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Greg Mathers is an account executive at Drishti and is based in the U.K.
There are many key performance indicators (KPIs) that point to the health of an organization. These figures provide quantitative values that give insight into an organization in order to zoom in on problems in the business quickly. If done correctly and meaningfully, KPIs can translate the performance of a business into revenue. In manual assembly-focused manufacturing — alongside KPIs like on-time delivery, waste and rework times — direct labor efficiency (sometimes called direct labor productivity) is among the most important.
What is DLE and how do you calculate it?
DLE is an attempt to test the predicted output of operations versus the actual production hours that were worked.
Monthly DLE Value = ∑ (labor standard time hours x quantity of good production + labor setup time)
∑ Attendance hours of direct labor
Perfect world example:
An example, with perfect quality, perfect attendance and nice round numbers only making one product:
A company making widgets where each is supposed to take 30 minutes with no setup time will produce two units per attendance hour worked on the floor.
If there are two operators working for eight hours each we would expect the following DLE for the day:
(0.5 hours work time to complete assembly x 32 good completed units + 0 setup time)
(2 workers x 8 hours)
= 16 / 16 = 100% DLE
An organization would calculate this DLE for each of its lines and cumulative for the organization over a longer period of time (week, month, quarter, year, etc.) to gauge its performance.
While the calculation is straightforward, the reality is usually not so clean. Assembly time does not usually line up perfectly with assembly hours worked, and setup time – a reality of manual assembly – already guarantees a less than 100% efficiency rating. Still, businesses know how long a product should take to assemble via their standardized work program and benchmarking activities, how much product they intend to produce in a period of time and how many employees they have scheduled to work in a given work week.
During real production, the variables of the DLE equation fluctuate below ideals — as cycle times vary, quality kickbacks and product rework occurs and employee absenteeism (and overtime as a result of needing to catch up) happens DLE strays from its predicted and intended percentage.
What levers improve DLE?
In order to improve DLE, organizations seek to stabilize the aforementioned variables. There are several ways to do this:
Improve standardized work both to regularize the process to a more predictable cycle time — with the goal of reducing cycle time and minimizing rework time.
Reduce quality kickbacks generally — remember, only good product counts toward the calculation.
Manage employee absenteeism, or at least being able to react to it more effectively.
Improve training practices.
Proper equipment maintenance and materials flow, ensuring minimal material and setup hangups.
Improve feedback loops both for line workers and their leadership, to quickly address issues — a reality of the day-to-day manufacturer, no matter how mature — in order to minimize their effects.
Drishti can drastically improve DLE
Drishti presents the unique ability to help regulate each one of the variables that can potentially improve DLE. Drishti’s action recognition AI analyzes workstations continuously and in real time across entire lines and plants to deliver key insights about what is happening right where the battle for DLE is won and lost. Drishti analyzes your standardized work processes to measure the efficacy of the process itself. You are able to see whether or not your line workers are staying within expected cycle times and whether they are doing the steps correctly and in order.
Workers can be delivered instant feedback and corrective training on the spot. In this way, Drishti both supports the workers to do more regularized, efficient work than ever before while also aiding and reducing training time for new and experienced employees alike. With improved training and support from the system, employee absenteeism may improve — at the very least, organizations can expect to be better poised to react with more employees trained effectively and quickly on multiple processes.
Insights from Drishti allow leaders to fix problems and improve processes fast — making continuous verification and improvement a reality for the first time. Additionally, leaders can balance and find bottlenecks across their production lines faster than ever before.
By mitigating employee variability, process control, line balancing and key data insights delivered at speed to all stakeholders in the assembly process organizations using Drishti can drastically improve their DLE by pressing on the variables that matter most.
Interested in finding out more about how Drishti can help you? Check out our solutions page for more info on upgrading to AI-powered production.
