Avoid Supply Chain Forecasting Errors

How to Avoid Forecast Error in the Supply Chain

Demand forecasting is an intricate science with many moving parts, and it can be challenging to get right, especially if you’re just getting started. With ecommerce continuing to grow, creating a similar shopping experience across channels creates a level of demand from customers that can be very challenging to plan for. However, forecast errors in your supply chain can be devastating to your business’s productivity and bottom line, so you must avoid mistakes as much as possible.

With the proper planning processes in place, it is possible to achieve demand forecast accuracy and enjoy the benefits of demand planning without the risk. This article will discuss how demand forecasting errors affect your business and where they originate.

What Is Forecast Error?

Forecast error occurs when there is a difference between the forecast demand and the actual demand. Determining the difference can involve various calculations, but in general, the more significant the difference between these two factors, the greater the impact on your bottom line and the greater the risks will be. Dangers of forecast error include:

• Overstocking inventory, significantly increasing costs
• Running out of stock of critical items, risking that the customer may go somewhere else for them
• Increased lead times for new items with shorter sales histories

Forecast accuracy determines all kinds of factors about how you run your business. It helps you decide what to buy and when to buy it. It also tells you what to stock and where to do so. It could even determine how you hire personnel and where you allocate your resources. In the end, forecast accuracy determines whether you can meet your customers’ needs.

Types of Forecasting Errors

To avoid forecasting errors effectively, it helps to understand the different types you may encounter. Being prepared for potential issues can help you mitigate inaccuracy and generate reliable forecasts that keep your business moving forward.

Under and Overestimation

Underestimation occurs when forecasted demand is lower than actual demand, while overestimation is the opposite. If you expect demand to be lower than what it actually is, you could have many dissatisfied customers and lost sales. Overestimating demand can lead to excess inventory and potential waste. Keeping up with technological advancements can improve accuracy and reduce errors.

Random

Random errors are the unpredictable variations between your forecasted and actual demands. These variations can come from various uncontrollable events, making them difficult to eliminate completely. You can minimize random errors by reviewing real-time data and continuously updating your forecasting models.

Systematic

Consistent inaccuracies in your forecasting processes are known as systematic errors. These errors are due to factors such as outdated forecasting models, flawed or inadequate methodologies or underestimation of an external factor’s impact. Regularly reviewing your forecasting techniques and processes can reveal systematic errors. Artificial intelligence (AI) tools can also help generate accurate demand forecasts with minimal systematic errors.

Underestimating Lead Times

Accurately predicting lead times is critical to receiving the products and materials you need right when you need them. If you underestimate lead times, you risk disappointing customers and failing to meet their demands. Monitoring and predicting lead times with precision will help you minimize related forecasting errors.

Ignoring Potential Delays and Seasonal Variance

Failing to consider the effects of delays and seasonal demands can lead to forecasting errors. Delays can happen for various reasons, from weather events and holidays to driver shortages and other supply-chain-specific challenges. Factoring in delays and surges in demand related to the season can help you effectively manage inventory needs.

Overlooking Other Departments

Forecasting should be a collaborative process within your organization. You need data from all sources to make informed decisions about meeting customer demands. Every department has valuable information for your forecasting process. Your sales team, for example, will know what demand shifts are happening, while your marketing team may conduct promotions that increase demand for certain items.

Three Sources of Forecast Error

No. 1: Data Problems

If your data is incorrect, your forecasts will be inaccurate as well. Organizing, acquiring and checking data is a significant source of delay in implementing forecasting software because so many businesses neglect data until forecasting brings data issues to light. Therefore, keeping up with and mastering your supply chain data is critical to accurately forecasting your inventory demand and ensuring proper inventory levels.

Data anomalies are also something to keep in mind. Even if your forecasting data is up to date and perfect, inaccurate or inconsistent data can easily drive forecasts off track if you don’t manage your data properly. These potential risks make it critical to measure forecast error and make error calculations.

No. 2: Wrong Forecasting Method

Businesses use various forecasting methods to calculate their demand. For example, traditional forecasting techniques are referred to as extrapolative methods. These methods attempt to find patterns in an item’s demand history, then project that same pattern into the future.

Some of these methods include exponential smoothing and moving averages. However, these methods are designed to work on consistent and regular data, not intermittent data. If you try to use them with intermittent data, your results will be inaccurate. Beyond that, with the last two years of shifting purchase and demand behavior, historical data needs to be carefully reviewed to ensure current conditions are met.

Big-ticket and slow-moving items usually involve intermittent data that must be calculated another way. In these cases, regression analysis, also known as causal modeling, may be used. These models use data other than an item’s demand history to forecast demand. However, they require more skill to use.

Picking the correct forecasting method for your products is crucial to getting the right results. So know your product, know your business, and you will know which model to choose.

No. 3: Flaws in the Forecasting Process

While organizations may think they are working together, different people will have different priorities depending on their departments. Alternatively, the mistake may be evaluating the forecasts incorrectly. If time isn’t taken to assess each forecast properly, collaboratively, the business risks error in its estimates.

There are many phases in a forecasting process, and things can go wrong. Forecasting has a team component to it, and this is where the process may get messy. Sales and Operations Planning is often where the collaborative aspect comes into play.

During these meetings, different departments come together to determine what the official forecast will be for the company. This collaboration enables more effective communication to occur, so that all departments are aligned on goals and strategies. Clear communication between departments helps improve forecast accuracy, adding to better inventory management and ultimately increased customer satisfaction.

How to Calculate Forecast Error

You can use several different types of calculations to determine your forecast error. These calculations range in complexity and the type of information they reveal, but they can all help you make necessary adjustments to improve forecast accuracy and reliability. Forecast error values can reveal forecast quality and opportunities for improvement.

Two of the most common forecast error calculations include:

• Mean Absolute Percent Error (MAPE): First, you will subtract your actual demand from your forecast demand, then divide the value by the actual demand. To discover the absolute percent error, you will multiply the value from the first equation by 100.
• Mean Absolute Deviation (MAD): The MAD shows how your forecasted demand deviates from actual demand. Subtract forecast demand from actual demand for a few periods, then take the mean of those values to determine your overall forecast error.

Reduce Errors in Your Demand Forecasting

You don’t have to handle your demand forecasting all on your own. Blue Ridge Global offers a supply chain management tool that can help you with forecasting at all steps of the supply chain to ensure that you can confidently predict your business’s needs and deliver your customers’ satisfaction. Our smart software incorporates your data and can help you identify risks, optimize planning and even improve pricing. Try out Blue Ridge Global software for yourself and see what we can do.