Introduction of new technologies requires the development of innovative products, and opportunities for their adoption on all organisational levels, as well as applied use by the highly skilled personnel. The recent studies demonstrate that no industry produces more big locked data than manufacturing, creating a huge opportunity for improvement through advanced analytics. An excellent news is that the use of big data presents a $50 billion opportunity in upstream oil and gas facilities, with a huge budget in opportunity across other process industries.
Challenges still darken the bright sky of data innovations
Some manufacturing sectors are still rather reluctant to fully embrace new technology, creating certain challenges on the way to an entirely data-driven society. For decades, manufacturing businesses have relied on connected machines and the correlating data to help streamline operations. Now, innovative Internet of Things (IoT) technologies are taking machine data to unprecedented new levels – but also introducing a whole new landscape of challenges in data management, analysis and governance. IoT data is locked in its sources, and it’s difficult to move data securely and control its use. And extracting new types of data in new ways relies on costly and time-consuming support from your PLC or equipment suppliers, making improvements reactive and slower with no real-time insight. Without effective IoT data management, it’s hard to access the right information at the right time and make smarter decisions.
At the dawn of innovations
The technology is constantly evolving - changes never stop astonishing the world of industry. Businesses can already take advantage of a real-time visibility and control over their locked machine data. Assisted by a systematic approach to collect, transmit, process, store and analyze IoT data, companies now got a unique chance to obtain relevant, actionable insights in order to achieve meaningful business outcomes. The objective of this data-driven platforms is to translate data from high-value assets into actionable insights on machine utilization to improve overall operational outcomes. The main two ways to access machine data are as follows:
Accessing Real-Time Machine Data: reading the machine variables in a real time to deliver immediate insights into overall equipment effectiveness, and performance issues that could impact product quality. Machine data acquisition is passive, so the process does not interfere with critical operations of machines in production.
Accessing Machine Data from External Sensors: attaching the sensors directly to a machine with no need to change the machine’s historic control programming. One can extract data from these connected machines that may be stand-alone, connected via serial connection, or use proprietary network protocols. These sensors are often used to capture specific types of data (e.g., temperature, humidity, vibration, pulse inputs, etc.). As a result, the Data Control Module securely aggregates the data from edge devices within the plant, including sensor gateways and edge devices. It then applies and enforces the company’s custom-defined policies to ensure that only relevant data is distributed to business-critical applications.
Advanced analytics and machine learning make manufacturing really “smart”
Addressing to the data high volumes, challenges, and opportunity associated with data insights, big data analytics of today encompasses the inclusion of cognitive computing technologies into the visualization and calculation of manufacturing data. According to McKinsey, “advanced analytics solutions provide easier access to data from multiple data sources, along with advanced modelling algorithms and easy-to-use visualization approaches.” In other words, modern manufacturers get brand new ways to control and optimize all processes throughout their entire operations.Likewise, machine learning greatly facilitates the search of correlations and clustering within the tons of process data, although the path to quicker insights must leverage innovations in adjacent areas to address the scope of data available for investigation.
Further investigations and innovative solutions in machine data mining and manufacturing will result in a more well organized higher-level experience, where the industries take advantage of the data management, storage, and big data analytics capabilities available on the market aimed to improve production and business outcomes. By and large, an ability to handle machine locked data in a smart way lets you overcome numerous barriers of accessing valuable IoT data housed in new and legacy equipment across a diverse plant floor. Machine data is a business-critical asset, and many innovative platforms assist in moving relevant data to operational and business applications, unlocking and optimizing your machine IoT data in real-time, enforcing policies for ownership, privacy, security and governance as data moves from the edge to the enterprise and cloud. Making machine data usable and actionable puts your industry on the fast track to increase operational margins, minimize equipment downtime, and lower energy costs.