In my years of working on time series forecasting and inventory optimization projects, I’ve observed a recurring pattern across companies of all sizes—from small businesses to enterprises making a billion dollars a year. Regardless of scale, the way organizations manage their supply chain operations often boils down to the same thing: rigid, outdated strategies that either completely ignore uncertainty or imply unreasonably simplistic assumptions about uncertainty distributions. These assumptions might include a certain form of probability distribution function, constant variance, etc.
In the best cases, companies rely on point forecasts paired with static rules-based methods like DDMRP. And in the worst cases…well, let’s just say some practices are best left unmentioned.
I’ve been there as well, advocating these methodologies and implementing them in worldwide enterprises.
The problem with these inflexible approaches is obvious. When you ignore or oversimplify uncertainty, you're forced to choose between two suboptimal outcomes: bloated inventory levels that tie up working capital or frequent stockouts that lead to lost sales.
So the question becomes: how do we strike the right balance?
We live in a world where nothing is certain. Even the best point forecasts, while possibly unbiased, still carry uncertainty. And this uncertainty isn’t uniform. It shifts depending on the season, product category, supplier, or even geopolitical events. Ignoring this dynamic complexity is a surefire way to miss your KPIs—be it service level, profit, or revenue targets.
Forecasting demand probabilistically means not just predicting what might happen, but understanding how likely each scenario is. And that same philosophy should be applied to supply side planning. Lead times, for instance, shouldn’t be treated as static numbers. Instead, we need full lead time distributions—and even better, conditional lead time distributions that adapt to seasonality, supplier performance, and other relevant variables.
Now here’s the catch: insight alone doesn’t drive action. Even with the best probabilistic data in hand, decision-making still requires translating that complexity into optimal, executable plans. You can’t eyeball a million scenarios and expect to land on the right choice.
What’s needed is a system that does this heavy lifting for you—a decision support engine that simulates outcomes, evaluates trade-offs, and recommends the best path forward based on your business goals.
This might sound like something out of a Black Mirror episode, but it's not science fiction. At Hexight, we're turning this vision into reality. I personally saw the path to uncertainty management, it is possible to embrace uncertainty, delivering smarter inventory strategies that maximize profitability, improve service levels, and reduce waste.
If your supply chain planning still treats the world as deterministic, there’s a better way - reach out to learn more.
