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STATISTICS, ML & AI IN DEMAND PLANNING SOFTWARE: ARE ALL CREATED EQUAL?

September 7, 2023

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STATISTICS, ML & AI IN DEMAND PLANNING SOFTWARE: ARE ALL CREATED EQUAL?

In the realm of demand planning, it is essential to understand the differences
between statistical forecasts, machine learning (ML) methods, and artificial
intelligence (AI). These terms are often misused, leading to confusion and
obscuring the unique differences. This article aims to demystify the terminology
by shedding light on the crucial distinctions and highlighting how each approach
contributes to the field of demand planning.  

THE FOUNDATIONS: STATISTICAL FORECASTS  

Statistical forecasts form the backbone of demand planning. They rely on
historical data and utilize statistical techniques to predict future demand
patterns. Statistical models provide insights into historical trends,
seasonality, and other relevant factors. However, they have limitations when it
comes to adapting to changing circumstances and incorporating dynamic inputs.
Statistical forecasts are typically reliant on human intervention and lack the
ability to learn and improve automatically.  

EVOLUTIONARY LEAP: MACHINE LEARNING IN DEMAND PLANNING  

Machine learning methods have introduced a significant leap forward in demand
planning. ML algorithms utilize historical data to identify complex patterns and
relationships that may not be obvious to traditional statistical models. By
automatically learning from experience without explicit programming, ML
algorithms can improve forecast accuracy. ML-based demand planning systems can
process large volumes of data quickly, making them valuable tools for
identifying trends, detecting anomalies, and generating forecasts.  

Not all ML algorithms out-of-the-box can be applied to Demand Planning.
Therefore, in some cases, these algorithms could even give worse results than
statistical models. That’s why it’s crucial that these models can work in the
context of the business and can be trained to understand the real business
drivers and different scenarios. Otherwise, these models are prone to under or
overfitting, generating large swings in forecasts, or to making recommendations
that may fit the model but are infeasible or unactionable. Therefore, it
requires a lot of special product & business understanding, a process we call
semantic ontology, to describe the business scenarios to the models so that they
are able to generate accurate, current, and consistent results for the business.
  

Beyond Boundaries: Artificial Intelligence in Demand Planning  

Artificial intelligence represents the next frontier in demand planning,
encompassing broader capabilities that extend beyond statistical forecasts and
ML methods. AI enables machines to perform tasks that traditionally required
human intelligence, such as understanding language, recognizing patterns, and
making decisions. In the context of demand planning, AI solutions can
revolutionize the way forecasts are generated, analyzed, and utilized by not
just Demand Planners but break silos in the organization and find uses for
sales, finance, marketing, or operations departments.  

THE DISTINCTIVE FEATURES OF AI IN DEMAND PLANNING  SOFTWARE

Automation and Adaptability  

Unlike statistical models and ML methods, AI-based demand planning solutions can
be implemented quickly without the need for extensive manual configuration.
These systems can adapt to changes in demand plans and dynamically update models
without human intervention. This adaptability ensures that forecasts remain
accurate and up-to-date, even in rapidly changing business environments.  

Continuous Learning  

AI systems can learn from additional inputs, whether from sales teams, external
sources, or real-time data feeds. This continuous learning enables AI-based
demand planning solutions to improve forecast accuracy over time. By
incorporating new information, AI systems can better capture market dynamics,
identify emerging trends, and make more precise predictions.  

Clear Insights  

AI-based demand planning software provide in-depth insights into the drivers of
demand. They go beyond surface-level analysis by understanding the relationships
and interactions between various components and events. This deeper
understanding empowers planners to communicate effectively with other
departments, identify root causes of issues, and collaborate on demand-shaping
strategies.  

Enhanced Forecast Accuracy  

AI-driven demand planning solutions have demonstrated substantial improvements
in forecast accuracy. By leveraging advanced algorithms, historical data, and
external signals, these solutions can achieve forecast accuracy improvements
ranging from 10-15% based on historical data and planner input. When
incorporating additional external signals and order data for Demand Sensing, the
accuracy improvements can reach up to 30-40%.  

DIFFERENTIATING TRUE AI FROM MISUSED TERMINOLOGY  

It is essential to distinguish genuine AI solutions from those that merely claim
to utilize AI or ML capabilities. Many companies misuse these terms, creating
confusion and diluting the transformative potential of true AI. True AI demand
planning solutions, characterized by their ability to automate processes,
continuously learn, provide comprehensive insights, and significantly enhance
forecast accuracy, offer substantial advantages over statistical forecasts and
limited ML methods.  

HEURISTICS TO MAKE THE DISTINCTION  

A few heuristics to decide whether a technology is built on old methods or data
scientists manually tuning the models vs. actually on AI:   

 * Speed of implementation: it doesn’t take months for ML models to be
   parametrized  
 * An actual increase in accuracy  
 * Deals with outliers in a smart manner rather than just deleting them: able to
   use these to create causal relationships looking ahead  
 * Adjusts based on changing assumptions  

Conclusion  

Understanding the distinctions between statistics, ML, and AI in demand planning
software is crucial for organizations seeking to optimize their forecasting
capabilities. While statistical forecasts provide historical insights and ML
methods enhance predictive accuracy, true AI-based solutions revolutionize
demand planning by automating processes, continuously learning, providing
comprehensive insights, and significantly improving forecast accuracy. By
leveraging the power of AI, demand planners can navigate the complexities of the
marketplace more effectively, anticipate trends, and make informed decisions
that drive business success.  

If you’d like to know more about how we have created AI-First Demand Planning
solutions, we’re happy to deep-dive into the details with you.  Contact us
today.

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