Far too often “trend reports” in an industry are all about trends that only the largest companies can implement and take advantage of. The Fortune 500 has the resources to fill in the gaps that might not be apparent when innovative technologies and processes are first rolled out. This affects everyone else in the industry and adoption is initially slow due to the high cost and longer time to realize a return on investment (ROI).
I decided to consider small and mid-market businesses when identifying the innovative technologies and processes we are seeing in the manufacturing industry. The trends below, while not being the new kids on the block, are helping modest-sized firms to lead the manufacturing renaissance.
Smart manufacturing is the development of a connected ecosystem of people and equipment that communicate in real time. The benefits of smart manufacturing range far and wide: optimization of organizational systems, improved product quality, increased efficiencies in the allocation of resources, and amplified customer satisfaction.
In the nascent days of smart manufacturing, equipment would have to retrofitted or replaced with newer models that had sensors or another mechanism to provide data. The shop floor had to be outfitted with input devices and the software for people to enter data. Now, newer digital technologies have reduced the barriers to entry to smart manufacturing for even the smallest manufacturers.
Shop floor equipment already slated for replacement benefit from the slew of up-to-date models already equipped with standard connectivity and seamless software integration capabilities—it’s industry standard. Software vendors have also improved the user experience with simplified workflows and intuitive visuals—engaging the workforce and streamlining data analysis. These innovations are driving the adoption of smart manufacturing at a rapid pace.
It used to be that data was lumped into two categories. In the simplest terms, data was either considered an asset or liability.
- Data as an asset considered things like intellectual property, customer and vendor lists, and some financial data—such as pricing and margins.
- Other data, including most financial data (like invoices and receipts), quality metrics, and output data where considered liabilities.
Due to the expense of storage, most companies only kept what was necessary to fulfill regulatory compliance. With the decreasing price of digital storage, increased computational power, and ease of capturing data, companies are storing more data than ever before. But, until companies had a way to utilize that data, what was once categorized as a liability is still a liability and ended up as overhead. Now, mid-size manufacturers are able to invest in tools and processes that address how to use this data to increase profit and productivity and improve customer satisfaction.
The Internet of Things
The Internet of things (IoT) is now prevalent in our personal lives, with our phones, watches, and even our clothes using sensors to gather data. Your car and your home have connected devices that use the kind of data and automation that was once considered special effects in science fiction movies and shows. All of this is fueling manufacturers to add IoT to their products and capitalize on them. For example, creating new revenue streams and improving differentiation by utilizing the data collected to predict defects or issues in products before they happen. In the past, this kind of artificial intelligence (AI) was too costly to justify the price to the consumer, so most mid-market manufacturers could not justify the expense. Now, the drastically reduced technological costs have made it possible for any sized manufacturer to realize ROI, become more innovative, and disrupt entire business models.
Artificial intelligence is the ability for an application to understand what you are asking and infer the best possible answer from all the available data. With machine learning (just one of the applications of AI), AI has become one of the best and most essential collaboration tools manufacturers can use.
Utilizing visual and sensor data from the shop floor, AI can not only predict if a product is being manufactured correctly (thus reducing defects) but also decide and inform the operator on how to improve the manufacturing process. AI is enhancing our ability to manufacture the best product at a lower cost and mid-size manufacturers are in the best position to get maximum impact from this trend.
Workforce training is going through a disruptive renaissance. With low skills jobs, a person might learn via apprenticeship, trade schooling, or on-the-job training. In higher skills jobs, you went to college to learn how to program. We are seeing a real need for workers with mid-level skills—not doing piece work or understanding programming—but able to make decisions based on what is happening in the environment and the process. These “middle” skills can only be acquired by repeating different scenarios and gaining that knowledge. To accomplish this, we are seeing manufacturers utilizing concepts such as gamification and technologies such as virtual reality. Along with AI and a simplified UI, it is easier than ever to master these mid-level skills.
These trends are really not trends anymore. They have moved beyond hypothetical concepts or results only large corporations can achieve. The majority of smaller and medium manufacturers have either started to adopt or have implemented some of these innovations. We will only see these smart manufacturing mainstays accelerating as more applications are built out for each of these approaches.