For the past 40 years, manufacturers have been trying to live up to the Just-In-Time (JIT) method. While many would say they’re effectively implementing lean manufacturing principles, the vast majority are still carrying too much inventory and too much risk.

It can even be said that few companies outside of Japan truly managed to practice it in the first place. It’s taken a pandemic to bring this to light and prompt manufacturers to think about alternative approaches to resilience. 

But to consider an alternative approach, you need to understand why JIT can only handle predictable patterns of supply and demand, and why it can’t accommodate the uncertainty that is part of today’s operating environment. And that leads us to the traditional approaches to demand forecasting that many complex supply chains still rely on. 

Just-In-Time relies on an out-of-date approach to demand forecasting
  1. It uses historical data, like sales data, to “predict” future demand 
  2. It generates forecasts infrequently, matching pre-pandemic planning cycles
  3. It sets aggregate targets for SKUs at a product group or BU level

This backwards-looking, slow-moving, aggregate view of demand clearly wasn’t designed for the New Normal’s fast-paced, erratic levels of demand. So, getting the right amount of supplies into factories to match customer orders is obviously going to be near-impossible. But at the same time, the original rationale for this lean manufacturing methodology – minimising waste and inventory – has never been so critical.

So, how can manufacturers live up to lean manufacturing even with the New Normal?

Simple: the New Normal needs a new approach. Manufacturers need to find a way to map out volatile demand so they can optimise inventory, maximise revenue and better handle supply volatility as it happens. For that, they’ll need AI (artificial intelligence). 

AI-enabled demand forecasting combines a confluence of advances in AI and ML (machine learning), allowing you to truly forecast future demand with hyper-accuracy, set SKU-specific targets, and monitor volatile demand as it changes. From our experience, this new approach can generate sustainable value for any business, whether it’s cutting costs and freeing up cash flow for cost-focussed businesses, or enabling growth-focussed businesses to prioritise strategic priority accounts and maximise revenue. 

Are we out-of-time?

AI is already optimising supply chains in a number of use cases, but we see real value in how it can restore your view of demand. Once again, you can truly understand what is driving it and why, just like you did pre-pandemic. 

AI will be behind a significant shift not unlike the one prompted by Toyota in the 1970s. But this time we aren’t ditching its ideals or philosophy, like it did with its predecessor, Just-In-Case; we’re simply upgrading the technology behind it.

To find out more about our new approach and how it can generate value for your business, no matter its situation, access our latest article: ‘Forget Just-In-Time: there’s a new way to manage supply chains’.

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