The manufacturing industry is undergoing a digital transformation. New data collection and analysis opportunities are becoming possible, thanks to emerging technologies such as the Internet of Things (IoT), artificial intelligence (AI) and advanced analytics, which allow manufacturers to collect and analyze data from machines and production lines in new ways. The convergence of information technology and operations technology, also called Industry 4.0, promises to enable manufacturing at new levels of efficiency, quality, and flexibility.

The convergence of IoT and operational intelligence are key trends. The network of internet-connected sensors, devices, and machines that produce massive data is known as IoT. Operational intelligence refers to the analytics technologies, such as an operational intelligence platform, used to gain insights from that data and optimize operations. Combining the two allows manufacturers to leverage IoT data to drive smarter, more predictive decisions across the organization.

This article will examine the convergence of IoT and operational intelligence in smart manufacturing. It will cover:

• The Potential of Connected Machines and Production Lines
• Challenges of IoT Data Overload
• The Role of Operational Intelligence
• Use Cases and Impact on Efficiency, Quality, and Flexibility
• Implementation Challenges
• The Road Ahead
• The Potential of Connected Machines and Production Lines

Smart manufacturing initiatives involve IoT. Manufacturers can now get unprecedented visibility of their operations by outfitting machines, production lines, and products with sensors and connectivity. It includes monitoring machine health, asset utilization, product quality, supply chain flows, and others.

According to McKinsey, IoT applications in factories could create $1.2 to $3.7 trillion in value by 2025. Key sources of value include:

• Predictive maintenance: Reduce downtime by using IoT data to predict equipment failures before they occur
• Improved asset utilization: Optimize production scheduling and capacity planning with real-time equipment status
• Enhanced quality control: Detect defects and production issues faster
• Supply chain optimization: Better coordinate inbound supply and outbound fulfillment
• Improved productivity: Identify losses, gaps, and opportunities to increase throughput

These applications depend on data generated by connected machines and assets. IoT allows manufacturers to instrument their operations from start to finish effectively.

For example, automotive plants can track vehicle assembly from station to station on the line. Discrete manufacturers can monitor production at specific work centers. Process industries can outfit reactors, boilers, and other assets with sensors to optimize process flows.

In many factories, legacy machines lack native connectivity. However, new IoT devices, such as wireless sensors and industrial gateways, make it easier to collect and contextualize data from decades-old equipment.

As machines grow smarter and more connected, they generate exponentially more data. However, realizing IoT’s potential requires more than just data collection - it requires the right analytics capabilities to support operational intelligence.

Challenges of IoT Data Overload

The surge of IoT-based data is presenting manufacturers with both opportunities and challenges. On the one hand, the data exposes visibility gaps, performance losses, and improvement areas that were simply invisible before. On the other hand, the sheer volume of IoT data can quickly become overwhelming.

According to 99Firms report, 53% of adopters identify integration with existing technology as a primary challenge in IoT adoption. This “data overload” stems from several pain points:

• Sheer growth in structured and unstructured data volumes as more equipment, processes, and products are instrumented
• Difficulty consolidating IoT data across different machines, plants, and systems
• Lack of internal skills and best practices for managing and analyzing big data
• Immature analytics capabilities are required to translate IoT data into operational intelligence

For many manufacturers, IoT investments race ahead of their ability to extract value from the data. Their data infrastructure and analytics tools struggle to keep pace with the influx of IoT data from the factory floor. All that machine and sensor data risks sitting in silos – going unanalyzed despite its potential to expose transformational insights about operations.

To dismantle the IoT data overload challenges, one of the primes is to address these analytics gaps. Advanced analytics and artificial intelligence can generate operational intelligence that helps manufacturers make the most of their connected machines and plants. Additionally, custom manufacturing software development can play a crucial role in building tailored solutions that seamlessly integrate IoT data, optimizing workflows and enhancing decision-making capabilities.

The Role of Operational Intelligence

Operational intelligence (OI) is the use of data analytics to achieve real-time visibility and insight into business operations. It connects insights from analytics tools directly to operational decision-making - delivering the right information to the right people at the right time.

For manufacturers, OI brings IoT data together with other datasets – from ERP, MES, PLM, SCM, and other systems – into an integrated view. Advanced analytics extract insights from this aggregated data to answer key questions, monitor KPIs, identify performance issues, predict problems, and recommend actions in real-time. OI makes IoT data actionable across multiple functions:

Production and Plant Operations

• Optimize production scheduling, line balancing, and inventory levels
• Track equipment effectiveness, utilization, and performance
• Monitor product quality and predict potential defects
• Predict asset maintenance needs and prevent downtime
• Gain energy efficiency insights from HVAC, lighting and other sensor data

Product and Process Engineering

• Identify opportunities to improve production processes and methods
• Analyze machine sensor data to refine equipment settings
• Detect minute flaws in component design using quality test data
• Guide engineering change management decisions

Logistics and Supply Chain

• Gain visibility into inventory levels, logistics flows, and supply needs
• Optimize logistics planning and warehouse operations
• Orchestrate just-in-time delivery based on production statuses
• Monitor product location, condition, and handling throughout delivery

Business Leadership

• Provide KPI dashboards with role-based visibility into operations
• Enable drill-down root cause analysis on mobile devices
• Compare performance across multiple plants and product lines
• Deliver insights to support planning, budgeting and strategy decisions

OI makes IoT data more usable by more roles across the manufacturing organization. It’s the connective tissue between data collection at the machine level and decision-making at the management level.

