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In Person Interview: Chad Zollman of TGW

In our continuing series of discussions with top supply-chain company executives, Chad Zollman, chief sales officer for TGW North America, discusses technology and the growth and impact of e-commerce on retail distribution.

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Chad Zollman has served as chief sales officer for TGW North America since 2018. He is responsible for leading commercial efforts for the North American systems integration business, working closely with customers to design and implement end-to-end fulfillment solutions. Before joining TGW, he worked in the third-party logistics industry for over 20 years.

Q: How do you view the current state of the material handling market?


A: The material handling market is experiencing record-setting growth. Over the past few years, companies were contemplating automation to satisfy future demand; however, there has been an acceleration in strategic planning of two to three years due to the impacts of Covid on e-commerce business. The shift of purchasing habits in combination with labor market constraints has influenced executives to add or expand the automation content within their supply chain portfolios.

Q: What are customers' greatest needs as we emerge from the pandemic?

A: Customers are looking for flexibility—they want their automation investment to be nimble enough to meet future—and sometimes unknown—demands yet still deliver productivity and quality metrics that ensure service levels aren’t compromised.

Order profiles in distribution centers have changed over the last year, moving toward smaller orders with an increase in returns volume. If customers don’t have a fulfillment solution that can flexibly adapt to the current market needs, they constantly have to reinvent their supply chain, which equates to high costs and operational inefficiencies.

Q: Are customers asking for specific capabilities in their designs?

A: The most important design criteria for our customers are flexibility, responsiveness, and scalability. Flexibility in terms of adaptiveness: Business models change over time, and the fulfillment solution must be future-proof. Second, responsiveness in terms of speed and quality: End-consumers are expecting high service levels. Fast order processing and shipping are crucial in order to stay competitive. Lastly, scalability: At TGW, we design our end-to-end solutions based on standardized systems, which can be easily expanded with minimal impact on daily operations.

Q: Are there particular markets or trends that you feel will heavily influence future automation designs?

A: In general, e-commerce business will continue to grow. We are seeing retail and wholesale channels with flat or minimal growth trajectories. However, retail stores will never disappear, as they are an important tool for building a strong brand experience—especially for “bridge” and premium brands. In this regard, omnichannel fulfillment capability is a key success factor for retailers.

When it comes to the fashion and apparel industry, we see a strong trend toward sustainability. End-consumers are making decisions based on a brand’s philosophy and environmental footprint.

The last noteworthy trend would be the emergence of microfulfillment centers (MFCs). The MFC concept centers on proximity to consumers, decreasing the hassle of last-mile deliveries and improving the pickup experience. However, the application of the concept varies greatly depending on the physical characteristics of the inventory, temperature requirements, location (greenfield vs. brownfield), and business model (B2B vs. B2C). 

Q: What would you say are the most significant advances you've seen in your time in the industry?

A: The evolution of robotic systems—specifically, robotic single-item picking. At my time with TGW, our developments in cognitive robotics have been impressive. Using machine learning algorithms, the robotics can distinguish between “right and wrong,” fixing errors autonomously. We have extended this system with the application of digital-twin technology, which facilitates shared machine learning and the prediction of system behavior.

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