In today’s fast-paced world, product development life cycles are shorter than ever, global trade is freely accessible and customer expectations are highly dynamic. To add to it, the uncertainties brought in by the pandemic and the growing competition across supply chains are further pushing businesses to do their best to survive and grow.
Modern-day supply chains are full of complexities and the pressure on businesses to enable quick deliveries, maintain good customer relations, build long term resilience against unforeseen events and stay profitable is massive.
In fact, the pandemic has exposed some serious problems and loopholes in supply chain management, and has given a true wake-up call to enterprises to adopt flexible and proactive business processes, mitigate the risks involved in supply chain operations, and find ways to be more cost-effective and efficient.
Some of the top challenges facing supply chains include:
- Manual, redundant and time consuming planning/backend processes
- Suboptimal logistics planning and ineffective execution of on-ground operations
- Rigid operating systems that are not flexible and resilient enough for a pandemic-struck world
- Lack of visibility throughout disparate supply chain activities
- Inability to harness and use readily available business data for better decision making
Thankfully, advances in artificial intelligence are now transforming supply chains across the globe, adding brilliance in day-to-day business operations and decisiveness in strategic long-term planning. Global research and advisory firm, Gartner defines artificial intelligence as the technology that applies advanced analysis and logic-based techniques, including machine learning (ML), to interpret events, support and automate decisions, and take actions.
It is becoming increasingly clear that multinational conglomerates have no option but to infuse AI in their supply chain. They need geography and industry-agnostic solutions which means different aspects of AI come into play in such a scenario. For example, Natural Language Processing aids in geocoding, deep learning aids in routing and understanding traffic patterns, and machine learning helps in digesting historical logistics and supply chain data and gleaning insights from it.
As more and more businesses have started to realize the importance of digitization, automation and streamlined business processes, there is a rapid growth in the adoption of AI in the supply chain. According to a McKinsey report, 63 percent of businesses reported revenue increases from AI adoption in the supply chain.
Here are some ways AI and ML can optimize your supply chain, and help you build strong competence in the days to come.
Minimizing human dependency in the supply chain
From planning to execution and decision making, supply chain processes have traditionally been dependent on human intelligence, judgement and productivity. Manual operations often lack efficiency and accuracy. Implementing AI in your supply chain can eliminate time-consuming and error-prone manual work, automate repetitive tasks and processes across supply chain functions and make your entire logistics and back end supply chain processes faster, more productive and reliable. It also helps in analyzing heaps of business data, and arriving at meaningful insights for better business predictions and decision making.