Manufacturing AUTOMATION

Defeating Muda by leveraging real-time IIoT data collection

March 18, 2016
By Tom Goike

Mar. 18, 2016 – Many companies aspire to Lean manufacturing and its power to change the way things are done on the shop floor. In principle, Lean is the reduction of waste and the focus of continuous improvement throughout a product or process’s life cycle.

For decades, companies have achieved this through the collection and analysis of data driven from clipboards and subjective notes or comments. This data is then used to paint a picture of the current state and measure improvements throughout the Plan-Do-Act-Check (PDCA) cycle. The mega-trend of the Industrial Internet of Things (IIoT) that connects devices to produce real-time analytics has brought technology that catapults Lean implementation and Lean thinking into a proactive environment based on truly objective data.

Lean as a data-driven method for reducing downtime relies on the speed at which data is deciphered. Waste, otherwise known as Muda, has eight key components: Motion, Defects, Overproduction, Waiting, Non-Utilized Talent, Transportation, Inventory and Extra-Processing.

All Muda is anchored by one common non-recoverable asset — Time. Once Time is invested or applied to a product or process, its value is fixed. In Lean, the goal is to maximize the availability of equipment and people and minimize the non-value added time placed into a product or process.

Manual data collection is Muda at its finest:
• Motion to collect and deliver the data,
• Defects from subjective data collection,
• Overproduction by gathering data that may or may not be used immediately,
• Waiting for the data to be manually gathered, complied and deciphered,
• Non-utilized talent by requiring skilled workers to document and gather the data rather than add value to the product or process,
• Transportation through manual retrieval of the clipboards and data,
• Inventory of data that has to be manually filed and tracked until needed, and
• Extra-Processing by adding additional roles or responsibilities to the employees who are tasked with value-added work.


Consider this example: what if this seemingly random event occurs twice on each shift in a three shift, 24-hour operation, and its total duration is 150 seconds across 32 of 40 machines in the plant each shift. That plant loses eight hours of production, 40 hours per week, for a total of 2,080 hours per year.

I encountered this situation while trying to improve productivity in a company that introduced the use of Day by Hour charts for manual data collection. Break and lunch were considered planned downtime with no operator input required. During a Kaizen event, we noted that the operators left their workstations approximately two to three minutes before lunch and break. Through interviewing and reviewing the causes, it was found that the company didn’t provide enough microwaves for employees to heat their lunches, and they felt they needed a head start to get to the scarce appliances.

If real-time data collection were in place, a supervisor would have been able to review data hourly or daily, unfiltered by those who input the data into clipboard and spreadsheets. The supervisor would have instantly detected the underutilization of plant equipment during lunch breaks, uncovered the issue around lunch breaks, and addressed the loss with the purchase of a few low-cost appliances.

With subjectively collected manual data, a few minutes lost due to something seemingly random and obscure most likely will not make the clipboard. With real-time data collection, machine utilization is collected automatically and processed into readable and actionable reports instantly available on the device of choice to those who can make a difference.

Real-time data collection delivers Muda-free data, allowing all employees the opportunity to stop loss before it becomes unrecoverable. As a manager, imagine being able to know minute by minute or hour by hour if you will meet your daily requirements, and not just know that, but have the ability to change the course of the day based on real-time, accurate, actionable data.

IIoT software systems that employ the MTConnect manufacturing communications standard capture data as it occurs from each machine and operator to provide an accurate picture of the health of any given process. If the system also offers the ability to connect machines and related assets of any make or origin, then Lean becomes a culture of accuracy and accountability based on real-time data.

Analytics displayed on monitors above each machine and/or strategically placed in work cells provide clear, consistent and unbiased data and summary reports that inspire employees to collaborate on new ways to improve their processes and reduce the waste that lingers in the “Hidden Factory.”

This way, plants that have accepted subjectively collected data and allowed it to be absorbed in the cost of the product can literally transform profitability.

Real-time data is true and accurate because human decisions as to what is and is not Muda are removed. Culture is key in any Lean implementation, and like it or not, manual data collection allows those involved to influence the results and paint the picture of the situation as they see it. An operator who feels he or she is productive and working to their limit can often question the notes or observation of the person gathering the data.

Real-time IIoT software collects data from each machine, shares it across the plant from the shop floor right up to the top floor, and removes barriers to objectivity creating consistent Key Performance Indicators (KPIs) across varying processes, equipment or systems. This helps to diffuse any potential conflicts over subjective data collection, and provides information that helps increase both consensus, and profit.

Tom Goike is the former director of manufacturing excellence at Memex Inc., in charge of Roadmap to Success implementations for Memex’s flagship IIoT communications platform, MERLIN. When installed in a plant, MERLIN can achieve payback in less than four months with an Internal Rate of Return greater than 300 per cent. To learn more, visit

This column was originally published in the March/April issue of Manufacturing AUTOMATION.

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