What Is Overfitting and How Can You Avoid It?

When it comes to warehouse and supply chain operations, it’s often beneficial when things are an “exact fit.” When managers accurately anticipate the amount of stock needed during seasonal swings, for example, warehouses can avoid stockouts while still meeting consumer demands. But when it comes to forecasting, there’s an aspect of it that we don’t want to be an exact match, because it can lead to some factual issues with the forecasting model. This situation is called overfitting.

What is overfitting, and how can it impact warehouse and supply chain operations? As we’ll discuss, overfitting is an issue that can arise with data modeling and happens when a model aligns exactly with the supplied data. In forecasting, this prevents the model from acting appropriately. When this is the case, the forecasting model has trouble accurately operating with any other information than what was provided in the model, and it becomes ineffective.

When it comes to warehouses and supply chain operations, overfitting can have cascading impacts. For example, it can potentially lead to forecasting models which aren’t doing their jobs, resulting in inconsistent predictions, ineffective inventory control, and can subsequently lead to dissatisfied customers. 

Here’s everything you need to know about overfitting, and how to avoid it in your forecast models. 

The Basics: Warehouses and Data

To answer the question “what is overfitting?” and understand how to avoid it, it’s important to first realize why it’s relevant. To start with, data is incredibly important when it comes to supply chains and warehouses, because it can improve operations, ensure there’s a satisfactory service level, and reduce the amount of excess inventory that your company needs to hold (therefore reducing unnecessary overhead costs). 

This data is crucial to everything from the supply chain operations themselves to the bottom line of the company. Data tells us that reducing supply chain costs can directly lead to more profits for businesses (one study found that reducing costs from 9% to 4% led to profits doubling).

Forecasting is a common way of collecting some of this mission-critical data, and different forecasting strategies are used to help warehouse decision-makers anticipate the amount of stock that will be necessary and maintain the correct levels of inventory. But of course, for forecasting to be useful and translated to real-world warehouse applications, your forecasting models must function properly. 

Overfitting: When the Data Doesn’t Work

What happens when the forecasting models aren’t all that great at predicting the future? This can happen when a forecast model fits too closely to its training data. When this happens, the model becomes ineffective at incorporating new data, and it loses its ability topredict. This is what overfitting is.

Let’s look at this example: a warehouse uses historical sales data to forecast demand for its best-selling products. However, when the model is created, it starts to too closely mimic the data which was input and is unable to account for real-world variables and fluctuations. This might result in an incorrect amount of stock being ordered for the upcoming season. 

Why Does Overfitting Happen?

On the surface, it might not make a ton of sense: if forecasting models are supposed to work, why does overfitting happen? As it turns out, there are a few main reasons that overfitting can occur, leading to inaccurate forecast models in warehouse and supply chain operations.

• An insufficient amount of data is used: When limited warehouse data is used in a forecasting model, that model might not accurately identify underlying patterns, and be prepared for outliers. Smaller data sets can cause a model to memorize specific data points which results in overfitting, instead of accurately predicting based on patterns. 

• There’s too much “noise” in the data: If your data model contains too much irrelevant data (also known as noisy data), this can also lead to overfitting. For example, if you’re trying to forecast for seasonality but are using tons of data that’s not really relevant to the seasonal trends, this could end in overfitting.

• The forecasting model is too complex: When you’re using a complicated forecasting model (particularly one with a large number of parameters), you’re at a bigger risk of running into overfitting. If models aren’t properly constrained, they can become too sensitive to the training data, and will fail to operate properly. 

What Are the Consequences of Overfitting?

The multi-billion dollar warehouse industry relies heavily on data. For example, accurate forecasts are crucial to understand how many employees to keep on during certain seasons: during busy seasons, you might need to bring on more employees, and during the slower seasons, you might be able to cut back on labor costs. Considering the fact that a typical warehouse spends millions of dollars in labor expenses annually, this can be a critical factor in a warehouse’s profitability.

Here are a few examples of how overfitting can impact warehouse operations:

• Inaccurate demand forecasting: The most significant impact of overfitting, inaccurate demand forecasting can have cascading effects on warehouse operations.  For example, if a forecasting model is overfitting and capturing things like irregularities or random fluctuations instead of finding repeating patterns, it can lead to inaccurate predictions. This can potentially lead warehouses to overstock or understock inventory.

• Inefficient inventory management: Overfitting can also have an impact on the way warehouse managers manage their inventory. For example, if overfitting is occurring, the forecasting model might not accurately represent the dynamics between supply and demand, or how certain inventory items are related. This can lead to inefficient inventory control decisions, causing events such as excess inventory levels (which can become costly) or stockouts (which can impact the customer experience).

• Improper resource allocation: Resources are often allocated in a warehouse based on forecasting models. If they’re overfitting, they might not accurately represent where resources should be allocated, and in what quantities. This can lead to improper allocations when it comes to things like equipment, storage space, and labor.

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How to Avoid Overfitting in Warehouse Forecasting Models

While overfitting can have devastating side effects, there’s good news: it’s possible to take steps to avoid it. Here are some techniques to avoid overfitting in your forecasting models:

• Ensure your data is high-quality 

High-quality data, which is representative of your warehouse operations, is necessary to avoid overfitting in forecasting models. It needs to represent your operations, and specifically represent the parts of your business you’re forecasting for. Leaders can also take steps to “cleanse” data of inconsistencies, erroneous outliers, or any missing information, to have the best dataset possible to work with.

• Excel at demand forecasting

Demand forecasting in warehouse operations requires many steps. For example, you’ll need to consider seasonality, trends, holidays, and external factors such as market dynamics. Taking these known variables into consideration can improve your forecasting model’s ability to detect repeating patterns. 

• Experiment with different forecasting methods

There are plenty of different forecasting models and strategies warehouses can use to expertly navigate stock and inventory needs. Experiment with different models to see which performs the best for your operations.

• Use digital tools to accurately collect data

Accurate and useful forecasts rely on your warehouse data, which means that leaders need to be collecting and accessing warehouse data in a reliable way. By using digital warehouse tools, you’ll be able to do things like track stock in real-time, see exactly how products are moving, and distill key insights automatically. Certain warehouse tools can also address your forecasting needs for you. For example, StockIQ has features which include advanced forecasting algorithms which can handle seasonality, short life cycle products, events, promotions, new product introductions, and forecasting at all levels of your product hierarchies.

• Support your efforts with other inventory management strategies

While forecasting is vital for inventory management, it doesn’t need to be the only tool you use. For example, you can take advantage of real-time inventory updates, insights from the market, and signals from your customers about demand, to create a complete picture of your warehouse operations. 

Overfitting Is Avoidable When You Have the Right Tools

Now that you understand “what is overfitting,” you see how it can lead to an array of consequences for warehouses. It can lead to inaccurate forecasting models which aren’t able to accomplish their main purpose: accurately forecasting your future warehouse needs. But the good news is that with warehouse and supply chain tools, you can easily collect data, receive real-time inventory analytics, and receive the most accurate forecasts possible.

We know forecasting accuracy because it’s what we do. StockIQ is a supply chain planning suite which provides you with the tools you need to run efficiently, improve forecast accuracy, and reduce inventory levels.

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