In a world painted with data and guided by experiences there will be little tolerance in the visual display of too much data. Manufacturers of Smart IoT products however are doing their very best to simplify the user experience component at least.

Companies such as PTC and Reflekt AR for example are creating enterprise tools using augmented reality to visualize technical data for predictive and prescriptive maintenance.

There is a chasm however to be crossed when it comes to simply creating and distributing big data visually ‘in a proposed AR Cloud’ or Fog architecture, and this is driving intelligent companies big and small to consider edge computing from a value proposition standpoint.

In order to create something that delivers value, analytics, data science and ultimately use cases will be the determining factors in creating a Smart Data Ecosystem, designed specifically for the IoT space. While the Cloud is transforming technology architectures as we understand them today, Fog is the upgrade which promises true data ubiquity.

Fog computing architectures are true distributed models, and these will be designed to connect and transmit data efficiently.  Fog computing is the current answer to distributing quantitative amounts of data quickly and utilizing nodes which then speak with several IoT or Smart devices, referred among technologists as edge computing devices.

Edge enables hyper-connective smart devices. These are devices which are collecting and communicating data constantly. Large tech companies in the meantime are not only exploring edge computing for IoT but also trying to work out some fundamental problems in taking legacy data storage and transforming them into valuable purposeful data.

Success in this new digital transformation will be measured by end user adoption and the ability

to take unique data points and turn them into tangible value that can be experienced. Ultimately to experience this data, Mobile, Wearable AR, MR, Spatial and VR devices will be enablers, currently mobile being the most user friendly among the mainstream.

Ubiquitous Visual Computing (using edge enabled devices) is the front-end of data distribution and this will be experienced primarily with augmented, mixed and spatial reality devices or connected systems / dashboards, in either mobile, integrated or wearable technologies.

In business specifically there are areas of focus where the most value can be extracted using edge, fog, cloud and visual compute technologies these are:

• Predictive maintenance.
• Yield, energy, and throughput optimization.
• Next-level automation
• Digital quality management
• Digital performance management.

Predictive / Prescriptive maintenance.

Even though the term predictive maintenance has been around for many years, significant progress in data availability, machine learning technology, and cloud technology is enabling an entirely new approach that integrates diverse data sets and uses complex deep learning algorithms such as neural networks.

Typically, we see an increase in machine availability and a reduction in maintenance costs – based on the introduction of new predictive maintenance algorithms by 15%. In addition its easier to replace machine systems because digital platforms enable prescriptive operational standards allowing companies to replace or perform maintenance on equipment before breakdown reducing downtime and ultimately increasing productivity.

Tribal Knowledge, Business Intelligence and Analytics and Change Management are the 3 key components to be successful in implementing predictive and prescriptive IoT solutions.

Yield, energy, and throughput optimization.

With systems integration and data analytics companies are able to optimize throughput and performance and measure from a production and financial perspective. Overall optimization of process and production systems using IoT sensors, devices, software dashboards and hardware technologies such as AR can help companies scale more quickly across different sites and implement systems and support easily which is critical to success in a global economy.

Automation

Robotics will grow significantly in the next five to ten years. We will see cost reduction of industrial robotics at approximately 10% p.a. until 2020 and improvements in smart sensors and AI allowing the use of robots in more complex systems.

Connected systems will also create improvements in supply chain management, logistics and other business verticals where automation will add value, companies such as Amazon are already using this type of solution and further advancing it to meet the demands of consumers.

Digital quality management.

There are many benefits manufacturers who invest in Industry 4.0 and IoT will experience. From higher efficiency to cost reduction all of which are enabled by predictive and prescriptive information management.

Digital quality management is about holistically investing in the ecosystem of tools to enhance one’s business. Advanced quality control, including the use of new sensing technologies (e.g., computer vision, AR) and semiautomated quality control (e.g., robots, artificial intelligence), unlocks further value.

Precision engineering systems will be used to create more advanced materials, already in practice at companies like Boeing and Lockheed where 3D printing and advanced manufacturing in combination with 3D laser scanning, AR and a number of other processes combine to create aerospace technologies.

Digital performance management is a gateway to digital manufacturing due to its low resource requirements and rapidly deployable solutions. Lean management processes are accelerated by applications to build digital capability and a data-driven business model laying the foundation for edge computing devices to be used in manufacturing to produce any number of products.

Companies can experience between 20% and 50% gains from overall equipment effectiveness using digital performance management tools, and complimented with AR devices and remote management and maintenance systems, engagement is increased for frontline operators/

Data becomes persistent and reporting is improved significantly, allowing KPIs across previously siloed functions, plants, and business units to be shared and benchmarked for consistency and best-practice sharing. In addition, tribal knowledge is compiled into digital knowledge ensuring that there is less attrition when experienced staff retire and new staff have to assume their responsibilities.

Tribal Knowledge will be compiled and integrated into digital twins and training solutions and will be accessible visually using devices such as the HoloLens, Sensors will measure and

manage all types of equipment and ensure optimized performance in several industries, formerly blue collar workers in plant operations will be transformed into data scientists, and technologies such as AI, XR, Robotics, Edge and Fog Computing, 3D printing etc. will be used to enhance the way we do business in this fourth industrial revolution.