More volume, more speed, more SKUs (stock-keeping units), more accuracy, more channels. … Supply chain leaders are facing increasing pressure from the business to deliver more from their distribution centers, and many are turning to highly automated solutions as a reasonable response to these demands.
Afterall, automated solutions can provide many benefits such as:
But is automation the right answer for your particular operation? And if so, how much automation do you need?
DC automation projects represent one of the largest investments that a company can incur, and, as a result, these projects can become highly visible both inside and outside of the organization. For this reason, it is imperative that the project not only have a business case with sound financials but also go through a thorough design process—one that evaluates alternative options, defines a realistic baseline scenario, considers holistic business impacts, and tests for resilience. We call this an “intellectually honest” approach to design.
What makes a design intellectually honest?
During the approval process for a capital project, supply chain leaders are typically prepared to answer questions about scope, financials, and timeline. But they should also be ready to answer another set of questions to ascertain if the project can “hold water.” In other words, if circumstances change, will the project still be able to produce a return on investment? Essentially, this line of questioning involves determining if the design approach is intellectually honest.
Here are the four characteristics of an intellectually honest design approach:
To elaborate on this concept of an intellectually honest approach, let’s use a case study involving a food and beverage company that is looking to consolidate three existing manual DCs with limited capacity into one automated DC. This company distributes through three different channels: retailers, drop ship for its retailers’ e-commerce orders, and its own e-commerce operation. It is currently using a manual, cart-based picking process and is considering implementing a more automated solution to support its growth and improve productivity and service level. In the following sections, we will walk the case example through the four characteristics of an intellectually honest design approach.
A simplistic justification approach to DC design would compare the benefits of a technology with the current or base scenario. An intellectually honest design approach, however, looks for the solution with the lowest investment possible that meets the company’s business goals, and then compares that solution to the incremental benefits and investments of subsequent, more expensive alternatives.
Using the intellectually honest approach, our food and beverage company is considering two alternative picking technologies, which can be seen in Figure 1. One solution involves using a highly automated goods-to-person system. The other would utilize goods-to-person for a subset of slower-moving SKUs and a pick module for faster-moving ones.
[Figure 1] Alternatives considered for picking at the new automated DC
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When the highly automated Solution A is compared to a baseline manual operation, it has a very attractive payback (see Figure 2). The boost in productivity and much smaller footprint generates significant savings that results in a 4-year simple payback for the $11.5 million investment.
[Figure 2] Example of payback comparison for alternative picking solutions
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However, when compared to Solution B (an incremental alternative), the payback timeline of Solution A is almost twice as long (7.6 years). While Solution A offers higher productivity and footprint improvements, the blended Solution B approach does a better job of balancing desired business results and investment.
Even though the 4-year payback of Solution A when using the base approach is mathematically correct, it does not tell the whole story because it ignores the other viable and less expensive option in Solution B.
Another common mistake that companies make when assessing automation projects is assuming that they will continue to operate at the same current “base” scenario. In today’s fluid environment, the status quo of a current state is hardly a representative reference point, so resulting comparisons would be flawed and hard to justify.
A flawed baseline scenario could misrepresent the business case in either direction. First, assuming that the current performance and profitability is sustainable without investment could be overly optimistic and under-represent the benefits of the proposed automation. On the other hand, assuming excessive performance declines or unnecessary investments in the baseline could potentially skew the numbers to make the business case look unjustifiably better than it is.
Defining realistic baseline scenarios can be a difficult task, but there are certain elements that help:
If these three characteristics are met, it will make the business case credible and help to gain buy-in from stakeholders and decision makers.
Let’s consider what baseline scenario should be used for our case example involving the food and beverage company. Figure 3 presents three different baseline scenarios: 1) maintaining the current three DC network with the DCs possessing the same limited capacity, 2) expanding the distribution network by adding another manual DC, and 3) consolidating the three DCs into one DC that still operates manually. The figure also shows the key metrics that would need to be considered for each scenario. Different scenarios would require some differences in metrics.
[Figure 3] What is the most realistic baseline scenario?
