We have seen a full cycle in the supply chain industry with recovery from multiple port delays plaguing America that caused fissures in how business gets done and products and supplies meet their final destination. However, the work towards a total supply chain transformation remains in the early stages, making for an exciting prospect to consider.
Case in point: The White House announced further steps to strengthen America’s supply chain. It announced the launch of the Supply Chain Resilience Center (SCRC), a new government department tasked to collaborate with private organizations to secure U.S. supply chains better. Initially, the SCRC will focus on U.S. port infrastructure and security, then provide recommendations on reducing costs, limiting supply chain disruptions, and guaranteeing delivery reliability.
While it’s still early days, the White House AI Executive Order is a watershed moment in our nation’s growing industrial/technology future. Implementing AI in procurement and the supply chain promises to accelerate data sharing and increase collaboration. With the right strategy and governance, AI technology can transform older procurement practices into new breakthroughs in supply chain resilience and help organizations forecast any external volatility more precisely.
To accomplish this, it’s important that connected parties in academia, government, and private industry collaborate to shape how the potential of AI optimization unlocks new levels of efficiency, insights, and customer value in supply chain management and the manufacturing industry. This article on can add to that collaboration. Let’s explore the potential of AI in manufacturing with some examples.
Eliminating traditional data cleanses
AI is already a game changer for the supply chain industry, as it gives organizations much deeper visibility into real-time demand signals, inventory positions, and capacity constraints across supplier networks.
In addition, AI optimization greatly increases trust between buyers and suppliers by providing a “single source of truth” for data. Data techniques can cleanse, enrich, and standardize procurement and operations data across systems. This reduces the need for expensive traditional data cleanses while ensuring all parties work from the same pool of reliable and consistent data. As trust in data grows, so too can collaboration.
Having a single source of truth also plays a part in collaborative data governance. AI machine learning helps to establish and define clear ownership and responsibility for data. AI can also facilitate sharing KPIs and dashboards with all stakeholders to see progress towards shared goals.
Enhancing internal and external collaboration
I mentioned increased mutual collaboration of artificial intelligence in manufacturing and supply chain management above. This is an important facet for this next step.
Collaboration in the supply chain is a two-way street for increased communication and mutual decision-making. It can also include collaborative demand signal exchanges, inventory planning, and production scheduling needs.
Increased internal and external collaboration can help organizations stay on top of supply chain disruptions, such as delays or shortages. It can also help businesses meet objectives such as cost reduction, improved customer service, or optimized supply chain performance.
Sharing these supply chain insights between procurement, operations and key suppliers in a secure way can align planning activities and drive major efficiency gains. Procurement no longer operates from static or aggregated data but rather harnesses a digital thread across the value chain.
Moving towards autonomous procurement
What’s ahead? What can we look forward to in the coming year? I think we are on track toward AI-powered procurement.
AI lays the foundations for autonomous procurement through a potent combination of advanced analytics, data quality, and expanded collaboration capabilities. Today’s systems can handle repetitive sourcing and transactional activities. At the same time, managers and procurement leaders can focus more on non-repetitive activities that bring deeper value to an organization, like handling strategic negotiations and managing supplier relationships.
Organizations can improve their demand signaling across their supply chain by automating the work with an AI-powered MRO platform. AI systems can detect purchasing and consumption data patterns, from small increments to large ones.
Additionally, AI optimization in storeroom management and inventory tracking shows the entire supply network. This gives manufacturers the opportunity to access accurate MRO analytics with a few clicks.
While full automation of our processes may be ideal in some industries, it’s also crucial that we don’t lose sight of how humans can augment these processes. Over time, technology can take on more complex tasks. However, the human element remains critical.
As AI implementation speeds up in business, humans need to double-check our systems and ensure we’ve got the balance right. Procurement can work wonders when we oversee these AI-powered systems in supply chain management.
Founder & CEO of Verusen