7 Inventory Forecasting Tips To Fix Common Mistakes

October 19, 2021
8 min read

In this article:

Inventory forecasting is everything to direct-to-consumer businesses. Here are the seven most common mistakes retail brands make and how you can avoid them.

If there's one thing retail brands want to know, it's what will happen in the future. Especially when it comes to their business. 

That's why teams wonder things like:

  • Will customers want our new products?
  • How many items will sell in a particular month?
  • How much merchandise should we order?
  • Did we order enough inventory?
  • Should we stay lean, or should we order safety stock just in case?

As all humans do, teams try to predict the future. That way, they can hopefully prepare and in some way control the environment their business operates within.

To try and successfully predict the future, direct-to-consumer (DTC) businesses create inventory forecasts.

What is inventory forecasting?

Inventory forecasting refers to predictions based on historic data and market research of how many units the company will need in its inventory to meet projected customer demand. Accurate inventory control and forecasting helps businesses make operational decisions within a given forecast period.
The problem is that forecasts are inherently challenging to create.

The problem is that forecasts are inherently challenging to create.

True customer demand drives forecast accuracy, but knowing the absolute truth about what customers want at any given point is impossible.

Brian Berger, CEO of menswear brand Mack Wheldon, recently shared on the Shopify Masters podcast how his team struggles with this. He said: "In DTC or the e-comm world, it's particularly stressful because you don't really have any visibility into demand."

How to do inventory forecasting: 7 mistakes to avoid

How can a brand build an accurate inventory forecast to drive its operations without visibility into consumer demand?

There are many reasons why business data gets muddled and distorts a brand's visibility into what customers want—but there are ways to fix it. 

We've studied data from brands that work with us and picked up best practices from around the industry that I'll share with you today.

Below, we'll discuss the top seven mistakes retail businesses make with their data and how you can proactively fix them.

1. Keep data up-to-date to empower teams

When using data to create forecasts and do demand planning for future sales, retail brands use two inputs:

  1. Historical sales data
  2. Inventory data

The timing of that data matters.

By using the most up-to-date information, businesses create more accurate and more empowering inventory forecasts.

When inventory management systems use real-time data, teams can be more agile in their demand planning and proactive in their decision-making. 

That's because they no longer need to spend time updating systems before they can produce accurate forecasts. Teams have constant visibility into changes in sales trends. And they're equipped to take action when necessary.

Spreadsheets, on the other hand, are always reactive. Teams upload all the latest sales data so they can then analyze it or create a forecast. 

Plus, accidentally using outdated information can cause teams to make decisions that may hurt the brand's bottom line and cash flow. If sales and inventory information isn't updated promptly, forecasts can be thrown off considerably.

To remedy this, teams must find a way to shorten its time to access the most recent data. Ideally, brands would find a way to sync their systems. That way, they are always working with real-time data.

2. Never change SKU IDs on products

Another mistake businesses make is changing the SKU ID on a product.

For example, if you sell Product A using SKU ID "product-a," but later change it to SKU ID "product-ab," the data between those two SKU IDs disconnect. And the inventory forecasting system considers these two SKU IDs as two different products (essentially, Product A and Product B).

When a product's data is inadvertently disconnected in this way, it throws off the forecast and loses accuracy. The system breaks the past sales data into two isolated datasets.

There are different forecasting methods to synthesize these now-separate data sets. But it becomes difficult to connect those two data streams to get the complete picture and produce an optimal forecast.

Without the complete picture of historic demand, you can't create even a semi-accurate forecast of future demand.

3. Take into account inventory levels

Most forecasting systems do not take historical inventory levels into account. Instead, sales history makes up most of what is considered when creating an inventory forecast.

But when you don't consider inventory levels, there are big pitfalls in the accuracy of forecasts created.

For example, say we only look at historical sales data and see that sales suddenly drop off. Then, we are likely to interpret that sudden drop in sales as a natural decline in customer demand.

But what if customer demand didn’t drop off? What if customers did want to buy more of that product? And instead, there was no more product to sell during this period of time.

Without historic inventory levels, operations teams cannot see when a drop in sales data matches up with a stockout. And the data isn’t truly representative of customer demand, skewing forecasts and biasing a team’s decisions.

This can lead to poor operational decisions in the future: underestimating customer demand and, as a result, more stockouts.

With inventory data in hand, though, inventory forecasting improves meaningfully over time. And our retail inventory optimization platform Cogsy provides this functionality.

Cogsy tracks inventory levels of each SKU continuously and considers this data when building inventory forecasts. This increases accuracy and improves the likelihood that DTC teams can make optimal operations decisions with those forecasts.

4. Identify limited edition product drops as such

There are two types of products that most DTC businesses sell: evergreen products and limited edition products.

Evergreen products are those that brands sell in an ongoing manner and have everlasting appeal. Meanwhile, limited edition products sell for a specific amount of time. And there are two types of limited edition products: seasonal and one-time.

One of the most recognized limited edition products is Starbucks’ seasonal pumpkin spice drink that consumers flock to purchase every autumn. 

On the other hand, Nike shoes are famous for one-time limited edition product drops. Likewise, some retail brands use limited editions of a popular product to sell at specific retail stores. For example, Caraway sells their pot and pan set in gold and green exclusively at Crate & Barrel.

Brands release limited edition products like these to create hype around their products. It works—but it can also create chaos in the operational side of the business.

