What if all companies worked toward creating a transparent, efficient, and ethical supply chain landscape?

In this episode, we welcome Peter Smith, a seasoned executive with a rich background working for major companies such as Carters, Party City, and VF Corporation. Joined by our hosts Logan Ensign and Joel Beal, we're diving deep into the evolving landscape of consumer brands and the retail market.

Peter shares his insights on how brands can better collaborate with retailer partners, on the declining dominance of large monolithic brands, the rise of lesser-known brands, and the significance of data transparency and collaboration across various departments and external partnerships. We'll explore the challenges that new brands face as they strive to sustain initial success and navigate a rapidly changing market, and discuss the transformation in data availability and its crucial role in supply chain management.

In this episode, you’ll learn about:

• How data transparency can improve collaboration across sales, marketing, and supply chain department
• The different strategies your business can adopt to sustain success in rapidly changing markets
• Practical applications of AI that have a profound impact on supply chain operations

Jump into the conversation:

00:00 Critical data areas: inventory and supply chain
09:39 Data-driven market analysis improves product performance
13:04 Supplier challenges in negotiating discounts transparently
18:47 Increased data communication and transparency between organizations
23:13 High-quality global manufacturing accessibility for startup brands
29:58 Advocating for data transparency within organizations
35:58 Leveraging machine learning for supply chain insights
39:05 Machine learning replicates human intuition for guessing

What comes to mind when you hear the words ‘sales data’?

Alloy.ai’s resident expert on all things related to retail data, Manfred Reiche, says there’s a whole realm of information you’re likely missing out on when it comes to the sales data bucket. In this episode, we delve into the intricacies of point-of-sale data and its transformative impact on the consumer goods industry.

Listen in as we explore the critical role of data analysis in uncovering regional problems, managing inventory, and enhancing decision-making across various company sizes. From the importance of granularity in data to the necessity of breaking down silos within organizations, this discussion promises to shed light on how businesses can leverage vast data sets to drive success.

Three Key Takeaways:

1. Accumulating data isn't enough, the key is to convert it into insights that drive action and that’s where granularity and AI play pivotal roles
2. Encourage cooperation between different business units - breaking down silos can lead to more synchronized decision-making and better use of POS data
3. Transition from traditional methods to using real-time POS data for forecasting to better predict market behaviors and enhance supply chain decisions
   
   

AI in supply chains isn't just about crunching data—it's about creating bridges between complex info and everyday decision-making.

In this episode, Lasse Holmstedt, VP of Engineering at Alloy.ai, joins us to discuss the dynamic role of generative AI and large language models within the consumer goods sector. Throughout the conversation, we delve not only into the innovative application of AI technologies that encompass forecasting and data harmonization but also highlight the critical importance of maintaining clean and accurate data for AI effectiveness.

Listen in for a deeper understanding of the potential and pitfalls of using pre-trained AI models. Along the way we’ll also explore strategies to safeguard sensitive business data and discover how AI can transform sales and demand planning processes.

Three Key Takeaways:

• Explore leveraging textual and qualitative data in addition to numerical approaches to enhance decision-making processes while mitigating risk
• Consider applications of AI for enhanced visibility and predictive capabilities in inventory management, forecasting, and cross-platform product matching for more efficient operations
• Establish a clear data governance framework to ensure that data fed into AI systems is accurate, up-to-date, and free from errors, laying a solid foundation for reliable AI outputs