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5 Methods to Assure Better Accuracy in Your Demand Forecasting

5 Methods to Assure Better Accuracy in Your Demand Forecasting

 

Though every business needs a demand forecasting model, there is no perfect solution that works for everybody. While no one method is perfect at predicting demand, there are many ways that you can improve the accuracy of your demand forecasting to increase efficiencies and drive profitability.

 

What is Demand Forecasting?

Simply put, demand forecasting is the process of using historical data to estimate future demand for your product or service. Demand forecasting delivers vital insights into inventory turnover, cash flow, margins, risk assessments, and more.

 

Depending on your approach, you could be missing out on opportunities to improve the accuracy of your demand forecasting results.

 

Methods of Demand Forecasting

Companies approach demand forecasting in a variety of ways. Essentially, it can be broken down into two very specific methods: quantitative and qualitative.

  • Quantitative Demand Forecasting Methods

Quantitative data leverages hard numbers to express variables. For example, a person’s shoe size, height, and the length of their stride would be considered quantitative, as would the number of items sold, the specific size selection, and so on.

 

From a demand forecasting standpoint, quantitative methods look at statistical data, both in the present and historically, to predict the future. Under this heading, there are a few subheadings:

 

The Trend Projection Method is leveraged by companies that have historical sales data dating back at least a couple of years. Hard numbers that represent “normal” conditions are used to visualize a trend over time.

 

The advantage of the trend projection method is that it is based on actual data and can be validated in several different ways. The analysis is more exact, and it can be replicated or refined as needed.

 

The disadvantage of trend projection is that it does not address the underlying issues that may have catalyzed the trend. For example, a significant event, like a hurricane or a wildfire, could present you with a slanted view. More data is needed to support accurate projections.

 

Econometric Forecasting takes a more nuanced approach. This method considers the more complex relationships between demand and influencing factors around it. Companies develop an equation that is fine-tuned over time to deliver an accurate historical representation of the facts. Those values are then used to generate a forecast.

 

Econometric forecasting looks at how different aspects of the economy interact with each other. One example could be how spending relates to increases or decreases in income, interest rates, debt, unemployment, and so on.

 

Barometric Forecasting considers influencing factors in the present to predict the future. Once abandoned due to its failure to predict the great depression of the 1930s, the barometric method is a time series that analyzes current economic indicators and their movements. These factors include statistical and economic indicators like leading, concurrent, or lagging series to generate the demand forecast.

 

The advantage of barometric forecasting today is that it solves the problem of identifying the value of an individual variable. One of the most significant disadvantages of this method is that it can’t be used for long-term forecasting.

 

  • Qualitative Demand Forecasting Methods

The results of qualitative demand forecasting are typically more descriptive than what we see in quantitative methods. They don’t necessarily rely on numbers as much as they do expert judgment to predict future demand.

 

Here are some examples of qualitative demand forecasting methods:

 

Market Research. Companies reach out to their customers with surveys and questionnaires to assess potential demand. These surveys leverage demographic, economic, geographical, and personal preference data to arrive at their conclusions. For companies or products that do not have a great deal of demand or sales history, this method of demand forecasting can be highly advantageous.

 

On the plus side, your market research efforts will tell you if there is anything inherently wrong with your product and direct you to areas that you can improve.

 

On the negative side, market research takes a lot of time to collect and analyze the data, which might cause you to miss out on market share. Plus, market research is costly, tying up funds that perhaps could be better spent elsewhere.

 

The Delphi Technique is based on multiple rounds of surveys and questionnaires that are sent to panels of experts to collect responses. This approach is generally anonymous, but it can be adapted for face-to-face meetings and focus groups. Results rely on consensus to arrive at their conclusions. As different opinions are brought into the mix, consensus can change as new points of view are considered.

 

The advantage of the Delphi technique is that it invites opinions from a diverse group of thought leaders and subject matter experts without the group being present in the same room. This approach supports anonymity, which tends to support unbiased conclusions as there is no fear of rebuttal or repercussions.

 

There are a couple of disadvantages to the Delphi approach. One, it tends not to achieve the same results as a face-to-face discussion. When parties interact directly, ideas are broken down and reassessed until they produce a consensus, a desirable outcome that is inherently lacking in this approach.

 

Second, it often takes a lot longer to reach conclusions, which might not be in the best interest of the endeavor. Additionally, there is a risk that the insights you end up with will not provide the value you’re looking for.

 

Better Data, More Accuracy

There is no doubt that an accurate forecast will produce better results. Generating fast, timely, and precise forecasts will strengthen your organization from end-to-end. The demand forecast is the jumping-off point for all of your decision making, so there is a definite advantage in starting from a strong position.

 

Here are a few tips on how you can improve accuracy in your demand forecasting.

 

Question All Assumptions

All of the data you apply to your forecasting models should be derived statistically. The number of buyers in your market, the percentage of that group that is expected to buy, when they will buy (accounting for seasonal events, holidays, and known economic cycles), and so on. These numbers must then be validated before they are applied to the forecasting model.

 

Apply a Variety of Forecasting Methods

When you consider your data from a range of viewpoints, your forecast will be significantly more accurate. While one model might be more suitable to your company than another, considering as many angles as possible will highlight gaps in reasoning and provide a more flexible and accurate result.

 

Leverage Quality Data

Statistical demand forecasting relies heavily on historical data. The data you are applying to the forecasting model must be complete, correct, of optimum quality, and drawn from an appropriate period in your company’s history. This is the only way to accurately represent true demand.

 

Minimize Delays

Lapse times in data collection will seriously impact the accuracy of your forecast. Ideally, you want to pull your data in real-time to minimize these delays. Acting after the fact has a negative resonance throughout the supply chain, reducing operational efficiency and placing you behind the competition. An integrated planning process helps you avoid costly overstocks and stock-outs.

 

Periodic Reality Checks

Your forecasts should be checked regularly against actual sales figures as soon as they become available. The discrepancies you discover can then be used to fine-tune your forecast to reflect what’s really going on in the markets. Consider how the competition is doing and always be mindful of new players entering the market. As conditions tend to change quickly and often drastically, diligence and consistency are critical.

 

Leverage AI for Better Accuracy in Demand Forecasting

Demand forecasting is a critical business practice, but if the data you are working with is inaccurate, you are wasting time and resources.

 

Crisp leverages historical data as well as customer data, sales data, and socioeconomic trends. If you are still relying on manual forecasting methods to predict your company’s future, you may well be facing higher costs, waste, and inefficiencies across the organization.

 

Accurate forecasts, on the other hand, allow you to grow and scale while improving cost efficiencies from seed to shelf, from ideation to product creation, and from the factory floor to the customer’s door.

 

Reach out to us directly to learn more about how to get started, or schedule a demo today.