Transforming the Manufacturing Sector with IIoT

Although IoT and Industrial IoT share common technologies that work together as part of a cohesive system (sensors, connectivity, and data analytics), the difference comes in their general usages. IoT use cases generally focus on consumer convenience and smart devices, as demonstrated in our previous article with real-world examples such as the Nest Hello and the Assistme AIDMATE. In contrast to IoT, which mostly centers around human interaction with devices, IIoT focuses on disruptive technologies and intelligent machinery within the manufacturing and industrial sectors.

 


In the business environment where competitive pressure continues to rise, organizations are looking toward IIoT as a solution to revolutionize the manufacturing industry through data intelligence and process optimization.

 

Disruptive technologies such as machine-to-machine (M2M) communication, edge computing, blockchain, robotics and artificial intelligence (AI) are bringing new levels of automation and actionable intelligence that allow for comprehensive data to be gathered, analyzed and shared. From a business perspective, this is fundamentally changing the way organizations operate. By combining relevant technologies with newly available data, industrial equipment and processes are being optimized to enhance productivity, profitability, and reliability in ways previously unimagined. According to Microsoft’s IoT Signals, 94% of businesses state that they will be implementing IoT by the end of 2021¹.

 

As a subset of IoT, IIoT is increasingly gaining a foothold in the market as more organizations are realizing the exponential value of connected devices and the return on investment (ROI). Smart devices and equipment have significantly impacted the manufacturing, healthcare, and business landscapes, more so than in the home. By 2025, it’s predicted that the global worth of IoT will be $6.2 trillion, with the maximum value deriving from healthcare at $2.5 trillion, followed by the manufacturing sector at $2.3 trillion².

 

Advances in connected sensors, communication mechanisms, data management, and robotics are opening up entirely new value streams through the application of smart, accurate and automated manufacturing processes. Forecasts predict that the industrial IoT market is set to reach $110 billion in 2020³.

 

The IIoT Ecosystem 2

 

Within the IIoT ecosystem, here are four key findings to consider:

  • IIoT systems are characterized by their ability to execute awareness and autonomy that delivers actionable insights for manufacturers and business owners.
  • IIoT provides a wide breadth of tools and processes (embedded controls data analytics and telemetry) to bridge the gap between big data and the ability to leverage key insights to make intelligent business decisions.
  • IIoT is a modern development and productivity enabler for traditional Manufacturing Execution Systems (MES).
  • Manufacturers, across all sectors, leverage IIoT’s ecosystem trifecta of intelligence, connectivity, and insights to establish low-cost, quick to implement and scalable solutions that enable them to compete globally.

IIoT Value Drivers for Business

Successful applications of IoT in business are the building blocks of digital transformation. However, clearly defining a business case, selecting primary objectives and prioritizing each one to drive real business impact, is a critical first step to achieving long-term success.

 

As one of the most data-prolific industries, manufacturers leverage IIoT as a means of achieving targeted goals that save time, boost profitability and enable businesses to leap ahead of competitors.

 

Although many IIoT projects are still in the proof-of-concept phase, IIoT is already demonstrating its prosperity and versatility with live deployments in various organizations, proving what’s possible with different use cases across industry domains. As asset-intensive industries continue redesigning their business operations to incorporate IIoT, here are the top 12 use cases accelerating business efficiency and success:

 

IIoT- The Digital Compass

 

Use Case #1: Root Cause Analysis

Root cause analysis (RCA) is a system that identifies the origin of problems in factories or with machinery and develops an approach to solve these issues. RCA offers greater access to equipment data that tracks and records patterns, enabling organizations to learn from insights to support operational and tactical business decisions in real-time. These trends are analyzed to spot anomalies in the manufacturing process when production slows down, drops in quality occur, or there’s a decrease in the overall equipment effectiveness (OEE).

 

With the power of industrial IoT and AI, root cause analysis can be automated based upon data immediately received from the production process. RCA is based on the principle that effective management is more than merely addressing problems that arise, but rather finding ways to prevent problems by forecasting for the future.

