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5 Innovations in Manufacturing Processes and Their Effect on the Bottom Line

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5 Innovations in Manufacturing Processes and Their Effect on the Bottom Line

Manufacturing is a rapidly evolving industry. With a broad spectrum of sectors depending on manufacturing, modern facilities are often quick to adopt new technology that improves on their existing processes.

The rise of automation, artificial intelligence (AI) and data have created a wave of digital transformation. As manufacturing grows and becomes increasingly competitive, capitalizing on Industry 4.0 innovations can determine whether or not a company will succeed.

Here’s a look at five of these innovations and how they affect the bottom line.

1. Cobots

Robots aren’t new in the manufacturing industry. But as automation has grown, new approaches and technologies have emerged that can take its benefits further. Collaborative robots, or cobots, are one of the most significant of these upgrades to factory automation.

In a 2021 study, 44.9% of surveyed businesses said that robots are an integral part of their operations. Of those companies, 34.9% had adopted cobots. Cobots have slowly become more popular as manufacturers have realized the limits of traditional automation. Other robotic solutions are expensive and inflexible, making it difficult to scale, but not cobots.

Since cobots work alongside humans instead of replacing them, they typically automate fewer processes at once. Consequently, they’re often more affordable than traditional automation and easier to implement. Manufacturers can then automate one process at a time, slowly scaling up to meet demand or new challenges.

This incremental approach to automation removes the high upfront costs and disruptions of traditional automation. As a result, cobots enable manufacturers, especially smaller businesses, to scale up and down with ease. These companies can then enjoy quicker, higher ROIs.

2. IoT Sensors

Another growing innovation in manufacturing is the implementation of internet of things (IoT) sensors. While these technologies aren’t a manufacturing-specific phenomenon, they hold considerable potential in this sector. Perhaps their most popular and impressive use case is predictive maintenance.

Predictive maintenance improves on traditional maintenance schedules by avoiding both breakdowns and unnecessary repairs. According to a Deloitte report, it reduces maintenance costs by 25% on average. That’s an impressive figure on its own, but it also reduces breakdowns by an average of 70%.

Considering that an hour of downtime costs more than $100,000 in 98% of organizations, that adds up to considerable savings. Predictive maintenance isn’t the only application of IoT sensors in manufacturing, either.

Manufacturers can also use these sensors to gather data points throughout their operations. This data can then reveal areas of potential improvement, enabling ongoing optimization. The longer manufacturers use these technologies, the more they can save through them.

3. Additive Manufacturing

One recent innovation that is specific to manufacturing is 3D printing, also known as additive manufacturing. While this technology is most well known as a tool for hobbyists, it originated as an industrial production technique. Recent advances have made it a more viable solution, leading to a comeback in industrial manufacturing.

Additive manufacturing lets manufacturers produce parts and products as a single piece instead of assembling multiple smaller components. Like mil-spec buffer tubes, which are made of a single piece of aluminum, this improves products’ strength and resiliency. As a result, they produce fewer defects, improving the company’s bottom line.

Since additive manufacturing adds material instead of cutting it away, it also reduces waste. Manufacturers can get more parts or products from the same amount of materials. 3D printers also typically work faster than traditional production techniques, leading to a quicker time to market.

Additive manufacturing is also more energy-efficient. Some products, like car batteries, require a lot of energy to handle the sensitive materials they need, leading to higher costs. By reducing energy consumption through additive manufacturing, facilities can increase their profit margins. Alternatively, they could reduce end prices, selling more with consistent profit margins.

4. 5G Connectivity

Like the IoT, 5G isn’t strictly a manufacturing technology, but it has impressive potential for the sector. 5G networks aren’t widespread enough yet to bring substantial improvements to the consumer sector, but they’re ideal for manufacturing facilities. Their higher bandwidth, increased speeds and lower latency let smart manufacturing reach its full height.

5G networks can theoretically support up to one million devices per square kilometer, ten times 4G’s limits. That will allow manufacturers to expand their IoT infrastructure to virtually every machine in the facility. Lower latencies will allow these interconnected systems to communicate more efficiently and reliably, unlocking Industry 4.0’s potential.

With all of these machines connected to one another, manufacturers could create cohesive autonomous environments. If a disruption occurs in one process, machines down the line could know and adapt to it, minimizing its impact. As a result, manufacturers could maintain higher productivity levels, minimizing their losses from lost time.

5G lets manufacturers use technologies like the IoT and automation to their full extent. This leads to higher ROIs for these significant investments.

5. Machine Vision Error Detection

AI has many use cases in manufacturing, but one of its most enticing is machine vision. Machine vision systems let manufacturers automate quality control processes at both the front and back end of production lines. This automation, in turn, improves the efficiency and accuracy of their error detection.

When Heineken installed a machine vision quality control system in its Marseille, France bottling plant, it highlighted this technology’s benefits. The facility’s bottling machine operates at 22 bottles per second, far too fast for human workers to spot any bottle defects without stopping it. The machine vision system, on the other hand, can analyze bottles at speed with a 0% error rate.

Machine vision error detection lets manufacturers increase production while maintaining the same level of quality. Since these systems deliver a level of consistency impossible for a human, they’re also more accurate. As a result, facilities will also produce fewer defects.

Fewer defects translate into less waste, and faster checking enables increased output. These factors combined result in an improved bottom line.

