I recently spoke with Jeff Kastning, Senior Manager of Logistics and Distribution at Brooks Sports, about the company’s recent implementation of a software-as-a-service (SaaS) labor management system (LMS). Brooks Sports, headquartered near Seattle, Washington, designs and markets high-performance men’s and women’s running shoes, apparel and accessories.
The company operates a 140,000 square foot distribution center (DC) that carries 12,000 SKUs and ships more than 6 million units per year. The facility uses a pick-to-carton methodology, where workers place the cartons on carts which they wheel through the warehouse.
Over the years, Brooks’ volumes grew and the company had to grow its staff. Five years ago, Brooks had 35 fulltime employees and used three or four temps per day. By 2008, Brooks was still employing 35 fulltime workers, but the number of temps it used daily had grown to 15 to 20 people.
In that same time period, Brooks realized that it had outgrown its IT infrastructure. In 2008 the company implemented a new Enterprise Resource Planning solution that also included a built-in warehouse management system (WMS). The WMS allowed Brooks to move from paper-based processes to real-time processes supported by RF scanners.
While Brooks did not notice a significant improvement in productivity from the WMS, the WMS did provide it with the data it needed to put in a labor management system (LMS). In particular, the WMS provided data on how much time elapsed between scans. Brooks planned to use the LMS to drive productivity improvements.
Brooks implemented an LMS – Labor Performance – from Integrated Management Systems (IMS), the company that handles its contingent staffing needs. IMS also, for some clients, has fixed bid contracts where it supplies all the labor at a customer’s site while agreeing to hit certain output levels. In order to make money in these types of contracts, IMS had developed a proprietary LMS, which the company decided to productize and develop into a SaaS solution.
Historically, it has been difficult for traditional, license-based LMS solutions to provide a payback of less than a year for warehouses that employed less than 70 people. However, the risks and costs associated with the IMS solution were far less than they would have been with a best-of-breed solution. Brooks felt it had little to lose in implementing the IMS solution. Further, following the ERP implementation, Brooks had little appetite for another large software expense.
Brooks began working with IMS in April of 2009 and the solution went live that November. IMS took a multivariable approach to setting standards. The six months was spent largely examining the WMS data to develop accurate labor standards formulas. These formulas are not based on tracking travel times or the use of time and motion studies. Instead, IMS used the WMS data and averages to set the standards.
For example, the data might say that it takes two minutes to pick a particular stock keeping unit (SKU). These are multivariable standards because the labor standards formulas use more than one parameter. For many picking tasks, in addition to the SKU as a factor in the equation, the number of items to be picked, the number of lines on the order (which tells you how many locations the worker needed to travel to), and the number of cartons on the cart that need to be filled are also used.
Developing the standards was an iterative process. The initial goal was to get workers to a baseline of roughly 80% of standard. While developing the standards, Brooks would occasionally see some employees hit a score of 30% or 180% for the day. That was an indication that there were additional metrics that needed to be included in the labor standard in order to make it accurate.
Brooks got their productivity improvements in two stages. First, once workers knew they were being tracked individually, warehouse productivity improved 10-15 percent to 83% of a fair, but efficient, daily goal.
After the solution went live in November, Brooks instituted daily productivity report cards and a three tier bonus structure:
- Tier 1, workers who work at 100-120% of standard during a day get a $1.50 per hour bonus.
- Tier 2, workers who work at 121-139% of standard during a day get a $3.00 per hour bonus.
- Tier 3, workers who work at over 140% of standard during a day get a $4.50 per hour bonus.
Bonuses are paid only to workers with good accuracy and who follow the safety rules. Further, bonuses are paid only for direct labor –i.e., when workers are actively working and using their scanners—rather than on break, getting training, in group meetings, etc. Productivity increased another 10-15 percent following the implementation of the bonuses.
Bonuses are calculated and shown to workers the next day. Following the day’s work, a supervisor downloads the data from the WMS, checks it, and sends it by email to IMS. IMS uses the data to create the daily report card and bonuses and sends this data back to Brooks, which then posts the results for every worker on whether they got their bonus and what tier they were in. This public posting creates peer pressure. If a particular worker sees that 70 percent of his colleagues got a bonus and he didn’t, he can’t help but take notice.
Prior to going live, Brooks educated the staff on what they were trying to do. The goal was not to burn workers out, but rather to develop fair standards and standard operating processes (SOPs) based on working smarter that would support a bonus program. The bonuses were designed to be self funding. In other words, both Brooks and the workers would win if they earned their bonus.
Jeff tells me this has been a “fabulous” solution for them:
- They improved productivity by 25-30 percent.
- The amount of time their staff spends on direct labor activities has increased from about 80 percent to over 90 percent.
- Increases in productivity allowed them to cut the number of temps they employ from 10-15 per day down to 1-2.
- The data has helped them improve their standard operating procedures. If Bob is the fastest worker at a particular task, they watch how Bob does the task and create the standard operating procedure (SOP). Bob becomes the lead and trains new employees on that process.
- The data provides an executive dashboard that summarizes metrics at a higher level and allows Jeff to quickly understand how the warehouse is performing. Jeff looks at the following performance indicators daily: percentage of direct labor, labor productivity percentage for the warehouse, and bonuses in dollars (remember, bonuses are good because both Brooks and the workers win!).
- They received payback only two months after they began working with IMS!
What Brooks and IMS accomplished with LMS is interesting to me for several reasons. I’ve rarely come across warehouses using a multivariable approach to setting standards or implemented an LMS in such a small warehouse. Also, I’ve never seen such a quick payback period for LMS. And I’d never heard of a SaaS LMS solution before—but I suspect it won’t be the last time.