APPLICATION OF INDUSTRY 4.0 IN MANIFACTURING
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Manufacturing is entering the fourth industrial revolution. Digitalisation - powered by the use of sensors, software, connectivity and big data analytics - is resulting. A good starting point in all of this is the McKinsey digital compass13, which identifies eight business value drivers. These comprise of: • Resource/process • Asset utilisation • Labour • Inventories • Quality • Supply/demand match • Time to market • Service/after sales Within these eight drivers come a total of 26 business levers – the Industry 4.0 applications that are changing the way that manufactured goods are planned, designed, made and repaired. Here we take a look in turn at each of these business applications, explaining what they are and how they are delivering benefit to the manufacturing industry.
APPLICATION OF INDUSTRY 4.0 IN MANIFACTURING
- 1. pag. 18 vers. 1 3.Industry 4.0 in manufacturing: 3.1 Applications driving real business value Manufacturing is entering the fourth industrial revolution. Digitalisation - powered by the use of sensors, software, connectivity and big data analytics - is resulting. A good starting point in all of this is the McKinsey digital compass13 , which identifies eight business value drivers. These comprise of: • Resource/process • Asset utilisation • Labour • Inventories • Quality • Supply/demand match • Time to market • Service/after sales Within these eight drivers come a total of 26 business levers – the Industry 4.0 applications that are changing the way that manufactured goods are planned, designed, made and repaired. Here we take a look in turn at each of these business applications, explaining what they are and how they are delivering benefit to the manufacturing industry. 13 Manufacturing’s next act - Cornelius Baur and Dominik Wee – Mc Kinsey
- 2. pag. 19 vers. 1 3.2 The resource/process levers 1) Smart energy consumption Automated building management systems connect sensors, actuators, controllers and other equipment over one IP backbone, enabling the monitoring of energy usage from machinery, lighting, HVAC and fire safety detectors systems. This information can be combined with broader datasets such as weather forecasting and real-time pricing of electricity and other utilities to give a more centralised and informed view of building performance. 2) Intelligent lots Industry 4.0 is proving to be a key driver of improved process effectiveness inside manufacturing plants. Digitisation of the supply chain through methods such as intelligent lots – characterised by smart information storage in products and pallets – is a good example of that, encouraging the adoption of just-in-time manufacturing. Through use of technologies such as RFID tags and sensors, with cellular 3G, LoRa WAN, NB IoT, Wi-Fi and Bluetooth connectivity, items such as containers, pallets and roll cages can be tracked and monitored prior to delivery and once stored inside the warehouse. This data can reveal information on location, inventory and temperature, and can act as the foundation for advanced technologies such as automated picking. 3) Real-time yield optimisation The networking of industrial assets through sensors, software and wired and wireless connectivity can be used to provide an accurate snapshot of equipment performance at any point in time. With the addition of machine learning, this evaluation can be progressed to become real-time yield optimisation in manufacturing environments – with outputs continually re-calibrated to achieve optimal performance, depending on a host of variable factors. This ensures that industrial assets are always working to maximum efficiency.
