Choosing the right tech for the right results

Investing in new technologies can be expensive and time consuming, and sometimes, the results aren’t worth the investment. While smart manufacturing — using advanced technologies to strengthen the efficiency and agility of traditional manufacturing processes ­— is now commonly part of business strategy, manufacturers still struggle with selecting and adapting new technologies to achieve the results they need.

Before one manufacturer found an ERP system that finally helped manage the business effectively, for example, the company went through years of frustration and expenses, even discarding one expensive system because it performed poorly.

Selecting the right technology to meet the needs of the business is complicated by the abundance of options, the time and cost involved, and the speed with which technologies are evolving.

Investigate new smart manufacturing options

As the use of artificial intelligence accelerates, the pace of technological change is accelerating and having a transformative impact on the manufacturing industry.

These are the key smart manufacturing technologies companies are adopting, as well as examples of their applications.

• Artificial intelligence: An umbrella term for technology enabling machines to perform tasks requiring intelligence to optimize manufacturing processes by recognizing objects and sounds, understanding language, and solving problems [21].

• Autonomous/intelligent robots: Smart machines that perform tasks without needing humans to control them. They acquire data from sensors and cameras, processed through neural networks, systems that mimic the human brain. These robots typically perform tasks related to material handling, processing, assembly, inspection, and inventory management that are difficult, repetitive or dangerous, reducing risks to human personnel.

• Cloud computing: Cloud-based services, such as servers, storage, databases, networking, software, analytics, and intelligence that support manufacturing operations and processes [22]. 

• Computer vision: Enables computers to understand and interpret visual information from images and videos to improve inspection, quality control, sorting, and process automation [23].
• Data analytics: Tools that analyze data on production processes, supply chain management and customer behaviour to identify areas for improvements and cost reduction.

• Digital twins: Software models that are exact replicas representing the attributes and operating behaviour of specific production lines, machinery, end products, or “real world” scenarios within a production process. These are maintained in a database through real-time updates. These virtual representations enable manufacturers to make quick production decisions by providing a real-time view of what’s happening now and in the future [24].

• Internet of Things (IoT): Networks of interconnected machines, tools, sensors and software that collect and share data to enhance efficiency, productivity and safety by supporting predictive maintenance, asset tracking, inventory management, quality control, production process monitoring, energy efficiency and supply chain optimization [25].

• Machine learning (ML): Enables machines to optimize inventory levels by analyzing data and detecting patterns and trends  that deliver forecasts of future demand [26].

• Predictive analytics: Analyzes historical data to reduce downtime ­by, for example, maintaining production schedules, minimizing rush orders, and reducing energy consumption and repair costs.

Plug-and-play, no code automation, making tech integration easier and more affordable

As factories continually upgrade and technology evolves, AI, IoT, big data, autonomous robots and other developments will transform manufacturing.

Traditionally, technology applications have been offered as isolated solutions, presenting significant obstacles for manufacturers. But now, plug-and-play models are rapidly emerging. With components that are standardized and easily integrated into existing systems, this modular approach facilitates quick installation, compatibility with existing infrastructure, and easier user training.

Six takeaways to effectively leverage innovation and technology

All manufacturers at the MNP roundtables agree that innovation is essential to gaining a competitive advantage, responding to changing customer demands, accelerating turnaround, and reducing waste and costs, regardless of industry sector or organization size.

When contemplating introducing a new technology, start by prioritizing tech choices based on their potential impact on business objectives. With this foundation, Jason Lee on MNP’s digital services team, offers the following takeaways for manufacturers to consider when leveraging technology to innovate.

1. Determine the ROI [27]

To establish whether a technology investment is worthwhile, Lee suggests assessing the total investment and the potential return on that investment. These questions can help guide your decision-making.

• What problem does this need to solve?
• What outcome do we want to achieve?
• How does this technology drive results?
• What is the potential impact on our business, employees, and customers?
• What key performance indicators (KPIs) will determine success?

2. Focus on resiliency

To remain competitive, manufacturers need to anticipate and respond to change. Advanced digital technologies such as AI, which enriches data and analytics, provide real-time insights that make the value chain more resilient and sustainable.

The pandemic highlighted how manufacturers that reacted quickly and utilized advanced manufacturing technologies were able to generate new opportunities. Today, with constant disruption in the industry, digitalization and automation are crucial.

Lee says manufacturers should always ask, " How can we innovate, automate, optimize, and develop predictive capabilities?"

3. Data quality is key

Before leaping into AI, Lee cautions, “Keep in mind that the risks of AI are often associated with “data garbage in, data garbage out.”

Data is at the heart of manufacturing decision making. Yet management teams looking to leverage their data to use AI often discover that poor data quality is a major obstacle to achieving optimal results.

Lee says that while maintaining data quality may be challenging given its growing volume, it is essential. He suggests the first step toward using these technologies is to evaluate and improve, when necessary, the quality of the data that you will be using. This is essential to ensure your organization makes the right data-driven decisions.

4. Getting started doesn’t have to be complicated

When it comes down to utilizing AI effectively, Lee says it doesn’t have to be complicated. It’s simply about “how do we make this more efficient, more automated, more predictive, more optimized.”

He suggests bringing together a small team and including external experts to gain a fresh perspective on the best ways to develop AI solutions for specific manufacturing issues.

5. Technology governance is essential to reduce risk and achieve goals

Governance is critical for advanced technologies such as AI to ensure they are deployed responsibly, securely, and ethically. The article Maintain control with an AI and data governance framework outlines MNP’s suggested best practices for AI and data governance.

As organizations increasingly rely on technology, strong governance adds value by ensuring this tech mitigates risks and supports business goals.