In today’s supply chain ecosystem, manufacturing buyers are beginning to shift away from a purely transactional mindset. Seeing more and more supply chain partnerships taking on a collaborative, “Vested” approach is great news for the health of these relationships, but the complexity of the modern global supply chain can make this extremely difficult to put into practice. 

As complexity in global supply chains increases, AI becomes an increasingly important enabler of collaboration, helping to empower the stronger, more collaborative partnerships that today’s manufacturing buyers expect. 

As AI tools continue to offer increased visibility and transparency to supply chain operations, it’s only natural that manufacturing buyers will increasingly expect these types of resources and insights from their partners. A good example is how Walmart uses machine learning in its global supply chain to help improve demand forecasting and customer trend tracking, enabling it to reduce overstocks and stockouts by shifting inventory based on local demand. 

As these types of integrations become more commonplace across the board, manufacturing buyers expect their partners to keep up. They look to their partners to use AI to enable faster, smarter results that encourage collaborative innovation and process optimization. They expect partners to be more proactive about common issues like delays or product shortages. And above all, they expect the large influx of data to improve performance measurement. 

For supply chain partners, it becomes critical to understand how to integrate AI in a way that enables proactive (or even predictive) supply chain management. AI integrations that enable improved manufacturing forecasting or increased manufacturing agility can help define the overall strategy partners use to drive mutually beneficial results.  

However, both sides need to understand how AI will be used, and what tasks it will be helping with. Transparency in AI use is critical for building and maintaining trust, and partners should be able to clearly demonstrate how it delivers value to both parties.  

Increased expectations for supply chain partners can be daunting, especially if partners aren’t ready with the right data and technology setup. 

A report from OroCommerce advises partners to focus on developing “unified” tech networks, arguing that tech debt from fragmented systems limits AI access to clean and accessible data, while also harming employee productivity. Providing a single, authoritative data source built for B2B operations streamlines processes for manufacturing buyers with deeper tech integrations that ensure accurate information at every stage. With 67% of procurement professionals making the majority of purchases through digital channels, these unified networks, powered by AI, help ensure a friction-free process for all parties. 

This means that supply chain partners need to adapt quickly to ensure their systems are fully ready for data compatibility and integration. Terms like clear data governance, transparent algorithms and decision logic may seem confusing at first, but understanding how they come into play will help partners identify clear use cases for AI and related tech applications, helping them build trust and confidence in their tech use. 

Ultimately, these applications shouldn’t just be focused on producing prettier spreadsheets in a shorter amount of time. The goal is to create a more proactive and supportive environment, where partners can act as true collaborators, using AI-powered insights to deliver worthwhile strategic change. 

Collaborating to Create Supply Chain Wins 

Of course, the strength of a supply chain partnership relies on both parties. When individual partners already have a strong tech foundation, they become better positioned to evolve and improve their partnership so they can focus on shared goals and innovation. 

Key to this is building a shared data ecosystem that gives both sides full visibility into the relevant KPIs for their partnership. Clearly defined, measurable and mutually beneficial outcomes should drive the partnership, rather than simply focusing on direct transaction costs. This requires partners to break down data silos within their organizations and establish a base foundation of trust. Without trust and alignment in AI usage, partners will struggle to fully integrate their tech options or use them to their full potential. 

Ultimately, AI is just one part of the picture in these successful collaborations. However, proactively including AI in discussions regarding the scope, goals and KPIs of any supply chain partnership should be a priority to ensure both sides are in alignment and have clear goals for how to use AI to further enhance collaboration related to other goals. 

The demand for shared intelligence, as well as supply chain agility and transparency, are on pace to become the standard expectation, rather than a unique differentiator in global supply chain partnerships. 

The tools may change, and the demands may seem more stringent, but in reality, this reflects the baseline of trust and knowledge-sharing that has always been foundational for successful supply chain collaborations. AI-enhanced partnerships don’t change the principles behind these truly collaborative partnerships, but they do make these goals more attainable than ever. 

Kate Vitasek is Distinguished Fellow, Global Supply Chain Institute at the University of Tennessee, and founder of Vested.