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The figure illustrates how the business case could vary depending on the baseline scenario that is considered. For example, while baseline scenario 1 represents current operations, it also creates some comparison challenges. This scenario limits the volume and revenue growth that the business case could consider, while it also assumes that current revenue and profitability levels will continue. Furthermore, the scenario is better suited for comparing three separate DCs to one consolidated DC than for comparing manual versus automated operations.
Baseline Scenario 2 does a better job of meeting expectations for volume and growth. But it still makes it challenging to accurately compare an automated solution versus a manual one, as it would be difficult to factor in the differences in cost structure and ability to meet other future requirements such as changes in the number of SKUs or to service level agreements.
Scenario number 3 provides the best reference point for most of the automation. Nevertheless, exploring different scenarios was important to determine the broader impact to the business that the project entails.
It’s a common practice to use only the improvements to a DC’s own P&L for the financial justification for automation. As a result, you may hear statements like, “this equipment should pay for itself with better productivity.” While this may be a mathematically correct statement, it may not necessarily be representative of the full value that the automation could bring to the organization. Implementing an automated distribution center operation will have a significant impact on the business that goes beyond the “four walls” and will involve many different stakeholders.
A more holistic approach to the business case would include these outside impacts and assess the benefits to the entire business of adding a new or improved capability in the DC.
For our case study, there are many factors that should be considered. The two main “inside the four walls” impacts are:
There were also four “outside of the four walls” impacts that should be considered:
Figure 4 outlines the specific metrics and business impact for each of the factors listed above.
[Figure 4] Some factors to consider for the business case
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This past year and a half have shown us an extreme example of changing conditions. The shift from retail store sales to online orders during the pandemic meant DCs had to go from shipping cartons with large quantities of product to bags with single units. While the pandemic was an unusual situation, changing conditions are not.
An important part of an intellectually honest design process, then, is to test the boundaries of the proposed solution and understand how resilient it is to change. In other words, if conditions change, will the proposed solution remain viable, or will the anticipated savings and benefits disappear?
Let’s look at three conditions to consider when assessing how flexible an automation solution may be.
Technology thresholds: It’s important to check the throughput rates that the technology can handle and how much it can be scaled up. Knowing where this threshold lies can help you determine a point at which the technology may not work or may become a hindrance to the operation. In this respect, autonomous robotic solutions often have the advantage over fixed material handling equipment (MHE). That’s because robot solutions can often be scaled up simply by adding more units, whereas scaling up MHE solutions might require more disruptive installations.
Labor availability and cost: If labor becomes scarce or wages increase beyond a certain threshold, automation may be better justified. Understanding those tipping points should be a key part of making decisions about the use of automation.
Change in growth and/or order profiles: Growth projection will always have variations, particularly when there are several channels. You need to factor in that certain channels or order profiles (such as small, direct-to-consumer orders) are more labor intensive than others.
By performing a sensitivity analysis over certain metrics, companies can determine how sensitive the business case is to changing conditions. The sensitivity analysis can identify “guardrails” that define the conditions under which the business case is favorable. In our case study (see Figure 5), we did a sensitivity analysis for labor cost and overall growth to see if the more automated Solution A would make more sense than Solution B’s blended approach under different conditions. The analysis helped to show that:
[Figure 5] Sensitivity analysis for labor cost and overall growth
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The sensitivity analysis also showed that Solution B’s more targeted automation approach is more resilient to changes in labor cost and growth than Solution A. Even if growth and/or labor costs came in below current levels, Solution B still showed a four-year payback period versus manual operations (not depicted in the figure). Therefore, the sensitivity analysis helped to determine that Solution B was the right choice even under a reasonable uncertainty over labor cost and growth.
An honest answer
Frankly, it’s an exciting time to consider automated solutions for your distribution operations. Both mature and emerging technologies can provide attractive alternatives to address different operational needs.
But it is important to make sure that this excitement and enthusiasm for the power of technology does not run away with itself. Many factors go into a distribution center automation project, and there is no silver bullet to meet a company's business goals. A strong business case with sound financials is a must for any new capital expenditure project, but it is also important to take it to the next step and submit a proposal with an intellectually honest design approach.