The adoption of Industrial Internet of Things (IIoT) solutions is rapidly transforming manufacturing operations. For OEMs, IIoT unlocks invaluable data insights that can boost productivity and inform smarter decision-making.
However, to fully capitalize on the potential of IIoT, OEMs must embrace certain best practices. This article explores tips for OEMs to enhance productivity through data-driven decision-making enabled by smart manufacturing IoT solutions. With the right strategy, OEMs can leverage IIoT data to optimize processes, reduce costs, improve quality, and gain a competitive advantage.
Tips For Boosting Productivity with Data-Driven Decision-Making in OEMs
The manufacturing industry is undergoing a digital transformation driven by IIoT technologies. For OEMs, integrating smart manufacturing solutions unlocks game-changing productivity enhancements through data-driven decision-making. However, to fully leverage the potential of IIoT, OEMs need an effective implementation strategy. Below there are tips to help OEMs boost productivity by enabling data-driven decisions with smart industry IoT solutions.
Leverage IIoT Data for Informed Decision Making
At its core, IIoT is about capturing and analyzing data from connected devices and systems to generate insights. For OEMs, this data visibility enables informed decisions backed by real-time analytics. Rather than relying on gut instinct, OEM IoT solution can help to:
- Identify inefficiencies in processes and optimize operations.
- Enhance quality control by detecting issues early.
- Predict failures through condition monitoring and schedule predictive maintenance.
- Adjust production based on real-time demand fluctuations.
- Gain visibility into inventory levels and supply chain flows.
By basing decisions on data patterns and trends, OEMs can drive continuous improvement across the organization.
Key Applications of IIoT Driving Productivity
Several key IIoT applications are powering smart industry IoT improvements for OEMs:
- Predictive Maintenance: IoT sensors on equipment track performance parameters like vibration, temperature, etc. Advanced analytics can detect anomalies and predict failures, enabling proactive maintenance to reduce downtime.
- Asset Tracking: RFID tags and GPS enable real-time tracking of asset location and status. OEMs gain visibility into equipment, inventory, and shipments to optimize utilization.
- Inventory Management: Automated inventory monitoring with IIoT solutions like barcode scanning and RFID improves stock visibility. OEMs can optimize inventory costs and production planning.
- Quality Control: Connected sensors can monitor product quality variables like temperature and pressure. Statistical analysis helps rapidly detect deviations to prevent rejects.
- Energy Management: Smart meters and IoT sensors enable tracking of energy consumption. Data analytics facilitates reducing wastage and optimizing energy usage.
Asset tracking with RFID has increased inventory accuracy at one aerospace manufacturer from 92% to 98% after installing tags on over 10,000 components and tools.
Management of spare parts is now automated, and real-time location data from the IoT network ensures the right parts are delivered to production lines as needed. This has increased inventory turns from 18x per year to 22x, lowering carrying costs by over 15%.
Adopt a Phased Implementation Approach
For most OEMs, adopting smart manufacturing IoT solutions requires a technology overhaul. Trying to implement everything together can be overwhelming. OEMs should take an incremental approach:
- Start with a pilot project in one process area to demonstrate ROI.
- Once successful, scale the rollout across the plant and progressively add new capabilities.
- Involve operations teams extensively during design and implementation.
- Maintain focus on solving critical pain points instead of attempting to connect everything.
For example, a metal fabrication company implemented IoT in three phases over 18 months. Phase 1 integrated sensors into key production machines in welding and assembly to pilot predictive maintenance. Phase 2 expanded this to other facilities after successful cost savings. Phase 3 involved integrating sensor data from machines into SAP and custom analytics dashboards for managers to track OEE downtime reasons and optimize processes in each plant.
Prioritize Cybersecurity from the Outset
With an exponential increase in connected devices, IIoT systems pose cyber risks. OEM IoT solutions must prioritize security by:
- Performing cybersecurity risk assessments during the design phase.
- Implementing effective secure data encryption and strong access controls.
- Regularly patch and update firmware/software.
- Adopting emerging standards like ISA/IEC 62443.
- Providing cybersecurity training to employees.
An incremental rollout and addressing security proactively from the start enables a smooth smart industry IoT adoption.
Leverage Advanced Analytics for Maximum Value
While IIoT provides invaluable data, OEMs need advanced analytics capabilities to transform it into actionable insights. Integrating solutions like machine learning, AI, and big data analytics allows OEMs to derive meaningful patterns from complex data. Key focus areas include:
- Predictive maintenance: ML algorithms can detect equipment failures before occurrence.
- Demand forecasting: Big data analytics on past demand, sales, and external factors improves demand predictions.
- Process optimization: Algorithms can continuously fine-tune manufacturing processes.
