The Non-Obvious Value of Robotics in Ecommerce Fulfillment

Consumers want a transparent delivery experience.

According to Retail TouchPoints’ last mile of retail study, a whopping 98.1% of ecommerce consumers say their delivery experience influences brand loyalty.

This means every step—from landing on a product page to receiving the package—plays a critical role in their buying decisions.

Yet, many retailers still struggle to meet these expectations.

According to a recent Project44 study, 73% of online retailers and shipping providers say they struggle to offer real-time tracking to their customers.

Curious to learn about what needs to happen on the fulfillment side to enable a best-in-class delivery experience, I sat down with Akhilesh Srivastava, the founder of Fenix Commerce, an end-to-end ecommerce delivery management platform.

Akhilesh shared insights on:

• The data needed for transparent delivery

• Current industry challenges

• The emerging role of robotics

… and much more.

Let’s dive in 👇

Data for a Transparent Delivery Experience

In the early days of e-commerce, delivery was unpredictable. 

Packages often arrived late, if at all, and customers had no way to track their orders. 

But companies like Amazon and eBay realized early on that revolutionizing delivery was essential for success in the online marketplace, and this insight set the stage for a series of innovations that reshaped the e-commerce delivery landscape.

As the Director of Delivery Experience and Returns at eBay, Akhilesh had firsthand experience of the early days of shipping:

Akhilesh quickly recognized that every e-commerce retailer on earth would eventually have to provide an “Amazon-like” experience if they wanted to stay relevant, and this led him to start FenixCommerce.

At a high level, ecommerce retailers need the following data to provide a transparent delivery experience:

• Product Placement: This includes knowing the product's location, inventory levels, demand forecasting, and reordering timelines.

• Warehouse Operations: Data on how products move within warehouses, including storage, picking, packing, labeling, and transfer to carriers.

• Carrier and Transport Data: Data on transport times, costs, and variability based on shipping options and routes.

Current Industry Challenges

While it sounds straightforward to gather this data, the reality is more complex. 

Many different systems need to work together to provide retailers with a clear and detailed picture of what’s happening.

I discussed industry problems in-depth with Akhilesh. 

Here are the four biggest blind spots he identified:

• Visibility Gaps: These gaps often occur between different stages of the supply chain, such as between when a label is printed and when the first carrier scan happens. "There’s often a time lag between when a label is printed and when the first carrier scan occurs, which can create a 'black hole' in tracking," Akhilesh explained.

• Disparate Data Sources: Information is fragmented across different systems like Order Management Systems (OMS), Warehouse Management Systems (WMS), and Transportation Management Systems (TMS). "Data is often siloed across different systems, making integration a significant challenge," Akhilesh noted.

• Lack of Standardization: Not all warehouses collect detailed data about internal processes, making it hard to predict delivery times accurately. "The lack of standardized data capture practices means that visibility into the internal workings of warehouses can be limited," he added.

• Limited Carrier Data: Tracking becomes difficult once a package enters a carrier’s system. "The handoff to the carrier can be a major visibility gap, especially if the carrier does not provide real-time tracking data," Akhilesh said.

The Role of Robotics

Akhilesh believes that robotics will play a major role in transforming the e-commerce fulfillment industry, beyond just automating processes.

In a nutshell, the “transparent delivery experience” problem is a data problem.

Many steps in the fulfillment process—like picking, packing, and handing off packages—are still done manually, which can lead to data gaps. 

But, as robotics becomes more common in logistics, it will digitize the flow of goods.

According to LogisticsIQ, the global warehouse automation market is expected to reach $44 billion by 2028, with a 15% CAGR between 2023 and 2028.

Logistics operators primarily think about operational efficiencies while considering robots today, but beyond streamlining operations, robots collect an incredible amount of data that can significantly improve the delivery experience for customers.

Here are some key use cases for robotics in ecommerce today:

• Picking Robots: These robots use advanced sensors and AI to pick items and pack boxes, which speeds up order fulfillment and reduces errors.

• Internal Transport: Automated Guided Vehicles (AGVs) and Autonomous Mobile Robots (AMRs) move goods within warehouses, cutting down on manual labor and speeding up operations.

• Stock Monitoring Robots: These robots have sensors to track inventory levels, report discrepancies, and ensure timely restocking.

• Robotic Arms: These robots load and unload goods from trucks, reducing physical strain on workers and minimizing damage risk.

• Sorting Robots: They sort products by size, type, or destination for shipping, improving accuracy and efficiency.

• Delivery Robots: Ground-based robots can navigate cities to deliver packages, reducing delivery times and costs.

• Tracking Systems: Robots with tracking capabilities provide real-time updates on shipment status, improving transparency and customer satisfaction.

