Machine / Operator Analytics, Employee Engagement, and Productivity

“The simple act of paying positive attention to people has a great deal to do with productivity.” – Tom Peters, Author of In Search of Excellence: Lessons from America’s Best-Run Companies

Modern Industry has always sought to measure productivity. To do this, a whole science of statistical analysis was born. One birthplace of modern analytics was the Hawthorne Work’s plant, the manufacturing division of Western Electric, on the eve of the great depression. In this massive factory, which employed tens of thousands at its peak, managers and researchers implemented cutting-edge management techniques and applied newly imagined statistical analysis to measure the impact employee conditions (and other things) had on productivity.

The researchers at Hawthorne Works were interested in a variety of conditional variables in work environments including lighting, rest times and work scheduling, recreational activities and updated break facilities, safety precautions, and healthcare. In their pursuit they developed the first known modern cycle time measurement- a ticker tape with a hole punch for each piece assembled- so the researchers were able to track productivity by throughput.

Measuring Performance Alone Won’t Raise Productivity

Out of this research, modern psychology coined the “Hawthorne effect”, the largely anecdotal principle that observation itself affects worker productivity. For years, this justified measuring employee performance alone as a way to impact production, but subsequent studies show that just monitoring them encourages workers only to keep quota but does little to actually spur increased productivity.

Turns out that researchers and experts were getting worker motivation all wrong and more recent thinking on the subject points to employee engagement as the single most important factor to boosting productivity. The productivity boosts at Hawthorne Works weren’t caused by observation, it was a result of attention. By engaging with workers and asking them about how they went about their job, the researchers and managers had stimulated them to think about their work and, perhaps inadvertently, let them know they mattered.

In a 2014 article on the subject in ACM Interactions magazine,  author Zeljko Obrenovic outlines the some of the most important takeaways from the Hawthorne Works study, namely that:

“At that time the prevailing view was that people went to work purely for money and to earn a living. The Hawthorne studies showed convincingly that this view was deeply flawed. Instead of treating the workers as an appendage to ‘the machine,’ the Hawthorne studies brought to light ideas concerning motivational influences, job satisfaction, resistance to change, group norms, worker participation, and effective leadership.”

Engagement as Productivity Booster on the Plant Floor

Tom Peters has written about business excellence and productivity for decades. His take on it is that organizations need to focus on “Productivity Through People. People get productive when they have a stake, are empowered, constantly trained, informed, and urged to see the job as a matter of perpetual improvement.”

Measuring performance alone won’t increase their performance, he says, even when they are well aware they are being observed, but talking with them about their performance will.

Increasing Employee Engagement and Productivity with Manufacturing Analytics

We’ve touched on this subject briefly in previous blogs, see 3 Things Successful Manufacturers Do With Analytics and There’s Only One Place To Find ROI From Mingo. In these blogs, we urge manufacturers to install scoreboards, use data dashboards and alerts and hold a data-driven daily production meeting as well as encourage great habits and behaviors. Analytics should be leveraged as a shared knowledge base providing plant-wide visibility into the data– not to reward or punish performance– but to analyze, understand and learn.

It’s been demonstrated that real-time scoreboard tracking line and cell performance does positively affect productivity. This isn’t because of a “Hawthorne effect”, meaning workers fear accountability for bad numbers but is likely tied to increased employee ownership of the process (“my numbers”) and a competitive drive based on performance (“my effort”). When this exists, you can also create an employee incentive program that actually works. It also provides employees with real-time feedback on what they are doing and how it might affect the performance on the cell, the line, and the plant.

The journey of continuous improvement starts with asking the questions:

• What is affecting downtime?
• Which machines are going down and why?
• What are operators doing that reduces or contributes to downtime?

This line of inquiry will lead to daily engagement around performance between managers and operators.

Likewise, machine operators can identify downtime reasons and input relevant information on what part and job they are running and be encouraged to suggest changes to reduce changeover times, for example, all of which can be measured through objective manufacturing KPIs.

A great article on this appeared on the website thefabricator.com in which the author seeks to answer the question, “How do we effectively engage our front-line employees in ongoing improvement?”

His suggestions are to:

• Get Employees Involved in Performance Metrics
• Institute Ownership and Accountability in 5S (See more on 5s here)
• Have Employees Hold Daily Stand-up Meetings
• Institute Peer Audits
• Institute Autonomous Maintenance

We wholeheartedly agree. Using analytics, each of these steps will help to create a company culture that “owns the numbers” and values evidence-based decision making. They give managers and operators the tools to monitor (individual and aggregate) performance, measure quality, analyze continuous improvement efforts, and, last but not least, a way to engage their workers. The formula is fairly straightforward:

A more informed workforce = a more engaged workforce = a more productive workforce.