By Afaq Ahmed
Leading companies are investing in industry 4.0 and leveraging its emerging technologies to make informed decisions in a near real-time environment for the benefit of the entire value chain. As a result, they have experienced breakthrough improvements in profitability, customer satisfaction, time-to-market, efficiency, quality, safety, cost, energy etc. Quality and operational excellence professionals have a significant opportunity to lead this transformation for the benefit of their organizations.
Let’s start by looking back at previous industrial revolutions and their effect on quality. Prior to the start of the industrial era was the craftsmanship era. In this era, the master craftsman was responsible for the quality of his products. He dealt with customers face-to-face to address cost, delivery, and quality issues. The first industrial revolution started somewhere between the end of the 17th century and early 18th century in Great Britain—with the mechanization of the textile industry using steam and water as energy sources. The mechanization led to a shift in mentality from quality to quantity of work—and quality suffered as a result.
The second industrial revolution started in the early 19th century by means of electricity. The mass production system led to further division of labor, which saw quality deteriorate even more.
The third industrial revolution started in the early 1970s. The main theme of this era was the automation of production machinery using electronics, information technology, and communication technology. This is the era of rapid advancements in the quality field. The big bang in quality started with statistical quality control and grew into a thriving engineering discipline. Several quality philosophies, methods and techniques such as TQM, Kaizen, quality circles, Plan-Do-Check-Act, quality tools, ISO 9000 standards, the Malcolm Baldrige criteria for performance excellence, the EFQM award, operational excellence, six sigma methodology, and lean manufacturing were applied to achieve excellence.
The industrial landscape is changing once again and this time changes are dramatic due to rapid speed. Companies who will take advantage of this will be leaders in their industry. The fourth industrial revolution has many names such as industry 4.0, digital transformation, digitization, smart factory etc.
So what is industry 4.0? It is hard to find an exact definition. This is because it’s an initiative and not a scientific term with an exact definition. So what does this initiative entail? It entails digital integration of advanced technologies with objects and humans along the entire value chain to achieve cutting edge competitiveness. Advanced technologies include state-of-art communication/information systems and computational elements such as big data, advanced analytics, artificial intelligence, virtual and augmented reality, machine learning etc. Objects include products, machines, computers, cell phones, tablets, advanced robotics, self-guided vehicles, sensors, 3-D printing, etc. Other key terms associated with the industry 4.0 initiative are Internet of Things (IoT), Industrial Internet of Things (IIoT), and Cyber Physical Systems (CPS). IoT means network of connected objects. IIoT means industrial network of connected devices such as production assets. Cyber physical systems are interacting networks of physical and computational elements.
The foregoing elaboration of industry 4.0 is necessary to understand how industry 4.0 technologies are connecting machines, data, and computational elements with people to make smart decisions. Let’s look at some examples of the application of industry 4.0 technologies to quality.
Manufacturing processes control and improvements
I led many Statistical Process Control (SPC) implementation projects. One such project was implementing SPC on a machining line consisting of several different types of drilling machines.
The process involved many variables, such as:
- Drilling machine (design, vibration, speed)
- Drill (geometry, size, wear)
- Material (physical and chemical properties)
- Cutting fluid, if used (flow rate, type)
- Machinist (skill, experience)
One of the main goals of SPC is to identify and eliminate special causes acting on the process to achieve statistical control and subsequently calculate the process capability (Cp, Cpk etc.). If the capability is below customer requirements, then actions were taken to reduce variation due to common causes by understanding the effects of underlying process variables on process outputs through the application of advanced statistical techniques, such as design of experiments (DOE).
Usually, after achieving the required process capability, gains are anchored by monitoring the process using a control chart. Since processes are dynamic in nature, this presented an uphill task to maintain the control of the process due to insufficient integration of relevant process information and end-to-end data processing. Other constraints included:
- Degree of confidence on results—since it was dependent on sample size
- Limitation of software packages in applying the best predictive model to the real-time data to predict future outcomes
- The iterative application approach to DOE, in which one broad experiment is split into several experiments to build-on learning from each successive experiment
The process data collected by measuring quality characteristics of several machined parts over a time period will be mere numbers. Without context it does not breed knowledge and wisdom. If this data is plotted on a variable control chart, the hidden information in the data can be exposed and used to improve the process. For example, a study of a variable control chart may exhibit that there are points outside of control limits. This observation or information can now be synthesized with statistical theories. Using these theories, a conclusion can be drawn that the machining process is not in control and there are special causes acting on the process. The knowledge gained about the process can now be used to take actions to achieve specific goals. These actions may include gaining deeper understanding of special causes of variation acting on the process and eliminating them to achieve statistical control. Industry 4.0 technologies—such as machine learning, big data, advanced analytics, and artificial intelligence—have now made it possible to predict the outcome of a process and take actions to prevent undesirable outcomes.
