Warehouse automation & the true cost of labor

Warehouse Automation & the True Cost of Labor

Calculating the direct costs of manual labor

Markus Waibel, Verity Co-founder & COO

Automation has created enormous value for companies that have embraced it. However, while that value is easy to see and assess in hindsight, the benefits of any new automation initiative need to be assessed before it is introduced. Doing so is challenging since there are important differences in how machines and humans perform work.

One factor at the heart of business cases for automation is the cost of human labor. While it is just one of the many factors that contribute to the value and ROI of automation, the cost of human labor is distinct because it can be calculated with a certain degree of accuracy. That said, determining the true cost of labor a company faces in a warehouse can be surprisingly nuanced, and gaining a complete picture of these costs can be challenging. For example, initial calculations for an inventory control task by staff of a Scandinavian warehouse proved to be off by a factor of 3.9 (or 390%) for blue-collar workers and 2.2 (220%) for white-collar workers when compared to the company’s actual costs. The size of these discrepancies points to an important reality for any company considering warehouse automation: an inaccurate assessment of the cost of labor can dramatically alter the business case, leading companies to overinvest or underinvest in automation, costing them money and putting them at a competitive disadvantage.

A basic assessment of a company’s costs related to a human labor task includes four aspects: 1) The labor costs incurred by the employer for having the task performed, 2) the employees’ efficiency in performing the task, 3) other directly related costs, such as equipment required for the employee to perform the task, and 4) costs for related work, such as task preparation or task reporting.

Costs for performing a task with human labor can be estimated as follows:

Cost of task performance =
task labor cost / labor efficiency + equipment cost + related cost

1. Task labor cost
Statistical data for hourly labor costs are available for most industries on a country level (see appendix). These include salaries and wages, taxes, social security contributions, and other costs such as those for training or recruitment. Though the actual costs for a specific location will likely differ from the national averages (e.g., costs tend to be higher close to large urban centers than in rural areas), these data make it easy to benchmark actual data provided by a site. They can also be readily used in the absence of site-specific data, such as when performing a first assessment or canvassing of potential application of automation technology across a portfolio of possible installations.

2. Labor efficiency
If task labor costs are determined using bottom-up time estimates (e.g., work time required per work piece), inefficiencies tied to performing the task need to be accounted for. These include task-specific inefficiencies, such as setup time, time for displacement to and from the location of task performance, task switching costs, and general inefficiencies specific to the task at hand, such as meeting times or time for task coordination between team members. If task labor cost estimates are built top-down (e.g., “23 work pieces completed per workday”), these inefficiencies may already be fully accounted for, and this correction factor may not be required.

3. Equipment cost
Many tasks require specialized equipment to perform. For example, inventory control in a warehouse requires a scanner interfacing with the warehouse management system as well as lifting equipment for accessing warehouse locations at height.

4. Related costs
Many tasks require preparation before they can be tackled and are only concluded once analysis and reporting are completed. Automation often greatly reduces the requirements for such related work. However, since such work is often performed by a different group of employees, it is frequently forgotten when developing the business case for automation. For example, inventory control tasks in a warehouse require creating a detailed task plan accounting for the specific requirements of customers, auditors, and other stakeholders. Once the inventory tasks have been completed, the results need to be analyzed and reported. An automation system like Verity’s greatly reduces preparatory work since tasks can be set up once with a few clicks, and then reused manually or automatically performed according to schedule. The system also automates reporting by providing analysis and automatic reporting functionality in a cloud dashboard. This means related costs, which can often amount to an additional 70% in time spent by office staff for inventories, can be almost fully eliminated as well. For warehouse inventories, this work preceding and following the actual task execution is often performed by office staff rather than staff on the warehouse floor, resulting in different labor cost, labor efficiency, and equipment cost for that related work.

When each of these four categories are properly accounted for, the result is a much more complete picture of labor costs. However, no two automation cases are alike, and specific cases may require a closer examination of the costs associated with specific aspects or even the addition of other categories. For example, when automating inventory control tasks, it is worth noting that many wall-to-wall inventories are performed on public holidays or weekends to avoid interference with the warehouse’s regular operation. Although the labor cost data in the Appendix include “industry-standard” overtime, this data reflects only the industry average across all work, typically resulting in overtime on the order of 2%. For many European countries, overtime pay requirements for inventories performed entirely on weekends are much higher, resulting in a 50% or as much as 100% extra pay requirement, dramatically increasing the benefits of automation.

The true cost of labor matters, and the expected reduction in labor costs is often a key factor when determining if an automation project is financially beneficial. Calculating those costs correctly may be the best way to ensure that high-value automation projects are given the green light to move forward. Failing in that mission can lead to poor business decisions that not only harm the bottom line today, but also put the company at a competitive disadvantage tomorrow. Succeeding—using concrete facts that reveal the true cost of labor—can support wise business decisions that improve operations for years to come.

Addendum: Reference labor costs for Europe and the US

The tables below provide reference data as compiled by the national statistic agencies in the EU and the US. Rates for “blue-collar” warehouse workers as well as those performing “white-collar” office work (e.g., data analysis, reporting) are provided. The tables use the following terminology:

Reference labor costs – blue collar work

Note: “:” indicates data that is unavailable
1 Total benefits include paid leave (USD 2.91 / 7.3%), supplemental pay (USD 1.3 / 3.2%), insurance (USD 3.99, 10%), retirement and savings (USD 2.12, 5.3%), and legally required benefits (USD 3.27 / 8.2%)

Reference labor costs – white collar work

Sources

Eurostat, Data extracted on 16/07/2021 17:57:23 from [ESTAT]. Dataset: Labour cost levels by NACE Rev. 2 activity [LC_LCI_LEV__custom_1153026], last updated: 21/04/2021 23:00. Online: https://ec.europa.eu/eurostat/databrowser/view/lc_lci_lev/default/table?lang=en

U.S. Bureau of Labor Statistics, Economic Release, June 2021: Table 4. Private industry workers by occupational and industry group and Table 4. Employer Costs for Employee Compensation for private industry workers by occupational and industry group [Mar. 2021], Online: https://www.bls.gov/news.release/ecec.t04.htm#ect_table4.f.3 and supplemental online materials: https://www.bls.gov/web/ecec.supp.toc.htm