Too many companies buy warehouse equipment and technology based on a "best case" scenario. Using an "engineered" approach to evaluating the return on investment will provide a more accurate picture of cost and productivity benefits.
Technologies for the warehousing and distribution center environment have progressed more in the past 10 years than they did in the previous 30 years, and new options are emerging virtually every day. Today companies that operate warehouses and distribution centers can choose from a vast array of advanced technologies and equipment solutions that promise to deliver attractive productivity benefits. These new technologies—from automatic pallet-wrapping machines to remotely controlled material handling equipment, and everything in between—can deliver tangible benefits, but most require substantial financial investments.
Few organizations have any margin for error when making decisions about new technology and equipment; competitively, one wrong investment decision can erase an operational advantage. Yet many investments in technology and equipment eventually fail to deliver the promised gains. One reason why this happens is that vendors' initial estimates of cost and productivity benefits often are based on a "best case" scenario. Those estimates often prove to be inaccurate, because each facility has unique physical, process, and data constraints, and it can be difficult to determine beforehand what a technology or piece of equipment could accomplish in a particular environment.
Article Figures
[Figure 1] Current and projected values for order-picking elementsEnlarge this image
[Figure 2] Projected labor savings with automated pallet jacksEnlarge this image
Moreover, the "cool factor" of new technology and the complexity of operations can distract decision makers from taking all the steps necessary to understand exactly what an investment will mean to the balance sheet. Most continue to take a high-level approach to evaluating the potential impact, using broad assumptions to estimate future performance and often failing to factor in support and maintenance requirements. Further complicating matters is the fact that vendors often do not have the opportunity to dig into the details of individual operations—an exercise that is necessary if they are to accurately quantify the benefit for a prospective customer. As a result, they use best-case examples, developed under ideal conditions, to promote their products.
Before any company commits to a large capital investment, it should have a realistic view of the cost savings to be gained from the new technology or equipment, as well as the likely impact it will have on operations. Instead of a best-case scenario, an "engineered" approach is a more effective method for evaluating potential capital investments. An engineered approach entails studying the current-state operations at a "micro" or elemental level (similar to the approach engineers use when creating engineered labor standards) and pinpointing the specific elements that will be affected by introducing a new technology. The degree to which each element will be affected can then be assessed using common work-study techniques and/or realistic estimates made by subject-matter experts.
In short, an engineered approach to evaluating new technology or equipment predicts the outcome that a future labor standard will require, which correlates directly to the labor savings one could expect from the technology. This approach develops a savings estimate that reflects the reality of a particular facility and operations—thereby improving a company's insight into the bottom-line impacts of cost-saving initiatives and reducing the potential for costly mistakes.
Consider the operational impact
Prior to embarking on any evaluation of new equipment or technology for warehouses and distribution centers, it is critical to have a true understanding of current operational performance, from receiving through shipping. With that information in hand, a company will be able to make the accurate "before and after" comparisons an engineered approach provides.
The first step of an engineered approach is to identify the specific aspects of an operation the company is targeting for improvement, and how each will change—for better or for worse—as a result of introducing new technology or equipment. Sometimes a vendor may include those specifics in its sales pitch, but most of the time someone who has the necessary expertise and is intimately familiar with the operation will have to pinpoint exactly what will change.
It is very rare for a large capital investment to have only positive impacts on an isolated aspect of an operation. For example, an automatic pallet-wrapping machine may reduce the wrap time per pallet but increase the amount of time that it takes to prepare the pallets to be wrapped. Remote-assisted material handling equipment may expedite the order-picking process by reducing the number of steps required, but it also introduces delays while the operator waits for the equipment to respond to the system's commands.
The next step is to consider how the introduction of new technology will impact other areas of the operation—both upstream and downstream processes, as well as maintenance and support functions—if at all. Consider the example of the automatic pallet-wrapping machine mentioned earlier. The machine may in fact wrap pallets more efficiently than a person could do manually. Automation could, however, create a bottleneck in the pre-wrap staging operations. If studies indicate that upstream bottlenecks would be introduced as personnel wait to utilize the equipment, then the buyer must evaluate how many units it would need to purchase in order to prevent those delays.
