Chapter 3. Value of Data in Industry 4.0 Era
With the rise of Industry 4.0 technologies, every machine, production line, product, and overall facility generates terabytes of data daily in the form of performance metrics, sensor readings, images, videos, log files, and more.
This manufacturing data holds immense value because it provides insights into how the entire production system is functioning. Data reveals inefficiencies, bottlenecks, quality issues and more that were previously invisible to manufacturers without sophisticated monitoring systems. Advanced sensors can now detect subtle changes in vibration patterns, temperatures or other readings that could signal impending maintenance needs or quality problems.
With the right analytics tools, manufacturers can examine historical production data trends and identify correlations to optimise processes. For example, data may show that a specific machine tool starts producing defective parts after 100 hours of run time. Predicting such failures in advance through pattern recognition helps schedule maintenance during planned downtime rather than unexpected breakdowns disrupting schedules.
On the factory floor, real-time data streaming from equipment, wearables, and other IoT devices allows managers to receive alerts on KPI deviations or anomalies. Issues that previously took hours or days to surface, like inconsistent weld quality, can now be addressed instantly. With a live dashboard showing live equipment health, output, and OEE, supervisors no longer need to inspect stations manually.
Data also powers predictive quality control, where computer vision analyses visual data from cameras, scans, and sensors to identify defects with greater accuracy than humans. Predictive maintenance goes a step further, using vibration signatures and other signals to forecast exactly when critical components may fail rather than just detecting anomalies. This helps achieve optimal service intervals, saving both downtime and maintenance costs.
As manufacturers recognise data's value, it is now seen as a key strategic corporate asset rather than an IT or engineering issue. Forward-thinking companies invest in tools and platforms for collecting, storing, and processing vast volumes of operational data. They also focus on data governance with strict security controls, considering competitive sensitivities around IP and proprietary manufacturing know-how. With well-managed manufacturing data harnessing IoT and advanced analytics, Industry 4.0 visions of vastly more efficient, optimised and autonomous "smart" factories will become achievable realities.
Generation of the new oil
Massive amounts of data are now being generated across manufacturing facilities due to the widespread deployment of Internet of Things (IoT) technologies under Industry 4.0. Sensors, RFID tags, automated machines, smart devices and other systems are producing volumes of operational data on a scale never seen before. This deluge of data from the factory floor holds immense value that can be unlocked through analytics for optimising business decisions. Every connected component in today's digital factories acts as a data generator, be it a sensor measuring vibration, a camera inspecting product quality or RFID tracking material movement. Major aircraft manufacturers like Airbus recognise that aircraft are a a wealth of valuable operational data. For example, a modern twinjet like the A350 contains over 10,000 smart sensors that generate terabytes of information on each flight. Sensors carefully monitor essential systems, structures and engines to ensure safety and efficiency. Similarly, sensors on machines, robotic arms, conveyors, feeder systems, and material handling equipment seamlessly track production metrics.
Smart Vision Systems Arrive
Computer vision has transformed how manufacturers leverage the enormous volumes of visual data generated across factory operations and facilities. Networks of distributed smart camera systems scanning production lines at ultra-high speeds can detect defects far beyond human capabilities. Equipped with integrated machine vision lighting solutions, these systems identify even the subtlest product irregularities or anomalies through high-resolution digital imagery.
Beyond just assembly lines, advanced camera technologies provide a virtual window into all manufacturing processes and assets through a digital sensory network. High-definition cameras integrated directly into machinery, robots, conveyors and other industrial equipment capture live video streams and photographic data at production points. Additionally, fleets of autonomous drones outfitted with specialised cameras are deployed by some manufacturers to capture surveillance-style aerial footage and imagery. These drones allow inspection of hard-to-reach and confined areas that stationary cameras cannot access, such as rooftops, exteriors of tall structures, and hazardous work environments.
Since computer vision is revolutionising how manufacturers extract real-time and strategic insights from the enormous troves of visual data captured at each step of production using networked smart camera systems, when paired with Industrial IoT and analytical technologies, it opens new frontiers for achieving the vision of data-driven decision making that characterises Industry 4.0.
