AI and Predictive Workflows in Supply Chain Management

We all know this story. Yet…

It was December 2019, and most of us heard about a bizarre disease outbreak in the Wuhan province of China but it was just another news for most of us. But the world was unaware of what was in store for it the New Year.

Come January 2020, and the disease, by then named COVID-19, spread to other parts of Asia and then worldwide in early 2020. The World Health Organization (WHO) declared the outbreak a public health emergency of international concern (PHEIC) on 30 January 2020.

The world almost came to a standstill – life as we knew it until then got over. Factories around the globe shut down for indefinite periods, and with that every other industry related to manufacturing including logistics and supply chain came to a sudden stop with a jolt.

But the period that followed also witnessed the rise of Artificial Intelligence (AI), as the pandemic prompted industries to adopt rapid digitization. If AI was considered an add-on service before 2020, it soon conquered grounds to be the leading technology that every industry has been chasing since.

The COVID 19 also illustrated just how fragile the global supply chain can be, highlighting the need for smarter tools to reduce delivery times and cut costs. The role played by AI in supply chain management revolution has been nothing insignificant, and is in the upward trajectory since the past half-decade, with no sign of slowing down. According to Marketsandmarkets Research Pvt. Ltd.: “AI in supply chain market is estimated to be USD 14.49 billion in 2025, and is projected to reach USD 50.01 billion by 2031, registering a CAGR of 22.9% during the forecast period” compared to around USD 500 million to USD 1 billion in 2018.

Let us discuss how and what aspects of AI automation found such an unprecedented footing in the supply chain industry.

Role of AI and Predictive Analytics in Supply Chain Management

The role of supply chain in supporting other industries like retail, manufacturing and logistics is crucial. However, these sectors have historically struggled with fragmented data and a lack of cohesive analytics strategy, as was evident during the COVID 19 times.

AI along with predictive analytics finds practical application in supply chain operations. With the help of AI solutions and predictive analytics, supply chains found new means to accomplish tasks such as forecasting, route optimization for drivers, cutting down on fuel consumption and lowering operational costs.

Let us break down the role of AI and predictive analytics in supply chain management:

• Enhanced Route Optimization: With the help of Internet of Things (IoT) and AI, it became possible to better plan and track the movement of goods from the origin till delivery. This largely helped logistics providers and supplier networks deployed across the supply chain to optimize logistics networks.
• Intelligent Inventory Management: AI tools for real-time inventory management can enhance supply chain visibility by automating documentation for physical goods and intelligently enter data whenever items change hands.
• Real-time Response to Queries: AI agents take natural language query and analyze data to deliver relevant responses. AI agents can work across business functions, such as procurement, supply chain management and logistics planning. These AI agents can go far beyond routine tasks and are instead making informed decisions based on the internal and external data sources that are input.
• Demand Forecasting using AI: With the help of AI and predictive analytics, businesses can now forecast demands — seasonal or otherwise – by analyzing people’s purchase history, and other factors.
• Smart Inventory Management: Using AI-driven automation and predictive analytics, businesses can maintain optimal stock levels while avoiding excess. Smart insights help in keeping warehouses lean and support better use of warehouse space, which boosts operational efficiency and costs, and also helps teams adapt according to changing market trends.
• AI-based production planning: Predictive analytics algorithms can forecast demand trends, which can help optimize production, thereby eliminating wastage and maximizing resource usage.
• Improved Supplier and Carrier Coordination: Real-time scheduling with the help of AI and predictive analysis reduces delivery gaps and miscommunication. With shared forecast, teams can plan more accurately with suppliers and carriers.
• Enhanced Customer Satisfaction: Accurate order fulfilment, faster deliveries, and reduced delays go a long way in making happier customers.

Conclusion

So, are you looking for the best AI solutions for logistics and supply chain optimization? DeepKnit AI can provide proactive AI solutions to meet your needs, so that your supply chains remain functional and smart for the longer run.

With our intelligent predictive analytics solutions, we can optimize and future-proof your supply chain.

Feel free to reach out to our experts who can guide you in the right directions.