Manufacturing IT leaders are working to overhaul aging, rigid supply chains, with global geopolitical events adding still more urgency to that task. Priorities include:
- Optimizing demand planning tools to better forecast demand changes
- Reducing lag time in responding to supply chain disruptions
- Automating tasks from manufacturing to logistics
Generative artificial intelligence (GenAI) capabilities can help achieve these priorities by focusing on specific supply chain tasks and processes. “Early adopters of AI-enabled supply chain management have reduced logistics costs by 15%, improved inventory levels by 35%, and enhanced service levels by 65%,” according to a recent article[1] in the Georgetown Journal of International Affairs.
Yet most manufacturers don’t have the necessary in-house AI expertise and tools to achieve these results. Partnering with a systems integrator (SI) that has AI expertise in the supply chain makes it possible for manufacturers to deploy task-focused AI in weeks.
Manufacturing IT leaders can act in three key areas.
1 – Identify critical data sources to unify the supply chain’s data foundation
The biggest obstacle to deploying AI quickly and at scale is a fragmented data environment. AI capabilities require large amounts of data to train the underlying models. A unified data foundation enables AI to ingest and digest current information to enable faster, optimal responses to unexpected internal and external changes. These can include demand changes, raw materials availability, production schedule changes, and many more.
IT leaders must initiate an inventory of existing data sources and how they are, or are not, being used. The inventory should include key data sources that are currently under-utilized. Unstructured information is one such resource. Text, video, audio, even social media and much more lack conventional data models. But they can be valuable in forecasting, demand analysis, and in generating contracts and other documents such as safety and technical manuals.
A systems integrator with expertise in supply chain, AI, and data management can be a key partner in this effort. TCS and its close collaborator Microsoft can help IT leaders focus this data inventory process, execute it quickly, analyze and prioritize the results, and replicate the needed data into easily accessible, cloud-based data lakes.
2 – Short-list the key supply chain tasks for automation
GenAI copilots – such as those co-developed by TCS and Microsoft – can “take over” an array of supply chain tasks. These focused co-pilots can analyze events or changes, uncover their root causes, and recommend optimal responses. They can give early warning of potential or emerging supply chain behaviors. And they can do all these faster than existing manual processes and human-decision chains.
IT leaders can balance these task-focused automations with a larger, long-term view of interconnecting these automated processes across the entire supply chain.
TCS and Microsoft combine supply chain knowledge, an AI platform with task-oriented copilots, and cloud-based services for data and development. Together, these capabilities enable IT leaders start automating supply chain functions within a few weeks.
3 – Establish a culture of AI experimentation
Coping with fast-changing technology is not a brand-new problem for manufacturers. IT leaders can leverage the expertise of a SI partner like TCS to spread AI knowledge and skills through the enterprise’s business and technology teams.
At the same time, IT leaders can explicitly encourage and incentivize AI experimentation and small-scale pilots throughout the supply chain. A hands-on approach, with permission (and expectation) of “failing” creates common ground for business and IT teams to collaborate in finding out how AI strengthens the supply chain.
The bottom line
Manufacturers needs to act on adopting AI and GenAI across the value chain to remain ahead of the curve. Partnering with TCS and Microsoft is key to building and developing a successful AI-driven strategy and reaping the benefits across supply chain and manufacturing operations.
For more information, see the TCS White Paper “Next generation manufacturing enterprise: powered by GenAI.”
[1] The Role of AI in Developing Resilient Supply Chains | GJIA (georgetown.edu)