- 3. pag. 20 vers. 1 4) Routing flexibility Modern manufacturing requires new levels of plant adaptability as companies look to become more responsive to changing customer needs. This has resulted in the increasing use of flexible manufacturing systems, which use IoT-enabled technologies to help companies become more reactive. Routing flexibility, for instance, represents the ability of manufacturers to cope with factors such as equipment breakdowns to enable continuation of production for any given component. This could be achieved in several ways, such as manufacturing a particular part through different routes or by continuing operation on more than one machine. Routing flexibility is performed by employing hierarchical models of job shop activity, allowing dynamic simulation of production activities. This flexible approach increases factory adaptability by maximising asset utilisation and increasing uptime. 5) Machine flexibility As the implementation of Industry 4.0 methodologies result in manufacturing becoming a more decentralised, autonomous process, so machine flexibility is likely to become an exciting area of development. This trend will be driven by the use of standard interfaces and intelligent infrastructure that enables a far more modular approach to industrial networking and automation, with ‘plug and produce’ modules encouraging the swift reconfiguration of production line facilities. That might
- 4. pag. 21 vers. 1 mean quick-fit data/communication cables for robotic arms, or high-level sensors that interface with Ethernet – eliminating the need for a standard I/O module. Such machine flexibility drives more dynamic manufacturing lines, with much faster and intuitive maintenance. 6). Remote monitoring and control Manufacturing plants are complex ecosystems with hundreds or even thousands of pieces of equipment working seamlessly together for an end result. When you add in the fact that some production plants don’t stand in isolation – being perhaps part of a global network of facilities – then the importance of having visibility of all operational processes becomes clear. IoT-enabled architecture offers this insight, in real-time, from anywhere in the world. Engineers can plug into networked systems through tablet, laptop or mobile dashboards, allowing them to drill into the performance of individual assets. This remote monitoring and control can be used as a primary means of identifying and eliminating bottlenecks and reducing waste. 7) Predictive maintenance The combination of sensors and wireless connectivity means industrial equipment of all kinds can be monitored in real-time, with data analytics powered by machine learning then used to identify trends and anomalies. Instead of performing traditional calendar-based maintenance using the periodic examination of equipment, or adopting ‘if it ain’t broke, don’t fix it’ strategies, engineers can track patterns of failure more effectively, spotting any potential problems before they occur.
- 5. pag. 22 vers. 1 By unleashing the potential of truly predictive maintenance regimes, manufacturers can reap enormous benefit through the eradication of unplanned downtime and associated costs, particularly when it comes to mission-critical pieces of equipment. 8). Augmented reality for MRO The days of maintenance professionals referring to well-worn instruction manuals as they repair machinery in manufacturing plants is becoming a thing of the past. These days, workers are just as likely to be equipped with augmented reality headsets – providing them with a wealth of information such as computer-aided data, diagrams and drawings in their line-of-sight as they go about their tasks. Such capability is being driven by rapid advances in image recognition technologies, computing power, wireless connectivity and the Internet of Things. The benefit of augmented reality-enabled maintenance is clear: immediate access to the right information, delivered in an intuitive way, means employees can perform higher quality work in less time, with reduced errors.
- 6. pag. 23 vers. 1 9) Human-robot collaboration Lightweight, space-saving robots that can operate alongside humans without safety caging are delivering new levels of flexibility within smart factory environments. These collaborative robots, which are fitted with a suite of sophisticated motion, vision and positioning sensors, can perform a host of repetitive and dull jobs, freeing up workers to add value in other areas. 10) Remote monitoring and control Cloud-based remote monitoring answers two primary questions. Where are my assets? And how are they doing? That information – accessible in real-time with the information delivered to dashboards on mobile devices – frees up the worker in the manufacturing plant to an enormous extent. Having information on factors such as temperature, pressure, volume, energy consumption, loaded hours and unloaded hours within instant reach, workers can make smarter decisions based on more reliable data, which improves productivity and increases uptime. 11) Digital performance management Digitalisation provides the opportunity for manufacturers to monitor more than just the performance of machines. Other cost drivers such as materials and manpower can be assessed, and KPIs automated, through digital performance and operations management systems. Such an approach provides a more accurate means of cost allocation across an organisation, and therefore an ability to improve cost calculations and overall financial performance. 12) Automation of knowledge work We are familiar with robots taking over manual tasks that were previously performed by humans. But what if automated technologies could also start performing some aspects of ‘knowledge work’ currently performed by employees? The phrase was first adopted by McKinsey to mean the use of computers to perform tasks that rely on ‘complex analyses, subtle judgments, and creative problem solving’. A report from the McKinsey Global Institute, suggested automation of knowledge work will be high up in its list of top ten disruptive technologies by 2025. This automation of business processes could have a big impact on manufacturing in areas such as procurement, marketing and customers’ services – not necessarily replacing humans, but complementing them in certain roles.
