Employee data collection has become integral to lean manufacturing with the dominance of Industry 4.0 in the past decade. Peak performers in high-volume, high-efficiency industries credit their success to collecting, organizing, and interpreting data about every strategy implemented from the factory floor to the head office.
But while aggregated data is a powerful tool, employees are often wary. This is because it is a tool that can be wielded unfairly against workers who never had any managerial power to fix problems in the first place. From the perspective of factory floor workers, it is a tool always wielded against them, and never for them.
Even workers who are highly competent worry that one mistake will be used by computers and algorithms to unfairly justify why they are weak links. Especially on an assembly line, performance is measured by precise timing and detailed measurements. A rigorous quantitative assessment may stress workers to the point of ill-performance or even quitting. Employers don’t want to risk pushing workers back into the job market, but they also want to make sure that work is optimized and efficient.
Implementing employee data collection without a clear strategy (and cross-company communication!) will strain positive coworker relationships. These key relationships are the root of a strong company culture.
So how should you ethically collect employee data in a way that encourages employees to be their best, and doesn’t loom over the factory floor like a fickle Big Brother?
Now, keep this question in mind as we go over a few strategies below. Let’s look at the big picture first:
Now, you may have sharply picked up that we seem to be positioning workers and employers as opposing forces in conflict. Of course this is not always the case, but let’s consider the worst case scenario here, in which the company culture is weak, undefinable, constantly changing, or even missing completely.
There is a huge gap between what management thinks data collection is, and what workers think data will be used for. Management has to change its approach to see the situation from the workers’ perspective in order to begin building trust through an establishment of Standard Operating Procedures (SOP).
You may think the ultimate goal of data collection is to separate the wheat from the chaff, that is, the solid workers from the less reliable ones. This, however, is the worst application of data collection because it is short-sighted. It appears helpful at first, but it doesn’t get to the core of organizational problems.
So what if you fire a single poor-performing employee? That doesn’t mean the next hire will be able to pick up the disorganized mess the first worker left behind. Data collection is NOT about employees, it is about the systems wherein employees work and upon which they rely.
The goal is not to put undue pressure on a certain few to smarten up or else, it’s to put in place systems that streamline and optimize employees’ work, habitual, and quality assurance standards. At the end of the day, good data always points to something that is a problem regardless of which employee is manning that post. You should be thinking long-term about strengthening company culture through workforce standardization.
Let’s say the collected employee data points out bottlenecks in production. Rather than blaming the individual at the helm of that production post, managers should reconsider how that production post works within the line. Remember, we’re aiming for a systemic analysis. If one worker has an issue with a process, then no doubt others are also having issues that need to be ironed out through SOPs.
By referring to automatic SaaS reports, managers can immediately see that this “isolated problem” is actually an insidious one. Once identified, management can work immediately to fix it company-wide. Workers then feel empowered to point out potential improvements instead of worrying that bringing up problems will endanger their individual jobs. It’s a win-win!
Collecting data includes more than just tracking employees’ daily attendance. Other types of initial employee data collection include time/resources used per item at each stage of production, or also total time spent on prep, team strategizing, catch up tasks, or resources saved due to creative new floor strategies. Basically, there’s no end to what you can translate into data!
Because the whole goal of employee data collection is to tease out the problems and hidden wastes scattered throughout the entire manufacturing process, sometimes you don’t even know where to start or what to look for. Fear not!
There are many ways to collect data if you yourself are not a math whiz. SaaS (Software as a service) has become a leading infrastructural tool in workforce planning and analytics because it is “on demand” for when you need it: you don’t need to start and stop a stopwatch for every item that comes down an assembly line. Automatic systems like work instruction software can help you identify and build stronger SOPs using data collection. SaaS solutions ensure that work is streamlined and reports can be instantly accessed for dynamic results. Perhaps most importantly, they are optimized to be easy to use, and can be customized to your specific needs as a manufacturer of any size.
Ironically, even though employee data collection can feel intimidating to employees, the best kind of negative feedback is data-based. Pointing out individual efforts is sometimes necessary to improve the team as a whole.
Let’s say that Employee X is failing to hit quality standards at their assembly station. Which critique would Employee X be more inclined to accept?
ANSWER: Option A sounds accusatory and assumes that Employee X is careless, which may harm their pride and workplace motivation. However, option B situates the quality problems as a failing in applied SOPs for all workers on that station. It positions problem-solving as a collaborative effort that boosts workplace confidence.
After all, what if the problem is strangely simple? Maybe Employee X has quality issues at the assembly station because they are helping Employee Y at the next station, and introducing an intermediary setup station in the assembly line would be a perfect and easy solution.
There are many incentives you can build into your SOPs that will respond well to data collection (and no, they aren’t pizza parties or Christmas bonuses). If employees can trust that pointing out workflow issues leads to collaboration, improvements, and even promotions in title or responsibility, they will readily do so. As a result, you will begin to see more dynamic, responsive data that clarifies issues using workplace analytics. The possibilities are virtually endless, but here are some effective incentives to build into your SOPs from the ground up:
Ultimately, data collection is not an end in of itself, it is a tool to be wielded for deeper, more structural purposes like improving production line efficiency and boosting worker retention. These goals are necessarily more structural and integral to long-term operations than a few ill-performing individuals, and management must clearly communicate this overview in order to reassure workers.
So now that we’ve gone over some methodology, let’s revisit the question we phrased above:
Employee data collection can seem daunting at first, but by pairing automatic data collection systems with a company culture of cooperation, dedication, and encouragement, you will see results. This is because you have joined quantitative data with a qualitative approach for a fully researched gameplan. As the manufacturing world continually speeds ahead with upgrades to technology, make sure to keep up at a pace where your whole team can run alongside you confidently.