The COVID-19 pandemic has caused significant disruption and volatility to supply chains across industries, and they have become increasingly challenging to manage. As a result, organizations need flexible supply chain operations and infrastructures in the face of dynamic market conditions on top of navigating the environmental impact of existing supply chain conditions. 

Supply chain management (SCM) and artificial intelligence (AI) will address their challenges, improve operations, and enhance decision-making from sales, production, procurement, and logistics. 

AI engineers in SCM are pivotal in helping organizations fix SCM operations and processes and are instrumental in implementing AI in the supply chain. AI supply chain operations are the future of supply chains, improving transparency and visibility into all areas of marketing, planning, and distribution.

Modernized SCM Capabilities

SCM automation is being used in many different capacities to help streamline processes and operations across manufacturing, warehousing, inventory, and transportation. AI engineers in SCM are providing the support to operationalize and implement AI initiatives by utilizing critical job capabilities like analysis, model development, and prediction. 

AI engineers help build the AI applications and systems that enhance SCM's efficiency, productivity, and performance. Here are some of the ways that AI engineers are supporting SCM functions:

Predictive Analytics

Predictive analytics is used for demand forecasting, which is critical post-COVID as supply chains overcome volatility and fluctuating demand. Predictive analytics is also used for inventory management to optimize inventory levels by specifics such as regions or countries. Additionally, predictive maintenance is helping organizations preemptively anticipate machine downtime and ensure ongoing operations. Predictive analytics is even being used for route optimization to improve shipping and logistics. 

Digital Twins

Supply chains are building digital twins that are virtual representations of physical environments to run behavioral simulations. This requires end-to-end data integration and helps companies improve performance tracking, enhance visibility, bring predictive capabilities, and enable automation. Digital twins help businesses improve decision-making, reduce operational costs, enhance product and service development, and overcome operational bottlenecks.