Use Cases and Impact on Efficiency, Quality, and Flexibility

Converging IoT and OI unlocks new opportunities to drive smarter manufacturing along multiple dimensions:

1. Improved Efficiency

IoT data combined with OI gives manufacturers visibility they’ve never had before into where they are losing time, capacity, material, and money. This intelligence can drive significant efficiency gains:

• Reduce unplanned downtime: OI helps plants predict equipment failures before they occur based on IoT sensor data. This predictive maintenance can reduce unplanned downtime by 30-50% or more.
• Streamline changeovers: Analyzing changeover times, OI identifies ways to optimize setup processes. This can yield 20%+ improvements in changeover efficiency.
• Cut energy costs: Applying OI to IoT-enabled metering exposes opportunities to streamline HVAC, compressors, lighting and other energy usage. Manufacturers can achieve 10-20% reductions in energy consumption.
• Increase throughput: OI helps production planners optimize scheduling and line balancing to reduce bottlenecks. Manufacturers can squeeze 5-10% more throughput from existing capacity.

Cumulatively, these OI-driven efficiency gains based on IoT data create millions in operational cost savings and capacity recovery for manufacturers.

2. Higher Quality

Combining IoT sensors with OI analytics also gives manufacturers greater ability to build quality directly into processes, rather than just inspecting quality at the end:

• Detect defects faster: IoT data helps predict potential quality issues based on production parameters and asset performance. OI delivers these insights to workers and engineers while production is still running so they can address root causes. This real-time feedback loop reduces quality escapes by up to 80%.
• Continuously optimize processes: By applying OI to analyze trends in IoT sensor data from machines and quality tests, manufacturers can continuously fine-tune production processes. This prevents tiny process deviations that lead to defects.
• Trace genealogy end-to-end: IoT gives individual components and products a “digital twin” that tracks their genealogy. OI analyzes this genealogy data to quickly trace quality issues back to specific batches of work center operators, suppliers etc.

Instead of inspecting quality at the end of the line, manufacturers can use IoT and OI to build quality by design – leading to 40%+ reductions in scrap and rework costs.

3. Greater Flexibility

The insights derived from OI and IoT data also give manufacturers much greater production flexibility:

• Respond to demand shifts: By combining IoT production data with supply chain signals, systems can manage the rapid reallocation of capacity as demand fluctuates. This minimizes lead time even for highly customized products.
• Adapt processes: When introducing new products, OI helps engineers quickly analyze IoT data to optimize machine settings, tooling, workflows, and quality checks for fast changeovers.
• Support customization: IoT product genealogy data combined with OI gives manufacturers the traceability required for mass customization. It ensures the right process steps are applied to each unique item.

This data-driven flexibility allows manufacturers to introduce new products cost-effectively in weeks rather than months and rapidly switch between product variants based on customer demand.

Implementation Challenges

While the potential impact of converging IoT and OI is compelling, realizing that potential requires overcoming some key implementation challenges:

Integrating Disparate Data – The first challenge is getting all the datasets to converge in one place. IoT deployments often introduce new data silos – with sensor data stranded locally on machines. Pulling this IoT data together with IT systems (ERP, MES, SCM) and other data sources in context is difficult but essential. It requires an underlying IoT data infrastructure.

Analytics Complexity – Layering advanced analytics and AI/ML on top of IT/OT data is the second challenge. The specialized skills needed to build and maintain these systems are in short supply. Analytics complexity also makes it harder to achieve scalable, governed solutions that deliver timely, trustworthy insights users can act on.

Organizational Alignment – Finally, manufacturers must align their organization, processes, and culture to take advantage of IoT/OI convergence. This requires breaking down silos between IT, OT and analytics teams. It also involves change management across the business to get various functions using – and acting on – the new intelligence.

The Road Ahead

As manufacturers implement Industry 4.0 initiatives, the convergence of IoT and operational intelligence will continue accelerating over the next decade. Here are three trends to watch:

1. Analytics become more tightly embedded into machines and processes. As power computing advances, more analytics logic will be pushed directly to network edge devices – allowing the mining of IoT data to occur at the source. This “edge intelligence” will enable faster cycle times for translating IoT data into operational decisions – driving the next level of smart manufacturing.
2. 5G and new network architectures enable new classes of IoT data. Expanding 5G connectivity and new network designs like Time Sensitive Networking (TSN) will support more bandwidth and real-time capabilities for IoT devices. This will enable the streaming of data that has never been leveraged before, such as vibration signatures and other sensory information, which translates to more visibility and smarter decisions.
3. Cloud and containerization provide more analytics flexibility. Cloud platforms and containerization will allow manufacturers to harness analytics and data science capacity on demand rather than building out fixed data centers. This will provide the elastic scalability needed to absorb growing IoT data volumes cost-efficiently.

Together, these trends will give manufacturers new possibilities for innovating with data. Production lines, machines, and products will grow more intelligent and interconnected. At the same time, analytics and simulation will become even more integrated into engineering, operations and business decisions – driving the next wave of optimization in smart manufacturing.

Conclusion

The manufacturing industry stands at the cusp of a new level of operational intelligence. As IoT and OI converge, manufacturers gain unprecedented visibility into – and control over – their operations. Factories grow more data-driven, insight-led, and customer-centric.

However, this digital transformation also presents organizational and technical challenges, which are required to integrate IT, OT, and analytics. Manufacturers who successfully navigate this convergence stand to sustain significant competitive advantages through smarter flexibility, quality, and efficiency. Those who fail to adapt risk disappearing entirely.

The convergence of IoT and operational intelligence represents the next major evolution in industrial automation. It promises to reshape manufacturing as we know it – but only for those manufacturers ready to become data-driven.