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Do you run an eCommerce business on Amazon? 

If so, you’re likely well aware that product demand on the marketplace can fluctuate. Sales can ebb and flow due to seasonality, market shocks, discounts and promotions, changes in consumer behavior, and more.

When this happens, a seller’s job becomes a tricky juggling act, especially when it comes to balancing cash flow and stocking levels. And to make matters worse, changing demand tends to create a bullwhip effect that travels up the supply chain, which only magnifies inventory issues.

So, how can you mitigate these problems and prepare for future customer demand fluctuations? 

Here, demand forecasting is an invaluable tool for your business. Let’s discuss what it entails. 

What is Demand Forecasting? 

Sometimes referred to as inventory forecasting, this is a business process wherein an eCommerce retailer estimates how much inventory they need on hand to meet consumer demand as it fluctuates over time. It leverages historic sales, market trends, and industry experience to estimate optimal inventory levels. 

While that may sound complex, the demand forecasting method is about finding harmony. 

  • Naturally, you don’t want too much inventory. Overstocking ties up cash and potentially results in wasted shelving and warehousing costs—not to mention potential spoilage. 
  • On the other hand, understocking can cause you to run out of product in the event of a consumer demand spike. This can result in lost revenue and relevance, unhappy customers, and missed opportunities to take advantage of cost efficiencies. Worst of all, it could compel would-be or existing customers to bring their business to your competitor. 

By practicing demand forecasting, you can find an equilibrium—the optimal boundary range.  

How Do You Prepare a Demand Forecast? 

So, what do you need to take to start forecasting your inventory? Here are some tips:

Set Your Boundary Ranges

For starters, you’ll need to identify the forecast boundaries. That begins with the time period the forecast will cover. Generally speaking, an Amazon seller will do a 30 or 90-day forecast, although in some cases, the forecast could extend over the course of a year (we recommend against a full year for inventory buying purposes). Whatever your time period, be aware that the farther out from today the forecast stretches, the less precise it will be. 

After selecting a time frame, your next task is to estimate the base demand levels. Here, leveraging historical sales data and understanding what sales forecasting is, can help you improve demand forecast accuracy. Additionally, you may need to factor in the inventory turnover rate and the manufacturing production cycle. 

Consider Factors and Trends

There are numerous variables that can impact a product’s future demand fluctuations as well as optimal stocking levels, including:

  • Lead times – How long it takes a product to arrive after ordering. 
  • Reorder point – The point at which you need to reorder stock before running out. This is calculated by multiplying lead times by daily average sales. 
  • Demand seasonality – The typical demand pattern, including peaks and lulls in buyer interest in and customer demand for a specific product. 
  • Inventory perishability – The time an item can sit on shelves before it goes bad or becomes obsolete. 
  • Safety stock – How much stock you require to act as a buffer before running out. 

As a general note, outlying events shouldn’t be included since they’re statistically anomalous.  

Customize the Forecast to the Product 

Not all products share the same demand and sales velocity. And that’s true even for analogous products. For instance, eyeglasses may be sold all year round, whereas snow goggles tend to be more closely tied to the winter months. They may serve similar purposes but still have wildly different demand fluctuations.  

Knowing this, it’s better to create separate inventory or demand forecasts for products or product categories. Treating all inventory items as if they were the same will likely render your forecast meaningless. 

New Products Are Tricky 

If you’re working with a new product, you won’t have historical sales data to leverage. This makes a process that is already subject to conjecture even more reliant on guesswork. That said, you can minimize the speculation by performing the following actions: 

  • Conducting competitor analysis on similar products
  • Analyzing similar products that you have previously sold
  • Researching search trends
  • Hosting focus groups and surveys 

Utilize the Right Technologies 

Demand forecasting accuracy relies on a wealth of data points. Manually gathering, categorizing, and then making sense of that data is a massive undertaking. But with the help of inventory management systems like Crystal, the process becomes much easier.

Crystal is an Amazon-approved application that leverages your historic sales data to create a boundary range of potential demand levels. This makes it possible to identify your understock and overstock levels, as well as the optimal equilibrium range.

Demand Forecasting with Crystal 

With so much saturation on Amazon, sellers need to do everything in their power to gain a competitive advantage. And with the help of demand forecasting, your team can find an ideal balance between cash flow and inventory levels, to better plan a seamless budget vs forecast

Want to enhance your forecasting? 

Crystal is the solution. It’s a tool for Amazon FBA business to create confidence over what is usually a nebulous process. 

How does it work? Check it out to see for yourself.

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