The Drug, Chemical & Associated Technologies Association (DCAT) recently released its latest study from DCAT Research & Benchmarking, “The Emerging Role of Artificial Intelligence (AI) in Supply Chain Management.”
 
The bio/pharma industry is moving quickly to apply AI in critical areas, most notably drug discovery. Still, it has not been clear to what degree supply chain management has been targeted as an opportunity by the industry.
 
Since the implications of AI are so significant for companies across the bio/pharma supply chain, the DCAT Research & Benchmarking Committee wanted to benchmark the uptake of AI in supply chain management.
 
To shed light on the development, DCAT conducted an online survey containing 20 content questions and several demographic questions. A total of 41 companies participated in the survey, including 12 bio/pharmaceutical companies and 29 suppliers.
 
The main objectives of the study were to:
 

• Gauge the status of AI in supply chain management in the bio/pharma industry;
• Understand the challenges when implementing the technology in the bio/pharma environment;
• Understand what the barriers to adoption are—where it is not being tried or adopted;
• Help bio/pharma companies and their suppliers understand how the adoption of AI will impact how they work together and how it might change their relationship.

 
See key findings from the study below:

Strategic Importance of AI

According to the survey results, companies that are already using AI in supply chain management are doing so as part of a corporate commitment to the technology.
 
Over 80% of companies already using AI for supply chain management consider AI to be “very important” to achieving corporate strategy objectives, but only 20% of non-user companies view AI that way.

Common Uses of AI in Supply Chain Management

Among respondents that are actively using AI in supply chain management, the most common use cases are for demand forecasting, logistics, and inventory management. More companies are also pursuing initiatives in supply chain risk management.

Barriers to Adoption

The non-user responses indicate that adoption is being held back largely by a lack of understanding of the benefits and implications of supply chain AI applications and the absence of expertise to mount an effort to pilot an AI application.

How AI Systems are Being Used

Most AI users use the system to identify problems and opportunities and to recommend what actions to take; none are allowing systems to act independently.
 
Machine-learning applications are most common; just 20% have experience with generative AI.
 
All users are leveraging vendors for applications and support, but about 30% have been building in-house capabilities as well.

Challenges to Implementing Supply Chain AI Applications

Data availability and confidentiality are by far the biggest challenges for implementation of supply chain AI applications. Most AI adopters are feeding their applications with a combination of internal and external data.
 
Non-adopters expect to have similar problems when they do finally adopt. GXP compliance and acceptance by regulatory agencies are also concerns.

How Will AI Impact Suppliers?

Late adopters will need to prepare now to respond as their customers implement AI-based systems with which they will have to interact. Per the survey results, over half of respondents with AI experience indicated that their suppliers will be expected to provide more data about their operations, requiring suppliers to invest more in their information technology.
 
Adopters all expect suppliers will need to be nimbler as AI applications are likely to lead to more frequent changes, such as redirected shipments and altered specifications.
 
Person-to-person relationships will be augmented, if not supplanted, by interactions with AI-driven systems.