Covid-19 has left one very destructive economic issue in its wake: disruption to global supply chains. The rapid spread of the virus in 2020 prompted shutdowns of industries around the world and, while most of us were in lockdown, there was lower consumer demand and reduced industrial activity.
As lockdowns have lifted, demand has rocketed. And supply chains that were disrupted during the global health crisis are still facing huge challenges and are struggling to bounce back.
However, there is hope on the horizon, and it is coming in the form of Artificial Intelligence (AI). The technology is improving the supply chain in myriad ways, from optimizing inventory management to enhancing warehousing and storage processes to automating critical elements of the supply chain. If properly executed, supply chain AI can improve logistics drastically at a time when every minute counts.
Ashwini Thota, Analytics and AI Leader at Bose Corporation, shares how AI can help mitigate supply chain troubles.
How supply chain AI has the ability to improve logistics drastically at a time when every minute counts.
AI is revolutionizing the supply chain industry. It’s fundamentally transforming logistics by automating and optimizing various processes, such as demand forecasting, inventory management, and route optimization. This can lead to increased efficiency, reduced costs, and improved delivery times. Additionally, leaders are increasingly relying on AI to early-detect potential issues in the supply chain, such as bottlenecks or disruptions, allowing organizations to address them before they become major problems. With the ability to process large amounts of data and make complex decisions quickly, AI can also enable real-time tracking and visibility of goods in transit.
How can AI-driven technologies provide continuous surveillance of warehouse, retail, and industry inventories, and can autonomously order new materials when supply levels reach a critical level?
Cloud computing, coupled with advances in AI technologies, can provide continuous surveillance by using various techniques such as computer vision, machine learning, and sensor data. For example, computer vision, a subfield of AI, can be used to track and count items in a warehouse or store, while sensor data can be used to monitor inventory levels in real time. Machine learning algorithms can then be applied to this data to predict demand, identify patterns and anomalies, and make recommendations for replenishment.
When supply levels reach a critical level, AI-driven systems can autonomously order new materials. This is done by setting up rules or thresholds in the AI system that trigger replenishment orders when inventory levels fall below a certain point. The AI system can also take into account other factors, such as lead time, supplier availability, and shipping costs, to make the best replenishment decision.
This approach is known as “autonomous replenishment” or “AI-driven inventory management,” which can improve efficiency and reduce costs by minimizing stockouts, overstocking, and waste. Furthermore, it can also help to ensure that the right products are always in stock and readily available to meet customer demand, which is especially important for businesses that rely on fast-moving products or just-in-time delivery.
How AI can help capture accurate data on market trends, customer behaviours, and related metrics to predict short-term and long-range supply needs?
Organisations can combine external and internal datasets to understand the customer comprehensively.
Natural language processing (NLP) can be used on textual to analyze customer feedback, reviews, and social media data to identify patterns and trends in customer behavior and preferences. Product reviews are one such example. With the advent of digital marketplaces like Amazon, customers are increasingly sharing their opinion about the product, purchase experience, price, etc. This direct access to customer reviews is allowing organizations to develop machine learning algorithms to make predictions about future demand and identify opportunities for new products or services.
Big data analysis can also be used to gather and analyze large amounts of data from multiple sources, such as sales data, weather data, and economic indicators. This data can be used to create detailed profiles of customers and market segments and to identify patterns and trends that can be used to predict future demand.
AI-based forecasting models can also be used to predict short-term, and long-range supply needs. These models use historical data and patterns to make predictions about future demand, taking into account a variety of factors such as seasonality, promotions, and special events. These predictions can then be used to optimize inventory levels and identify potential shortages or surpluses.
AI-based technologies are helping organizations to forecast demand, optimize inventory levels and make better decisions in every stage of supply chain management, which can lead to improved efficiency, reduced costs, and increased customer satisfaction.
What are the main factors or trends shaping supply chains across the world?
There are several factors and trends that are shaping supply chains, including:
Globalization: The increasing interconnectedness of the global economy has led to more complex and far-reaching supply chains, with goods and services being sourced and delivered from locations all over the world.
Digitalization: The rapid advancement of digital technologies, such as the Internet of Things (IoT), big data, and AI, is enabling more efficient and transparent supply chain management by providing real-time visibility of inventory and logistics.
Sustainability: The growing awareness and concern about the environmental and social impacts of business activities are leading to a greater focus on sustainability in supply chain management, with companies striving to reduce their carbon footprint and improve their social and ethical performance.
E-commerce: The continued growth of e-commerce is leading to increased pressure on supply chains to deliver goods quickly and efficiently, with many companies turning to technologies like drones and autonomous vehicles to help meet these demands.
Resilience and Agility: The uncertainty caused by global events such as pandemics, natural disasters, and trade disruptions has made companies focus more on supply chain resilience and agility, which means being able to quickly adapt to changes in demand and supply, and minimize disruptions.
Government regulations: Governments around the world are increasingly enacting regulations aimed at improving supply chain transparency, security, and ethical behavior, which companies must comply with.
These factors and trends are driving the need for more sophisticated and innovative supply chain management practices, which are being enabled by technologies such as AI, IoT, and automation.
How can technology play a pivotal role in catering to the changing supply chain requirements for e-commerce?
Technology can play a pivotal role in catering to the changing supply chain requirements for e-commerce by providing solutions for automating logistics and inventory management, optimizing transportation routes and delivery schedules, and enabling real-time tracking and visibility.
Some of them are as follows:
Automation: Automation technologies such as warehouse robots, drones, and autonomous vehicles can be used to improve the speed and efficiency of order fulfilment, which is critical in e-commerce, where customers expect fast delivery.
Real-time tracking: IoT technologies can be used to track inventory levels, shipments, and deliveries in real-time, providing e-commerce businesses with the visibility they need to ensure that products are always in stock and readily available to meet customer demand.
Predictive analytics: Machine learning and artificial intelligence (AI) can be used to analyze customer data and predict future demand, allowing e-commerce businesses to optimize their inventory levels and make better sourcing decisions.
Personalization: AI-driven technologies can be used to personalize product recommendations, promotions, and marketing messages to individual customers, which can improve customer engagement and increase sales.
Fraud detection: Advanced security solutions such as blockchain, biometrics, and encryption can be used to secure online transactions and protect against fraud while providing a seamless payment experience for customers.