 

Use Case #2: Predictive Maintenance

Predictive maintenance technologies track all the workings of machinery and gain granular visibility across all operations. Manufacturers use this overview to reduce the chances of system failure and equipment degradation. According to Information Technology Intelligence Consulting, 98% of organizations state that a single hour of downtime costs them over $100,000⁵. Therefore, it remains a key priority for manufacturers to ensure that all equipment functions optimally through the application of predictive maintenance.

 

Due to significant advances in IoT and machine learning, applications with complex algorithms are now in place to make predictions and decisions based upon new data without the need for human involvement. In fact, McKinsey reported that sensor data used to predict equipment failure in the manufacturing environment can reduce maintenance costs by as much as 40% and cut unplanned downtime in half⁶.

 

Use Case #3: Supply Chain Optimization

As the supply chain becomes more complex with more components at each stage, it has become increasingly difficult to streamline this process. This is where IIoT offers a solution that enables each component and machine to ‘communicate’ with one other — M2M communication — to perform automated tasks based on data received at each entry point within the supply chain.

 

Sensors monitor these activities across the supply chain, providing access to real-time information by tracking inventories, timelines for material and resource availability, equipment performance, and progress.

 

Use Case #4: Digital Twins

A digital twin is a digital replica of a process, product or service. This virtual model is created using computer-aided engineering and is integrated with IoT, machine learning and big data analytics that merge real-time data from its physical counterpart onto an interactive visual interface. Pairing virtual and physical worlds allows for data analysis to identify problems before they occur, prevent downtime, and assist in future planning through digital simulations.

 

IIoT offers an end-to-end information flow that highlights key insights across the entire product lifecycle and value chain. As more “things” become connected to the Internet, having a digital equivalent gives manufacturers key insights to advance operational excellence and create other what-if scenarios to determine business outcomes before execution.

 

Use Case #5: Asset Tracking

Securely tracking the location of valuable assets and goods is critical to business operations. With IoT-enabled asset tracking sensors, manufacturers are able to remotely track equipment and fleets in real-time. This offers companies mechanisms to reduce risk, save money, and prevent theft. Leveraging sensors improves manufacturing management and reduces the operational costs of a factory. By using tags and sensors to monitor facilities, organizations gain key insights on how to optimize space usage and production performance.

 

Radio Frequency Identification Tags (RFID tag) are the sensors used to track vehicles and merchandise, transmitting information to an RFID computer program for organizations to have oversight on each of their assets. Vehicles can also be tracked through an Electronic Logging Device (ELD), which attaches to a commercial motor vehicle (CMV) that synchronizes with the engine to electronically and accurately record all vehicle movements and driving activities.

 

Use Case #6: Inventory Management

IoT systems in manufacturing lower inventory carrying costs and reduce inventory management errors. Embedded sensors and smart inventory management are catalysts for driving real-time visibility across inventory systems, warehousing, production cycles, and distribution centers. Beyond this, smart inventory management offers manufacturers real-time actionable insights that can improve the allocation of raw materials, efficient scheduling, transportation logistics, and ordering timelines.

 

Use Case #7: Quality Control Mechanisms

Quality control (QC) and validation mechanisms often contribute significantly to the total cost incurred by a company. As a result, quality control best practices suggest that organizations monitor the production process itself, rather than waiting until there's a finished product.

 

IIoT enables connected machines to use intelligent capabilities to conduct quality testing during production to ensure that the process is error-proof. Furthermore, IIoT ensures that quality standards are consistent and puts mechanisms in place for quick reactions to any issues with the quality of raw materials, processes, or finished products.

 

Use Case #8: Smart Monitoring

With smart monitoring, businesses are able to collect diverse data-sets and analyze internal systems. This enables organizations to bridge the gap between machines and business processes in order to gain the relevant data-driven insights needed to resolve issues and optimize manufacturing performance.

 

Smart sensors monitor machinery and factories in real-time to provide actionable insights that can be used to improve customer experience, address operational setbacks and maximize IoT opportunities. These smart sensor readings are connected to an application dashboard that visualizes data gathered from machinery, detailing any performance anomalies and alerting manufacturers of any urgent issues. Smart monitoring practices enable businesses to reduce maintenance costs, improve processes, strategically plan for the future, and utilize energy more efficiently.

