How AI Can Fix Broken Supply Chains

Making supply chains smarter with AI has become necessary. Enterprises hope to lower costs and risks, increase efficiency, and stabilize supply channelsto withstand any scenario.
Supply chains globally are broken and are in dire need of repair. The decreased workforce, a shortage of proper skills and constant disruptions have become a seemingly permanent situation even after the pandemic has normalized. These emerging gaps are already being taken over by intelligent technologies like AI, with algorithms and bots working alongside humans on factory floors as well as assisting across all stages along the supply chain. One market survey shows that 25% supply chain professionals worldwide plan to invest in AI over the next three years.

But why do supply chains need fixing?

Mostly because they are not agile enough to withstand changing market conditions or disasters like the COVID-19 pandemic. As an example, manufacturers want to produce smaller batches faster to address spikes in demand across geographies and markets. But they need to deal with inflexible, slow-moving manufacturing models that only support production at scale and are not nimble enough for just-in-time batch production/ production on demand. Even well planned lead times are easily upended by disruptions, the impact cascading down the entire supply chain with potentially disastrous outcomes. Legacy infrastructures and fragmented technical ecosystems make it impossible for supply chain managers to get the right data in time, track movement along the chain etc. This creates friction, slows down inventory turns and escalates costs. Besides, a disparate system would mean that data is being sourced from multiple points thus increasing chances for error.

Applying AI

AI-driven supply chains unlock efficiency by avoiding shortages, reducing costs, increasing sales and improving customer service. AI is perhaps the only way to handle crisis situations systematically, ensure end-to-end visibility and enhance decision-making with AI-powered data collection and analysis, and ML algorithms To begin with, AI offers deep contextual intelligence instead of flinging random data at supply chain managers. Combined with ML, AI provides rich insights into multiple aspects, including logistics and warehouse management, inventory optimization, and customer service. AI takes over the heavy work of data collection and analysis at scale and speed, offerings recommendations to address disruptions, opening new transparency into data, and delivering insights beyond simple statistics for a real view of supply chain performance.

Factories and logistics

On the factory floor, AI helps unravel complex sensor data and monitor machine performance to prevent expensive line shutdowns that can cause production and shipping delays. Several manufacturers are also using AI to manage logistics. For instance, automatically sorting out packages past expiration dates for packages, recording accurate data and ensuring timely shipping.

Inventory management

Maintaining inventory levels and stock distribution is the foundation of supply chain management. But inventory management is a complex process, that involves avoiding understocking, overstocking, or sudden stock-outs. Automating inventory management using AI reduces errors and makes the entire process more predictable by identifying patterns and trends. AI/ML tools are already being used by distributors to cull out granular information for each location and stock item, including specific details like if an item is a return or an outgoing order, if it’s in the warehouse or the store shelf, if it’s in for repair, and so on. AI can also offer insights on storing costs split by locations so that managers can reroute and distribute stock efficiently, and help maintain stock levels to continuously address demand and enhance reverse logistics.

Supplier and customer management

Enterprises typically receive 265 Bn service calls resulting in an expense of USD 1.3 Tn per year. A large proportion of these calls are from suppliers and customers with basic queries. AI-based chatbots can easily address routine queries and even handle predictable issues. Chatbots reduce response times and free up call center agents to address more complex queries.

Fleet management

AI-powered fleet management tools ease the burden on fleet managers by providing them with better insights extracted from data such as real-time tracking of delivery vehicles and weather and traffic data. AI tools can also highlight bottlenecks and recommend alternative routes or delivery schedules thus reducing unplanned fleet downtime, delivery costs and fuel inefficiencies.

Summary

AI-based supply chain management tools are powerful and can help managers tackle even the most inimitable challenges. AI can be applied throughout the ecosystem by better equipping enterprises to handle constraints and operational disruptions, boost efficiency, improve demand forecasting, automate operations and mitigate rising costs. According to a recent study, the Global Artificial Intelligence In Supply Chain Market size is valued at USD 4.8 Bn in 2020 and is expected to reach USD 14.3 Bn by 2028. Obviously, AI is increasingly being seen as a technology vital to driving effective decision making and building resilient supply chains.