In the context of supply chains and logistics, many companies have been exploring AI-driven solutions to enhance their workflow efficiency and overcome the complexities involved in managing the movement of goods from companies to the end consumer. The fast-evolving situation left many supply chain operators grappling with an unprecedented level of uncertainty as long-standing conventional supply chain management models proved insufficient to cope with the scale and complexity of the disruptions. The predicament compelled supply chain organizations across industries to seek more innovative tools and technologies. Consequently, a substantial number of them turned to AI solutions due to the advantages of the technology.

The next wave of technology – artificial intelligence (AI) – is already making sense of the deluge of operational data streaming in from a plethora of devices and cloud applications. This technology is also applying advanced mathematics to create products, processes, and systems that can adapt and learn.

The CSAI® examination tests members in major instructional areas in Machine learning and its subset which is Artificial Intelligence in Supply Chain management

Machine learning is becoming mainstream in the operational technology portfolio. Baseline forecasts for new products are instrumental in determining new product introductions and go-to-market plans. When applied to demand signals, AI can determine changing demand behaviors and optimize inventory levels and replenishment plans to feed the continuous loop of product life-cycle management.

Sales and operations planning is the ultimate collaborative decision-making process. Companies can apply AI technologies to sales and operations planning and other massive supply chain data pools to manage demand volatility, supply constraints, production scheduling, and dynamic distribution. AI can augment human interaction by allocating resources, assigning people, and scheduling processes.

Machine learning capabilities apply algorithms to massive operational data feeds to discover insights to track and predict supply chain disruptions, providing new levels of visibility into day-to-day operations. These capabilities can also recommend alternative actions for unplanned events and transportation disruptions. Weather data integrated with operational data can predict potential problems and alert transportation and logistics service personnel with recommended actions.

Topics covered in the certification tests are

• Machine learning in Supply Chain
• Machine learning (ML) techniques
• ML algorithms
• How to apply ML in demand forecasting, sales and operation planning (S&OP), and inventory management
• ML in production planning and predictive maintenance
• Forecasting
• Advanced analytics techniques using engine downtime predictive modeling example