Taking Measure of Picking

Feb 01, 2006 10:30 PM  By

We don’t have to tell you that improving profitability is the number-one driving force in today’s operating environment. Honing your picking productivity is one way to reduce labor costs, extend facility capacity (buying time until your logistics network is expanded), and improve employee retention — all of which contribute to profitability.

But improving your picking efficiency has subtler benefits as well. It can help boost service levels and worker morale, reduce your dependence on temporary workers, and provide management with better tools and metrics, among other pluses. So how do you improve picking productivity?

If it is true that what gets tracked gets done, the question is: “Is the picking function in the distribution center getting the right things done?” To maximize your picking operations, it pays to track the productivity and accuracy of individuals, work groups, and processes. Establishing goals for the facility and its workers builds an internal culture that will never be content with status quo.

Pickers often operate at a high level, but management is frequently ill equipped to manage and track the productivity. By measuring productivity you may find that your DC layout needs improvement and that your picking methods need modifications.

Performance vs. UPH

Two methods are commonly used to determine the productivity of individuals and teams. The first is performance, which is calculated by comparing the time it actually takes to complete an activity with an engineered standard. The second, and more popular, method is to compare the associate’s hourly production, expressed as units (for instance, orders/lines/cartons) per hour (UPH), with the activity’s historical UPH.

Let’s say Mike is a picker in the outbound picking operation. During 60 minutes Mike picks 200 units from 80 locations (see chart below). Mike’s UPH is 200, which was calculated by dividing the number of units picked by the number of hours worked in the operation. His performance is 100%, which was determined by dividing the total time Mike earned by the total number of minutes considered standard for the operation.

Now let’s look at Sally, another picker in the outbound picking operation. During her 60 minutes, Sally picks 130 units from 100 locations. Her UPH is 130. But her performance is 105%, which takes into account that she traveled to more locations than Mike did.

So although Mike had a higher UPH, he did not perform as highly as Sally. That’s because UPH is not always the best measure of productivity. It does not take into account variations within an operation that may make a task easier for one associate and more difficult for another. Given this complexity, an unfair productivity measure will often leave associates exhibiting illogical behavior. Therefore, if you want to change the behavior of your picking staff, you must use the appropriate metrics.

Case in point: In February 2004 a $1.1 billion manufacturer/marketer of footwear wanted to improve its distribution operations by implementing a pay-for-performance incentive program within its 300,000-sq.-ft. distribution center. But the engineering team had trouble accurately measuring performance among the pickers. The facility had four cart-pick modules and three pick-to-carton modules in which the cartons were delivered to the picker via conveyor. Each of the pick-to-carton models contained sections of case-flow racks, pallet-flow racks, and full-pallet static racks. Many of the picking modules were similar in layout, process, or product mix, but few were similar enough to accommodate a common productivity measurement.

Because of the differences, the engineering team had to determine separate productivity measures for nearly every module in the DC, taking into account multiple variables rather than a single measure of UPH. Though the variables were similar in definition from operation to operation, the pickers earned varying time credits for each.

Across the operation, three common variables were used to measure productivity: number of units picked, active locations visited, and shipping cartons filled. Prior to the start of the program, management committed to a rigorous schedule to ensure that all DC workers took part in a one-on-one coaching and feedback session.

The result of the program: The picking operations improved productivity more than 60%.

Customizing your metrics

Straightforward UPH measurements may not measure the true productivity within a picking operation because of the variety of factors that affect picking operations, including picking method, product mix, and picking accuracy. For that reason, you need to tailor your metrics to your operation.

Let’s start with variables regarding picking methods. Most distribution facilities have a variety of picking methods. This makes a universal picking productivity value difficult within a warehouse. To determine the mix of methods used in your facility, try a “with/to/from” analysis:

  • With what are you picking: forklift, guided vehicle, on-foot worker?
  • To what are you placing the picked item: carton, pallet, tote?
  • From what are you picking: pallet, bin, carousel, static rack, flow rack?

Next, consider the mix of product being picked. In a furniture warehouse, for instance, there are large physical differences among types of furniture. An employee who has to pick sofas and armoires all day will certainly not complete as many picks as an employee picking ottomans and dining chairs. If you use a simple UPH measurement as your productivity indicator, you are creating an environment where associates will cherry-pick their work to ensure desired productivity results. This results in disbelief in the fairness and equity of the program, in addition to animosity toward those workers who seek opportunities to cheat the system.

