Gartner’s Supply Chain Symposium always presents a unique opportunity for supply chain leaders to exchange actionable ideas for how to improve their operations. On the conference floor and within various keynote sessions, it was clear that addressing macro-level challenges was top of mind. How do you build resilience in the face of geopolitical instability? How do you navigate increasing network complexity? How do you address the ever-present pressure of rising costs?
We were fortunate to have insightful discussions with both supply chain executives and several of Gartner’s leading analysts, uncovering some common challenges and trends that have become prevalent in the industry.
The New Problem Space: Wrestling with Supply Chain Unpredictability
Well-run supply chains can help companies create a stronger customer experience and differentiate themselves from competitors — but doing so requires dealing with unprecedented complexity.
Consider common scenarios like launching a promotional campaign for a specific geographic region or introducing a new SKU. How well can your network and operations support that? Can your systems and processes handle the surge in demand?
Supply chain unpredictability has led to some key areas of investment across industries:
- More Flexibility: The ability to quickly pivot and adjust shipping strategies to meet evolving needs and account for outside factors.
- Optimization and Cost Reduction: Leveraging data and technology to identify inefficiencies and drive down shipping expenses without compromising performance.
- More Ties into Customer Experience: Recognizing that shipping is a crucial touchpoint for customers and building supply chains to serve them.
- Risk Reduction Across Shipping: Building resilient shipping networks that can withstand disruptions and minimize potential negative impacts.
The Role of AI and Machine Learning
Unsurprisingly, a common topic of discussion was how organizations can harness their data. It’s clear that supply chain leaders know they need to find ways to leverage AI/ML, but recognize they also need to do so in a way that solves concrete problems.
A major area of focus for Shipium’s customers is moving away from static decision criteria — they’re leveraging machine learning to do things like:
- Use Ground Methods More Frequently: By replacing static carrier-provided TNTs with ML-powered TNTs that account for outside network factors, they’re consistently able to select the least expensive services that are predicted to meet the delivery dates promised to customers.
- Set Dynamic Carrier Limits: Rather than meeting contractual commitments using static rules, they’re leveraging Shipium’s ML models to send the right mix of shipments to meet thresholds at a lower cost rather than using a carrier exclusively until the volume commitment is met.
Data leverage and machine learning can help shippers improve decisions and meet their desired mix of speed, cost and accuracy.
Wrapping Up
This year’s Gartner Symposium offered a great lens into how today’s operations leaders are managing the complexities of their supply chains, and specifically how they’re leveraging technology to create better outcomes.
If you’re looking into ways to do the same, reach out to our team here to discuss your use case and see if we can help.
Anurag Allena
Product Marketing Lead
Read Anurag Allena's Shipium blogs. Anurag is a Senior Product Marketing Lead at Shipium and an experienced software marketing professional.