anticipates when issues are likely to occur can significantly reduce downtime. Although this was traditionally tackled via TPM (Total Productive Maintenance), which advocated for preventive maintenance based on MTTR (mean time to repair).

Remote Asset Management: Remote monitoring of asset utilization broken down by location, department, user etc. can maximize asset utilization that saves time and money.

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Aren’t we there yet?While IOT has lot of potential for advancing lean manufacturing, it it still early on: There are gaps in technology and business processes. and the right change agents. A robust data infrastructure is required that works across machines & things while breaking down protocol barriers so that communication is real time. Both Lean experts and IT Teams need to converge for implementing such systems. Additionally, there is no one size fits all technology, but using the right devices for the right problem coupled with right data analytics is key for process optimization.

IOT gateways, edge computing frameworks, cloud and event management platforms are all converging lately to deliver an end-to-end IOT solutions but is still not mature. Azure IOT, Splunk, Elastic Search, SAP, etc. are all trying to fit the IOT solution in a box.

At Eigennet we have evaluated several IOT platforms, IOT gateways, sensors and produced benchmarks. We are open to distribute our finding to any mid-large scale company planning toimplement an IOT solution.

IOT(INTERNET OF THINGS)

“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 systems at DFKI

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References: 1) ITU-T Study group 13, Next Generation Networks – Frameworks and functional architecture models - Overview of the Internet of things, http://www.itu.int/ITU-T/recommendations/rec.aspx?rec=y.2060

2) Markus Löffler and Andreas Tschiesner: Report: The Internet of Things and the future of manufacturing, McKinsey and Company 2015

Websites:

http://www.iv-i.org/ http://wm.iv-i.org/public/Scenarios/

About Eigennet

Eigennet, LLC is a certified minority owned small business with primary focus in data analytics. We provide consulting services for end-to-end data analytic pipelines for custom applications, business intelligence, predictive models, machine learning, constructing Hadoop based data lakes, enterprise security monitoring and many more.

Emerging Technology: Capture and index disparate data from IoT sources and visually display the information in configurable dashboards. Detect patterns and trends by viewing IoT data in real time, over specified time periods or overlaid on maps. Obtain intelligence from all IoT networks to understand the needs and behavior of citizens and to develop strategies that improve services and the publics well-being, all without specialized skills.