How to Take Warehouse Productivity Measurement to the Next Level

Every logistics manager knows that personnel are  among the main costs when operating a warehouse and, therefore, they must constantly look for ways to increase productivity. 

There are many ways to measure this productivity, ranging from the most general to the most specific. In this article, I will talk a little about the traditional ways of measuring productivity. But above all, I will focus on one of the most complex and systematized ways of measuring it. 

Traditional Methods 

Within the traditional and more general methods, we divide the total units of measurement (pieces, pallets, liters, kilograms, order lines) processed by a unit of time (time, day, month, year); for example, kilograms received per hour during 2021. Additionally, it could be divided between the labor cost associated with each process to obtain the labor cost per storage unit; for example, the assortment could be calculated as the average cost of the assorted case during the month of May. 

Such indicators can be applied to something more specific, such as operator-level productivity; for example, by placing the name of the operator on the assortment sheet and then capturing that information in a spreadsheet and calculating productivity. Here’s an example: Pedro’s productivity during the first quarter was 20 lines of assorted orders per hour. It’s not the best, it’s not the most efficient, and it’s not the most accurate, but in a warehouse without technology, it’s something that can help measure productivity and generate improvement initiatives, such as publishing indicators, focusing on less productive operators, setting improvement goals and focus on improving the process. 

Measuring Productivity With Technology 

There is a significant variety of software and hardware that allows you to systematize processes and from there measure productivity, but I will focus on the typical warehouse management software: a Warehouse Management System (WMS). Any WMS records the activities that operators perform: receiving, putaway, cyclical counting, replenishment, picking, shipping. From there, reports can be designed with productivity per operator. 

Getting real-time indicators like assortment lines per operator or pieces received per operator is relatively straightforward with the right technology. 

The advantages against a manual system are clear: real-time and more accurate information, which allows the generation of more efficient improvement initiatives, such as the payment of bonuses or incentives based on productivity. 

Taking it to the Next Level 

The next level can only be reached by specialized software and consists of calculating the exact time each warehouse operation must take. Generically known as a Labor Management System, it consists of implementing "engineering standards" and comparing them against the execution (goal time/actual time) to determine each operator's productivity. 

But how do you calculate the exact time? Below, I explain the steps to be performed, although they may vary slightly depending on the software used: 

1. The first step is to configure the warehouse in a three-dimensional way so that the location in aisle 3, rack 22, and level 3, has coordinates x, y, z 

2. The average speed (e.g. km/hr) of each vehicle used inside the warehouse is then calculated; for example, hand truck, pallet jack, order picker.

3. The time it takes each vehicle to reach each of the different levels of the rack is calculated, differentiating, for example, single-deep and double-deep locations

4. All the steps required to perform the activity and the associated time are determined through the engineering standards. For example, for a case assortment (which will be different from a pallet or piece assortment) the following steps could be defined: 1) take the RF gun; 2) point to the location barcode; 3) scan the location; 4) read the instruction (like pick three cases); 5) save the RF gun; 5) pick cases; 6) accommodate cases; etc.  

Steps 1 and 2 calculate how long it will take any operator to move from point A to point B. Step 3 will calculate the time to obtain and deposit a case/pallet at any level of any rack type within the warehouse. And finally in step 5, based on the philosophy MOST (study of times and movements), calculate the most accurate time possible to carry out any activity within the warehouse. The sum of travel time between point A and point B + times in locations + process times is how the system will calculate the goal time. 

Depending on the configuration of the software, you can determine how the attributes of the products affect the goal time; for example, determine the difference between handling a small and lightweight case and a large and heavy case. Supplying five  cases of paracetamol is not the same as supplying five cases holding 50-inch TVs. 

Due to the great effort involved in setting a standard with engineering measurements and MOST methodology, it is important to determine the most expensive and significant processes within the warehouse (e.g., picking, putaway and replenishment). For other activities, averages can be generated, such as cyclical counts per hour. 

With a traditional system, we can compare operators with average productivity and calculate the following: 

1. Operator 1 had a productivity of 1,000 replenished pieces per hour for 8 hours, so 8,000 replenished pieces total in a given day. 

2. Operator 2 had a productivity of 975 for 8 hours, so 7,800 during the day. 

With this information, we could conclude that Operator 1 has a better performance, but actually, we don’t know exactly which type of pieces each one replenished, or the total distance each operator had to travel. However, with an advanced system, the following could be calculated for the same example: 

1. Operator 1 had to take 7.5 hours (goal time calculated by the system) to complete the 8,000 replenished pieces, but concluded the activity in 8.0 hours (real time that it took the operator), resulting in a 94 percent performance. 

2. Operator 2 had to take 8.5 hours (goal time calculated by the system) to complete the 7,800 pieces, but finished in 8.0 hours (real time that it took the operator), resulting in a 106 percent performance. 

We now know that operator 2 actually performed better. 

The benefits of reaching this level of sophistication are evident; the calculation of performance is much more accurate and allows for setting fair and achievable goals. Most importantly, operators will know in real time how they are performing, allowing them to take corrective action immediately. The warehouse can now generate fairer incentives. It’s important to make constant revisions to adjust the metrics and improvement process implemented along with the tool. 

Another benefit is that such a system can give you visibility of non-productive times in processes, dead times, processes of greater complexity, etc. It also gives you visibility of volume peaks for a  specific time and allows you to generate simulations that can be performed, such as calculating the man-hours that are required to complete a peak day in the operation and thus calculate the amount of personnel who must work that day. 

Finally, we are convinced that such a system also increases the motivation of employees, as they feel they are evaluated more fairly, boosting the commitment of the workforce. 

*This article has been written in collaboration with Salvador Hernández Pliego, who has experience in productivity and supply chain issues.