- Anomaly detection: Unsupervised ML models can identify abnormalities indicating quality issues.
Upskill Teams to Adopt Data-Driven Culture
To truly adopt data-driven decision-making, OEM IoT solution needs team and processes that can handle big data and analytics. Key steps include:
- Hiring data scientists and analysts for specialized analytics.
- Training employees on interpreting insights from IIoT data.
- Implementing collaborative tools for data sharing across departments.
- Instituting KPIs and metrics to track data-driven improvements.
- Automating repetitive analysis tasks through ML and AI.
- Changing processes to become data-oriented.
With an analytics-focused workforce, OEMs will be able to realize the full potential of IoT solutions. A mixed approach includes on-site data analytics training conducted by vendor experts monthly. This hands-on training helps employees learn data visualization, querying, and interpretation skills. Companies can sponsor employees annually to pursue relevant online certifications in areas like IoT, machine learning, or SIEM systems. This helps develop analytical talent able to maximize IoT investments.
Overcome Resistance to Change Through Engagement
The workforce is crucial for the success of any digital transformation initiative. OEMs often face resistance from employees, fearing job losses from increased automation and digitization. To overturn perceptions, OEMs should:
- Communicate how IIoT aims to augment human capabilities rather than replace jobs.
- Provide adequate change management and upskilling programs to align workforce competencies.
- Engage teams throughout the IIoT implementation journey.
- Highlight how data-driven insights can make processes easier and empower employees.
- Gaining employee mindshare and support ensures the smooth adoption of data-driven decision-making.
Key Challenges in IIoT Adoption
While promising improved productivity, IIoT also brings certain implementation challenges OEMs should be prepared for:
- Legacy Technology Integration: Integrating IIoT solutions with legacy systems like ERP and MES can be tricky, requiring customized interfaces.
- Data Management: The high data velocity and variety from IIoT systems necessitate robust data management capabilities.
- Cybersecurity: The exponential increase in attack surfaces with IoT devices makes security more complex.
- Talent Shortage: Most OEMs need more staff skilled in handling IoT, data science, and advanced analytics.
- Interoperability: Lack of standardized protocols can make connecting disparate devices and systems challenging.
- Upfront Costs: Substantial upfront investment is needed in sensors, connectivity, analytics, etc., leading to unclear ROI.
By partnering with the right IoT/analytics service providers, OEMs can overcome these technology and talent challenges on the path to IoT adoption.
This can be understood with the help of an example. One medical device manufacturer faced legacy system integration challenges initially. But custom APIs and middleware developed in-house helped migrate sensor data from the factory IoT network to their 20-year-old MES. This was more cost-effective than a full MES upgrade. Now, analytics is improving quality workflows, and older systems are able to support an IoT implementation.
Choose the Right IIoT Platform
The IIoT platform is the foundational technology that integrates devices, systems, and applications. OEMs have the option of custom-building or buying a platform. Developing a proprietary IIoT platform requires tremendous time and resources. An attractive alternative is opting for an off-the-shelf industrial IoT platform.
When evaluating options, OEMs should check that the platform:
- Provides easy onboarding and integration of sensors, machines, and software systems.
- Supports industrial communication protocols like Modbus, OPC-UA, etc.
- Offers embedded IoT security capabilities.
- Comes with visualization dashboards and analytics tools.
- Has capabilities for edge and cloud data processing.
- Enables application development.
- Is hardware agnostic and open standards compliant?
- Choosing the right platform speeds up IIoT adoption and total cost of ownership.
The Future Outlook of Data-Driven Manufacturing
As IIoT becomes ubiquitous, manufacturers will increasingly automate decision-making using AI/ML. OEMs will leverage simulation, digital twins, and immersive analytics tools. Additional advances we expect include:
- Democratization of AI with low-code analytics platforms.
- Rise of Industry 5.0 with human-machine collaboration.
- Convergence with AI, ML, blockchain, AR/VR, and 5G.
- Transition to cloud-based software and analytics.
- Growth of edge computing to support real-time applications.
Data-driven manufacturing powered by IIoT will be the catalyst propelling OEMs into the future of the smart industry. Adopting the right strategy today will help OEMs lead this revolution.
Moving Forward with OEM IoT Integration
Data-driven decision-making enabled by IIoT solutions has the power to transform manufacturing operations. To maximize productivity gains, OEMs need to adopt best practices encompassing the workforce, analytics, platform technologies, and implementation approaches.
While the data deluge from Industry 4.0 technologies brings certain challenges, the exponential performance improvements justified the investment. Manufacturers who embrace data-driven decision-making early will be best positioned to reap the benefits of smart manufacturing IoT, paving the path for data-fueled factories of the future.