512-800-6031 editor@ramreview.com

It has been said before: All data is not created equal. In my  “Dispatches From The Plant” newsletter column of Jan. 4, 2022 (see link below), I touched on the dark and bright sides of the data tsunamis flowing into (and through) plant environments these days. I also stressed the need to understand, identify, and, ultimately, analyze “the right data” if we really want to fuel lasting reliability improvements. To that end, I pointed to the importance of making data actionable. Here, I’m drilling a bit deeper into those issues.



Click Here To Read The Jan. 4, 2022, Newsletter Column
“Fuel Reliability Improvement With The Right Data”


Let’s start by taking a quick look at this real-world example: an account of one organization’s recent data-discovery journey. At this plant, production and labor data are collected by machine operators on tickets and forms, then keyed by others into a master database. To make the information more useable, data is printed out in spreadsheets. Some is then converted into graphs for reports or used to measure progress toward defined business goals.

Data collection continues with scrap production and material waste measurements. Quality data is collected from multiple sources for two separate reports: production defects and customer complaints. The defects are identified and categorized by QC inspectors through random inspections. Customer complaints are supplied by those who run a customer-feedback process. Production-machine downtime is also written on sheets with a duration and a reason, then summarize later in spreadsheets by department.

The plant’s maintenance work orders also capture machine work, problems, repairs, parts used, and labor. Most of that data is viewed separately, and the improvements are targeted by departments. In turn, the results are narrowly focused actions that lead to slow gains and short-lived improvements. There can be more. There must be more.

WE MUST MAKE DATA ‘ACTIONABLE’
Data used to chart a path for reliability improvement and measure progress along the way is essential to business success. But it doesn’t start with data.

The key element in business improvements is asking the right questions. Andreas Weigend, the former Chief Scientist at  Amazon.com and author of more than 100 papers on the application of machine-learning techniques, might  have said it best: “You have to start with a question, not with the data.” Here are several data-related success factors for improving an organization’s performance in an evolving reliability-improvement work culture:

1.  Big opportunity. Start by focusing on improving something that is very important to the organization: Where is the organization most at risk, where are failures most penalizing, where could breakthrough improvements be revolutionary to business success? These opportunities for improvement can be expressed as dire needs, a burning platform, response to regulatory issues, market changes, balance sheets, or changes in the organization due to buy-outs, mergers, or acquisitions.

Whatever the reason, start by defining the big opportunity for improving your organization’s performance. Specific opportunities for focused improvement are then defined. Be prepared to answer the question: Why are we doing this?

2.  Right data. Identify and gather the right data. From where does the data come? Is the information easy to access? Is the data reliable and trustworthy? In the early years of Total Productive Maintenance (TPM) we learned that machine performance data should be collected and analyzed by those people closest to the machine, the source of the data, and often the source of improvement. With the explosive rate of the IIoT, much of the data will likely come directly from the machines and equipment.

3.  Information. Ask what the data is telling you. Here is where the improvement teams question the relationships among production efficiency losses, unplanned machine downtime, quality defects, customer complaints, scrap rates, and maintenance work (labor and parts). These collective data are now the information that guides improvement.

4.  Knowledge. By connecting the information from the combined data sets, the improvement team can look for connections to the big opportunity for improvement. Armed with the knowledge between the information and the big opportunity for improvement, the improvement team is prepared to begin making improvements that will benefit the organization in a notable way.

5.  Action. Develop a bias for action. Data analysis can be an attractive end to some. To others, it’s analysis paralysis. But, taking purposeful action is what gets things done in the organization on the plant floor. Action begins with root-cause analysis to determine the connections between what was learned from the data and the causes of poor (and successful) performance. Action continues with the corrective actions to address the root causes and putting countermeasures in place to eliminate the cause, or at least to minimize the penalizing effects.

6.  Wisdom. Nurture the individual, team, and organizational learning that takes place from the specific improvement process. Ask the question: Are there similar problems that could be identified and eliminated in this manner? The wisdom to leverage additional improvements with the same body of knowledge is a powerful step in creating a culture of reliability improvement.

7.  Creative/Collaborative People and Machines. Weaving together all six of these steps will result in an essential organization-wide behavior that I call Creative/Collaborative People & Machines. “Creative” meaning new ways of using data as a foundation for purposeful improvement. “Collaborative” is two-fold: People from different parts of the organization working together to make data a tool for reliability improvement providing data that people use to improve performance.

As I wrote in my previously referenced  newsletter column, “Data is the fuel that drives the reliability-improvement engine and tells us how well our improvements work.” So, I repeat, let’s find effective ways to make “the right data actionable” for the good of the organization, employees, customers, community, and owners.TRR


ABOUT THE AUTHOR
Bob Williamson is a long-time contributor to the “people-side” of the world-class-maintenance and manufacturing body of knowledge across dozens of industry types. His vast background in maintenance, machine and tool design, and teaching has positioned his work with over 500 companies and plants, facilities, and equipment-oriented organizations. Contact him directly at 512-800-6031 or bwilliamson@theramreview.com.


Tags: reliability, availability, maintenance, RAM, asset management, Big Data, actionable data