And of course, you should factor picking accuracy into productivity measurement. When an operation begins undergoing productivity measurement, the workers typically make a point of working faster to meet the standard. Unfortunately this often results in a higher rate of mistakes. You can curtail this behavior by deducting points, in a sense, for picking mistakes, thereby lowering their overall productivity scores. This concept is known as factored performance.

Let’s reconsider the performance of Sally, assuming that her accuracy was measured as 94% during her hour of work. To determine her factored performance, we would multiply her performance percentage and her accuracy percentage: 105.0% × 94.0% = 98.7% factored performance.

Rather than being measured by her normal performance, Sally will be judged by her factored performance, which is significantly lower and illustrates the need for an improvement in picking accuracy. Additionally, in performance incentive programs, you can establish minimum accuracy levels to prevent associates from earning bonuses unless they meet and maintain a minimum accuracy rate.

Managing change

To ensure that your program is effective, you must guarantee that the productivity measurements are not only accurate but also fair and equitable. The best way to prove this is through on-the-floor coaching sessions, which ensure that positive work practices are recognized and negative work practices are identified and avoided.

Generally, four factors affect an employee’s level of performance:

  • skill — talent and proficiency
  • technique — efficiency and effectiveness of methods
  • activity — time spent staying on task
  • rate — level of effort expended

Only after you’ve coached your workers so that they can maximize these four factors should you put in place the surrounding reward structures and recognition programs.

Timeliness of feedback is also imperative. Performance data should be posted immediately instead of allowing time between effort and reporting. This allows workers to more easily understand the link between work practices and performance.

Take the art of the “long view” when developing the measurement program. It should be a lasting program, not a one-time project. The standards should be modified only when changes in process, equipment, layout, or systems require it. If you do need to change a standard, do so immediately to avoid losing credibility. The program should include all employees — supervisors and management as well as the pickers themselves. Try to make it entrepreneurial in nature, so that the employees feel as if they are in business for themselves.

Recognize that this is not an engineering or technology solution but rather a shift to a performance-driven culture. Employees will alter their mindsets only if they see the point of the change and agree with it — at least enough to give it a try.

The formula for success

Successful project implementations come down to several important factors:

  • multiple, specific productivity variables customized to each operation
  • supervisor involvement with employees on the floor
  • coaching of associates and feedback in the areas of skill, rate, technique, and methods

A good measurement formula therefore would be:

Objective standards + timely performance feedback + coaching and skills improvement = success in measuring.

Many of the companies heralded for their supply-chain best practices tend to be fairly open with benchmarking and facility tours, which showcase their capabilities. Such best-in-class firms continually look to further streamline, standardize, and simplify.

H.J. Heinz of H.J. Heinz Co., one of the country’s leading food condiment companies, has been credited with inventing the public factory tour. One of his founding principles, dating from 1876, stated, “To do a common thing uncommonly well brings success.” His statement captures the very essence of the important subtleties you need to manage when tackling a picking productivity measurement project.


Eric Watterson is a project manager for XCD Performance Consulting, an Atlanta-based logistics, supply-chain management, and operations consultancy. Jeremy Davidson is an account executive in the Nashville, TN, office of Fortna, a provider of distribution software, material-handling systems, DC design, and operations-planning services.

Factoring in Tracking Technology

In distribution facilities that use a warehouse management system (WMS) with radio frequency (RF), data-capture capabilities allow management to establish a detailed production history for every associate. In warehouses with little or no capability for data capture, the level of detail in the automated productivity measurement is minimized.

In such facilities you can nonetheless effectively record time and production manually. Data entry clerks, however, are generally required to complete the sensitive task of summarizing time and productivity data.

Depending on production tracking capabilities, you have several options for calculating productivity:

  • use customized spreadsheet and database applications
  • capitalize on the WMS functionality of existing enterprise resource planning (ERP) or niche applications
  • procure best-of-breed bolt-on applications for labor management
  • leverage the warehouse control system (WCS) already managing picking tasks and automation equipment

As a note of caution, our experience tells us that industry buzz phrases such as LMS (labor management system) tend to take on a life of their own on as a sole operating strategy. But the companies heralded as supply-chain “best performers” do not think in terms of IT projects.

Rather, their philosophies start with tough management solutions, not a new technology that promises salvation. To them, technology is only a specific enabler of management solutions. A meaningful solution can exist at some level with little or no technology just as well as with the most modern systems in place; the results are in the implementation.
EW/JD