 

Use Case #9: Remote Monitoring

In the industrial sector, it can be expensive and risky to monitor equipment out on the field without any mechanisms of remote monitoring in place. As a result, manufacturers have begun to implement environmental sensors to continuously monitor conditions and alert management when quality thresholds or parameters are not met.

 

By deploying IoT connected sensors, manufacturers are able to have a live overview of the production process and machine performance-from afar. Based on this data, operations can then be adjusted remotely according to the specific needs of the business at any given time within the growth cycle.

 

Use case #10: Process Automation

Among the many reasons for putting industrial IoT standards in place, a few driving forces stand out: efficiency, cost reduction, risk management, and enhanced value streams.

 

Process automation is largely facilitated through the implementation of robotics. In 2020, the projected global revenue from robotics within the industrial sector is set to reach $18.5 billion⁷. This boosts operational efficiency and is a critical business driver among manufacturers adopting IIoT. Process automation leads to faster production cycles and paves the way for entirely new business models.

 

In addition, IoT offers an effective inventory method called just-in-time (JIT) manufacturing, whereby materials and goods are scheduled to arrive or be replenished exactly when needed during the production process. This method can drastically optimize an organization’s productivity levels, capital allocation, and reduces manufacturing waste. However, organizations are generally required to forecast demand accurately. Automating processes that would otherwise require human involvement to improve efficiency and save time provides the rationale for deploying IoT into the industrial sector.

 

Use Case #11: Workplace Safety and Security

Accidents and risk management within the workplace still remains a significant challenge for employers in many industries. As a solution to this, IoT technologies such as video monitoring and remote monitoring can help improve employee safety and security.

 

In addition, IoT combined with big data analysis can optimize worker safety and security by monitoring specific key indicators such as employee absences, property or vehicle damage, and any additional losses or damages that may occur during regular business operations.

 

Sensors can also accurately monitor temperature, humidity levels and can detect other environmental conditions that may cause harm to employees. Mechanisms are then put in place so data from worksite sensors deliver fast and accurate messages to emergency services in the case of a disaster.

 

Use Case #12: New Business Models

With IoT-backed solutions, manufacturers are transcending product-based business models into services-based models to create new opportunities across the value chain. Smart connected products are rich in untapped data and revenue potential which has opened up new ways for the manufacturing and industrial sectors to conduct business — by using servitization to innovate new offerings.

 

One of the most exciting things about IoT is that business value can be created far beyond the problem that IoT was originally meant to solve; this leads to entirely new product capabilities and innovative services.

 

From Smart Components to Smart Organizations

Looking at the enterprise infrastructure, initial implementations of IIoT begin with applications of smart sensors before scaling up and transitioning into smart organizations, which use data-driven insights to drive more sustainable, resilient, and efficient processes.

 

IIoT The Connected Enterprise

 

Using a technology-driven approach, smart manufacturing enables companies to identify opportunities for automating operations and using data analytics to improve overall production performance. As more machines become connected through IoT, they are able to effectively communicate with one other, supporting greater levels of autonomy in the production process. Organizations that harness the power of collective learning practices and data insights are well prepared to progress in the future.

 


At NBT, we're always searching for IoT innovators looking to scale their business. If you'd like to discover how NBT can help transform your IoT idea into a real-world application, just reach out.


¹ Azure, https://azure.microsoft.com/en-us/resources/iot-signals/

² Intel, http://www.intel.com/content/www/us/en/internet-of-things/infographics/guide-to-iot.html

³ Morgan Stanley, https://www.morganstanley.com/ideas/industrial-internet-of-things-and-automation-robotics

⁴ LHP Engineering Solutions, https://lhpes.com/internet-of-things

Information Technology Intelligence Consulting, https://itic-corp.com/blog/2016/08/cost-of-hourly-downtime-soars-81-of-enterprises-say-it-exceeds-300k-on-average/

McKinsey, https://www.mckinsey.com/business-functions/mckinsey-digital/our-insights/an-executives-guide-to-the-internet-of-things

⁷ Statista, https://www.statista.com/statistics/745476/industrial-sector-revenue-from-robotics-worldwide/

Genpact, https://www.genpact.com/downloadable-content/insight/making-the-iiot-promise-real.pdf