An Expedition Into Demand Forecasting With Machine Learning Models

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An Expedition Into Demand Forecasting With
Machine Learning Models
In a fast-paced business landscape, evolving
consumer choices pose one challenge for
businesses Demand Volatility. Blame geopolitical
changes, social media influence, fierce
competition amongst enterprises, and sometimes a
global pandemic. Traditional Forecasting
mechanisms cannot always give accurate results
based solely on historical data. Moreover, what
about a variety of data sets and multiple points
of consideration that directly impact the
dynamics of consumer demands? Thankfully, we have
AI and Machine Learning (ML) to our rescue,
revolutionizing Demand Forecasting. In this
article, we dive into the realms of Machine
Learning Demand Forecasting and gauge how it
surpasses traditional forecasting methods to
offer deep insight into the future purchase
predictions of a thriving consumer base.
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What exactly is Demand Forecasting and what are
the Traditional forecasting methods? Demand
forecasting is the process of predicting customer
needs for a product or service in the future. It
helps make adjustments to inventory, or rather
inventory decisions, and an informed supply to
meet consumer needs. Traditional forecasting, or
Statistical forecasting, encompasses methods like
linear regression, simple exponential smoothing,
ARIMA, ARIMAX, and more. These methods offer a
high level of transparency but are only based on
historical data and apply to a perfect scheme of
situations that are not necessarily prone to
disruptions. Do we completely discard the
traditional methods? We will analyze this later
in this article after we uncover
Machine Learnings capabilities in
forecasting. How does Machine Learning
revolutionize Demand Forecasting? Machine
Learning, on the contrary, works on multiple data
sources, including many variables that would
affect consumer demand. It does not just depend
on historical data of purchase behavior gathered
over, lets say, the past two years and considers
current factors and drives a high degree of
predictive analysis. Machine Learning models are
built on data-driven predictions that consider
internal and external factors influencing a
product or services demands. Some of the data
sources that Machine Learning utilizes are
marketing polls, macroeconomic indicators, weather
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forecasts, local events, social media influence,
competitors activity, and historical data. It is
safe to categorize these data sources as
structured data like past purchase orders,
customer POS information, inventory, and sales
transactions, and unstructured data like social
media, marketing campaigns, reviews, and
more. ML forecasting models use complex
mathematical algorithms and understand
complicated relationships in datasets while
adapting to volatile conditions. Some popular ML
forecasting models include Artificial neural
networks, Classification and regression trees
(CART), Generalized regression neural networks,
and Gaussian processes. While traditional
forecasting models mostly use linear regression
methods, Machine learning models use a
combination of linear and non-linear methods, to
arrive at a prediction. The result is a high
level of accuracy of forecasts and minimum loss
function. It has been observed that the error
metrics like Mean absolute percentage error, Root
mean square error, or Weighted root mean square
errors are significantly lesser in an ML model
than in a Statistical model. Having said that,
ML works best for predictive analysis with
volatile demand patterns and short-to-mid-term
forecasting while launching new products or
services and dynamic business environments. For
example, the leading dairy brand Granarolo
achieved 85 to 95 accuracy in Forecast by
integrating machine learning with its existing
systems.
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How To Maximize the Benefits of ML in Demand
Forecasting? To maximize MLs offerings,
enterprises must choose one compatible with their
existing ERP or Inventory management system for a
smooth operation. Businesses must know the data
sources from which the solution would pull
information as it gives accurate results only
with the help of a large and high-quality
dataset. Organizations must conduct extensive
training programs for their staff to seamlessly
use ML solutions. Businesses can also choose to
buy ERP or WMS with an in-built model or build a
custom model that requires ample investment.
Finally, the ML solution needs to be tested
thoroughly to see if the level of accuracy in
predictions is acceptable. Otherwise, a brand
would never have the correct inventory to suffice
its consumer needs due to incorrect predictions
by an ML model. How are Traditional Forecasting
models still relevant? Though ML models offer a
holistic approach to forecasting, we cannot
completely discard Traditional Forecasting
models. Statistical forecasting models offer a
high transparency level and are perfect for mid
to long-term planning. They are apt for products
or services that have survived the storm of
demand volatility and would never run out of
choice. Conclusion While Statistical Forecasting
methods have been used for ages, the rising
demand for models that predict masked market
trends and
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navigate volatility has led to ML-driven Demand
forecasting. As with every complex AI-based model
with high computational prowess, Machine Learning
has its requisites to perform optimally, like a
smooth integration with existing systems,
investment, and resource training. When
integrated and utilized well, it can help
businesses forecast accurately, and promote
operational efficiency and cost reduction along
the supply chain. MLs offerings, coupled with
human intervention, can aid in strategic
decision-making for better growth and
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