Another important consideration is the impact the solution may have on a facility's physical layout and traffic patterns. Some questions that must be answered include:
Can the equipment be positioned so that it does not impede the traffic flow?
How will the equipment interact with other pieces of equipment in the workspace?
Will the pre- and post-trip inspections or preventive maintenance programs for the equipment need to be modified and/or introduced to ensure the safety of those working with it or in its vicinity?
In addition, it is important to understand the degree of reliability the new solution must have and the maintenance that will be needed to support the new solution. Many people fail to consider that certain skills will be required and costs will be associated with maintaining the equipment or defining alternate procedures to continue operations during machine downtime and maintenance. These are just a few examples of the types of considerations that are often overlooked or omitted in the sales and business-case evaluation process used by most buyers.
Baseline versus future state
Once the buyer understands the potential impact of a new technology or piece of equipment, it is important to gather a baseline value (often measured in time when it comes to labor savings) for each step of the task that is being examined. Each step should be broken into smaller steps called elements. Elements that will be unaffected by the new technology can be ignored, which allows the buyer to isolate the true differences between the operation before and after the new technology or equipment has been implemented. There are various methods of collecting the times required to carry out each element, including stopwatch studies and predetermined time-and-motion studies. Companies can use information from their existing engineered labor standards to help them quantify the current environment, as long as the current standards are accurate and have been updated within the last 18 to 24 months. Those that do not have engineered standards in place can still follow this approach, but it requires a bit more data gathering beforehand.
It is important to understand how a new technology will affect the structure of engineered standards or incentive programs that are in place to manage the workforce. A company that does not intend to adjust its engineered labor standards or incentives to reflect the impact of a new technology is not likely to get a true picture of the anticipated savings, nor is it likely to achieve the benefits it expects.
With baseline information about the current state of operations in hand, the buyer can then project how each element would be affected after the new technology has been implemented. Under ideal circumstances, a potential buyer would introduce the equipment or technology into a facility, train individuals in how to employ it, and then study how it performs and what impact it has in the environment in which it would actually be used. Testing the equipment or technology at a facility can reveal unforeseen pitfalls and shortcomings as well as provide fact-based information for subsequent discussions with the vendor.
Because many equipment and technology capital investments are large and complex, it may not be possible to "test drive" them at a working facility. In such cases, simulation models can be valuable. When using simulation models, however, it is imperative to document all assumptions used, as they should form the framework for any conclusions drawn from the data.
After assessments of both the current and future-state values of the affected areas have been completed, the next step is to calculate the differences, and then apply them to the labor model and affected processes in order to determine the new equipment or technology's cost and productivity implications. (See the sidebar for a sample calculation.) Companies that have a labor management system with simulation capabilities can send actual work assignments through the future-state model and feel confident that they are accurately applying the frequencies of their key labor drivers, such as cases per location, cases per assignment, pallets per assignment, and percentage of walk travel versus ride travel. For those that do not have this capability, it is essential to look at as large a data sample as possible in order to be confident that the labor-driver assumptions reflect the long-term operational environment.
Once buyers have quantified the impacts of the new technology or equipment, it can be easy to "fall in love with the number." Since so much effort has been put into calculating an accurate savings projection, many executives want to immediately plug that number into a return on investment (ROI) model and begin translating the savings into dollars. But it is very important to consider factors that cannot be quantified in the model just described. Examples of questions to be asked include:
Will the introduction of the technology create new bottlenecks in the operation that may interrupt the flow of goods?
Does the technology have the potential to be "process limiting"—that is, it would improve the overall average but would limit high performers in the warehouse?
When such questions remain, it may be wise to take a more conservative approach to estimating future benefits.
An accurate projection of the savings to be gained from a capital investment can be an extremely valuable tool when negotiating with the vendor. Suppose that the equipment or technology under consideration fails to meet the required ROI. In that case, the buyer could identify a lower price that would keep the equipment or technology as a viable option. If there is no price flexibility, then the buyer could require the vendor to make modifications to the equipment to compensate for the ROI shortfall.