- 7. pag. 24 vers. 1 13) Batch size Industry 4.0 is driving faster and more flexible production, with increasing levels of customisation. Taken to an extreme, seamless production systems should be capable of switching out of serial processes to manufacture a single part just as quickly and efficiently as they can make multiple parts. This ability to produce ‘batch size 1’ provides firms with the opportunity to achieve new levels of mass customisation – effectively ‘making to order’ for individual customers 14) Real-time supply-chain optimisation Supply chains in the era of Industry 4.0 are faster, more flexible, and more transparent. The combination of ubiquitous sensors and connectivity, in combination with big data analytics, means manufacturers can have total visibility of their incoming components, knowing the exact location and condition of each shipment. Automated handling ensures goods are picked and placed in exactly the right spot in the warehouse, while networked machines give feedback on real-time production rates. This creates an optimised loop back to the purchasing department.
- 8. pag. 25 vers. 1 15) In-situ 3D printing The performance of 3D printing equipment has improved dramatically in recent years, with the latest machines able to produce polymer and metal components in a reliable and repeatable manner. This has led to 3D printers escaping the confines of traditional prototyping roles to provide a flexible means of making spares or replacement parts. In this way, manufacturers can reduce the need for warehousing in favour of on-demand production of some components in or near its own facility, providing greater supply-chain resilience. 16) Digital quality management We’ve discussed Industry 4.0, but what about Quality 4.0? It represents the use of big data analytics to deliver a shift in the way that quality is measured. It’s no longer good enough just to measure quality by looking at the integrity of products – digital quality management provides an opportunity to integrate quality throughout the value chain, from supply through to delivery. According to Sparta Systems’ 3 Steps to Quality in the Cloud14 , a move away from old-style quality measurement based on paper records towards adoption of a digital quality management system in the cloud can result in lower costs, better compliance and an improved user experience. 14 https://www.qualitymag.com/ext/resources/files/white_papers/sparta/eBook---3-Steps-to-Quality-in-the-Cloud.pdf
- 9. pag. 26 vers. 1 17). Advanced process control Modern facilities comprise an intricate network of production processes controlling a multitude of factors such as feedstock, temperature and other operational targets. Increasingly, the application of advanced process control is being used to provide a common platform for procedure optimisation through activities such as data collection and analysis and dynamic modelling, with a view to improving quality, increasing throughput and reducing energy use. The software can be used to oversee a broad range of variables such as feed rates, inlet air temperatures and powder moisture, making minute adjustments to improve product quality and boost plant performance. 18) Statistical process control Numbers matter in manufacturing, with a slew of statistics available to provide insight into quality during the production process. This data is captured in real time, and then plotted on graphs with predetermined control limits based on the capability of the process. Variations that occur outside of the parameters provide insight into faltering operations, which can have an impact on quality. 19) Data-driven design to value Ubiquitous sensors are changing the way that discrete manufacturers bring products to market. By connecting sensors to prototypes, and using the data created from testing to get a better idea of real-life operational scenarios, manufacturers are able to develop better-performing products that are more closely aligned to their customers’ needs. This approach, known as data-driven design to value, doesn’t stop with prototypes, though. Sensors fitted to products out in the field continue to provide operational insight, allowing further product refinement. 20) Data-driven demand prediction External factors can have an enormous impact on manufacturers. Sudden changes in consumer spending can, for example, drive a hole through the most careful forecasting and decision making. Enter data-driven demand prediction – cloud-based predictive economic intelligence software that provides a 360-degree view of future demand. In manufacturing environments, the software uses global data, analytics and expert services to identify future threats or opportunities to business performance across finance, sales, marketing and operations.
