How AI is already working for supply chains — and how it will grow in the future

The leading factors driving their desire to optimize processes weren’t what most of us would have predicted, like macroeconomic pressures or changing customer demands. Instead, their top two motivators were reducing costs — and harnessing emerging tech, like AI. The AI hype train has arrived, and supply chain pros are ready to board.

The enthusiasm makes sense; after all, many of the other factors contributing to the need for process improvement — such as supply chain disruption, ESG demands, and growing competitive pressure — could also be addressed through enabling AI (read on for details about this later in this post). Other sources agree: per Gartner, by this year (2024), 50% of supply chain orgs will be investing in AI. 95% of supply chain leaders surveyed by IBM affirmed that AI is crucial for innovation success.

The question is: Are the AI capabilities the supply chain world clearly wants actually ready for widespread adoption?

Right now, the answer is a resounding...“it depends.”

To help clarify, we’ve compiled a few real-world examples of how AI is already being used in supply chains — and some cases where we may need to wait a bit longer to see AI’s full potential.

How is AI already being used in supply chains?

• Mistake and flaw detection, from the factory floor to the back office. One of AI’s strengths is pattern recognition — and, correspondingly, detecting deviations. On factory floors, workers use AI tools like computer-vision systems to scan products for quality and consistency. In back offices, AI-powered Process Intelligence tools like Celonis help illuminate opportunities, bottlenecks, and common errors, such as incorrect or duplicate invoices.
• Predictions and demand forecasting. Whether preventing shortages or overstocks, working with more accurate predictions gives a major competitive advantage to companies across the supply chain. For example, many are using machine-learning tools to measure historical performance against current trends and events, so they can more precisely predict which products their clients will want (and when they’ll want them) — yielding savings in both warehouse space and costs.
• Disruption management. With end-to-end process visibility and AI, supply chain workers can now react to crises — from vendor issues to pandemics to natural disasters — much faster.For example, companies are using Process Intelligence tools like Celonis as early warning systems for their supply chains to know when disruptions happen, understand what’s causing issues, and come up with solutions. AI can even integrate weather data with shipping information to flag potential delays and pinpoint exactly where goods are (down to individual shipping containers) so nothing gets lost in the melee.
• Greener, more efficient shipping and logistics. Gone are the days of tossing everything onto a truck and leaving the route to Google Maps. From international cargo transportation to last-mile logistics, AI-powered tools are upending transportation and route planning, giving companies greater insight into their shipping emissions. In tandem, intelligent, responsive, AI-informed routes are helping companies save money and CO2 while prioritizing speed.
• Automating repetitive tasks. Companies are using Robotic Process Automation (RPA) to let AI take over some high-volume, low-value tasks. Back-office processes are still an integral part of the supply chain, and RPA is smoothing out many of the snags and delays that come from repetitive tasks formerly assigned to humans. However, many companies find that RPA alone has some significant limitations — enabling it with Process Intelligence tends to work better, generating much more value.
• Smarter vendor selection and lead-time forecasting. Using insights from AI, companies are more easily choosing vendors that match their needs, budgets, regulatory guidelines, and timelines. AI is also supporting better relationships with customers and vendors through better lead-time predictions. Celonis’ own Supply Chain Network Visibility App uses AI within the object-centric data model to give businesses a more accurate picture of how long every step of their processes will take, with less over- or under-estimating leading to more trustworthy timelines for everyone.
• Payment management. In supply-chain-related processes like Purchase-to-Pay and Procurement, AI is already helping companies stay on track, automatically flagging unequal payment terms and patterns of late or early payment. In some cases, AI is even suggesting better times to pay, or predicting late payments.
• Smarter factories and warehouses. When integrated with the Internet of Things (IoT), AI is helping companies better predict when equipment will need maintenance and repair, and sometimes even allowing robots to make autonomous decisions to keep processes flowing efficiently. This is one of the many ways AI is already working in factory and warehouse settings, among other uses like automated Inventory Management, inventory storage optimization, and even staffing and scheduling.