Supply Chain Now – “The Transformative Power of AI: 3PL Operating Systems”
Episode Date: June 11, 2025

Episode Overview

This episode explores the transformative influence of artificial intelligence (AI) on 3PL (Third-Party Logistics) operations. Hosted by Scott Luton and Kim Reuter, the show features a dynamic discussion with Lindsay Billing (EVP of Technology, National Logistics Services) and Paul Booth (Executive Board Advisor, OSA Commerce). Panelists dissect the practical impact of AI-powered operating systems on logistics, from automation to customer experience, and emphasize the vital foundation of clean, unified data. The conversation is punctuated by actionable insights, industry anecdotes, and a healthy dose of candid supply chain leadership wisdom.

Key Discussion Points & Insights

The Growth & Complexity of the 3PL Sector

• Market Expansion: 3PLs have grown rapidly—moving from niche outsourcers to a $335B industry (U.S., projected by 2029).
• Shift in Perception: 3PLs are no longer simple warehousing partners but are seen as innovation enablers and growth engines.

“15, 20 years ago, it was kind of unheard of for you to sort completely outsource that. But now it is everybody's game.” – Kim (01:48)

AI’s Current Role in Global Supply Chain

• Nascent but Promising: AI adoption is accelerating, but many organizations are still at basic use cases (like cost reduction, inventory management).
• Predictive Power: AI’s richest opportunity is forecasting consumer behavior and demand, providing a competitive edge in labor and inventory optimization.

“AI is still largely untapped as a tool in global supply chains… companies that are using AI, I think there's huge benefits there with cost reduction, decreased inventory levels and really just improved service levels.” – Paul (12:58)

• The Importance of Focus: Not all AI use cases add value. Pinpointing the processes where it brings the most measurable improvement is critical.

3PL Challenges: Context for Innovation

• Economic and Tariff Uncertainty: Volatility in global markets means planning is more complex.
• Commoditization Threat: 3PLs must avoid being seen as mere commodities; differentiated service is essential.
• Unpredictability: “No two days are the same”—3PLs must be agile and adaptive.

“Supply chain is the customer experience. It is revenue generating and it is always looked at as an expense... [3PLs] can become revenue generating partners.” – Kim (18:24)

Practical AI Use Cases in 3PL

1. Warehouse Automation

• Progression: Automation began with RPA, evolved toward machine learning, and now integrates with generative AI.
• Examples at NLS:
  • AI-powered conveyance, unit sortation, and “goods to person” robots (Exotec).
  • Autonomous Mobile Robots (Six Rivers’ “Chucks”).
  • Pilots of fully autonomous case pick and removal robots.
• Benefits: Faster, more accurate warehouse operations; redeployment of staff to higher-value roles.

“If Batman were to build a warehouse, that would be the warehouse that would be built.” – Scott (21:21)

• Data-Driven Decisions: AI determines optimal inventory placement, predicts hot sellers, and dynamically reallocates pick slots.

2. Demand Forecasting & Data Utilization

• Why It Matters: Anticipating demand informs staffing, inventory levels, and even carrier contract negotiations.
• AI in Practice: Machine learning draws from historical sales, market and weather trends, and even social media sentiment.
• Collaboration: 3PLs, as data hubs, can partner with shippers/brands on unified forecasting models.

“We have been running AI machine learning models because, you know, a 3PL is a wealth of data.” – Lindsay (25:22)

“He who holds the data wins.” – Paul quoting Steve Sinsing, Ryder (28:15)

3. Customer Service Transformation

• Personalization at Scale: For smaller “emerging” brands, AI-driven Level 1 support ensures individualized service without bloated teams.
• Balanced Approach: AI-enabled chatbots and BI tools are now genuinely useful, but human backup is necessary for more complex requests.

“If we can connect AI to the right data sources, then each of those emerging brands…can feel like they have individualized customer service…that is very exciting.” – Lindsay (30:47)

• End-Consumer Focus: Predictive analytics can improve end-customer experience by ensuring right product, right location, fastest delivery, and cost-effective shipping.

The Foundation: Data Quality & Integration

• Unified, Clean Data is Essential:

  “You can't just jump right to AI and skip the data. And again, if it's garbage in, garbage out…human intelligence feeds on data, right? Artificial intelligence is no different.” – Lindsay (40:37 & 41:58)

• Integrated Platforms: Connecting ERP, WMS, TMS, and other systems into a single “source of truth” enables actionable AI.

• Cautionary Tale:

  “If you take that situation and you put AI on top, you're just going to screw up faster and faster and faster…” – Kim (43:20)

Notable Quotes & Memorable Moments

• On Service Differentiation:
  “At NLS, we're really focused on—we're not a commodity service we provide. We are a growth engine and partnership with our customers to enable them for success and help them grow.” – Lindsay (16:00)

• On AI Application:

  "Don't use a hammer when you can use a wrench. …You need to be specific.”—Kim (14:27)
  “Let's not try and boil the ocean…” – Paul (38:06)
  “Everything else in supply chain is pretty predictable and controllable except the customer.” – Kim (39:24)

Actionable Takeaways

• Start with Data: Ensure you have clean, connected, unified data before AI implementation.
• Identify High-Impact Use Cases: Don’t jump to flashy tech (like drones) without measurable ROI—pilot first in critical areas.
• Partner for Success: 3PLs can leverage their data ecosystems to collaborate deeply with shippers and brands.
• Balance Humanity and Automation: AI supports, but does not fully replace, skilled supply chain professionals.
• Focus on the Customer: Use AI to enhance both business customers’ and end consumers’ experiences.

Timestamps for Key Segments

• [11:39] – Panel Views on AI’s Role in Modern Supply Chain
• [16:00] – Unique 3PL Challenges
• [19:15] – Warehouse Automation Use Cases
• [25:22] – Demand Forecasting & Data Utilization
• [30:47] – Customer Service Transformation
• [40:37] – Value of Clean, Unified Data
• [44:54] – Key Takeaways and Advice

Final Advice from the Panel

• Lindsay: The biggest impact of AI for 3PLs is around customer-centric predictive service, especially for handling peak seasons.
• Paul: Don’t rush; identify targeted opportunities for AI, start small, scale based on success.
• Kim: “Data. As always, you got to start with good data and then go through AI slowly, find a couple places that you think that you can apply it and then test and learn. Don't get all crazy and go straight for the drones.” (44:54)

Connect with the Panelists:

• Lindsay Billing (NLS): [LinkedIn] | [nls.ca]
• Paul Booth (OSA Commerce): [LinkedIn] | [osacommerce.com]
• Kim Reuter: Host, “The Morning Mood” podcast

This summary captures the main themes and insights from the June 11, 2025 episode of Supply Chain Now. For further resources or contact details, visit the podcast’s website or the panelists’ LinkedIn profiles.