Benefits for both sides
An engineered approach to evaluating equipment and technology has benefits for both buyer and supplier. For distribution executives, having a realistic sense of the anticipated savings from a capital investment not only assists in decision making but can also provide substantial support for the business case required to secure investment funds. Moreover, it can provide valuable information for price negotiations. Equally important, it provides fact-based information that is specific to a particular operation—something that will help buyers avoid making poor capital investment decisions that could disrupt operations and negatively impact an organization's performance.
For vendors, the use of an engineered approach can improve the accuracy of ROI projections and increase their confidence that customers will be satisfied with the results of an implementation. Finally, this approach can help vendors identify potential problems and unique environmental characteristics before a technology or piece of equipment has been completely installed, providing the opportunity to customize or adapt the product while improving the odds of a win-win situation for both vendor and customer.
Evaluating automation: one company's experience
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The management team of Company A's distribution center (DC) attended a trade show where an equipment vendor was showcasing a new electric pallet jack that automatically advances to its next location without the operator touching the controls. Company A's DC uses pallet jacks during order selection, which is the largest use of labor in the facility. The equipment vendor claims that its automatic pallet jack will improve productivity in order selection by up to 30 percent by eliminating the steps operators must take to return to the equipment controls, thus allowing them to walk directly to their next pick location.
When scaled to its facility, the 30-percent productivity improvement would represent a huge financial savings for Company A; even achieving one-third of that would be worth serious consideration. But before making a large capital expenditure, the company opted to take an engineered approach to evaluating the technology.
The company has engineered labor standards in place, so it already had baseline numbers for the potentially impacted areas:
The steps to and from the pallet jack to the pick location
The steps from the case-placement location back to the equipment controls
Grasping of the controls
The acceleration constant for their fleet of equipment
The vendor allowed Company A to test one of the automated pallet jacks at its facility to help in the decision-making process and hopefully close the sale. Company A invested several weeks in training an individual on the equipment so that the pallet jack would be operated as the vendor intended. An engineer then studied the equipment under normal operating conditions, focusing on generating values for the affected elements of the picking process. In studying the new equipment, the engineer discovered an additional factor to consider: a system-response delay before the equipment moves forward. Figure 1 shows a summary of the values the engineer collected.
The element values indicate that potential savings exist, but the overall savings cannot be determined until the appropriate frequency of occurrence of each element is applied to each value. In the absence of simulation capabilities in a labor management system, the frequencies can be calculated using the following:
Total cases selected
Total locations visited
Percentage of cases selected after short travel (from 9 feet to 40 feet between selection bays; manual travel will still be used for longer distances)
Percentage of locations visited after short travel (from 9 feet to 40 feet between selection bays)
Once the company calculated those frequencies and knew the elemental times, it simply had to "do the math." Figure 2 provides a summary of those calculations.
Several factors were not considered in this calculation, including, but not limited to, maintenance-support hours and the impact on congestion delays. With these factors excluded, the values shown represent a "best case" scenario. Based on the cost of the additional investment in this technology, the results of the study would need to yield at least a 10-percent savings in order to justify serious consideration of such an investment.
After calculating a solid value of the projected labor gains from the new technology, the management team decided not to purchase the equipment unless the vendor was able to significantly reduce the price or further enhance the equipment to provide additional gains at the same price. As it turns out, the vendor's projected gains of 30 percent were actually closer to 20 percent, but new pallet jacks would only affect 25 percent of the total labor component of order picking, thus bringing down the overall savings into the neighborhood of 5 percent.
Supply chain planning (SCP) leaders working on transformation efforts are focused on two major high-impact technology trends, including composite AI and supply chain data governance, according to a study from Gartner, Inc.
"SCP leaders are in the process of developing transformation roadmaps that will prioritize delivering on advanced decision intelligence and automated decision making," Eva Dawkins, Director Analyst in Gartner’s Supply Chain practice, said in a release. "Composite AI, which is the combined application of different AI techniques to improve learning efficiency, will drive the optimization and automation of many planning activities at scale, while supply chain data governance is the foundational key for digital transformation.”
Their pursuit of those roadmaps is often complicated by frequent disruptions and the rapid pace of technological innovation. But Gartner says those leaders can accelerate the realized value of technology investments by facilitating a shift from IT-led to business-led digital leadership, with SCP leaders taking ownership of multidisciplinary teams to advance business operations, channels and products.
