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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.
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.
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.
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:
Plex DemandCaster 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.