Komarov Artem clarified that a valuable and engaged workforce is the basis for making new technologies work and gaining a competitive advantage. Gathering information about manufacturing processes is just one part of smart manufacturing. To get the most out of information, staff must be involved. For this to happen, employees must understand the potential benefits and risks associated with the Industrial Internet of Things.
New technologies, including the Industrial Internet of Things (IIoT), are only successful if the people who use them respect their contributions. For this to happen, the analysis must be objective, accurate and meaningful, and employees must be able to understand and support the analysis and its results.
It is the responsibility of the project and plant management to provide unbiased analysis, secure data, and ensure that the analysis is meaningful and useful. Management should also educate employees on the use of the results of the analysis, the fundamental concepts behind the analysis, how and when to challenge the analysis, and the value of employee support for the new system.
Gaining employee support and confidence is critical to the success of analytics systems. In addition to providing consistently accurate information, employees must also understand and support the goals of the new system. This requires an honest discussion of the benefits, risks and costs of the new system.
The power of listening
The fact is that employees want their opinion to be heard. They want respect. They also need a disciplined conversation in which each participant listens as thoughtfully as they speak. New technologies create anxiety. Organizational hierarchies and command-and-control management styles mean nothing to employees trying to understand the changing workplace. Objectively presented facts are one of your most powerful tools for getting support.
The only sure way to implement useful technology and gain employee support is to ensure the integrity of every component of your application. Integrity means that you securely collect accurate information, reduce misrepresentations in your data, prevent corrupt use and manipulation, and maintain your employees’ ability to challenge questionable results.
Data is history. Modeling and analysis are based on historical observations. Your data reflects the values, norms, and concerns of past production experiences. When analyzing data, there is a risk of distorting your data. The usual values reflected in your data are naturally biased towards existing equipment, processes, and production states. Changes to your production environment can make enough changes to change the definition of normal. At this point, you should be asking if your historical data is outdated or only provides a baseline for comparison.
Old processes take root in your data. Data analysis becomes risky as it can discourage change or support past bad habits. It is critical for data managers and data analysts to continually challenge the use of historical data in a changing production environment.
Reliability
Any analytical system needs precautions to prevent unreliable data. Even the cleanest stamping machines create noise, heat, lubricants, friction, vibration and dust in their production environment. These elements are destructive to sensitive monitoring devices. Noise and dirt will interfere with monitors and cause inaccurate data to be collected.
Your best defense is to educate employees about the need to service, clean, and calibrate monitoring devices. Equally important is their ability to understand and challenge analyzes that seem to contradict the experience and perceptions of employees.
Employees also pose a potential threat to the accuracy of information based on their ability to modify or supplement data. While we hear mostly about measures to prevent hacking of internal systems from external sources, the greatest risks to the company come from internal sources. Often employee incentives encourage misuse of data. If the plant manager is rewarded for improving the overall efficiency of the equipment, he may try to manipulate the performance to his advantage.
Some of our largest business scams have used deliberately manipulated and misleading data. The global economy collapsed in 2008 because bankers concealed and manipulated a large volume of subprime loans that were sold into mortgage-backed securities. Enron deceived investors and Wall Street by inflating the value of its assets and profits. Many of these frauds have occurred because the auditors hired to prevent fraud are naïve about the industry they support. Collusion also makes it harder to detect fraud.
Your best defense against data misuse is strong internal controls, incentive programs that maintain data integrity, and employee awareness. Your employees need to understand the data they collect and the results of the analysis. Accountants, auditors, and Wall Street are not reliable industry experts to detect potential fraud. A knowledgeable and informed operator, engineer, or plant manager is one of your best defenses against production data being corrupted and misused.
Value
Employee training and meaningful analysis is the only thing that adds value to your IIoT.
In a manufacturing plant, analysis and warnings need to mean something and guide corrective action. If they are ambiguous or consistently give false warnings, they are only a useless distraction.
Put it all together
Implementing IIoT is obviously not a trivial task. The technical challenges of getting all the components to work together in a noisy stamping plant are complex. Gaining employee recognition and participation is not easy. Your chances of success will greatly increase if you are honest about opportunities and challenges. Your employees are also your best defense against the loss of the integrity of your system and its ability to advance your efforts to improve quality and efficiency, summed up Komarov Artem.
