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Demand and supply planning was as much an art form as a science for many decades. The process was guided by human intuition, gut feeling, and experience and was aided by tools such as calculators and spreadsheets. These were all combined to produce forecasting that was an assumption at best and highly variable at worst. Software increased forecasting accuracy, reducing the height of the peaks and depths of the valleys in terms of variance. But despite the improvement, and aside from common issues like human error and missing data, demand and supply forecasting were still highly biased because they relied on human analysis of yielded insights and manual data entry. The rise of advanced software introduced tools to help eliminate remaining human inputs. Automated data, alongside systems more open to integration with other platforms, eliminated data silos. And now, platforms with advanced machine learning (ML) algorithms are addressing bias forecasting.

What is Machine Learning?

Machine learning uses trends and interprets incoming data to hone and improve computer predictions. Using complex algorithms, computers are "taught" to learn the value of incoming data inputs and return them to the user in a relevant way. The greater the volume flow on critical data points, the more accurate the machine becomes.

How Machine Learning is Linked to AI

Machine learning is often considered a subset of artificial intelligence but is capable of providing high value in advanced software platforms without AI. The most common link between ML and AI is the use of at or near real-time data to render actionable insights immediately. ML is always considered a type of AI, but not all AI applications are the result of machine learning, and ML systems can deliver high value as a standalone feature of the software.

Types of Forecasting Bias

Calculating forecast bias can reveal how accurate the forecast is to actual demand. Bias may be positive to forecasting demand, or it may be negative, resulting in under forecasting. In the end, bias will always impact the analysis of the data under review. There are many types of bias forecasting, including:

1. Modeling Bias – Modeling bias occurs when a biased set of assumptions are used to build a model. One example would be a model that recreates the current state. Because the current state of demand has been steady or predictable, the model may have little room to account for the impact of disruption. Machine learning can identify and learn from trends to adjust the model.
2. Complexity Bias – This bias assumes that if some inputs are good, more and more complex inputs are better. But not all incoming data is relevant to creating an accurate forecast. Machine learning can identify the most important data and filter out anything irrelevant.
3. Confirmation Bias – When humans analyze complex data sets, they often zero in on what they’re more inclined to prefer as indicators. If people are accustomed to looking for certain variables, they may miss other signals in favor of their "favorites." Machine learning removes these predispositions.
4. Innovation Bias – Companies continually develop new products to remain competitive, and each of these products has a unique demand curve. Human analysis risks the observer using assumptions that may result in biased demand variables. Machine learning handles each product's variables while managing complex combinations and overlaps for material requirements.
5. Anchor Bias – Everyone has sat in meetings where managers firmly state a specific growth factor. The problem is that human planners may come away with bias that leads them to manage inputs to cluster at or near the number. Machine learning forecasting manages these inputs for relevance and shows actual growth or reduction.

Using Machine Learning for Accurate Forecasting

has released a new machine learning enhancement to improve demand forecasting accuracy and help planners interpret data more efficiently. Plex DemandCaster’s machine learning capabilities can help you unlock more accurate data-driven forecasting insights, address disruption, and automate your demand and supply planning. Contact us to learn more about our machine learning capabilities and how they can help you.