The demand for digital tools and data analytics in supply chain logistics is surging, driving a need for increased expertise and core data science teams across various functions. At the recent Automotive Logistics and Supply Chain Digital Strategies North America conference in Nashville, Tennessee, industry leaders discussed the push for digital democracy in the supply chain.
A director of data science and machine learning at a major automotive company highlighted the growing requests for machine learning (ML) and artificial intelligence (AI) applications. The pandemic and subsequent semiconductor supply chain disruptions underscored the importance of supply chain resiliency. “We must sense risks through all tiers, regardless of the component causing disruption,” the director noted.
The adoption of digital tools introduces new complexities, especially as data from third-party logistics providers is integrated. The use of digital twin technology in manufacturing systems exemplifies this complexity, particularly when building a digital supply chain replica with active data from various sources.
“Managing external data involves more than just technology—it’s also about people and processes,” stated a chief architect for the automotive industry at a technology infrastructure firm. The complexity necessitates a consistent strategy to manage these elements, emphasizing the need for automation.
Automation of processes will manage this complexity, drawing parallels to the shift from mass production to mass customization. “Future systems will provide levels of automation currently unimaginable, reducing the human-in-the-loop requirement,” the architect predicted.
Empowering individuals with data tools and training is driving a shift in roles, blurring the lines between IT and business. The democratisation of data enables business users to generate analytical reports independently, facilitating informed decision-making. “Providing tools to local teams allows them to make rational decisions based on data,” the architect added.
This capability has yielded quick wins for business teams, enabling a focus on long-term solutions rather than ad hoc reactions. “Understanding data requirements and usage is crucial for data democracy,” the director emphasized. Consistent data alignment across the supply chain is essential for efficient service delivery.
Consistency hinges on a common operating platform and semantic layer. “Building data products consistently with an enterprise master data management strategy is essential,” the architect noted. This approach supports automated business applications and accelerates decision-making processes, particularly in procurement.
Ultimately, digital technology is enhancing risk management and building supply chain resiliency. Predictive models and ML are used to assess supplier risks and prioritize actions to prevent inbound freight disruptions. These models help identify necessary changes in the supply chain, ensuring smooth operations and reduced reliance on premium freight services.
#ICTTMNews #BreakingNews #SupplyChainNews #DigitalTransformation #DataAnalytics #LogisticsInnovation #AIandML
