Strengthening your supply chain one link at a time.
When you have more data to understand it.
If Anna picks 100 units per hour and Barbara picks 50 units per hour, who performed better? You might say Anna since 100 is more than 50, but without further data, there is no clear answer to this question. While having some data is better than no data, this is an example of how basic labor throughput tracking does not paint a full picture of the effort an operator puts in to complete their work.
What if you were asked the same question but this time you knew for Barbara the time to pick 50 units including travel should take 90 minutes because she was picking to weight, which required her to walk to a scale and individually bag each of her products picked and Barbara took 60 minutes. Anna on the other hand, had an expected completion time for 100 units of 30 minutes with travel as all of her locations were close together and she did not have to do any special handling, but it took her 60 minutes—twice the expected time. With this information, it becomes clear Barbara performed better. These complexities are examples of things that would only be captured in a Labor Management System (LMS) that had configured multi-variable Engineered Labor Standards (ELS). Without this level of detail, it is impossible to know who is performing better.
There are multiple methods to develop ELS, including traditional time studies and other methods such as Maynard operation sequence technique (MOST). Time studies involve isolating unique processes, breaking them down into granular steps and timing each step. For example, if studying a Pick to Cart process, you might observe that the user has to get a cart, walk to location one, pick the correct number of units and after visiting all locations, return to a conveyor to induct the items from the cart.
When timing these processes, it is important to time multiple users who would be considered to have an average skill level and capture a large amount of data, then through the use of regression analysis you can ensure the data’s accuracy for your average performing employee. Using MOST can also be a time effective and accurate method of developing the same types of standards, as this involves evaluating a process in a consistent and repeatable format and using premeasured data for reasonable time expectancies for each step in a process instead of conducting manual time studies.
Once these standards are developed, an LMS can accept these standards as part of the configuration and help you measure your employee’s performance, relative to not just the type of task they have completed, but the exact complexity of their work.
Having this level of detail opens the door many possibilities as it relates to your workforce and can lead to productivity increases between 15-35% and major labor cost savings from overtime reduction and increased employee morale and retention. If management is able to see how each individual is performing, there are more opportunities to reward strong performance and hard work through use of Incentive pay and other employee engagement programs. When users are able to see how they are performing, and know that management sees the same information, and acts upon it fairly, there can be an increased sense of pride in their work and performance levels.
—Caroline Sharp, St. Onge Company
