After witnessing three industrial transformations kindled by steam power, electricity, and IT, the world is all set to usher in the fourth transformation, known as Industry 4.0. Industry 4.0 blends computers and automation (i.e. cyber-physical systems) and the Internet of Things (IoT) meshed with data and services to reshape manufacturing and give rise to smart factories.
We are early in the Internet of Things era, where cyber-physical systems interact seamlessly with the Web in unimaginable ways, without human intervention, in real time. Industry 4.0 depends on IoT gadgets and machines that can communicate and help us interact, and it will need a lot of data for input.
Machines talking to each other, discovering and analyzing issues in products in advance, automating assembly lines, and requiring minimum human supervision. What is the outcome of the mammoth amount of data, information, and complex processes spawned by various sensors, systems, machines, and shop floors? The answer lies ahead.
The significance of Big Data, IoT, and advanced analytics
Clive Humby, co-founder of retail data science company Dunnhumby, remarked “data is the new oil.” We are in an age where data has become more precious than anything. In today’s digital economy, analytics is the magic lever that turns crude data into the gasoline of consequential insights. The potential of large sets of data can be realized only when it generates significant, actionable insights.
We create technologies to increase the efficiency of work for greater profitability at a lower cost. Advanced Big Data and IoT analytics can bring a discernible value to the manufacturing table in the following ways.
1. Simplifying complex data for increasing efficiency
Ease of access to important information plays a key role in how an organization functions. The IoT creates tremendous opportunities for organizations by decentralizing decisions. Data analytics helps enterprises process and analyze essential information in useful ways. Measuring pivotal performance indicators, like productivity and quality, reduces time and cost and increases revenue.
For example, a car manufacturing giant can use analyzed data from climate sensors to determine that a plant’s weather is not optimal (it’s too humid or hot) for painting automobiles. This data helps the company shift the work to another plant, saving time and cost spent on equipment service as well as speeding delivery, optimizing cost, and augmenting its bottom line.
2. Rendering valuable insights
Advanced analytics enables manufacturing companies to improve production quality by identifying issues and avoiding product failures. Running efficient algorithms to tame vast, overflowing data can enrich boardroom decisions with fresh insights.
3. Benefiting from analytics
In the future, companies will be required to adapt to new technologies and work towards an integrated system, including real-time decision making and enhancing productivity, customer service, and innovation.
Some of the major tools in the analytics artillery are predictive methodologies, prescriptive analytics, machine learning, forecasting models, neural networks, and so on. Their use has unraveled hidden (and untapped) patterns, correlations, trends, and insights.
Stepping into the future
The age of Big Data and IoT technology has clearly unfolded a new path and way of living, but there are still some concerns. For instance, how can manufacturing industries adhere to this new concept? How will industries shed traditional practices and adopt a full-fledged, modern approach? The fact is, the transition will not be so easy.
- The first step is to devise a robust, cross-functional digital strategy. This translates to creating methods and ways to pan out value from volumes of Big Data. Some key points to remember are identifying problems and roadblocks and creating innovative solutions with in-house techniques.
- Companies have been using crowdsourcing, machine learning, data integration, and advanced analytics as problem-solving methods. The next step is making use of that evolving data to derive crucial information.
- Securing data is one of the most important concerns for any organization. It’s essential to use Big Data analytics techniques to identify security breaches and secure the organization and customer information.
The key to staying ahead of your competition lies in informed and timely transformation. Learn why Data is the Hidden Treasure Inside Your Business.Comments