In today's rapidly changing business environment, staying ahead of the competition requires constant innovation and adaptation. Supply Chain Management, a domain that is constantly under stress, has seen significant advances in recent years through the use of artificial intelligence (AI). By using tools and solutions augmented by the technological and methodological capabilities of AI, including machine learning (ML), companies can improve their demand forecasting processes and level up their operational excellence.
This directly impacts their efficiency and the need for them to achieve cost savings.
Machine learning is a type of AI that allows software applications to more accurately predict outcomes without being explicitly programmed. To do this, it’s made of algorithms that are able to automatically learn and improve their capabilities. Use cases include recommendation engines, fraud detection, cyber threat detection but also business process automation (BPA), and predictive maintenance.
Regarding Supply Chain Management, solutions augmented by AI technologies (AI-driven) provide decision-makers with critical information, enabling them to make more informed choices in aspects including demand forecasting, forecast returns, reducing out-of-stock, new product forecasting, and price optimization.
According to McKinsey, AI-based solutions for supply chain (including capabilities like prediction models and correlation analysis) leads to a better understanding of causes and effects in supply chains, improving: demand-forecasting models, end-to-end transparency, integrated business planning, dynamic planning optimization, automation of the physical flow.
ML models are designed to bring significant benefits to the business through their adaptability and accuracy. For example:
The supply chain, as well as all operations in the enterprise, can take advantage of the contribution of new technologies, including Automation and AI, and digital approaches such as Data-Driven strategy. Immediate expected outcomes include streamlined workflows and increased process efficiency.
Technological areas include the Internet of Things (IoT), artificial intelligence and machine learning, advanced analytics and predictive analytics, Optical Character Recognition (OCR), and warehouse robotics, but the list doesn't end there.
Forecasting is a key element of supply chain management. It provides managers with the necessary elements to plan production cycles more efficiently and to operate with the levels of agility and transparency that are required in changing production environments. Accurate insight allows them to compare market demand forecasts with actual inventory levels in order to ensure sufficient supply to meet delivery schedules. This capability is especially critical when products are stored on just-in-time basis constraints.
Effective supply chain forecasting empowers operations managers with the crucial operational intelligence required to proactively eliminate bottlenecks at their source. AI-driven platforms are designed to predict these bottlenecks so that they can be avoided. Indeed ML algorithms optimize distribution planning and logistics control processes. They can be used also to design simulation tools. This facilitates the balancing of key operational components such as inventory management, packaging, transportation, and outbound logistics.
