In a recent exploration of the evolving landscape of supply chain applications, the International Centre for Trade Transparency and Monitoring sheds light on the pivotal role played by Artificial Intelligence (AI) and Machine Learning (ML) in boosting business resilience. The story unfolds during a chance encounter with a local kite manufacturer, unveiling the potential of AI/ML solutions to enhance adaptability, navigate supply chain intricacies, and respond dynamically to market forces.
This real-world example of the kite manufacturer serves as a microcosm, illustrating how machine learning solutions can revolutionize manufacturing processes on a larger scale. The ability to swiftly adapt to customer demands, efficiently handle supply chain challenges, and competitively price products are key advantages highlighted in this narrative.
The article delves into the evolving nature of manufacturing business goals, emphasizing the transformative impact of pandemic-induced shifts in consumer behavior and economic uncertainties. To effectively navigate this complex ecosystem, businesses are urged to invest in digital infrastructure that enables quick responses to unforeseen challenges.
Three overarching goals for businesses seeking successful digital transformation are identified:
- Improving Profitability: With a focus on sustainable practices.
- Enhancing Workforce Efficiency: Retaining and improving operational productivity.
- Customer Growth: Building a robust product portfolio to meet future market demands.
AI and ML strategies emerge as instrumental tools to achieve these goals, leveraging the wealth of decision history data for heightened agility and competitiveness. The article introduces the concept of AI/ML Decision Engines, explaining their role in aiding businesses to make informed decisions swiftly.
Key Components of a Decision Engine Include:
- Digital Business Platform: Supporting big data pipeline, orchestration, and analytics.
- Machine Learning Platform: Enabling the development and maintenance of multiple models.
- Composable Architecture: Facilitating integration with various business systems.
The kite maker’s success story serves as a testament to the potential of AI/ML Decision Engines, which empower companies to address critical questions such as what to do, when to act, how much to produce, and why a specific action is recommended.
The article also explores the practice of demand forecasting, demonstrating how ML-based forecasting can leverage past information, incorporate external factors, and enhance adaptability to new product introductions or market expansions. Recommender systems, another application of ML, are highlighted for their role in improving customer ordering experiences and guiding workforce learning and development.
In conclusion, the International Centre for Trade Transparency and Monitoring underscores the transformative impact of AI/ML in reshaping traditional supply chain practices. The ability to achieve significant improvements in demand and supply planning, emulate the agility and personalization seen in the kite maker’s business model, and enhance the overall customer experience positions AI/ML as a strategic asset for manufacturers facing common supply chain challenges.