In an increasingly complex global competitive environment, real-time information is vital to help make decisions that optimize the efficiency and effectiveness of manufacturing enterprises. This also means being prepared to embrace disruptive technologies to acquire this real-time information, even if it can potentially cause radical changes to manufacturing processes. Recent examples of disruptive technologies don’t seem all that new anymore, such as IIoT, as well as cloud computing, mobile devices, social networks, advanced search engines, and big data analytics, as all are increasingly being used in manufacturing and other industrial operations to create the information-driven enterprise. So the problem of optimally managing and best leveraging this abundance of data and analyzed information remains for many manufacturers, especially those who have embraced some of the recent disruptive technologies, such as IIoT, and have seen a step change in the volume of data and analyzed information that is now at their disposal.
This problem of how to best leverage and utilize this abundance of IIoT-driven data and analyzed information is partly because most manufacturing business models are still designed around sustaining technologies, rather than disruptive technologies. Manufacturers must first thoroughly determine the problems they are trying to solve before defining their specific data and analyzed information requirements, and then create paths to map out where that data and analyzed information can go to best control and manage their operations.
Much has been written about digital transformation. However, most manufacturing organizations still have not fully figured out how to best leverage this new data and analyzed information to maximize efficiencies and effectiveness at either the manufacturing plant or enterprise levels. Whether collected continuously or intermittently, automatically, or manually; via IIoT low cost sensor direct to the cloud or traditional sensor to PLC or PAC; data that has been properly analyzed provides the foundation for real-time information. However, much of the data gathered today still lacks the five attributes needed to ensure a solid foundation to build upon: accuracy, authenticity, integrity, context, and timeliness. Accuracy means free from error. It also means that the data conforms exactly to the applicable truth or standard. Authenticity means that the data is of undisputed origin. Integrity means that the data set or packet remains complete as it undergoes a number of operations such as capture, analysis, storage, retrieval, update, and transfer. In context data is relevant to a particular person or function. Finally, timeliness means that the data is delivered at the optimum time needed to support either manual, guided, or automated operations. Manufacturers must ensure that the foundation of their data is solid, otherwise the analyzed information derived from this data will be wrong, and decisions made by the manufacturers as a result of this inaccurate business intelligence will do more harm than good to both production and profits, as well as safety.
In today’s manufacturing enterprises, both the information needs and potential information sources continue to expand rapidly. Traditional process control and information hardware and software technologies continue to become more capable, easier to use, more open, standards-based, IIoT-enabled, and less expensive to purchase, deploy, and maintain. Measurement devices continue to become smarter, smaller, less expensive, and incorporate more powerful communications capabilities, including IIoT. Advanced process control, including multi-variate and model-based predictive control, continues to be increasingly deployed at a growing number of manufacturing sites due to its favorable ROI calculations justifying their purchase and deployment.
At the same time, disruptive technologies, such as IIoT, are making an impact on industrial organizations. Both private and third-party cloud providers are helping to cost effectively improve industrial operations. Manufacturing functions, such as material and energy procurement, product quality, and production management, are provided through software-as-a service (SaaS) by third party providers. Virtualization technologies are reducing computing hardware, software, energy and IT support costs. Manufacturing is taking advantage of the big data analytics to convert data into analyzed information used for business intelligence. With IT/OT convergence, enterprise-level IT providers are offering integrated families of business applications that capture and analyze transactional data in real time using in-memory platforms. And with more manufacturers allowing employees to “Bring Your Own Device” (BYOD), mobile computing has become the normal use-case for enterprise IT and OT HMI/visualization, rather than the exception, with more operators, supervisors, and managers monitoring plant- and factory-floor performance using their own mobile devices. Finally, social networks are being used for virtual user groups both within the plant, between plants, and between the plants and their automation and enterprise suppliers.
Many industrial enterprises have a long history of being slow to replace their existing installed base of automation technology until new technology has matured based on the belief that this practice reduces deployment risk. Today, the risk of being a late adopter now exceeds the risk of being an early adopter. This is especially true regarding IIoT. Successfully adopting disruptive technologies can challenge traditional manufacturing enterprises. It is not unusual for big corporations to dismiss the value of a disruptive technology because it does not reinforce the current company business model. Today’s industrial enterprises cannot afford to wait to adapt their manufacturing and business processes to take advantage of the latest control and information innovations.
Manufacturers must also start thinking about how the next wave of disruptive technologies is going to affect their businesses and how they can be best leveraged in the shortest period of time. With, for example, artificial intelligence, machine learning, augmented reality, virtual reality, etc. making their way into the consumer world, it is just a matter of time before they each become mainstream technologies in manufacturing, rather than just case studies being deployed today by leading-edge manufacturers. Manufacturers must strive to be “Disruptive Ready”, embracing disruptive technologies rather than fearing them, and planning how these disruptive technologies can be best leveraged to increase productivity and profitability in an uber-competitive flat world.