Chapter 3
Building an AI-fuelled supplier collaboration strategy
Effective supplier collaboration is crucial for both a resilient and agile supply chain.
Our survey found that 53% of respondents are leveraging AI to enhance collaboration. However, significant challenges persist. Data sharing and integration (46%), risk management (41%), and building trust and collaboration (41%) were identified as the primary hurdles.
These challenges underscore the need for standardised data protocols, secure platforms, and collaborative frameworks to foster seamless and transparent communication with suppliers.
AI can play a pivotal role in addressing these challenges by automating data exchange, providing real-time risk assessments, and facilitating collaborative decision-making.
What challenges do you face when collaborating with your suppliers? (Respondents were asked to select all that apply)
Building trust and collaboration
Data sharing and integration challenges
Risk management
Ensuring sustainability and compliance
Innovation and collaboration
Lack of transparency
Conflicting priorities
Communication barriers
Supplier performance management
Managing supplier relationships
"For me, I'd say conflicting priorities, communication barriers and risk management are the key challenges. That being said, I don't think that building trust is necessarily a challenge. It is just a matter of having clarity on the priorities for both the supplier and the customer, and setting the conditions properly. I think we just need to be cognizant of what matters to both parties, and try and find the win-wins."
David Ruiz, Head of Digital Supply Chain Strategy & Execution
Do you leverage AI when it comes to collaborating with suppliers?
Yes
No
“AI is only as powerful as the people using it. The survey shows that lack of internal expertise is a barrier to AI adoption. To maximize impact, pharma companies should focus on change management, upskilling teams, and leveraging intuitive AI tools that simplify decision-making—like conversational AI assistants that provide instant insights without requiring technical expertise."
Toby Keech, SVP Sales, EMEA at project44
"While we do not currently use AI for supplier collaboration, I think the technology will be useful for certain processes, like evaluating several suppliers for a specific product or service. I think it also depends on your business model, however. For some, you may have a pretty fixed amount of suppliers, so it may not make sense to use AI in this way. The industry is also relatively slow changing, and contracts are usually fairly standardised, which again may limit how effective AI is for this purpose."
Stefano Chiei, Director Operations EMEA/EE, Advanced Bionics
Which areas have AI brought the most value to your supply chain over the last 12 months? (Respondents were asked to select three options)
Inventory optimisation
Customer service
Quality control and assurance
Logistics and distribution
Supply chain visibility and risk management
Supplier collaboration
Decision support
Demand forecasting
Cost reduction
Manufacturing planning and optimisation
"The fact that most of the use cases are internal is not a massive surprise, as most AI projects are built on enterprise data lakes. It’s interesting that the areas that are listed as having most value in another question (supply chain visibility, manufacturing planning) are not high on this list, which is likely because they require a lot of external data, and getting multi-party data into an AI data model requires a supply chain network which most companies don’t have."
Shirell James, Vice President, Blue Yonder
