Making decisions about what to improve and how to measure the rate of improvement requires a systematic use of data. But, more than raw data, data bases, or spreadsheets, it’s important to use the right data. Many organizations today are already awash in data, anticipating a tsunami of numbers, thanks to the Industrial Internet of Things (IIoT) and, as some are forecasting, the Internet of Everything. Professor Patrick Wolfe, executive director of the University College of London’s Big Data Institute noted, “The rate at which we’re generating data is rapidly outpacing our ability to analyze it.”
Data’s dark side emerges when unfiltered information is used as a threat, a smoke screen, or to obscure the facts. So it’s easy to see why some view data as a not-too-pleasant four-letter word.
Data alone can easily elicit anxiety, boredom, fear, sensory overload, and, in some cases, even excitement. Today’s business leaders must find ways to make data more user friendly to be successful in reliability/maintenance, in operations, and ultimately to the benefit of their organizations, their customers, and their stakeholders.
When organizations actually begin using their data, when they make data actionable for the benefit of the business, the employees and their customers all experience the bright side of data. Data is the foundation for eliminating problems and improving organizational performance.
What is data anyway?
When we delve into data we find digital data, bits and bytes, numbers and decimal fractions, text, alphanumerics, and mathematical symbols. Whatever the data looks like it is actually representing certain conditions or objects—and it is limitless.
Output from a machine sensor is also called data. This can be very useful, redundant, irrelevant, or totally useless. But, it’s still data. Real-time data is on-line. Archived data is off-line.
Amassing data for data’s sake can be a futile effort. It’s what we do with the data that’s most important—turning data into actions through smart, informed decisions.
Let’s take a quick look at one organization’s recent data-discovery journey. 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 and later summarized in spreadsheets by department.
Maintenance work orders also capture machine work, problems, repairs, parts used, and labor.
Most data is looked at separately and the improvements are targeted by departments. The results are narrowly focused actions that lead to slow gains and short-lived improvements. There can be more. There must be more.
Make data actionable
Let’s make data actionable. Data used to chart a path for continuous improvement and measure progress along the way is essential to business success. But it doesn’t start with data.
The key element in business improvement is asking the right questions. Andreas Weigend, former chief scientist of Amazon.com and the author of more than 100 scientific papers on the application of machine-learning techniques said it best: “You have to start with a question, not with the data.”
Let’s look at an example for improving an organization’s performance in an evolving continuous-improvement work culture:
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?
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.
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.
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.
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.
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 continuous improvement.
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 continuous improvement and machines providing data that people use to improve performance.
Data is the fuel that drives the continuous-improvement engine and tells us how well it performs. Let’s find ways to make the right data actionable for the good of the organization and its employees, customers, community, and owners. MT
Bob Williamson, CMRP, CPMM, and a member of the Institute of Asset Management, is in his fourth decade of focusing on the people-side of world-class maintenance and reliability in plants and facilities across North America. Contact him at RobertMW2@cs.com.