The rise of technology poses the question, can AI rescue global supply chains?
The last few years have put immense strain on global supply chains, whether due to port congestion, extreme weather events, manufacturing delays after the pandemic, the war in Ukraine, and more recently, conflict in the Middle East. The outcome has been shortages of goods across multiple sectors and skyrocketing costs.
In an attempt to regain control, companies are increasingly turning to advanced technologies like artificial intelligence (AI) and machine learning (ML) to extract visibility across their supply chain operations, improve forecasting, and implement measures that will help them become more efficient. While the challenges remain daunting, these cutting-edge solutions may hold the key to streamlining decision-making amidst the complexity and dizzying array of variables.
The advantage that AI and ML tools offer is the ability to provide diverse sectors with a view of the hidden patterns and relationships that can alter or affect outcomes. From the use of predictive analytics to make assumptions about the future and analysis that is delivering healthcare advancements, to how big data is enhancing social media optimization, these technologies are already giving organizations powerful insights. Some experts foresee similar breakthroughs being applied to manufacturing supply chains – not just by automating tasks, but by fundamentally optimizing processes and reducing costs.
The core advantage of AI and ML is its ability to continuously learn from data, enabling greater automation, rapid discovery of insights, and a competitive edge. Many organizations create data and even use it to inform their customer relationships, forecasts, and planning, but AI and ML technologies offer a way to deliver more. Unlike legacy systems that merely process and report data and are unable to scale, AI and ML models evolve and improve the more data they are fed and the better trained they are, leading to increased accuracy and better business results in the long term.
Revolutionizing logistics
When it comes to logistics, data is pulled from several diverse sources, and sometimes global locations. One key application of AI and ML is streamlining logistics by analyzing real-time data from international sources on factors like transport and traffic, weather forecasts and delivery conditions. AI can then optimize routes, schedules, and resource allocation for maximum efficiency and accurate delivery times. Machine learning is ideal for descriptive and predictive analytics, helping logistics teams move on from retrospective ‘what happened?’ questions to future looking ‘what will happen?’ insights that drive smarter real-time decisions and long-term planning.
Tracing the trail
Utilizing data through AI can also dramatically boost supply chain visibility and traceability, supporting the monitoring of goods and materials from the source to when they arrive with the customer. AI-powered models can instantly track materials, components and finished goods, highlighting quality issues, enabling precision for recalls, and ensuring regulatory compliance.
Manufacturers gain an unprecedented real-time view into the full length of the supply chain, taking in logistics and transport data across wholesalers, retailers, suppliers, and partners, allowing them to rapidly identify and address bottlenecks, delays, and other inefficiencies, and most importantly, to act fast.
Collaborative intelligence
Contrary to the common fear of being replaced, AI can empower individuals within teams to focus on higher-value tasks and innovation, helping to cut costs associated with human error and repetitive, time-consuming work. Rather than firefighting problems manually, AI improves cross-functional communication, aligns goals, and surfaces critical insights from the latest data to drive coordinated action.
Optimizing inventory
AI and ML can also transform inventory management by continuously tracking stock levels to predict demand surges, notify partners of shortages or oversupply, and reduce waste. Automated replenishment cycles ensure goods are produced and delivered exactly when needed, eliminating delays and surplus.
Affordable AI power
Research conducted by Stanford University, MIT, and the National Bureau of Economic Research at an enterprise company in the Philippines in April last year found that AI tools boosted customer service worker productivity by 14 percent on average. The customer service team was split between those with access to the AI-enabled tool and those that did not. Aside from cutting chat handling time and improving customer satisfaction, the agents using the tool also benefited from real-time suggestions on responding to customer queries.
While AI may seem daunting, many businesses already have AI-enabled tools that are underutilized. Even mainstream applications like ChatGPT offer AI capabilities that small companies can leverage to automate routine tasks so staff can prioritize strategic objectives.
AI: the golden solution?
AI is no longer the mysterious, futuristic concept it once was. In fact, most consumers encounter AI every day through technologies like voice assistants. For businesses, AI has the power to provide transformative opportunities to drive innovation, deliver a competitive edge, and provide cost savings. However, success hinges on having a modern data infrastructure that can meet the immense storage and processing demands of AI and ML workloads. With the right strategy and technological foundation, AI could be the golden solution that solves persistent and restrictive supply chain challenges.
For a list of sources used in this article, please contact the editor.
Lenley Hensarling
Lenley Hensarling is the Chief Product Officer at Aerospike. Lenley has more than 30 years of experience in engineering management, product management, and operational management at both startups and large successful software companies. He has extensive experience in delivering value to customers and shareholders in both enterprise applications and infrastructure software.