The mission of our Supply Chain AI Lab is to develop novel tools and methods for understanding and handling emergent outcomes in industrial systems. To that end, our team of researchers studies complexity science, emerging artificial intelligence technology, and nature-inspired and agent-based computing techniques.
Supply Chain Lab Project
Individual Dynamic Capabilities and Artificial Intelligence in Health Operations: Exploration of Innovation Diffusion
This research investigates the integration of individual dynamic capabilities (IDC), artificial intelligence (AI), and the Technology Acceptance Model (TAM) within health operations to evaluate their role in fostering innovation diffusion in healthcare. A convergent, multifaceted research approach encompassing quantitative and qualitative methodologies was employed, commencing with a systematic review of the extant literature.
Supply Chain AI Lab
We create methods to discover hidden patterns in data that yield useful insights for improving supply chain operations. These insights can be used to forecast deliveries, disruptions, find out hidden information, estimate risks, predict quality of goods and even estimate the best price for procurement negotiations. Once the system state has been predicted, then autonomous algorithms can control daily low-level operations to nudge supply chain systems to a more desired state. Some of our current activities include:
Concept X
Autonomous supply chains with agent-based systems
Concept Y
Study and detection of self-organization and emergence in supply networks
X and Y Model
Digital twins and their interaction
Our network enables us to collaborate with our affiliated companies from all involved centers on research and discussion of various areas, thereby pooling their expertise and facilitating global projects that create supply chain and logistics innovations. These innovations help companies compete in an increasingly complex business environment.
Predicting Hidden Supply Network Dependencies
Predicting and extracting dependencies from the "hidden" or "invisible" parts of a supply network is a key area of ongoing research for the Supply Chain AI Lab, which has led to a number of innovative and impactful approaches, collaborations and a start up.
Predicting Supply Chain Disruptions
The aftermarket segment's attractiveness has allowed Original Equipment Manufacturers (OEMs) of complex engineering systems to transition from a pure manufacturing role to a position as providers of through-life engineering services (TES). The provision of high-value engineered product sales in conjunction with product support services has enabled companies to offer TES strategies for their customers, ensuring the product's functionality throughout its design life. This strategy allows OEMs to enhance their competitive position in the market, securing a competitive edge and safeguarding or expanding their service revenue streams. To enable TES solutions, OEMs must have unique access to in-depth technical product knowledge and analytics on big operational data. However, the challenge lies in designing a reusable and optimized TES architecture that can be implemented across a wide range of industry use cases.
Fundamental AI
We develop the fundamental AI technology to address Supply Chain challenges in a trustworthy, explainable and reliable way. Some of our key foci include:
Uncertainty: A missing ingredient from many supply chain machine learning deployments is the measurement of uncertainty. Without reasoning on the uncertainty of these systems, manufacturers are exposed to risk of overconfident and erroneous predictions, hindering the adoption of promising ML techniques due to mistrust.
Cooperative AI: Supply networks are co-opetitive systems, where companies are embedded in a complex network that both are interdependent for their performance, but also might compete with one another. Can we build multi-agent systems, which learn to cooperate to achieve better outcomes at the system level?