“A sound data governance strategy supports advanced technologies, such as composite AI, while also facilitating collaboration throughout the supply chain technology ecosystem,” said Dawkins. “Without attention to data governance, SCP leaders will likely struggle to achieve their expected ROI on key technology investments.”
The U.S. manufacturing sector has become an engine of new job creation over the past four years, thanks to a combination of federal incentives and mega-trends like nearshoring and the clean energy boom, according to the industrial real estate firm Savills.
While those manufacturing announcements have softened slightly from their 2022 high point, they remain historically elevated. And the sector’s growth outlook remains strong, regardless of the results of the November U.S. presidential election, the company said in its September “Savills Manufacturing Report.”
From 2021 to 2024, over 995,000 new U.S. manufacturing jobs were announced, with two thirds in advanced sectors like electric vehicles (EVs) and batteries, semiconductors, clean energy, and biomanufacturing. After peaking at 350,000 news jobs in 2022, the growth pace has slowed, with 2024 expected to see just over half that number.
But the ingredients are in place to sustain the hot temperature of American manufacturing expansion in 2025 and beyond, the company said. According to Savills, that’s because the U.S. manufacturing revival is fueled by $910 billion in federal incentives—including the Inflation Reduction Act, CHIPS and Science Act, and Infrastructure Investment and Jobs Act—much of which has not yet been spent. Domestic production is also expected to be boosted by new tariffs, including a planned rise in semiconductor tariffs to 50% in 2025 and an increase in tariffs on Chinese EVs from 25% to 100%.
Certain geographical regions will see greater manufacturing growth than others, since just eight states account for 47% of new manufacturing jobs and over 6.3 billion square feet of industrial space, with 197 million more square feet under development. They are: Arizona, Georgia, Michigan, Ohio, North Carolina, South Carolina, Texas, and Tennessee.
Across the border, Mexico’s manufacturing sector has also seen “revolutionary” growth driven by nearshoring strategies targeting U.S. markets and offering lower-cost labor, with a workforce that is now even cheaper than in China. Over the past four years, that country has launched 27 new plants, each creating over 500 jobs. Unlike the U.S. focus on tech manufacturing, Mexico focuses on traditional sectors such as automative parts, appliances, and consumer goods.
Looking at the future, the U.S. manufacturing sector’s growth outlook remains strong, regardless of the results of November’s presidential election, Savills said. That’s because both candidates favor protectionist trade policies, and since significant change to federal incentives would require a single party to control both the legislative and executive branches. Rather than relying on changes in political leadership, future growth of U.S. manufacturing now hinges on finding affordable, reliable power amid increasing competition between manufacturing sites and data centers, Savills said.
The number of container ships waiting outside U.S. East and Gulf Coast ports has swelled from just three vessels on Sunday to 54 on Thursday as a dockworker strike has swiftly halted bustling container traffic at some of the nation’s business facilities, according to analysis by Everstream Analytics.
As of Thursday morning, the two ports with the biggest traffic jams are Savannah (15 ships) and New York (14), followed by single-digit numbers at Mobile, Charleston, Houston, Philadelphia, Norfolk, Baltimore, and Miami, Everstream said.
The impact of that clogged flow of goods will depend on how long the strike lasts, analysts with Moody’s said. The firm’s Moody’s Analytics division estimates the strike will cause a daily hit to the U.S. economy of at least $500 million in the coming days. But that impact will jump to $2 billion per day if the strike persists for several weeks.
The immediate cost of the strike can be seen in rising surcharges and rerouting delays, which can be absorbed by most enterprise-scale companies but hit small and medium-sized businesses particularly hard, a report from Container xChange says.
“The timing of this strike is especially challenging as we are in our traditional peak season. While many pulled forward shipments earlier this year to mitigate risks, stockpiled inventories will only cushion businesses for so long. If the strike continues for an extended period, we could see significant strain on container availability and shipping schedules,” Christian Roeloffs, cofounder and CEO of Container xChange, said in a release.
“For small and medium-sized container traders, this could result in skyrocketing logistics costs and delays, making it harder to secure containers. The longer the disruption lasts, the more difficult it will be for these businesses to keep pace with market demands,” Roeloffs said.