- 10. pag. 27 vers. 1 21) Rapid experimentation and simulation Software has long since been used to increase the speed of experimentation. Now, though, advanced hardware such as 3D printers are being used to further accelerate time-to-market. The additive process is particularly suited to rapid creation of prototype parts, with the findings looped back into the design and simulation process for further refinement. Previously, manufacturers would have needed to pass each design iteration to its production department, or outsourced the work, causing delay. 22) Concurrent engineering The parallelisation of tasks – often referred to as concurrent engineering – can quicken the product development process. But structuring those parallel processes can prove difficult if collaborative tools aren’t in place as part of wider Industry 4.0 strategies. Engineers can then go about concurrent tasks without fear of accidentally overwriting one another’s files. Once the primary design is in place, the individual subassemblies will adapt. 23. Customer co-creation/open innovation Industry 4.0 is creating a more collaborative environment in manufacturing, which is increasing levels of customer co-creation and open innovation. Through this, the design process can be shared by other parties such as customers or suppliers, or students at a local university. Such an approach
- 11. pag. 28 vers. 1 builds transparency and trust, encourages lateral thinking and often results in more customer- centric products. It can also reduce time-to-market. 24. Predictive maintenance Digitalisation is transforming the servicing of industrial equipment. Under some after-care packages, sensors, software and connectivity enable the manufacturer of machines such as drives and motors to assess the performance of their products in-situ, helping the customer to avoid downtime by predicting problems before they occur. This IoT-enabled architecture is also leading to the creation of new business models based on servitisation, where the end-user effectively leases a service or a solution rather than buying a machine, therefore avoiding large upfront capital cost. This service is based on KPIs such as available uptime, giving the manufacturer clearer visibility of maintenance schedules. The seller of the service, meanwhile, receives predictable revenues. 25. Remote maintenance Traditionally, if a machine failed at a manufacturing plant, the in-house maintenance team was charged with carrying out the repair or calling the OEM to book a service engineer. Now remote
- 12. pag. 29 vers. 1 monitoring of in-situ equipment can be extended to remote maintenance, with experts from the OEM able to perform some tasks without the need for a physical presence. 26. Virtually guided self-service The application of artificial intelligence within manufacturing isn’t restricted to the production function – it’s also making its mark on customer service and after care. Virtually guided self-service denotes the use of virtual agents on company websites, helping customers to resolve problems. 3.3 Disruptive trends Consider an example of each disruptive trend: • Big data. • Advanced analytics. • Human-machine interfaces. • Digital-to-physical transfer. These changes and many others like them are sure to be far reaching, affecting every corner of the factory and the supply chain. The pace of change, however, will likely be slower than what we’ve seen in the consumer sector, where equipment is changed frequently. In coming years, the executives surveyed estimate that 40 to 50 percent of today’s machines will need upgrading or replacement To capture the potential, manufacturers can consider three moves. Primarily, companies can gather more information and make better use of it. With production data now available for the asking, executives rightly wonder about how to begin. Which data would be most beneficial? Which data leakages are causing the most pain? Which technologies would deliver the biggest return on investment for a company, given its unique circumstances? To sort through the choices, manufacturing leaders can use a “digital compass” . The compass consists of eight basic value drivers and 26 practical Industry 4.0 levers.
- 13. pag. 30 vers. 1 Strategists should also take Industry 4.0 into account as they contemplate the company’s future directions—the second way to capture the potential. The traditional manufacturing business model is changing, and new models are emerging; incumbents must be quick to recognize and react to these new competitive challenges. More specifically, executives must consider the following options—and watch for others that may be deploying them. Eighty-four percent of the manufacturing suppliers we surveyed expect new competitors to enter the market soon. • “Platforms,” in which products, services, and information can be exchanged via predefined streams. Think open-source software applied to the manufacturing context. For example, a company might provide technology to connect multiple parties and coordinate their interactions.
- 14. pag. 31 vers. 1 • Pay-by-use and subscription-based services, turning machinery from capex to opex for manufacturers. • Businesses that license intellectual property. Today, many manufacturing companies have deep expertise in their products and processes, but lack the expertise to generate value from their data. Manufacturers might offer consulting services or other businesses that monetize the value of their expertise. • Businesses that monetize data.