Tips on Achieving Better Demand Forecasting

Having the ability to make forecasts and predictions on future market trends and consumer needs is not just a good idea, it’s practically necessary to succeed in today’s turbulent economy. Making smart, well-informed forecasts doesn’t come naturally, but the right information combined with insights and tools like machine learning software and expert statisticians can make a world of difference. Here are some pointers on how to make better forecasts and give your business the edge it needs.

Choosing the Right Technique

Many techniques exist for creating a forecast for a product or service, each varying in scope, accuracy, costs and required data. This means that they are not interchangeable—a product that has just been launched lacks historical data to draw from, so more statistical methods such as time series analysis would not be of great use, but these have value for long-term planning regarding an established product. Start by determining the purpose and scope of the forecast and what factors are being considered, and weigh the cost of making the forecast versus that of making an erroneous decision.

Beyond Traditional Statistics

Traditional statistical methods have been a mainstay for decades, especially before the heavy computational requirements of machine learning were fulfilled. Today, they’re still valuable as a foundation for forecasting models. However, these traditional methods make a lot of assumptions, especially about the stability of market trends, and unexpected turning points can invalidate statistical models. Additionally, massive volumes of data, especially unstructured data, are difficult to interpret this way.

Machine Learning Has Limits

While the use of ML tools has enabled refined forecasts that otherwise wouldn’t be possible with statistics and human minds alone, misunderstandings about ML can easily lead to its misuse. Fundamentally, ML is based on deriving patterns from large sets of test data and applying them to new datasets. If the test data is flawed, the learned patterns can be nonsensical or limited. Even when it works, the algorithm lacks context for data—despite being called artificial intelligence, it doesn’t have the intelligence to make inferences beyond pattern recognition.

Turn to the Experts

Ultimately, statistical software and machine learning algorithms are tools, and tools need the right hands using them to do the most good. Knowing when to use traditional statistics, machine learning algorithms or other methods entirely is critical, and no one technique is a universal solution. Data scientists and industry specialists determine what factors are important, interpret output data, work out what predictive models are relevant and make a final decision based on these findings. This is why the best ML-based platforms include the means to consult with experts—and why we have experts in the first place.