Manufacturing companies have to contend with extremely high capital investments with slim profit margins. That’s primarily why so many companies decide to offshore manufacturing and production processes to low-wage developing nations like India and China. Due to the extremely low cost of human resources, it didn’t necessarily seem important or justifiable for companies to make a large capital investment in artificial intelligence and other forms of machine learning technology.
However, in recent years, the living standards and wages in countries like India have been rising considerably, which has made companies consider AI technology. China has already started making great headway in investing in a future driven by AI.
As a result of China’s drive towards embracing automation, jobs have been lost at a “blistering pace”. In fact, over the past year, China has hired more robot workers than any other country in the world. Furthermore, according to IFR estimates, the adoption of Artificial Intelligence is likely to grow by 75% by 2019. In the short run, many workers are likely to lose their jobs as robots take over a lot of repetitive tasks. However, in the long run, workers will have to be empowered to take on higher-skilled jobs like programming or maintenance.
Furthermore, as AI-driven specialization continues, manufacturers will have to devise AI that can’t simply automate tasks but also accomplish tasks that were, as yet, not feasible, such as the custom configuration of products to meet individual customer needs.
Artificial Intelligence was conceived as far back as in the 1950s. However, they only entered popular consciousness and gained some form of acceptance when machine learning processes allowed them to discover patterns in a body of data in order to perform without specific programming, i.e. when they could start learning and evolving for themselves.
Without machine learning algorithms, computers can only be used to perform pre-programmed tasks. That’s basically a simple machine. However, many tasks, especially those involving perception, can’t be slotted down to specific rules and instructions. When it comes to manufacturing, robots can only truly take over human tasks if they have a sense of perception and an ability to learn for themselves.
Machine Vision is one of these applications that makes the sense of perception a reality. It’s easy for manufacturers to come up with cameras that are even more sensitive and better trained than the human eye. However, AI has the ability to identify patterns in the images shown via the camera and take actions based on them.
Machine Vision can be used to train a robot so that it can sense what’s happening in its immediate environment. As such, the robot can’t just steer clear of dangers and disruptions, but it can also help humans steer clear of obstacles.
An example of the aforementioned challenge in the automotive industry’s adoption of self-driving vehicles with features like Advanced Emergency Braking Systems. These are vehicles with the ability to sense when an obstacle or obstruction is coming up even before the human behind the wheel is made aware of them. As such, it can automatically take corrective measures to avoid the obstacle.
This same technology can also be applied to workplaces and factories. Self-driving forklifts and conveyors, for example, can be equipped with these features so that they can avoid obstacles and prevent workplace accidents from occurring.
In addition to simple obstacle avoidance, robots are also being programmed for collaborative efforts, nicknamed “cobots”. These robots can work alongside human colleagues and take instructions from their human counterparts, including instructions that aren’t included in their original programming. Furthermore, the humans don’t even need to have an understanding of programming to pass instructions, they can simply use plain speech as AI is becoming increasingly responsive to human language, as evidenced by a study conducted at MIT.
As we’ve already illustrated above, Artificial Intelligence is making robot-human collaborations a lot easier. However, the impact of this collaboration is likely to extend to areas and processes that have absolutely nothing to do with robotics as well.
In Supply Chain, AI algorithms can be used to determine patterns in regards to market demand charted over a period of time, demands from various geographic markets, and socioeconomic conditions. Furthermore, these conditions can be analyzed in relation to macroeconomic cycles, political changes, environmental changes, and weather patterns. All of this information can be harnessed to accurately understand market demand and thus make organizational changes related to raw material sourcing, staffing, finances, equipment maintenance, and consumption of energy.
AI can also greatly enhance the efficiency of the manufacturing process by sensing failures or disruptions in operating conditions, factory tooling, and predicting possible breakdowns. Once it has diagnosed or detected the problems, it can also offer recommendations for preemptive modifications to prevent those patterns from reoccurring in the future. AI has already been deployed in this capacity in various industries and for a wide range of purposes. For example, machine learning is used in Facebook and Google server farms to predict failures or malfunctioning in ‘blades’. Furthermore, this tool is also used to generate highly personalized consumer email alerts in various organizations.
