How the AI revolution in manufacturing is boosting operational success

Companies often explore ways to find new operational efficiencies using emerging technologies, including innovations in artificial intelligence, or AI. While some believe AI and machine learning may be considered innovations of the future, they're both becoming more commonly implemented by businesses to drive efficiencies—generating stronger results and revenue. And, in some cases, keep business afloat during trying times. For instance, the COVID-19 pandemic's stake in global supply chain issues.

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—think of important tasks like inventory management. 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 positively impact business, such as:

• Collecting data from hundreds of sensors to optimize equipment performance and minimize downtime
• Helping train industrial robots that perform habitual tasks, allowing them to learn each time to achieve higher accuracy and speed
• Improving safety conditions for humans working with heavy equipment and robots, expanding options for efficiency and quality control
• Using satellite imagery for better natural resource planning and allocation
• Implementing natural language processing to handle invoices, orders and contracts faster with reduced risk of errors or fraud
• Creating 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 reaping the benefits of AI.

Starting the transformational journey

A proof of concept or pilot of an AI project can be a smart bridge to full-blown implementation. 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

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.

Adaptability in times of constant change

Agility in the face of changing market dynamics remains a core feature of resilience and competitiveness. To gain optimal advantages from AI and acquire high-quality training data, consider:

• Refining your implementation strategies
• Working to understand your strategic objectives and potential use cases
• Harnessing resources from across the organization
• Tapping into external partner networks that will work with you and provide data

Investments in AI can provide tools for companies to stay successful during times of disruption. If you're still not convinced, just know AI in manufacturing market size is currently valued at $2.3 billion and is expected to climb to $16.3 billion by the year 2027. Talk to your banking partner about a plan to reap the rewards from the latest innovations in AI.