Overtime

Overcoming Overtime Obstacles in Manufacturing

Manufacturing Workers Assembling a Tunnel

A report released by the U.S. Bureau of Labor Statistics last December found that manufacturing employees worked an average of 3.6 overtime hours per week. Another survey of shiftwork practices reported that while most 24×7 labor environments maintain moderate amounts of overtime (averaging less than 200 hours per shift worker annually), employees in a significant number of these companies can average up to 500 hours a year.

Manufacturing facilities are among those 24×7 operations. Many shift workers want to get as much overtime as possible to supplement their income, so overtime distribution is usually detailed in union contracts or specifically defined by work practice. While less than 10% of U.S. manufacturing employees are part of a union, plant managers note that many employees in non-union plants see their schedule (overtime included) as “absolute”.

When an employee requests overtime, or when peaks in production demand dictate the need, plant supervisors must distribute overtime fairly—and be able to justify the process if there is an employee grievance about not getting enough hours.

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How You’re Handling Overtime with Outdated Processes

Distributing overtime equitably relies on someone recording each employee’s overtime hours on a paper or spreadsheet. When the time comes for overtime hours to be issued, the supervisor manually flips through union contracts (if applicable) and/or chooses by seniority—whether this is done through tribal knowledge (“I know who my people are”) or by looking through a separate log of employee information varies from plant to plant. This process repeats every week, as a properly kept roster will help settle disputes between employees and the supervisor; the manual recording acts as an audit log.

Let’s say you’re at the point where your facility is pretty confident with its ability to keep track of overtime hours. That’s great—but that’s only part of the puzzle. It’s a retroactive process.

How do you limit unnecessary overtime before it happens? Paper processes in particular make this nearly impossible. Without real-time visibility into employee hours and how that matches with current demand needs, managers can’t accurately anticipate their overtime costs. They’re tracking OT hours, but they’re not really leveraging that information strategically beforehand or scrutinizing why the hours are being assigned in the first place.

How To Reduce Unnecessary Overtime in Manufacturing

What if that audit log could be automated? And what if the OT management process could be proactive?

That’s where automated scheduling comes in.

Automated shift scheduling helps reduce unnecessary overtime in the manufacturing industry, driving overtime costs down by 19%. Perhaps this is linked to the finding that organizations that are behind on automation consider about 27% of their overtime costs to be unbudgeted?

This is because, with an automated employee scheduling solution, managers will be assured that when an employee has clocked in overtime, it will fit an anticipated, necessary need.

Here’s how it works: Your weekly schedules will always sync up with production demand. You input company restrictions based on your specific business needs to prevent unnecessary overtime. Each employee is then assessed by these rules as the schedule generates automatically.

The best part? You have an automatic log of overtime hours, making life that much easier if grievances ever occur. No manual entry here!

The Indeavor Solution

Looking to stop spending so much time managing OT and instead focus on business-driving decisions at the plant? Indeavor can help. Click here to request a demo.

Indeavor is our workforce management SaaS solution which offers clients an end-to-end, cloud-based employee scheduling, time & attendance, and absence management system. Indeavor integrates with your human capital management and enterprise resource planning systems to create a robust platform that provides you with real-time employee data.

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