​​The data from these robotic systems, combined with AI and predictive analytics, can lead to more accurate delivery predictions, better inventory management, and improved overall supply chain efficiency.

Future Roadmap

This progression in e-commerce delivery seems inevitable: Customers demand transparency, pushing retailers to require more data from their logistics partners. In response, logistics operators adopt advanced technologies like robotics to not only optimize their operations but also meet retailers’ and consumers’ expectations.

I left the conversation with Akhilesh with an intriguing question: Could a new business model emerge where robots justify their costs through the value of the data they generate, instead of being seen as a capital expenditure for logistics operators? 

This potential shift could redefine the economics of robotics in the supply chain, making the investment more accessible and appealing to a broader range of companies.

This conversation has been edited for length and clarity.

Tell me about your background and what led you to founding FenixCommerce.

Akhilesh Srivastava: I've been in the industry for 26-27 years, primarily focusing on retail supply chain logistics. 

At IBM, I consulted for large brands, providing solutions to improve their supply chain operations. When I joined eBay in 2014, my role was to enhance the shopping experience by managing shipping and global returns. We aimed to improve conversion rates by providing clear delivery promises and simplifying the returns process. This experience made me realize the impact of accurate delivery promises on customer satisfaction and sales. 

In 2017, I founded FenixCommerce to address the issue of disparate data across supply chains. Our platform consolidates data from various sources to improve delivery predictions and customer experiences.

How does FenixCommerce handle data from different parts of the supply chain?

Akhilesh Srivastava: We integrate various data sources, including order timestamps, WMS data, and carrier scans. By consolidating this information, we can provide better predictions and visibility throughout the supply chain. 

For example, we use machine learning models to predict delivery times more accurately than static carrier estimates. 

Our system tracks every step, from the order placement to the final delivery, ensuring that we capture all relevant data points. This comprehensive approach allows us to identify potential delays and provide real-time updates to customers, enhancing their overall shopping experience.

What are the biggest challenges in data visibility for supply chains?

Akhilesh Srivastava: One major challenge is the lack of visibility within warehouses and during the first carrier scan. Many systems don't communicate effectively, leading to gaps in tracking. 

For instance, after an order is placed, it goes through various stages within the warehouse, but not all of these stages are captured and reported. This can create a black hole where the order seems to be stuck without any updates. 

We work with retailers and 3PLs to capture these intermediate events and improve overall visibility. By providing granular data, we can offer more accurate predictions and ensure that customers are well-informed about their orders.

Are there any emerging technologies that you see playing a significant role in the supply chain?

Akhilesh Srivastava: AI and machine learning are crucial for predicting delivery times and optimizing operations. These technologies allow us to analyze vast amounts of data and identify patterns that can improve efficiency. 

For example, we use AI to predict how long it will take for a package to move through various stages of the supply chain. 

Robotics in warehouses also play a significant role by automating repetitive tasks and generating a lot of data. This data can be used to enhance visibility and make more informed decisions. 

The challenge is to integrate all this data effectively and make it actionable, ensuring that every part of the supply chain is working in harmony.

Do you help retailers in determining optimal product placement in warehouses?

Akhilesh Srivastava: Currently, we don't focus on that aspect, but it's a significant issue for retailers. 

Proper inventory placement can reduce shipping costs and improve delivery times. Some progressive retailers are looking into this, but it's still an area with a lot of potential for innovation. 

The idea is to place products closer to where the demand is, reducing the distance they need to travel and speeding up delivery. This requires advanced data analysis and predictive modeling, areas where AI and machine learning can be particularly useful. 

By optimizing product placement, retailers can improve their efficiency and provide better service to their customers.

What key technologies will shape the supply chain in the next 5-10 years?

Akhilesh Srivastava: AI and robotics will continue to be transformative. 

The ability to process and use vast amounts of data will be crucial. Effective data capture, cleansing, and integration will unlock new efficiencies and improve customer experiences. 

For instance, AI can help predict demand more accurately, allowing retailers to stock the right products in the right locations. Robotics can automate many aspects of warehouse operations, from picking and packing to inventory management. 

These technologies will work together to create more efficient, responsive supply chains that can adapt quickly to changing conditions.

Any thoughts on new business models emerging due to these technological advancements?

Akhilesh Srivastava: While nothing specific has emerged yet, I expect new models to develop, especially around reducing CAPEX and using data for value creation. 

The funding market has been challenging, but we might see innovation in business models in the next few years. 

For example, we could see more subscription-based models where retailers pay for access to advanced analytics and AI-driven insights. This would reduce the upfront investment required and make these technologies more accessible. 

Additionally, as data becomes more valuable, we might see new ways of monetizing it, such as selling insights to other companies or using it to improve other parts of the business.

PS: Do you have any questions for Akhilesh? Let me know at [email protected] and I can pass it on.