Interview with Stefano Fascio about Supply Chain Innovation
Supply Chain Innovation
In the ever-evolving landscape of global commerce, the concept of supply chain innovation has undergone significant change, clearly reflecting the dramatic shifts in the dynamics of both customer and supplier markets. In this interview, Stefano Fascio, Supply Chain Director at Nissha Metallizing Solutions, explores the latest trends and developments that have reshaped supply chain strategies, focusing on the imperative need for flexibility, risk mitigation and integrated solutions. Through insightful discussions, we will explore how companies are adapting to these changes by leveraging data management and artificial intelligence to improve operational efficiency and customer satisfaction.
“What does Supply Chain Innovation mean in 2024 and what has changed in recent years?”
“In recent years we have seen many changes, changes in the structure of both the customer and supplier markets, the opening up of new markets, new regulations. There have been events that have made us reassess the advantage of having convenient and distant suppliers versus supply chain solutions with lower risk and greater flexibility. And this is not only due to the criticalities that have emerged or single events that have caused supply interruptions. Our customers' needs have changed. Markets do not always want the lowest price, but are often interested in different types of support. And not just services strictly related to product supply, but services and support with a broader scope. In terms of the supply chain: support for the development of integrated solutions along the entire supply chain, the ability to provide timely information, the implementation of a low-risk system with alternative solutions in case of critical issues to ensure continuity. It has become increasingly difficult to predict and plan, so we need to build organizations that are flexible and can respond quickly to market demands. We need to strike a balance between detailed analysis of each event, which can lead to changes in operating methods, and organizational models that allow for self-regulation of the external environment. In the past, we moved from a functional organization to a process organization. Now the next step is to move from process management to data management through artificial intelligence solutions.”
“But isn't the goal always linked to revenue and margin growth through customer satisfaction?”
“Of course, but today, when we talk about satisfaction, we have many more targets and many more figures to satisfy. Not just customers in the strict sense, but a wide range of stakeholders. The objectives are linked to social responsibility, the environment, the community, governance and safety. This means not only structuring a supply chain capable of taking advantage of the best market conditions, but also ensuring continuity and respect for the human, environmental and social resources involved. Think, for example, of the EUDR or CO2 emission targets. This requires a different approach both in the design phase of the system and in the operational and decision-making management phase.”
Can you give us concrete examples?”
“Consider the process of approving a new supplier or vendor rating. In addition to traditional criteria such as quality and delivery performance, we are increasingly evaluating suppliers' ability to ensure safety in their facilities, to treat their employees fairly, and to choose solutions with the lowest environmental impact. The most common questions are: How do you manage energy? Do you have combined heat and power plants? Which renewable energies do you use? Do you have recovery facilities for the products or sub-products you use?
These are different investments and approaches, and our goal is to select suppliers based on these capabilities as well. And this makes all the difference.”
“But doesn't this multiply activities at the operational level? How can you pay attention to everything and be more flexible?”
“Systems are much more complex and the amount of information to manage is much greater. Artificial intelligence will help. It is no longer enough to simply automate. We need to make evaluations and decisions supported by systems that are able to manage this amount of data for us. We would not be able to do this on our own. We need to delegate the more operational part to AI systems and devote ourselves to more value-added content. For example, we receive thousands of emails. Of those emails, probably less than 10% are useful for decision making. We spend hours reading and separating important information from less important information. AI is very good at managing text and identifying data, leaving us to make decisions. That leaves us with the value-added part.
Another important element is integration. Precisely because the market is more complex and there are numerous aspects to manage and coordinate along the supply chain, joint actions are needed by all the actors involved: suppliers and customers. The right integration will allow everyone to avoid inefficiencies at the operational level or duplication of activities already carried out. However, integration requires a very strong and open commercial relationship. We need many more partners than suppliers.”
“What other applications could AI have?”
“With regard to the difficulty of forecasting markets, I believe that AI can provide us with valuable support in demand planning. We have access to a lot of information that we get through different media: newspapers, market analysis, customer reviews, internal reviews. It is not easy to analyze this vast amount of information and derive actionable insights to structure the supply chain. Often this analysis is based more on personal experience. AI can more easily compile the data and interpret it according to the interaction models we provide. This is an interesting exercise because it forces us to formalize how different pieces of information interact and influence demand, such as product cycles, seasonal demand, market trends and the economies of different countries. By better analyzing this information, we can structure a supply chain that is more in line with market expectations, reduce the risk of disruption and maintain adequate stock levels.”
In conclusion, integrating AI into market forecasting and supply chain management offers a promising way to improve operational efficiency and better meet market demands. By harnessing AI’s ability to process and analyze large datasets, businesses can move beyond reliance on personal experience and promote a more data-driven approach to decision making. This not only improves the accuracy of demand forecasting, but also helps to anticipate market shifts and adjust supply chain operations accordingly. As we move forward, the key to realizing the full potential of AI lies in developing robust commercial relationships with partners, embracing innovation and continuously refining our understanding of market dynamics. Through these efforts, we can build a more resilient, responsive and integrated supply chain that is well equipped to navigate the complexities of the modern market landscape.