By Ramya Nirmal
In my experience working in the manufacturing domain, the conversation around technology has always been about efficiency, but now it’s about something more. It’s about relevance. I’ve seen the industry go from analog to digital, and now, we’re standing on the brink of the AI revolution. Everyone is talking about GenAI, but the conversation often gets stuck on the big, futuristic, ‘next-generation’ problems. I believe the real power of AI isn’t in solving the problems we’ll face in five years. It’s in simplifying the problems we’re dealing with right now, today. It’s about making day-to-day activities easier, decisions sharper, and the supply chain a little less chaotic. The next big thing could be a small one, but with an impactful improvement.
Inventory run-rate is a core metric for suppliers. a core, fundamental activity. Can AI help us with that? Absolutely. It’s not about completely reinventing our entire supply chain; it’s about giving us better details on raw materials and helping us plan delivery times more effectively. This is where AI becomes a practical, powerful tool, not just a buzzword.
AI and ERP integration is the key to unlocking true efficiency, connecting our operational data with our customer-facing insights in a way that just wasn’t possible before. Let me give you a few specific examples where I believe AI can provide immediate, tangible value.
The Challenge of Delivery Timelines
This is one of the most frustrating pain points for any manufacturer and for customers. How do we give a realistic delivery timeline? We might tell a customer their order will arrive in 14 days, knowing full well that there’s a lot of wiggle room in that number. We’re building in a buffer because of all the unknowns: potential vendor delays, production line issues, and transportation hiccups.
But what if we could narrow that down? What if, instead of promising 14 days, we could confidently say 10 days? That small improvement doesn’t just improve our efficiency; it has a ripple effect throughout the entire supply chain. It builds trust with our customers and helps them plan their own operations better.
This is where AI’s predictive capabilities shine. Remember the Stanley Cup example? Sales soared, with some reports indicating that the company jumped from $94 million in revenue in 2020 to $750 million by 2023. Consumers were buying multiple Stanley cups in different colors, turning them into collectible items. When demand for a product suddenly shoots up, we need to adapt quickly.
AI can analyze historical sales data, current demand trends, and even external factors to forecast demand with a precision that’s impossible for us to achieve manually. It can use this information to predict realistic delivery timelines, helping us manage our customers’ expectations and, more importantly, helping us to meet them. It’s about taking the guesswork out of our promises.
The Problem of Dynamic Pricing
Manufacturing isn’t like retail. It’s a complex, multi-variable equation. The cost of raw materials changes constantly due to market fluctuations, geopolitical events, and sourcing challenges. Think about it: a tariff in one part of the world, a new trade agreement in another, and suddenly, the cost of a key component shifts.
In the past, our pricing would be reviewed, maybe once a quarter. But in today’s market, a quarterly review is like trying to navigate a Formula 1 race with a map from the previous venue.
AI changes this. By integrating with an Enterprise Resource Planning (ERP) system, AI can analyze real-time data on raw material costs, geopolitical news feeds, and even demand signals. It can help us implement dynamic pricing that adjusts in real-time, not in response to a crisis, but proactively. It’s about giving our sales team the right price, right now, so we can maintain our margins without losing our competitive edge. This is a real problem, and it’s one AI can solve today. I feel that ERP personalization allows us to tailor our operations to meet the unique needs of each customer, one at a time.
Predicting the Unpredictable: Vendor Delays
Another major pain point: vendor delays and failed deliveries. We receive a batch of raw material from a supplier, but a week later, we find out it’s a failed batch. Who tracks that? How do we prevent it from happening again? The old way is to be reactive. We deal with the problem once it’s already here.
But what if we could predict it? What if we could use AI to analyze historical data from that vendor? Past delivery times, the frequency of failed batches, the time of year, or even the geopolitical climate. An AI model could look at all of this and tell us, “This batch of raw material from this specific vendor has a 30% higher probability of being delayed or failing quality checks.”
I have seen firsthand how an AI-driven customer experience changes everything, shifting our focus from reacting to problems to proactively anticipating customer needs.
Armed with that insight, we can be proactive. We can order from a backup supplier, put in a different quality control process, or simply have a plan B ready. We’re not just reacting to problems; we’re getting ahead of them. It’s about using data to make our day-to-day activities simpler and more predictable.
In my mind, this is the true value of AI. It’s not about the grand, abstract problems of tomorrow. It’s about the tangible, daily challenges that keep us up at night. It’s about finding that small workflow that we can improve, that little bit of predictability we can add, and that one less phone call we have to make to a vendor. AI is the tool that lets us do that. And in an industry like manufacturing, where small efficiencies lead to massive gains, that’s a game-changer.
We’re not just building products; we’re building a smarter way to work. We’re using AI to move from a reactive stance to a proactive one, simplifying our jobs and improving our business, one small, crucial problem at a time.
AI, in my mind, is the brain, and the ERP is the heart—the central repository of all our data. When they work together, they give us a 360-degree view of the customer, their complete history, and their long-term value to us. AI takes that raw ERP data—past orders, service tickets, and communication history—and transforms it into a personalized experience. It helps us anticipate customer needs, proactively solve their problems, and offer a truly unique service that goes far beyond just selling them a product.
Customer experience personalization is no longer a nice-to-have; it’s the core of building trust and lasting loyalty with our partners. We’re not just building products anymore; we’re building a smarter way to work, a more human way to connect with our customers.
