Next Level Digitization: Better Sustainability with AI

Many manufacturing companies have chosen to work with the rapid evolution of tech rather than against it. Yet, plenty of holdouts are still resistant to moving away from traditional means and towards digitizing successful, long-standing processes/workflows. If this sounds like you or your business, though, it’s time to shake things up and finally embrace the role it can play within your daily operations. Digitization is an incredible workhorse for sustainability, with AI capable of improving products, profits, and maintenance; you just need to know how to implement it and use it in different scenarios!

How AI Is Here to Help

As a general rule, people tend to be a little suspicious or wary of artificial intelligence and associated technologies － it’s one of the main reasons why AI hasn’t fully taken over manufacturing yet. But this bold and brave form of technology doesn’t have to prove a source of harm. It can actually serve as an amazing form of help.

For one thing, AI can assist us in making intelligent decisions, whether they be related to high-level business direction or more on-the-ground daily operations. Artificial intelligence (and the machine learning that powers it) isn’t like the average person. Where humans have to take countless hours, days, weeks, and even beyond to analyze and draw conclusions from data, AI can do all this in a fraction of the time. This then allows it to better understand anomalies/outliers, make better predictions, and ultimately, make faster, more informed decisions than we ourselves are capable.?

All of that provides incredible value and not just in the ways one might expect. Artificial intelligence and related systems increase efficiency at nearly every level of a manufacturer’s operations. Leveraging historic data along with other metrics, they improve predictive maintenance, decrease machine downtime, minimize material waste, increase yield, enhance product quality, and accurately track demand. As one would expect, that adds up to dramatic cost savings and greater sustainability across the board!

At Circle View, we're building a feature that will allow manufacturers to ask an AI Assistant questions such as, "Based on the past month's data, what should we improve immediately to reduce costs?" You'll get an immediate response based on accumulated data from mulitple sources.        

Business Scenarios & Digitization

However, as explored in a 2022 article about the lessons learned and successes of?artificial intelligence in manufacturing, AI isn’t always an immediate smash hit for sustainability. Sure, nearly any business can put it to work and significantly benefit from doing so, but the ‘why’ of it has to be clear before you see such a payoff. It has to be used properly, and the crux of this lies in actually having a reason for AI’s presence.?

“Successful AI implementation boils down to the three P’s: problem, persona, process,” author Katie Rapp explains. “You have to carefully define a suitable problem for AI to solve. You need the right people involved… And process － you need an approach that identifies the right way to tackle the problem.”?

The kind of AI needed will vary dramatically from company to company, depending on each manufacturer’s strengths, weaknesses, customer relationships, and work culture. What works for one will definitely not work for another, impacting AI’s best use cases and specific value proposition.

Gartner’s foundational document, “Manufacturing Industry Scenarios?in 2023: Leading Through Innovation,” does a great job breaking this all down. Rather than trying to analyze digitization’s impact on countless unique businesses, they identify four major scenarios most manufacturers are likely to fall into: systematic, opportunistic, unconstrained, and experimental.?

The very first of these is characterized by a resistance to change, a primarily control-driven approach to innovation where the driving force is (first and foremost) financial benefit. Here, AI is best used to improve iterative performance and reduce costs, utterly contrary to the bleeding-edge product improvements assistance it provides unconstrained scenario manufacturers. A different way to do things, yes, yet one that tackles exactly what is needed in a given business model.

The same thing happens across each scenario. Competitive, research-driven opportunistic businesses aren’t as value-centric; thus, they don’t need AI for cost-cutting. It’s instead best implemented for market sensing that can, in turn, help them differentiate their products and fill a gap that exists in the market.?

Similarly, experimental manufacturers? They may not have a need for differentiation, but they can use artificial intelligence to assist with customer-first product configuration.

So, while each business type has different needs and goals, digitization can still go a long way toward bettering overall sustainability. It simply needs to be implemented in a way that makes sense for the scenario at hand. Need help with how to do this in your own company? We’ve got a few tips and tricks that should help.

Tips & Tricks for Implementation

• Be sure to define clear objectives. For those a little overwhelmed, go back to the basics outlined by Rapp. What problem are you trying to solve － predictive maintenance, inventory optimization, downtimes? What are you hoping to achieve, and how do you plan on doing so? Answering these questions should help you define clear objectives that can appropriately guide you toward the AI solution(s) appropriate for your needs.

• Start with a pilot project. After seeing all the ways digitization can improve business sustainability, it’s tempting to go all-in on AI, but this isn’t necessarily the best strategy. Though beneficial for most, it won’t work perfectly for every company or every manufacturing stage or process. So, play it a bit safe at the start and begin with a small-scale pilot project to evaluate the effectiveness of AI in your specific manufacturing environment. This will help you to identify potential challenges, refine your approach, and later demonstrate the value of AI to any stakeholders.

Request a Circle View pilot today and begin your jounrey.        

• Prioritize scalability and flexibility. AI is flexible, able to slot into many industries, but not all solutions will be the right fit for a manufacturing environment. Do your research and be sure to opt for those that are scalable and adaptable to changing manufacturing requirements. Look for platforms that can handle large volumes of data, accommodate future expansion, and seamlessly integrate with existing systems for a winning digitization strategy.

• Invest in employee training. AI implementation is more successful when employees are equipped with the necessary skills. Provide training programs to help your workforce understand the technology, its benefits, and how to effectively collaborate with AI systems. This won’t only foster morale and improve the work environment but also maximize the potential of artificial intelligence.

• Monitor and refine continuously. AI implementation is an iterative process. Continuously monitor the performance of AI systems, gather feedback, and make necessary adjustments. Regularly reassess your objectives to align with changing business needs and leverage AI's capabilities to their fullest.

The old ways of doing things certainly have their strengths, although AI has a ton to offer the manufacturers choosing to adopt it. Between improved costs, product design/quality, machine maintenance, and more, digitization proves a serious sustainability tool. As long as implementation is on point － tuned to each business’ scenario and approached with care － there’s little to lose but a whole lot to be gained.?