Advanced Sensors Navigate Industry 4.0
The last decade has witnessed smart sensor technology take yet another transformational leap towards the objectives of Industry 4.0. New generations integrate multi-sensing and energy harvesting ability, enabling autonomous equipment networking with edge and cloud analytics capabilities.
A major advancement in smart sensor technology has been the development of self-powered energy harvesting models that solve the challenge of powering devices in remote locations where wired power is impossible or impractical. Through innovative techniques such as thermoelectric generation, vibration-based electricity conversion or solar photovoltaics, these smart sensors can operate permanently without the need for battery replacement. As they are wire-free, energy harvesting sensors ease deployment and maintenance significantly in difficult environments such as conveyor belts, rail-mounted systems, unstable structures or extreme temperatures that traditional sensors could not withstand. In addition, wireless smart sensors compatible with mobile robotics and equipment now provide the flexibility needed for reconfigurable automation through on-the-fly sensing and decision-making that follows production as it evolves.
Turing data oil into power
Manufacturers leverage analytics tools to derive actionable intelligence from their Industry 4.0 data pools. Advanced algorithms find patterns, spot anomalies, predict outcomes and prescribe optimisation measures. Managers can access insights through intuitive dashboards at their fingertips for rapid fact-based choices.
Condition monitoring analytics track equipment health to detect subtle deviations indicating impending faults. Predictive maintenance analytics forecast failures in advance, helping schedule servicing optimally. Process mining of production data uncovers bottlenecks and waste for continuous improvements.
Quality control powered by machine learning examines visuals, readings, and patterns to identify likely defects proactively. Demand forecasting based on sales, inventory, and customer data helps to precisely match production to actual customer needs. Resource allocation and line balancing are optimised by leveraging production simulations on digital twins of real assets.
Data also helps enhance customer experience and engagement. Through analytics of customer profiles, touchpoints, service records and sentiment, businesses craft personalised experiences and targeted marketing campaigns. After-sales service levels also rise when diagnostics and remote support capabilities are leveraged.
Value Story I – OEE optimisation through data
Overall Equipment Effectiveness (OEE) is a critical performance metric widely used in manufacturing industries to measure how effectively a production line or equipment is utilised. At its core, OEE indicates the percentage of planned production time spent making quality products.
OEE is calculated by multiplying Availability, Performance, and Quality. Here are the equations for calculating the three factors of Overall Equipment Effectiveness (OEE):
OEE =Availability x Performance x Quality
most manufacturers track OEE data routinely through manual recording of downtime logs, production counts and defect inspections. OEE levels are then benchmarked across similar equipment, shifts or plants to identify underperforming areas for corrective action. Poor OEE can equate to hundreds of thousands of dollars in lost output annually. As such, driving higher OEE is a key lever for manufacturers to boost productivity, minimise production costs, expedite deliveries and strengthen competitive advantage in their industries. Continuous OEE monitoring and enhancement efforts have become business-critical undertakings at world-class factories.
However, emerging industrial technologies now unlock vastly richer operational data views. The falling costs of sensors and edge devices enable multivariate real-time data capture even from legacy machinery. For example, welding lines equipped with minuscule IoT sensors embedded in robotic arms, positioners, and joints can measure metrics like speed, pressure, and temperature with millisecond resolution.
Value Story II – Supply chain operation optimisation
The digitalisation of industrial systems has created an explosion of machine-generated data from sensors, RFID tags, embedded computers, and enterprise transactions. This deluge of structured and unstructured 'Industrial Big Data' holds immense potential for improving supply chain visibility and performance. However, harnessing insights from data spanning the entire extended enterprise presents challenges from volume, velocity, and variety standpoints.
overview of operations and supply chain optimisation
Effective supply chain management relies on accurate demand signals and end-to-end tracking of materials, components and finished goods. Traditionally, these operational aspects involved manual record-keeping and long feedback loops. However, the connectivity of IoT now enables real-time data collection on factors like customer purchasing patterns, warehouse inventory levels, shipping container conditions, and vehicle traffic conditions. These disparate data streams offer a transparent view of supply and demand synchronisation when aggregated and analysed.