Limited edition product drops can skew forecasts because of the finite number of units available to sell. When those units sell out, inventory forecasting software might assume that customer demand dried up. 

In reality, this was a successful product drop where all available units were purchased. But the inventory forecast may view the product as a failure. 

As a result, the forecasting model may make recommendations for the future that won’t serve the business’s best interests.

To avoid skewing data, an improved forecasting technique is to identify limited edition products as such in the inventory management system. By doing so, you can forecast evergreen products separately from one-time drops.

This allows the inventory forecast to create an optimal purchase order for the future.

When retail brands release new versions of older products, it can skew forecasts.

For example, if Product A's Version 1 sold well, the team should link that demand to Product A's Version 2. This infuses some of Version 1's historic demand into Version 2's initial forecast at the SKU level.

Once Version 2 builds up enough sales data of its own, you can unlink the two versions and create a separate forecast for each unique SKU.

It's possible to transition that data from one SKU to another. A team might do this when they replace a product with a newer version or an alternative product altogether. In both cases, it's inferred that the demand for those products would be similar.

By linking the demand for all versions of the same product, the inventory planning team can know where new versions of products were introduced and how those new products changed sales. That way, inventory forecasting becomes more accurate.

6. Analyze each channel separately

Each channel has a different demand curve that you should treat separately.

If you compare DTC to wholesale, for example, they have different intervals at which products are ordered. So, you need to analyze them separately to correctly understand the data.

When you analyze omnichannel retail data, information seems homogeneous. This means that teams might see spikes and fluctuations in sales trends and interpret them as seasonal trends. In reality, this may indicate periodic wholesale orders.

Each channel should be identified analyzed separately to normalize those demand curves. That way, they don't skew your inventory forecasts or the business's cash flow.

Regarding wholesale orders, the one metric that positively influences forecast accuracy is the sell-through rate (how much of the inventory a brand sends a wholesaler actually gets sold).

As mentioned earlier, actual customer demand drives the accuracy of inventory forecasting. Working with wholesale, the sell-through rate provides your brand the data to determine that actual customer demand.

For example, if a brand sells a 10,000-unit replenishment to a wholesaler every quarter, that data isn't helpful for demand forecasting. Operations teams need to know the sell-through rate, which predicts how much product customers want to buy and how the brand should prepare accordingly.

Admittedly, data on sell-through rates are hard to come by

That's because it takes time and effort to relay that information from the wholesaler to the brand. As a result, the sell-through rate data is likely outdated, incomplete, incorrect, or costly. 

But it's worth pursuing because of how much it improves forecast accuracy.

7. Keep human error to the bare minimum

Most retail brands rely on spreadsheets to manage their inventory and demand planning. But accurate inventory forecasting in Excel or other spreadsheets is hard to do because they create various problems for operations teams.

First of all, spreadsheets only ever reflect a single moment in time. They are not dynamic. When a team consults a spreadsheet for a forecast, it's likely already outdated.

Secondly, they are prone to human error. One inadvertent finger flick can skew data exponentially. Add the many team members working on the same spreadsheet simultaneously, and the potential errors multiply.

In other words, spreadsheets (and their errors) don't empower teams to make proactive decisions or even the best in-the-moment decisions.

That's why, when possible, teams should rely on real-time synchronized data with a product like Cogsy instead. Forecasting tools like this essentially eliminate human error.

With inventory levels and purchase orders streaming in from inventory management software and sales data from an e-commerce provider (like Shopify or proprietary website), Cogsy gives teams transparency into what's happening in real-time.

You can then link the supply chain and average product lead times to the sales forecast. As a result, your team gets a complete picture of their inventory turnover, so they can better optimize their inventory forecasts.

Better data, better forecast, better business 

The more accurate data businesses have on hand, the better their decisions.

By infusing real-time data into their forecasting process, DTC retail brands know where they stand and where they can expect to be in the future. With that information in hand, they can chart a path that ensures they get there.

But to do this, they need accurate inventory forecasting. That means:

  1. Shorten the time it takes to update data in your systems to empower teams.
  2. Avoid changing SKU IDs for the same product.
  3. Take inventory stock levels into account when doing demand forecasting.
  4. Identify limited edition products to interpret their data accordingly.
  5. Link demand for all versions of the same product.
  6. Analyze each channel separately.
  7. Avoid using spreadsheets to minimize human error.

There’s no way a DTC business can create an accurate forecast with skewed data. Avoid these seven mistakes to empower retail brand teams to make the best operational decisions possible.

Inventory forecasting FAQs

Here are answers to common questions about inventory forecasting.

What should you do if you forecasted too much inventory?

Inaccurate inventory forecasting can cause a business to overstock the warehouse with products that lack demand. To avoid negative cash flow and get rid of excess inventory, a company can resort to one of the following techniques:

What is the difference between qualitative and quantitative inventory forecasting?

Quantitative forecasting involves the analysis of measurable, historical data to make predictions. Qualitative forecasting relies on information that cannot be measured or quantified in any way. 

What is time series inventory forecasting?

Time series is the forecasting technique that relies on time-stamped data to make specific predictions about future trends. It involves a historical analysis of trends to make smart inventory planning and management decisions.

Want to build a successful inventory strategy?

Request a live demo to learn how Cogsy reduces your stockouts—so you always convert your customers' demand.
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