Exploring the Venn diagram of two branches of data science, plus how they impact businesses in the CPG industry
by Sophie Guimaraes | June 21, 2021
Artificial Intelligence (AI) is everywhere these days — blink and there’s a new application for it, a new take on it, and maybe even a new name for it. But what seems like more of the same can actually be something entirely distinct. A chief example? Artificial Intelligence and Machine Learning.
While AI and Machine Learning (ML) are fundamentally interconnected, they do not mean the same thing. That they’re interchangeable terms is a common misconception many people have these days — even CPG professionals who utilize AI or ML to help them do their jobs more efficiently, especially when it comes to demand planning and forecasting. In this article, we’ll dive into the key differences and similarities between AI and ML and explore how they can be applied to businesses across a variety of industries, particularly CPG.
What Is Data Science?
Data science refers to the scientific process of drawing knowledge from structured and unstructured data, and then applying those insights across a variety of functions. For example, CPG brands employ various algorithms (or models) to their own internal or external datasets, and then use those learnings to make key demand planning decisions across their supply chain.
What Is Artificial Intelligence?
AI can elicit images of rapid change, the future, and maybe even dystopia — but these stereotypes have varying degrees of truth. Rapid change, yes; AI is a field of data science that’s rapidly growing, changing, and affecting our world. Future, not quite: AI is here, now, in today’s world and vastly applicable. And dystopia? Definitely not. AI shapes your life in ways you may not even realize: You engage with it every time you hop in an Uber or Speak with Amazon Alexa, for example, or every time you scroll through your social media feeds.
Artificial Intelligence is a branch of data science in which machines (i.e. computers) can make decisions and execute tasks in a way similar to — and assumedly better than — humans: they use logic and rules, and can sense and adapt and act. AI is sometimes referred to as a “digital brain,” but it can be more simply understood as a concept in which machines carry out intelligent tasks that mimic human behavior in certain situations. It has its own shortcomings when compared to human intelligence, but in many respects, AI — especially when it’s industry-specific — can easily exceed the predictive capabilities of a person, and can therefore succeed in accomplishing a task or achieving a goal more efficiently and effectively.
What Is Machine Learning?
Machine Learning is a data science technique that’s used to achieve AI. Machine Learning can very simply be summed up as a branch (or subset of) AI in which algorithms acquire knowledge by continuously identifying and drawing inferences from patterns amongst various datasets, and then apply these learnings to maximize the performance of a task. In the case of demand planning, a machine learning model will learn from the data it’s fed, and then automatically iterate within itself and ultimately identify new, precise, optimal solutions with little to no human intervention, which improves outcomes, such as accuracy.
(ML itself has subsets, including Classical Machine Learning, deep learning, and neural networks — the specificity increases the further you go down the data science food chain. The most significant distinction, though, remains between AI and ML.)
AI & ML: A Venn Diagram
You don’t say “square” when you mean “rectangle” — nor would you say “artificial intelligence” when you mean “machine learning.” The differences and overlaps between AI and ML may not seem glaring, but if these new technologies play even a small role in your business, it’s vital to know their applications inside and out — and have a complete understanding of the forces that pave such a powerful path forward.
Once again, Artificial Intelligence and Machine Learning can seem like the same thing, and while they’re definitely correlated, it’s important to distinguish between the two. The simplest way to put it? One is about executing tasks; the other is about learning on its own.
ML is a subfield of AI and describes the process by which machines learn and adapt through iterative algorithmic activity, learning on its own without additional programming or human intervention. In other words, learning from experience. AI describes when machines execute tasks that mimic the capabilities of a human; AI utilizes the information that ML provides and becomes the digital brain of your business.
Concluding Thoughts: Applying AI
The relationship between Artificial Intelligence and Machine Learning actually mimics another oft-confused one: demand planning vs. demand forecasting, two terms used interchangeably but are decidedly different. Demand planning is the process of forecasting and shifting your operational strategy in order to meet the demand that was forecasted; demand forecasts are the actual result, the output of your planning process. One is a function of the other — just like AI and ML. More and more, businesses in the CPG industry are adopting and investing in innovative technologies that can bolster their demand planning process and unify their supply chain operations, like AI. Why? Because AI can automate a number of tasks and do them quicker, and more accurately than, a team of humans. Among the benefits of AI are the ability to:
- Eliminate time-sucking and growth-hindering manual work.
- Raise your base demand forecasting accuracy.
- Keep your team lean.
- Bring agility to your business.
- Help your third-party relationships run smoother.
- Deliver actionable insights fast.
Unioncrate is an AI-powered Integrated Business Planning (IBP) platform that delivers demand forecasts with unmatched accuracy, collaborative visibility, and actionable intelligence — enabling CPG brands to plan and execute agile supply chain strategies at the click of a button.