The data collected and analyzed by these machines require sensors to be placed in the processing equipment at the factories and suppliers’ facilities. As such, these sensors can track inventories and front-end inputs to monitor quality and make predictions.
Due to AI’s ability to analyze trends and make forecasts, they can be used to help manufacturers predict demand even before it’s time to create the products. A recent study conducted at Indiana University found that machine learning algorithms could even accurately analyze Twitter feeds to scan public sentiment and determine stock-market movements.
Consumers from across the globe constantly express their sentiments regarding products and brands online through social media platforms and through conversations with Google and Amazon chatbots. As such, all of that information can be analyzed to predict demand for specific products and brands, thus giving businesses an edge in crafting their marketing strategies accordingly. In addition to consumer behavior, this same tool can also be used to predict (and sway) political sentiments.
In addition to making predictions about demand and supply, AI can also be used to connect all the different pieces of the supply chain, from the manufacturers to the delivery systems and trucks. In most cases, there’s a slight dissonance between different aspects of the supply chain which lowers productivity. Issues arising in one aspect affect all the other aspects of the supply chain as well, but they’re not usually communicated effectively. Artificial Intelligence can be used to connect the supply chain and provide a seamless experience for the manufacturing business and the end-users. In the long run, a well-connected supply chain can drastically reduce the cost of operation.
One of the biggest struggles that manufacturers constantly deal with is determining what’s the right amount of inventory to purchase. Purchasing too little inventory can cost them heavily on lost revenue if demand exceeds their available inventory. Purchasing too much can lead to overspending if enough demand isn’t generated to utilize the inventory.
Most businesses tend to err on the side of caution and overspend on inventory so that they don’t fall short of market demand. Constant fluctuations of public sentiment and demand make it almost impossible to accurately predict demand.
However, as we’ve highlighted in the previous points, artificial intelligence can accurately predict market demand based on current public sentiments expressed through online channels and through a thorough analysis of market trends. As such, manufacturers can stop relying purely on speculative gut instinct and start relying on some hard facts and informed predictions, thus cutting down on overspending and increasing revenue.
‘Machine-to-Machine Communication’ is currently a hot buzzword in the field of artificial intelligence and manufacturing trends. The benefits associated with machine to machine communication are too wide and diverse to explain in detail in this article, and even wider to neglect entirely.
Artificial Intelligence, with support from the Internet of Things, allows machines to communicate with each other and with the rest of the supply chain and management. As such, a machine can use AI to detect if there’s an issue with another machine or if it’s about to break down, and then communicate that information to the relevant personnel. As such, managers are able to fix issues before they even arise, significantly cutting downtime and ensuring that their processes run smoothly.
At the beginning of this article, we briefly touched upon the fact that there are limitations to AI that are preventing it from being adopted widely by manufacturing industries.
Predictive Maintenance can be handled by processes like machine learning, machine to machine communication, and advanced analytics. However, Artificial Intelligence goes a step beyond even those individual technologies.
According to a study conducted by Price Waterhouse Coopers, adoption of predictive maintenance technology amongst manufacturing companies is at 78%, adoption of manufacturing execution systems is at 73%, adoption of digital twins is at 60%, and finally, adoption of robotic process automation is at 59%. As opposed to these, the adoption of artificial intelligence is trailing behind at only 29%.
As is evident from the aforementioned data, manufacturers are currently focusing on technology that’s primarily geared towards cutting costs. However, eventually, market demand will necessitate the adoption of AI in manufacturing processes to go beyond cutting costs and elevate productivity levels.
Beyond manufacturer hesitance, the two primary limitations of AI today are Skills and Data.
Artificial Intelligence is a relatively new but rapidly growing field. As such, AI expertise is in short supply but in great demand. IBM predicts that demand for data scientists will grow by 93% by the year 2020 and demand for machine learning experts will grow by 56%. Furthermore, Glassdoor ranked data scientist as the best job in the U.S. currently because of its great demand.
However, as demand continues rising, companies are finding it difficult to fill those roles with skilled professionals. Instead, companies are now resorting to providing advanced training to their own expert staffers.
Despite the risks and growing pains of adopting a new form of technology on a mass scale, Artificial Intelligence is set to revolutionize the manufacturing industry pretty soon.