Jason Kra kicked off his presentation at the Council of Supply Chain Management Professionals (CSCMP) EDGE Conference on Tuesday morning with a question: “How do we use data in assessing what countries we should be investing in for future supply chain decisions?” As president of Li & Fung where he oversees the supply chain solutions company’s wholesale and distribution business in the U.S., Kra understands that many companies are looking for ways to assess risk in their supply chains and diversify their operations beyond China. To properly assess risk, however, you need quality data and a decision model, he said.
In January 2024, in addition to his full-time job, Kra joined American University’s Kogod School of Business as an adjunct professor of the school’s master’s program where he decided to find some answers to his above question about data.
For his research, he created the following situation: “How can data be used to assess the attractiveness of scalable apparel-producing countries for planning based on stability and predictability, and what factors should be considered in the decision-making process to de-risk country diversification decisions?”
Since diversification and resilience have been hot topics in the supply chain space since the U.S.’s 2017 trade war with China, Kra sought to find a way to apply a scientific method to assess supply chain risk. He specifically wanted to answer the following questions:
1.Which methodology is most appropriate to investigate when selecting a country to produce apparel in based on weighted criteria?
2.What criteria should be used to evaluate a production country’s suitability for scalable manufacturing as a future investment?
3.What are the weights (relative importance) of each criterion?
4.How can this methodology be utilized to assess the suitability of production countries for scalable apparel manufacturing and to create a country ranking?
5.Will the criteria and methodology apply to other industries?
After creating a list of criteria and weight rankings based on importance, Kra reached out to 70 senior managers with 20+ years of experience and C-suite executives to get their feedback. What he found was a big difference in criteria/weight rankings between the C-suite and senior managers.
“That huge gap is a good area for future research,” said Kra. “If you don’t have alignment between your C-suite and your senior managers who are doing a lot of the execution, you’re never going to achieve the goals you set as a company.”
With the research results, Kra created a decision model for country selection that can be applied to any industry and customized based on a company’s unique needs. That model includes discussing the data findings, creating a list of diversification countries, and finally, looking at future trends to factor in (like exponential technology, speed, types of supply chains and geopolitics, and sustainability).
After showcasing his research data to the EDGE audience, Kra ended his presentation by sharing some key takeaways from his research:
China diversification strategies alone are not enough. The world will continue to be volatile and disruptive. Country and region diversification is the only protection.
Managers need to balance trade-offs between what is optimal and what is acceptable regarding supply chain decisions. Decision-makers need to find the best country at the lowest price, with the most dependability.
There is a disconnect or misalignment between C-suite executives and senior managers who execute the strategy. So further education and alignment is critical.
Data-driven decision-making for your company/industry: This can be done for any industry—the data is customizable, and there are many “free” sources you can access to put together regional and country data. Utilizing data helps eliminate path dependency (for example, relying on a lean or just-in-time inventory) and keeps executives and managers aligned.
“Look at the business you envision in the future,” said Kra, “and make that your model for today.”
Turning around a failing warehouse operation demands a similar methodology to how emergency room doctors triage troubled patients at the hospital, a speaker said today in a session at the Council of Supply Chain Management Professionals (CSCMP)’s EDGE Conference in Nashville.
There are many reasons that a warehouse might start to miss its targets, such as a sudden volume increase or a new IT system implementation gone wrong, said Adri McCaskill, general manager for iPlan’s Warehouse Management business unit. But whatever the cause, the basic rescue strategy is the same: “Just like medicine, you do triage,” she said. “The most life-threatening problem we try to solve first. And only then, once we’ve stopped the bleeding, we can move on.”
In McCaskill’s comparison, just as a doctor might have to break some ribs through energetic CPR to get a patient’s heart beating again, a failing warehouse might need to recover by “breaking some ribs” in a business sense, such as making management changes or stock write-downs.
Once the business has made some stopgap solutions to “stop the bleeding,” it can proceed to a disciplined recovery, she said. And to reach their final goal, managers can use the classic tools of people, process, and technology to improve what she called the three most important key performance indicators (KPIs): on time in full (OTIF), inventory accuracy, and staff turnover.