SKU "Revolution" or Just Packaging Hype?
Decoding the SKU Revolution The packaging industry in India, as one report claims, is undergoing a revolution driven by "AI-driven SKU intelligence." The promise? To solve "SKU sprawl," that familiar nightmare of thousands of product variations leading to carton confusion and inflated costs. But is this genuine disruption or just the latest tech buzzword applied to an old problem? Let's break down the numbers. The article states that a single FMCG company can operate with 8,000–12,000 active SKUs. Okay, that's a big number. But what's the baseline? How has this number changed over the last decade? Without that context, it's impossible to tell if this is a genuine crisis or simply the normal state of affairs in a complex market. (And let's be honest, most markets *are* complex.) The core argument is that AI can optimize packaging by predicting demand, algorithmically matching product dimensions, and continuously recalibrating decisions. Sounds great in theory. The article claims that freight costs can be reduced by 15–30%. Now, *that's* a measurable claim. But what's the methodology behind that calculation? Is it based on real-world implementations, or is it a theoretical projection? Details matter.AI Packaging: Hype or Hyper-Efficient? Show Me the Data.
The Algorithm's Edge—or Just a Sharper Spreadsheet? The article highlights that AI can analyze demand patterns across thousands of SKUs and recommend exact quantities. This is where the methodological critique comes in. What kind of data are these AI models trained on? Are they relying solely on historical sales data, or are they incorporating external factors like seasonality, promotions, and competitor activity? If it's just historical data, then we're essentially back to the "historic average" method, only with a fancier interface. Then there's the claim that AI can solve SKU "overlap," where multiple packaging types perform the same function. This is a valid point. I've seen companies carrying redundant packaging options that add unnecessary complexity to their operations. But can AI really identify and eliminate these overlaps more effectively than a well-trained packaging engineer? That's the question. Consider the statement: "India’s packaging consumption is growing at 12–15% annually, among the fastest globally." This sounds impressive, but again, context is crucial. What's driving this growth? Is it purely organic, or is it fueled by factors like increased e-commerce penetration and changing consumer preferences? If it's the latter, then AI may be just one piece of the puzzle, not the silver bullet. The piece also mentions that AI allows MSMEs (Micro, Small, and Medium Enterprises) to operate with the sophistication previously reserved for blue-chip companies. That's a nice sentiment, but it glosses over the practical challenges of implementing AI solutions in smaller organizations. Do these MSMEs have the data infrastructure, technical expertise, and financial resources to effectively leverage AI? Or are they simply being sold a dream? One thing I find genuinely puzzling is the lack of discussion around the potential downsides of AI-driven SKU intelligence. What about the risk of over-optimization, where packaging becomes so tightly tailored to specific products that it lacks flexibility and resilience? What about the ethical implications of using AI to make decisions that could impact jobs and livelihoods? These are important questions that deserve to be addressed, not glossed over. Cold, Hard Data or Warm, Fuzzy Marketing? So, what's the verdict? Is AI-driven SKU intelligence hype or hyper-efficient packaging? The truth, as always, lies somewhere in between. The technology has the potential to transform the packaging industry, but it's not a magic wand. It requires careful planning, robust data, and a healthy dose of skepticism. Without those ingredients, it's just another overhyped tech trend destined to disappoint.