New Technologies Make Manufacturing More Profitable

These five technologies aren’t the only ones pushing manufacturing forward, but they are among the most notable. As more facilities embrace these innovations, manufacturing is becoming a more profitable industry.

Technologies like these improve efficiency, minimize errors, optimize operations and more. Manufacturers that can capitalize on them early will ensure their future success, and those that don’t may quickly fall behind.

machine

Future in Maintenance: Will Machines And AI Replace Maintenance Workers?

A widespread narrative on work in the future is that machines will take care of everything. Robotics, artificial intelligence, and modern algorithms powered by new energy sources will replace the way this world works. People will be out of jobs as they will not be able to compete with machines powered by AI. Leading to widespread unemployment and dispossession of the masses. The state of affairs is, allegedly, no different for maintenance activities in the future and looks bleak for maintenance employees. 

Now, is this the bleak future we face, or is it ‘immanentizing the eschaton’ as Willian F. Buckley puts it? For that, we have to take a look at the current state of maintenance automation, the potential evolution of the same, and historical precedents for such radical changes.

Maintenance automation: A Swiss army knife?

Maintenance activities have become a lot simpler with the help of technology. Managing maintenance schedules to predictive maintenance can be accomplished with the aid of modern technology. Everything with some level of computerized decision-making is broadly termed automation. But there are varying degrees of automation depending upon the entity making the decisions in various processes.

All processes in an industrial environment are formed by one or more of the following functions.

1. Monitor function

2. Advice function

3. Decide function

4. Implement function

The control of each of these functions can be handled by a computer or a human. Based on this, there are ten levels of automation starting from complete manual control to full automation. In full automation, all the functions in the process are controlled by a computer. The different levels of automation and who controls different functions in each of those levels are illustrated in the table given below.

The aim of all automation advancements is to reach the level of full automation. Today in most automation instances, computers control only one or two of the functions that form the process. The common narrative is that technological improvements snowball and compound to an exponential degree to deliver fully automated systems in the not-so-distant future. This will lead to the take over of all maintenance activities by machines and AI replacing all maintenance workers.

But what the narrative misses out on is the law of diminishing returns. According to the definition from Investopedia, “The law of diminishing marginal returns is a theory in economics that predicts that after some optimal level of capacity is reached, adding an additional factor of production will actually result in smaller increases in output.”

Applying the law in maintenance automation, after a period of compounding a ceiling is reached from where incremental improvement requires a disproportionately high amount of time, resources, and effort. This follows the trajectory of an S-curve as shown above. The progress in automation will follow a snail’s pace after a critical limit is reached. 

The real-world impact of the S-curve can be seen everywhere in technological advancement. The capacity of semiconductor chips was supposed to grow exponentially to infinity. “Faster and faster processors every year” was the narrative pushed during the initial phases of semiconductor development. Today, semiconductor manufacturing is fast approaching the physical limitation and the cost of improving the tech is orders of magnitude higher than earlier.

A similar ceiling for innovation will also hit the march to full automation of maintenance activities in manufacturing facilities. The cost of implementing incremental automation will rise exponentially after reaching a critical limit. Till the critical point automation technology will rise exponentially at a minimal cost. The problem is that no one really knows what is the critical point for maintenance automation or for any other technological evolution.

In the future, there will be an exponential rise in the technology driving maintenance automation. But it will not completely eliminate the need for human workers in maintenance activities. Maintenance automation brings about improvement in processes, efficiency, and in turn bottom line. But after a critical limit, an incremental increase in efficiency comes at a huge cost.

Horse buggies were replaced by cars and taxis. A lot of coachmen lost their jobs due to the transition. In addition to that, horse merchants, workers taking care of horses, carriage makers, all lost their jobs. But plenty of new jobs were created in the process of transitioning into automobile-based transportation. Cars and taxis were unheard of before the existence of automobiles. Plenty of new jobs such as cab drivers, car salesmen, car dealers, mechanics, etc came into being. This is the sort of creative disruption that always happens in free-market capitalism and maintenance automation would be no different.

Creative disruption

The most plausible scenario, for maintenance automation, is where human workers work in conjunction with machines and artificial intelligence. Software and algorithmic tools will be used extensively for process automation intelligence. Robotic arms and other robotic devices that can be programmed to perform regular tasks would be created. But since there is a lot of variability in a lot of maintenance tasks creating custom programmed robots for each instance would be cumbersome. 

The tasks that require flexibility and dexterity will be exclusively carried out by human maintenance technicians. They will have assistance from cobots. ‘Cobot’ is an abbreviated form of ‘collaborative robot’. It is specially designed robots that assist human workers in accomplishing their tasks. This form of creative cooperation will be commonplace for maintenance activities of the future.

The bottom line is that machines and AI will take over a lot of mundane and repetitive tasks. This frees up human capital to deal with more creative and complex tasks. While on the one hand, a lot of traditional maintenance jobs will no longer exist. But on the other hand, plenty of never seen before jobs will be created. Machines and AI would be a net positive for all maintenance activities and jobs in a plant, in the long term.

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Bryan Christiansen is the founder and CEO of Limble CMMS. Limble is a modern, easy-to-use mobile CMMS software that takes the stress and chaos out of maintenance by helping managers organize, automate, and streamline their maintenance operations.