By: Debendra Mohanta, Area Vice President and Country Head-Japan

In last one decade, the Internet of Things (IoT) has come from conceptual to actual. IDC now predicts that by 2020, the IoT network will consist of 29 billion connected devices. Also, experts are estimating that the data from these devices will yield insights that drive economic value of more than $11 trillion by 2025.

In the manufacturing industry, IoT and IoT Analytics will play a critical role in the years to come.

Manufacturing has been part of the IoT throughout its entire history. Many companies have been embedding sensor-based technology in their devices for decades, without fully realizing their potential. Manufacturing was one of the first adopters of robots and automated processes—many of these machines signaled distress, with a sensor providing notification and addressing the problem before the machine stopped working, thereby avoiding downtime.
Today, thanks to the power of IoT, new data processing technologies, and availability of analytical forecasting models, the entire manufacturing value chain, from concept to completion and beyond, can now take advantage of this sensor technology.

For a modern example, some of today’s most sophisticated fighter jets are built almost completely out of outsourced parts. With a digital supply chain supported by advanced predictive analytics coordinating three principal partners, nine countries, 40,000 individual parts, and thousands of suppliers, a major manufacturer of these jets predicts that it will soon be able to build one jet per day, a process that used to take months or years.

As this example illustrates, IoT and analytics are bringing innovation to manufacturing, improving interoperability across a large set of assets, and linking machines, products, computers, people, and analytical resources into one ecosystem.

Emerging ideas and tools in analytics for IoT can help manufacturers make sense of it all, and make decisions to improve their operational efficiency and overall business results.

Some of the major tools in the analytics artillery are Predictive Methodologies, Prescriptive Analytics, Machine Learning, Forecasting models, Neural Networks, and so on. Their usage has led to the unraveling of hidden patterns, correlations, trends and untapped insights.

There are many areas where IoT Analytics can be used. Some of the potentials areas are product quality improvement, process controls, operations management, process design and improvement, predictive maintenance/asset management, supply chain management (Inbound and outbound), safety and facility management.

Some of the important areas where IoT analytics will play critical role are given below.

Predictive Maintenance
Maintaining high value equipment at maximum production capacity, is the single most important goal for any manufacturer. Predictive maintenance that ensures minimum unplanned downtime, is critical to maximizing return on equipment investments.

IoT sensors on critical assembly equipment can deliver data in real-time that enable managers to make decisions rapidly, and trigger actions to maintain production lines at maximum capacity.

Monitoring, predicting, and acting to prevent failures on the complete installed base of machines, is an extraordinary analytical challenge that requires new ways to capture, monitor, and act on information in real- time.

IoT Analytics focused on predictive maintenance, can help these organizations plan and target their support and maintenance resources much more efficiently and effectively, in order to maintain their equipment in the field at peak operating performance.

There is an optimal schedule for maintenance and repairs: not too early, not too late. Machine learning algorithms can compare maintenance events and machine data for each piece of equipment to its history of malfunctions. These algorithms – through analytics – can derive optimal maintenance schedules, based on real-time information and historical data. This can help maximize equipment utilization, minimize P&E expense, and avoid surprise work stoppages.

Operating Efficiency
Ensuring maximum throughput through an operating line, is another area where IoT Analytics applications can add significant value. Complex assembly operations rely on a reliable stream of sub-components and a consistent supply chain flow.

IoT sensors and devices can provide early indicators of supply chain imbalances. Analyzing this data flow can yield many points of insight and potential actions for streamlining the flow of components, processes, and human resources applied to a particular production application.

Carefully balancing component supply with operational throughput can reduce excess inventory, accelerate the assembly process, and reduce capital requirements. All of these benefits deliver increased profits and improved customer service.

Supply Chain
From tracking products to inventory management, the supply chain is ready for its own IoT-led disruption — and the transformation is already well underway.

Inventory Forecasting
IoT data provides critical information to change the way manufacturing and distribution companies understand procurement operations. For instance, transit or retail stock levels can be closely monitored, as well as within 3PL distribution centers and warehouses, so that companies receive advance warning on any shipping errors to reduce data-entry errors and prolonged cycle times. The collection of data intelligence with pattern analysis over time, enables accurate forecasting and intervention should faulty operations occur. As a result, inventory planners, production and procurement managers can be better informed and equipped for executive decision-making on materials to hold, build or buy.

Raw Material Sourcing
Manufacturers want to minimize the inventory that they keep on hand and prefer just-in-time delivery of raw materials. Sensors and RFID tags reduce the cost of capturing supply chain data, which will help in IoT Analytics, which will help manufacturers have more visibility into the history of their supply chains and they are able to see large patterns that might be invisible in only a few months of data. This analytics can give manufacturers greater lead-time to adjust to supply chain disruptions. It also allows them to reduce supply chain costs and improve margins.

Quality Assurance
Thoroughly tested products still have post-sale problems. Customers may not report problems to the manufacturer, but still complain about the product to their friends and family on social media. This social stream of data on product issues can augment product feedback from traditional support channels. Hadoop stores huge volumes of social media sentiment data. Manufacturers can mine this data for early signals on how a product holds up throughout its lifecycle. This ability to learn about issues quickly and take early action to protect a product’s reputation is powerful for winning and maintaining customer loyalty.

Conclusion
Although the market for the IoT Analytics is still in nascent stage, many first adopters in the manufacturing companies are executing pilots programs where there is substantial potential to augment productivity and efficiency gains. In order to remain competitive, the focus of manufacturing companies should be to differentiate themselves through efficiency optimization supported by IoT Analytics. Combining IoT data with analytics provides manufacturers with a 360-degree view of operations and a significant monetization opportunity. This extends beyond traditional end customers to the larger ecosystem of partners and stakeholders.