Technology · July 20, 2020

How AI in Manufacturing Can Boost Operational Success

The manufacturing industry has used automation, robotics and complex analytics for years. Now, as these technologies have become more affordable, companies of all sizes are using various types of artificial intelligence, or AI, to take their operations to a new level.

AI is the concept of enabling machines to process information and carry out tasks in a way that humans would consider thinking or learning. Most AI in manufacturing is narrowly focused and task-oriented. Machine learning, or ML, is a subset of AI that involves extracting knowledge from data by observing patterns. ML uses pattern matching to map inputs of data to predict results.

Machine learning in manufacturing can make automated decisions from more data, potentially faster than humans could. This capability can improve accuracy and bring tangible results to the bottom line.


AI use cases in manufacturing

AI and ML can fully automate complex tasks. These systems require less manpower to maintain and can be adjusted quickly based on changes in manufacturing strategy and production plans. Companies use various types of AI in manufacturing to generate higher margins and transform operations, and the possible benefits are numerous.

  • Collect data from hundreds of sensors to optimize equipment performance and minimize downtime
  • Help train industrial robots that perform the same task over and over again, allowing them to learn each time to achieve higher accuracy and speed
  • Improve safety conditions for humans working with heavy equipment and robots, expanding options for efficiency and quality control
  • Use satellite imagery for better natural resource planning and allocation
  • Use natural language processing to handle invoices, orders and contracts faster with reduced risk of errors or fraud
  • Create a recommendation engine to suggest products or materials based on earlier choices and uses

However, the biggest challenge for many small to medium-sized manufacturers is finding the best way to start using AI.

Starting the transformational journey

A proof of concept or pilot of an AI project can be a smart bridge to a full-blown implementation. It can also help you get buy-in from top leadership and manage risks more effectively. Observing the first round of results across a shorter duration—typically a few weeks—allows for incremental planning. The first step on the journey involves two carefully balanced goals:

  1. Pinpointing the most important questions data can help your company address
  2. Finding out what types of data your company collects or can access

Finding a valuable business question aligned with your company's overall strategy can make rallying stakeholder support easier. Even expanding the budget to get more insights into a strategic goal may be more practical than putting resources toward a limited-value project.

Remember that machine learning and predictive analytics require massive volumes of data. Typical sources can include:

  • Sales and customer data: Purchase history, demographics and engagement, website, email and call center data
  • Vendor and supply data: Orders and contracts
  • Operations data: Processes, accounting, labor and administrative

Look out for relevant data that's publicly available, such as information on social media, prices for materials or competitor information. Break down the time and costs needed to close critical gaps in the data.

Next, if the question points to a particular process, map it out to determine the decisions made during that activity. Show how insights from the data could make an impact across operations, and find out how much information you'll need to make sound business decisions.

Looking to address customer loyalty? Think about how information in the customer profile and history are linked to customer churn. If minimizing malfunctions is the challenge, find out what sensor readings or other data you can map to failures.

Determine an acceptable accuracy threshold, and evaluate the data model's performance against benchmarks. If necessary, tweak your parameters. Define project goals around this framework, and determine how you'll measure progress.

Create a budget aligned with the goals and measurement process. Factor in time for training internal and external stakeholders, considering outside partnerships and expertise as needed.

AI isn't just a buzzword word anymore. AI in manufacturing can be a powerful tool for predicting and adapting to dynamic conditions. As more companies employ machine learning in manufacturing, the stakes and the opportunity cost of not adopting may become especially steep for businesses under the pressure of volatile margins.

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