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AI for Advanced Supply Chain Optimization

Automation and demand forecasting are at the forefront of supply chain improvements that leverage AI and machine learning (ML), but organizational culture change is critical to drive true optimization.

Supply chain optimization impacts every industry, from retail to manufacturing, transportation to warehousing. Machine learning and AI bring additional opportunities to tighten supply chain logistics using new sources of data and new techniques that can radically improve operations, most notably at the hyper-local level.

The market for AI in the supply chain is expected to reach $21.8 billion by 2027.

Successful AI Integration Into the Supply Chain


High-Value Use Cases

Optimization of Scheduling, Production, & Display Planning: The adoption of data science and machine learning techniques allows companies to optimize logistics and determine factors that affect performance, thus increasing productivity. It helps especially built-to-order producers, as AI helps harmonize constraints automatically.

By using embedded technology as part of the Internet of Things, (IoT) companies are better able to understand their customers. The more connected devices that can track and analyze customer movement throughout a facility, the easier it is to understand customer needs and optimize for maximum impact.

Demand Forecasting: Data science and machine learning techniques make it possible to track all the factors that can influence accuracy in demand forecasting at a scale that wouldn’t be possible for analysis by humans. Information such as weather or real-time sales can improve warehouse management and self-management of inventory systems.

Effective Selection of Suppliers: In an ideal world, AI systems would analyze big data sets and automatically sort out suppliers according to their delivery performance (e.g., on-time, in-full), their credit scoring, and internal evaluations that then enable people to make better decisions when selecting reliable suppliers, thus improving their customer service.

Automation: When it comes to machine learning and AI in the supply chain, a key step to optimization is automation – removing steps from the process that humans previously had to do manually. This frees up human energy to perform more strategic tasks and develop further system improvements.

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Chatbots: Increasingly, companies are turning to AI to deal with frontline supplier issues. Companies are able to reduce the time spent engaging with suppliers, as chatbots can tackle a large portion of common issues and transactions.

Best Practices

The potential gains of AI-powered systems in supply chain operations are huge; currently, data is siloed and hard to leverage. While AI isn’t magic and there are challenges to overcome, there are a few key steps that are critical when starting the journey towards Enterprise AI within the supply chain:

  • Explore datasets from multiple sources to build projects encompassing greater value; getting creative with data acquisition and prioritizing smooth (and privacy-compliant) access to datasets as they evolve is critical.
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  • Design and deploy data solutions into production for real-time insights.
  • Automate the workflows associated with these insights.
  • Carefully monitor the models in production to prevent drift and ensure they function as intended.

Dataiku for Supply Chain Operations

Dataiku is one of the world’s leading AI and machine learning platforms, supporting agility in organizations’ data efforts via collaborative, elastic, and responsible AI, all at enterprise scale. Hundreds of companies use Dataiku to underpin their essential business operations and ensure they stay relevant in a changing world.

Specifically, Dataiku can help organizations introduce AI-driven processes into the supply chain by:

  • Creating a collaborative space to explore and drive value with data. Dataiku allows everyone (from data scientist to business analyst) to contribute to data projects and engage with data to improve processes.
  • Enabling both immediate and robust analysis. Visual analysis features can inform snap decisions while robust machine learning modeling can ensure enduring predictions and optimizations.
  • Putting AutoML and operationalization within reach. Putting machine learning models into production and automating AI workflows doesn’t have to be a hassle; Dataiku makes this process a seamless part of a data project, enabling organizations to extract more value from their data faster.
  • Maintaining security and data privacy regulation compliance at every step of the data pipeline. Features like activity monitoring and fine-grained access rights by dataset, project, etc., allows for the controlled and targeted use of sensitive data (e.g., transaction information) without exposing it to everyone.
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