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Operations enhancement with data management
Modern industrial machinery generates tremendous amounts of real-time data from embedded sensors monitoring processes around the clock. Manufacturers are collecting multivariate data streams directly from equipment, reflecting key OEE metrics like uptime/downtime durations, production throughput in units per hour, and quality inspection results on parameters such as dimensions, finish, or contaminants. Equipment-generated data holds immense potential for operations optimisation when harnessed properly.
Supply chain optimisation
Effective supply chain management relies on accurate demand forecasts to synchronise production and logistics with customer requirements. Traditionally, demand prediction involved extrapolating past manual sales records with limited context. However, businesses can now access diverse real-time data sources for more predictive analytics.
Point-of-sale transaction systems automatically capture customer purchasing patterns nationwide at a granular stock-keeping unit level. Machine learning algorithms can discern demand patterns across regions and seasons when linked to calendar information and contextual factors like weather, holidays and sports events. Modelling multiple years of comprehensive historical demand data improves forecast accuracy by up to 25%.
Additionally, sensor-enabled warehouses and distribution centres continuously report inventory levels and throughput by individual stocking locations. Integrating this internal visibility with projected external consumer demand enables automated replenishment rules triggered within enterprise resource planning systems. Replenishment algorithms optimise safety stock levels while avoiding over-ordering to minimise necessary working capital.
Value Story III – product design and development revolution
The modern product design and development process has undergone a revolution in recent years, driven largely by the abundance of available data and analytical capabilities. Gone are the days when the design was an isolated activity focused solely on engineering specifications and limited market research. Today's product development demands a highly cross-functional, data-driven approach that leverages real-time customer and process insights from ideation through manufacturing and the product lifecycle.
As manufacturers digitally transform, data collection capabilities have expanded dramatically across the entire value chain. Sensors, IoT technologies, digital platforms and automated processes continuously generate vast troves of operational, customer and product performance data. This wealth of information plays an invaluable role in shortening design cycles, improving quality, and minimising risks. When properly analysed, data reveals deep customer insights and helps uncover inefficiencies that previously went undetected with traditional methods. It has become a strategic asset that boosts competitiveness when incorporated systematically into new product development.
Data now permeates the front end of the product design process, facilitating more user-centric outcomes. Customer feedback platforms and digital touchpoints give voice to the voice of the customer like never before, providing a constant stream of qualitative and quantitative input. Sentiment analysis and predictive modelling extract meaning from unstructured data sources to understand latent needs. Combined with demographic and usage data, these insights help design more targeted, human-centred solutions from the seed of an idea. Frequent iterations and rapid prototyping fuelled by customer data also allow for faster concept validation and refinement to ensure full resonance with target markets from the start.
Value Story IV – New Ways Of Customer Management
Customer data plays a crucial role for companies in the manufacturing sector. Data analysts work to ensure organisations effectively capture and use customer information to strengthen relationships and drive sales over time. Retaining existing business and customer feedback is important for product quality, satisfaction, and repeat orders.
Companies collect various types of customer data through different systems. CRM databases commonly store contact details, purchase histories, support interactions and more. ERP systems provide insights into order fulfilment, billing, payments and any outstanding issues. Integrating these sources allows gaining a holistic view of each touchpoint across the customer journey.
For new client acquisition, market and customer data are analysed using machine learning techniques. This helps identify promising prospective accounts with similar profiles. Clustering buyer attributes informs targeted marketing campaigns aimed at nurturing leads. Qualification scores assist sales teams in prioritising the most viable opportunities.
For existing accounts, predictive models alert representatives of accounts nearing renewal points or at higher risk of churn. Well-timed outreach through customised campaigns aims to retain business and acquire feedback on better-serving customer demands.
Manufacturers seek to develop long-lasting, trusted partnerships beyond individual transactions by strategically leveraging customer data across systems. A deeper understanding facilitates cross-selling new solutions, co-developing tailored offerings jointly, and co-creating future innovations aligned with client needs.
Chapter 3: Value of Data in Industry 4.0 Era
Chapter 4: Evolvement to Data-driven IT/OT Convergence
Chapter 5: Introduction To Artificial Intelligence
Chapter 6: Demystify AI and ML
Chapter 7: Industrial AI Use Cases
Chapter 8: Industry AI – Trends and Forecast
Chapter 9: Drive Industrial AI Strategies
Chapter 10: Implementing Industrial AI
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