Machine learning and artificial intelligence (AI) have amazing potential to simplify, accelerate, and improve many aspects of our everyday lives. Early results have simultaneously created huge excitement and demonstrated its frightening potential.
In one example, Facebook was forced to shut down an AI engine after developers discovered that the AI had created its own unique language that humans could not interpret. Researchers at Facebook discovered that the chatbots had deviated from the script and were communicating in a new language developed without human input or intervention.
Despite this, the positive hype surrounding these technologies and the level of investment are set to grow exponentially, impacting every part of our personal and professional lives. So, the question essentially becomes: Are you ready to embrace machine learning and AI?
Terms like predictive analytics, data science, cognitive/deep learning, machine learning and artificial intelligence are often used interchangeably, but it’s important to note the distinction.
AI is any device that can perceive its own environment and takes actions that maximize its chance of success. AI includes systems that read and interpret written language and natural language processing applications like Amazon Alexa.
Machine learning is a type of AI, where computers have the ability to learn without being explicitly programmed. Machine learning programs teach themselves to grow and change when exposed to new data. This is the true definition of on-the-job training.
Start fast, evolve steadily
So, where to start? This is one of those instances where getting started is more important than where to start. Building AI capabilities like machine learning is an evolutionary process and lends itself to short, focused discovery, design, prototyping, and delivery cycles. This will ensure ideas, budgets, and outcomes are qualified early and will provide a proven framework to maximize success.
For example, many of the use cases for AI and machine learning in supply chain planning are already yielding significant benefits with greater business insights and smarter decision making through supply chain process optimization.
Use case #1: Demand planning & forecast accuracy
A high-value application for machine learning in the area of supply chain optimization is a “best-case” algorithm for demand planning & forecasting. Best-case algorithms automatically switch to the most appropriate calculation method and input parameters based on real-time demand information. This will ensure the creation of the optimal forecast for every product at every stage of its lifecycle. The algorithm evaluates forecast errors for each cycle and recommends or automatically adopts the forecasting method that will produce the best result.
Use case #2: Inventory optimization
Inventory optimization continues to be a costly problem for many organizations. Multi-echelon inventory optimisation (MEIO) using machine learning automatically adjusts optimal safety stock values and inventory parameters at every echelon of the supply chain to meet customer requirements while minimizing the overall inventory investment. MEIO strives to maintain the optimal balance of components, work in process, and finished goods inventory.
Achieving success through these emerging technologies and capabilities will be an evolutionary process. Understanding how machine learning & AI can positively impact your organization will take time, but that shouldn’t stop you from embracing the future by taking advantage of the machine learning capabilities already available today.