Source: dqindia.com
The next step in industrial technology is about robotics, computers and equipment becoming connected to the Internet of Things (IoT) and enhanced by machine learning algorithms. Industry 4.0 has the potential to be a powerful driver of economic growth, predicted to add between $500 billion- $1.5 trillion in value to the global economy between 2018 and 2022, according to a report by Capgemini.
Artificial Intelligence (AI) is a significant part of Industry 4.0
AI is one of the emerging technologies already being utilised by manufacturers to improve product quality, efficiency and for cutting down on operating costs. We are starting to see a working relationship between humans and robots, an area which is benefiting from the use of AI in the manufacturing plants. The smart factory which is made up of hyper-connected production processes comprises of various machines that all communicate with one another, relying on AI automation platforms to collect and analyse all types of data including images, standardised code text and categorized fixed field text.
A recent IDC survey of global organisations that are already using AI solutions found that only 25% have only developed an enterprise wide AI strategy. Many organizations are applying AI to increase their efficiency. However, there are immense quantities of data that have not even been digitized or organized in a way that enables AI to use them.
Furthermore, numerous organizations lack the right people, such as data scientists, to analyze whatever data they have. BFSI, healthcare, logistics which are manual labour-intensive industries deals with a lot of data requires AI solutions to produce effective results. Manufacturing companies that are looking to innovate and deploy AI need to look towards implementing integrated automation platforms that use bots built with fractal science technology for undertaking admin-based roles and tasks. Fractal science is based on the premise of self-similarity and that networks of neurons carry similar but not identical signals about patterns. Fractal science is a more deterministic science, which needs a smaller representative data set for training. With Fractal technology one needs a thinner infrastructure, lesser computing power and it becomes much easier and faster to implement.
These types of AI, known as multi-tenancy platforms, enable multiple bots to reside on a single machine and perform several different processes at once. It also allows personnel to use the same machine to perform other functions at the same time, meaning that less of the human touch is required to fulfil roles. This, in turn, means that a significant number of workers can be re-skilled and retrained at a faster rate, to take on the more technical and complex work such as designing and programming, effectively moving them off the production floor and on to higher positions. This is something that is already happening in some parts of the industry. These bots will not only speed up the manufacturing process but also aid human workers in decision making. Bots can collect, process and analyze structured and unstructured data in the form of algorithms and system messages in real-time.
Manufacturing warehouses mainly use unstructured data, like handwritten paperwork and inventory checklists, as part of their day-to-day workings. Intelligent automation platforms that are built on fractal science will be instrumental in transforming the modern warehouse.
As a result of this, manufacturers will be able to cut down on production downtime, while also optimising the overall operational efficiency of the manufacturing lines.
AI in manufacturing process
Original equipment manufacturers (OEMs) that are successfully operating in smart factories and implementing industry 4.0 are using AI in their production processes. Manufacturers who have undergone a digital transformation and can organize and utilise their data sets are taking advantage of the ability of AI and machine learning to improve quality control, standardisation and maintenance through producing predictive analyzes of equipment functionality and radically streamlining factory lines. Many companies are now aiming to implement AI in their production processes, but far less have an AI development plan and to a greater extent, unsure of the appropriate type of automation platform to use.
CIOs and CTOs are starting to buy into the smart factory revolution in their quest to achieve optimum operational efficiency in their manufacturing processes. However, a big part of their role in managing the deployment of AI is first to identify the processes that need automating. They must then decide on the right automation platform for achieving the set goals. This is a fundamental step in deploying and managing AI as wrong decisions will undoubtedly lead to a waste of resources.
Business leaders also need to establish buy-in from workers at all levels and effectively implement a management plan which will take into account the effects of automation on the organization’s workloads and roles. This will also help pinpoint which roles need retraining.
Future of manufacturing
Companies have stepped up their efforts to embrace digital transformation to align themselves with evolving consumer needs. With manufacturing being one of the backbones of global economy, the implementation of technology in this sector is all about unleashing the true potential of products and solutions for the consumers. Analytics and Internet of Things will play a major role in industry 4.0, identifying patterns, behaviours and bringing real time data to the fingerprints of manufacturers. As manufacturing operations will keep growing, organisations will also need to find a way out of dealing with the deluge of data and complexity of analytics, they will have in front of them.
The way ahead transforms the evolution of manufacturing into cognitive manufacturing, which essentially means the collection of data from the entire gamut of manufacturing processes such as employee biometrics, factory logs, manuals and equipment sensors. These intelligent assets will enable the machines to optimize performance with the use of connected sensors. Manufacturers will be planning smarter resource optimization on the back of data collected from various smart factory workflows and processes. Having a data deluge will no longer be a problem for manufacturers as cognitive technologies working on the back of machine learning and artificial intelligence will predict patterns both in structured and unstructured data to provide real time information helping the industry to make data driven decisions.