Demand didn’t follow forecast. Now what?

Date Posted: August 24, 2018

There are many explanations companies routinely give when inventory management issues arise, whether it’s too much unsold inventory or too many empty shelves.

“The market is unpredictable.”

“It’s impossible to accurately forecast everything.”

It’s not that these explanations are inaccurate. In fact, they’re pretty much universally true. What’s puzzling is that, despite these realities, brand manufacturers still pour all their energy into rigorous planning and forecasting, and yet take a more frenzied approach to responding when performance deviates from forecast. In other words, they lose the discipline they need for a fast, coordinated response in the face of changing demand.

The need for balance

Truly demand-driven organizations are investing as much time and effort into what Alloy calls Demand Response Coordination as they are into planning and forecasting. This term refers to the structured process by which companies respond to rapid changes in demand, such as:

  • Your new product launch performs exceptionally well, and its sales exceed forecast, while sales of existing products drop off more quickly than predicted
  • A retailer decides to change a promotion for your product at the last minute, leaving you with excess inventory that’s costly to reallocate
  • A tastemaker organically promotes your product on their Instagram, causing an unexpected spike in orders

Many teams struggle to execute a coordinated response in situations like these. They often determine what to do based on gut feelings and judgment calls, doubting that sufficient data exists to help solve the problem, or thinking that any data would take too long to analyze. The fate of millions of dollars in inventory and tradeoff decisions, like whether to air freight product from another DC or to let a store stay out of stock, come down to little more than intuition! The strategic nature of demand forecasting decisions seems to have been abandoned.

In contrast, Demand Response Coordination is a system for staying on top of true demand and using scientific data analysis as the basis for proactively responding to unexpected changes.


Preparing for Demand Response Coordination

At some companies, responding to unexpected demand changes can feel a lot like “fire-fighting.” We suggest there’s a better way.

It starts with the acceptance that it’s impossible to accurately forecast everything. That means organizations must be prepared with the data, analytics and tools they need to methodically identify changes in demand as they happen, and then coordinate a response across sales and supply chain teams. It’s not a question of “if,” but rather a question of “when.”

In the remainder of this post, I’ll cover some of the key considerations when helping your company move from haphazard responses to Demand Response Coordination.


1. Analytics

As the saying goes, you can’t manage what you can’t measure. Measurement starts with visibility into your demand network, including orders and shipments, customer sell-through data (POS data) and inventory status for your customers, DCs and 3PLs.

One of the key challenges companies face at this stage is that the volume of data is too big to process. Many existing data management systems and BI tools are simply not equipped to handle the scale of standard and ad hoc query computation and analysis for an ever-growing number of item-door combinations. Ever had to wait for Excel to calculate a complex formula across a massive data set? Or had a complex SQL query time out? All too often, the result is that users end up focusing their efforts on basic reporting, and forego ad hoc analysis entirely.

Demand Response Coordination is a system for staying on top of true demand and using scientific data analysis as the basis for proactively responding to unexpected changes.

Most companies will silently struggle with this for years, thinking that they’ve got their bases covered just because the data exists somewhere. But what companies need is to have the necessary data in one place AND be able to use it easily to measure demand.


2. Management by exception

With analytics in place, the challenge becomes identifying which insights require action. Exception management is the concept of identifying and acting on the biggest risks and opportunities. That said, for most teams, finding those exceptions is too manual of a process to be done at the item-location level. Automating exception management makes granular change response possible, helping you focus on solving issues, rather than trying to identify them in the first place.

Ever wish you could be notified when an order you’ve shipped to a customer is at risk of delay? Or when there is a significant mismatch between how much a customer has ordered and the number of units they typically sell? Proactive alerts are key to management by exception, helping ensure changes in demand that require a response are surfaced for action.


3. Data-driven decision making

Optimizing for demand requires understanding not only what exceptions needs your attention, but also what the business impact is of solving them. Assuming you have limited inventory across your network (if you don’t, maybe you’re tying up too much cash), servicing demand everywhere there’s a gap isn’t always the best option. This is often where gut feel, rather than data, is used to make tradeoff decisions.

Imagine Customer X consistently orders 20% more inventory than they need and always cancels the order right before you ship it. Given that this may impact your ability to service other customers, would you still fulfill the order each time?

You’re probably thinking, “It depends.” You want the higher volume order, but not at the cost of missing service levels for other customers. Optimizing for demand in this scenario requires the ability to discern whether to fill the entire order or to allocate the inventory elsewhere.

If you’ve captured real-time product availability across every DC, retailer, door and SKU, and you’ve been able to anticipate your customers’ long-term and short-term needs through predictive forecasting and proactive alerting, you can now make a more informed, data-driven decision to fill the order, or not. No gut feelings necessary.

In summary

Solving for inventory management challenges doesn’t need to be a crapshoot every time your forecasts are off. By applying the same level of rigor to a coordinated demand response as you apply to forecasting and planning, your organization will soon develop the reflexes needed to thrive in a constantly-changing environment.

This post was originally shared on Medium.

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