The Role that Robots and Smart Solutions Play in Efficient Supply Chains

Automation is essential for overcoming current supply chain challenges.


Digital transformation and automation are key to creating efficient supply chains. Florian Pestoni, CEO of InOrbit, shares his insights into technologies that can help overcome current challenges and the opportunities for software companies to integrate these solutions with existing management systems to transform operations.

Can you share some insights into the current challenges and the outlook for supply chain challenges in the next few years?

Pestoni: Supply chains are struggling, and it’s not likely to get better any time soon. From production woes for key components used in thousands of products to labor shortages at e-commerce fulfillment centers, manufacturers, third-party logistics vendors and retailers are facing growing challenges as they try to keep up with demand.

Which tech solutions can supply chain partners implement to overcome those challenges more easily?

Pestoni: For decades, supply chain operations remained largely unchanged, relying heavily on human labor and low automation. Forklifts and paper orders were the norm, except in some production facilities like automotive factories that embraced just-in-time, low inventory manufacturing for relatively fixed production lines.

In light of ongoing disruptions, from climate change-related events to the still ongoing COVID-19 pandemic and rapidly changing consumer habits, supply chain operators must find ways to increase flexibility, resilience and efficiency by embracing new technologies, from digitization and AI to smart robots.

How can robots help create efficient supply chains?

Pestoni: New generation of smart robots, with a degree of autonomy and a variety of form factors and functions never seen before, is transforming supply chain operations from omnichannel fulfillment to intralogistics. These robots rely on advanced sensors, such as LiDAR and depth-perception cameras, as well as more efficient energy storage and motors.

However, it’s the advanced software supported by powerful mobile processors that makes the biggest difference. Applying advances in computer vision and machine learning, these robots can operate in unstructured environments, often alongside and in collaboration with human workers. These robots can operate around the clock and adjust dynamically to new data: mobile robots can reroute as needed to avoid obstacles; pick-and-place robots use AI-driven path-planning to pick items from a bin or conveyor belt; depalletizing robots can quickly break down pallets with custom grippers.

Are there market gaps that innovative ISVs can help fill?

Pestoni: Modern supply chains run on software, but legacy software designed for a simpler, less-automated world can hold back progress. Advanced warehouse execution systems (WES) combine existing solutions such as warehouse management systems (WMS) and traditional warehouse control systems (WCS) for a more dynamic view of material flow. Building integration with a rapidly growing variety of robots is critical.

The impact of robotics goes well beyond just augmenting labor. Robots can collect massive amounts of very detailed data. Vendors who are able to capture data at scale, run efficient analytics and generate insights can help take supply chain operations to a new level.

What advice do you have for ISVs working to create efficient supply chains?

Pestoni: ISVs looking to make an impact in this space must be able to bridge information technology (IT) and operations technology (OT), two disciplines that historically have been largely siloed. For instance, while IT teams have embraced the benefits of the cloud when it comes to flexibility and scalability, OT teams in traditional industries are still clinging to fixed production lines with largely disconnected equipment. This limits their ability to rapidly iterate and adjust dynamically to changing conditions.

Realizing this difference in expectations, ISVs must help supply chain operators make the transition to more data-driven, dynamic operations.