AI enables accurate demand forecasting

Many companies struggle with forecasting demand. Whether you run a small business or a large enterprise, the challenge of predicting customer behavior and inventory levels has never been easier. Even large organizations such as Target and Walmart that can afford teams of data scientists have recently reported that they are struggling excess stock due to poor demand forecasts.

In this time of global uncertainty, many companies have adopted a just-in-case mindset. They relied on archaic forecasting methods, sifting through old data and drawing poor conclusions based on past problems.

But understanding demand accurately should not be such a big deal in 2023. Even as we battle the post-pandemic turmoil, we now have clear alternatives to outdated forecasting tools — thanks to artificial intelligence (AI). And we don’t need endless amounts of historical data to access the real-time patterns needed to accurately forecast demand. In fact, AI-powered demand sensing has been shown to reduce inventory errors in supply chain management by up to 50% 50%according to McKinsey & Co.

Why does effective demand forecasting depend on AI?

Today’s forecasts are mostly based on old and inefficient methods, leading to massive misconceptions and inaccuracies. These inaccuracies limit sales forecasts, leading to over-corrections in capacity planning and supply chains that are incorrect from the start.

Every company produces data, of course, but almost all of it is trapped in silos and walled solutions that have been developed over many decades for specific tasks. Silos arise for noble reasons – they represent a company’s attempts to organize and become structured.

Frankly, silos are useful in many scenarios, but if the boundaries between them are too tight and there is a lack of effective communication, silos will have a negative impact on business, putting more pressure on processes. Inaccuracies are most common in organizations with many silos, because teams and departments just don’t have enough of a shared language. Rigid silos also make data, even good data, less credible.

Working with ThroughPut’s clients, I’ve seen AI make a difference in demand forecasting. That’s because it can draw from disparate datasets, using real-time patterns feel the ask around the corner instead of just assuming future demand from past events.

By using an AI-driven system, timestamped data is picked regardless of barriers and quickly builds a global vision of your virtual supply chain network. Supply chain AI takes the best cues from the noise constantly generated by your disparate data systems and turns the noise into a song you can understand.

In addition, AI is superior in analyzing and understanding data in large quantities; but it also doesn’t need a lot of information to learn. AI trained for real-world applications already senses which data signals to extract from an ocean of noise, so it can solve needs before they cause problems.

The quality of the data matters most, not the quantity, and delaying using AI to detect demand will only cause current supply challenges to stagnate and potentially get worse. From there, stock prices and shareholders suffer. We see this in all industries today: innovation laggards and slow adopters paying the price for relying on old forecasting methods.

What myths about demand forecasting need to be overcome?

Looking for the best possible accuracy, what other myths can we debunk in the world of demand forecasting?

A misconception running around tired companies is that demand forecasting can never be truly accurate, making it more effort than it’s worth. But if you can account for the margin of error, use high-quality data, and analyze patterns effectively, demand forecasting can be accurate and make tangible differences in the way your supply chain operates.

Another of the biggest misconceptions is that a company needs to go through a lengthy and expensive digital transformation, system integration, or cloud or data lake project, with armies of consultants and data scientists, to use AI-driven tools and the kind of outcomes it will get. need. While digital transformation can be beneficial in the long run, companies have an immediate need for better forecasting of demand that they need to act on sooner rather than later. Your company already has all the data it needs to solve these problems.

The bottom line is that improved accuracy in demand planning will result in higher sales and profits. When demand planning is based on old data and bad assumptions, inevitably there are inaccurate results, leading to ineffective decisions, vague customer service and ultimately lost business. AI can turn forecasting into demand detection: forecasting best estimates of likely outcomes; AI-powered question sensing sees the past and present while focusing on what is likely to come in the future.

By applying supply chain AI and predictive augmentation to your existing data, you can realize real demand sensing downstream, access much greater accuracy of the most requested SKUs, and ultimately achieve higher sales, profits and output – all in a more sustainable way.

Seth Page is the chief operations officer and head of business development at Through Put Inc.