Building trust in smart manufacturing

Collecting information from shop floor processes is just one part of smart manufacturing. To get the most out of the information, personnel have to be engaged. If that is to happen, the workforce need to understand the potential benefits and the risks that come with the industrial internet of things. nay/iStock/Getty Images Plus

New technologies, including the Industrial Internet of Things (IIoT), are successful only if the people using them respect their contributions. For this to occur, analysis must be objective, accurate, and meaningful, and employees have to be able to understand and support the analysis and its results.

Project and plant management are responsible for providing objective analysis, secure data, and ensuring that analysis is meaningful and useful. Management also must train employees in the use of analytic results, the fundamental concepts behind analysis, how and when to challenge analysis, and the value of employee support for the new system.

Gaining employee support and confidence is critical to the success of analytic systems. Beyond providing consistently reliable information, employees also must understand and support the goals of the new system. This requires an honest discussion about the benefits, risks, and costs of the new system.

Honestly, I’ve only seen dog and pony shows that can only be loved by a used car salesman. These offer no value.

The Power of Listening

I found a relevant story on a recent family visit to southeast Louisiana for the holidays. I sat at the hotel bar among a small group of offshore oil rig workers, a well service provider, and a farmer from Alabama. It’s been a long time since I worked on oil and gas, and even longer since I worked on a farm. Being someone who works behind a desk and whose opinions and grievances are quite different from theirs, I expected an interesting conversation when they began putting down battery electric vehicles (BEV).

They believed that BEVs are just something politicians are forcing on people to support an agenda. They heard that, of course, oil, coal, and natural gas are used to generate the electricity needed to charge vehicles. The materials used to build the vehicles and batteries also consume energy. They questioned whether there was any real benefit to BEVs.

I explained that I work closely with automotive suppliers and am aware of the advantages and disadvantages of BEVs. I respectfully explained they were correct that energy production and manufacturing have costs. I then discussed lifecycle carbon footprints, emissions studies, the issues with lithium mining and its tremendous use of water, and other factors.

What happened surprised me in today’s contentious environment. This was a group of intelligent and thoughtful men. They didn’t allow interruptions. They listened and absorbed what I said. I also listened to them. At the end of the evening, one of the guys told me he might consider a BEV or hybrid for his wife. We left friends, and we each had a new perspective.

The point is that employees, like these gentlemen, want their opinions heard. They want respect. They also want a disciplined conversation where each participant listens as thoughtfully as they speak. New technology creates anxiety. Organizational hierarchy and command and control management styles mean nothing to employees struggling to understand an evolving workplace. Facts presented objectively are among your most powerful tools to gain support.

The only true way to install useful technology and gain employee support is to build integrity into every component of your application. Integrity means that you reliably collect accurate information, reduce bias in your data, prevent corrupt use and manipulation, and support your employees’ ability to challenge questionable results.

Data Is History. Modeling and analysis are based on historical observation. Your data reflects the values, norms, and issues of past production events. With data analysis comes the risk of bias being introduced into your data. The normal values reflected in your data are naturally biased toward existing equipment, processes, and production states. Changes to your production environment might introduce enough change to shift the definition of normal. At this point, you must ask if your historical data is obsolete or provides only a baseline for comparison.

Old processes entrench themselves in your data. Analysis of data becomes risky because it can discourage change or support past bad habits. It is critical for managers and data analysts to continuously challenge the use of historical data in a changing production environment.

Reliability. Any analytical system needs safeguards to prevent data from becoming unreliable. Even the cleanest stamping plants introduce noise, heat, lubricants, friction, vibration, and dust into their production environment. These elements are destructive to sensitive monitoring devices. Noise and dirt will interfere with the operation of monitors and lead to the collection of inaccurate data.

Your best defense is to teach employee awareness of the need to maintain, clean, and calibrate monitoring devices. Equally important is their ability to understand and question analyses that appear contrary to employee experiences and perceptions.

Employees also are a potential threat to the accuracy of information based on their ability to change or make additions to data. While we hear mostly about steps to prevent outside sources from hacking into internal systems, a company’s greatest risks are from internal sources. Often, employee incentives encourage the misuse of data. If a plant manager is rewarded for improving their overall equipment effectiveness, they might attempt to manipulate production numbers to their own favor.

Some of our greatest business frauds used intentionally manipulated and misleading data. The world economy collapsed in 2008 because bankers hid and manipulated a large volume of subpar loans that were sold in mortgage-backed securities. Enron scammed investors and Wall Street by inflating the value of its assets and profits. Many of these frauds happened because the auditors hired to prevent fraud are naive about the industry they support. Collusion also makes fraud challenging to discover.

Your best defense against the misuse of data is a strong set of internal controls, incentive programs that support data integrity, and employee awareness. Your employees must understand the data they collect and the resulting analysis. Accountants, auditors, and Wall Street aren’t reliable industry experts to identify potential fraud. An aware and informed operator, engineer, or plant manager are among your best defenses against the misstatement and misuse of production data.

Value. Employee training and meaningful analysis are the only things that will give your IIoT value.

When auto manufacturers replaced gauges with warning lights, many drivers called them “idiot lights.” They are probably more intuitive for drivers unfamiliar with proper coolant temperature or oil pressure. However, probably the most useless thing in your car is the check engine light. When your oil light is on, you know to check your oil level or change your oil. When the battery light is on, you know to check your battery and alternator. What do you do when the check engine light is on? The ambiguity of this light makes it nearly worthless for most drivers. The problem could be major or minor. You just don’t know.

In a manufacturing plant, analysis and warnings must mean something and define remedial action. If they’re ambiguous or constantly issue false warnings, they are only useless distractions.

Pulling It All Together. Adopting IIoT is obviously no trivial task. The technical challenges to make all the components work together in a noisy stamping plant are difficult. The challenge to gain employee acceptance and participation isn’t easy. Your chances of success are much improved if you remain honest about the opportunities and challenges. Your employees are also your best defense against losing the integrity of your system and its ability to advance your efforts to improve quality and efficiency.