The future of supply chain management is in the hands of AI. Disruptions in the global supply chain started with the pandemic, creating damage of $4 trillion in lost revenue, and are still affecting the global economy. In this uncertain and hostile scenario, building resilience is more important than ever.
Supply chain resilience comes down to end-to-end visibility and collaborations across the whole chain. While companies have implemented supply chain solutions for decades, it is clear that extraordinary technological progress is opening up the door for new and exciting opportunities.
Let’s dive deep into the role of Supply Chain AI, in particular Generative AI and Predictive AI, for better supply chain management.
What is Generative AI?
In the last two years, generative AI has been on everyone’s lips thanks to tools like Chat GPT and Dall-E, which garnered a lot of attention and spread extremely fast both in professional and personal environments.
Generative AI is an advanced technology that generates human-like content from simple inputs. The kind of content it can generate (for example, visual or textual) depends on the data on which the model was trained. The implications of such a technology in the business world are many, from improving customer service – mentioned as the primary use case by 38% of executives in a recent poll from Gartner – to creating content for marketing and sales. But what are the applications of Generative AI services for supply chain optimization?
Generative AI in the Supply Chain: Use Cases and Benefits
Gartner found out that half of supply chain organizations plan to implement GenAI by the end of 2024.
Moreover, the survey gives us valuable insights into the most impactful use cases area. According to the data, code augmentation and staff assistant chatbots are the areas in which most companies focus their investments.
- Code augmentation
Generative AI can automate code generation, enhancement, or modification for supply chain applications like demand planning, inventory optimization, and logistics operations. Moreover, GenAI can generate new synthetic data based on historical data to augment existing datasets and improve analysis.
- AI-powered chatbots
Chatbots are increasingly being used to automate customer service, order processing, and inventory management tasks, streamlining the supply chain process. This potentially leads to increased efficiency and a significant cost reduction: AI-powered chatbots can handle 80% of routine tasks and customer questions and decrease the cost of customer support by up to 30%. (Source: IBM)
Besides giving us insights into the most remunerative areas for Generative AI applications, Gartner’s survey also shows us something else. Compared to other business functions, like marketing and sales, the application of Generative AI in the supply chain is somewhat behind. This is a precious opportunity for companies to be an early adopter and gain a significant competitive advantage.
What is Predictive AI?
Predictive AI uses statistical analysis and Machine Learning to identify patterns based on past data and predict future events. While there is no guarantee that its predictions will be correct, it can provide businesses with significant support in preparing for the future, anticipating events, and making decisions. Quality and quantity are essential for Predictive AI, which needs large, high-quality datasets to produce valuable outputs.
Predictive AI has many applications across industries and business units. Its ability to form predictions based on past data is beneficial in sectors like marketing, finance, and healthcare but is also widely recognized in supply chain management.
What is the role of Predictive AI in supply chain optimization?
Predictive AI is excellent in supply chain risk management as it can be applied in a variety of ways to anticipate and minimize potential disruptions:
- Gain visibility into potential trends that will affect customer demand;
- Predict and anticipate problems and disruptions before they happen to have time to prepare a solution;
- Enhance a company’s demand planning capabilities, predicting demand more accurately and allowing better inventory optimization.
- Reduce the risk of overstocking and understocking.
With Predictive AI, businesses can switch to a more proactive supply chain risk management, thanks to a higher visibility into future threats and opportunities.
The impact of Supply Chain AI on your business’ success
It is undeniable that Artificial Intelligence is revolutionizing every aspect of the way companies conduct business, and the supply chain is no exception. Generative AI and Predictive AI both use Machine Learning and data to produce their outputs, but they differ in handling structured and unstructured data and their use cases. Companies wanting to invest in Supply Chain AI need to understand which technology, or a mix of both, better suits their needs and goals.
Having an AI strategy in place is a bulletproof way to start your journey into the power of technology and the future of the supply chain. Corporations have been increasingly experimenting with AI in the supply chain, investing in areas with the highest ROI and investigating new use cases and applications. While predictive AI makes demand forecasting more powerful and accurate, generative AI is gaining attention due to its ability to strengthen inventory and logistics operations and handle customer service.
Everyone seems to agree about the value of technology for supply chain management and the importance of having a robust and resilient supply chain for success. To quote EY:
Whether you win or lose in the market may soon depend on having the best generative AI tools and the data quality to match them.
But rushing to invest in the technology without study, research, and a proper strategy might be inefficient at best and counterproductive at worst. Risks and limitations arise when implementation is rushed and poorly integrated into the business processes and supply chain networks, mainly regarding the lack of human control in automation and the quality of input data.
Innovation works when it is guided by strategy and understanding. With a firm plan, leaders can integrate predictive and generative AI into their processes, simplifying supply chain operations, eliminating inefficiencies, and reducing costs. The future belongs to those eager enough to conquer it, taking advantage of every bit of opportunity they encounter on the way.