Industry 4.0’s technology machine learning (ML) has the speed and capabilities to perform functions in real-time that were not possible before, such as, finding an accurate predictive model, learning from the real-time process data, providing an accurate prediction of future outcomes, and enabling people to take actions to prevent failures. ML can predict when a cutting tool’s life is reduced to a point where it can either catastrophically break, damaging the part in-production, or it can no longer produce good quality parts. This technology is applied both in manufacturing and in service industries. Some examples of its applications are: predictive machine maintenance, customer’s segmentation, improved accuracy of supply chain forecasting, optimization of navigational routes, inventory optimization, efficient utilization of resources, safety, optimizing shop floor operations, improving quality, customer service etc.
Supply Chain Management and Improvement
Industry 4.0 technologies such as real-time streaming of production/quality control data, cloud platforms, blockchain, and advanced analytics can be effectively used to manage and improve supplier performance. Traceability of raw materials and parts especially for aerospace, food, and oil companies is very critical and always have been a concern. For example, valves are one of the most commonly used commodity in the oil and gas industry. The performance of valves is dependent on the quality of the cast body. Even a very minute leakage of flammable gases from the valve body can cause fire and destruction. Valve’s castings are usually supplied by lower tier suppliers and therefore it is difficult to trace back to the sources of these castings. Using blockchain technology, it is now possible to trace back to the source of castings, thus improving the supply chain visibility.
The transformation journey to the fourth industrial revolution provides a plethora of opportunities for organizations to achieve operational excellence, but like all paradigm shifts, there are challenges en route. However, with careful planning, monitoring and management, the road to digital transformation can be fairly smooth.
Below are highlights of some major challenges:
- Getting top leadership buy-in is the foremost challenge in digital transformation. Generally, there is not much interest shown by leaders in industry 4.0 transformation because of issues that include cost, cybersecurity risks, understanding of benefits of IoT, and apprehension due to formidable challenges in the implementation. These hurdles can be overcome by educating leadership on the ginormous opportunities that industry 4.0 can create for the business, as well as an accurate breakdown of ROI, sound transformation strategy, and application of risk management practices to deal with cybersecurity issues.
- Another challenge lies with the quality and relevance of data to be used for analytics. The collected data is either inappropriate or inaccurate resulting in flawed analytics, which cannot be used for effective decision making. To benefit from industry 4.0 technologies, start by asking what are the focus areas of the organization? What data should be collected, analyzed, and used for real-time decision making?
- There are significant challenges to integrating industry 4.0 technologies with the existing IT infrastructure and physical devices, due to compatibility issues between legacy machines/data capturing devices, and gateways to digital platforms. Implementation teams can overcome such barriers by closely working with industry 4.0 technology service providers to identify compatibility issues as a part of implementation strategy and finding suitable solutions.
- Organizations implementing industry 4.0 technologies and their customers have major concerns about cybersecurity. Breach in IoT networks can cause sensitive data to leak or interrupt operations. Careful planning by the transformation team in collaboration with industry 4.0 technologies service providers can ensure the security of networks and safety of sensitive information. Security policies such as authorization to access sensitive data and implementation of risk management systems shall be considered during the strategy formulation stage. An emergency preparedness process and a business continuity management process shall be put in place to swiftly address security breaches.
The trend toward the digital transformation of organizations is not futuristic, it’s all happening right now in many leading companies. It is the harbinger of an emerging manufacturing economy. There is a wealth of information hidden in the big data generated as a result of the digitization of processes. This real-time information can be transformed into knowledge for people to take actions to achieve breakthrough improvements across the value chain. Every organization needs to carefully evaluate the benefits and challenges that industry 4.0 has to offer before embarking on the transformational journey. Quality professionals who embrace this shifting trend will add significant value to their organizations.
About the author
Afaq (Fayzee) Ahmed is the Principal Consultant and Trainer at TORQUE in Irvine, California.
His core skill set consists of operational excellence design and implementation, management systems implementation, business performance improvements, assessments, and supply chain improvements.
He has worked in wide variety of industries (aerospace, automotive and others) in the US and 15 years for Saudi Aramco. He earned a master’s degree in mechanical engineering from the University of Southern California, Los Angeles.
He is a senior member of ASQ, is an ASQ-certified manager of quality/organizational excellence, quality engineer and quality auditor, and an Exemplar Global-certified skill examiner and quality management systems lead assessor. He can be reached at email@example.com. www.torque-usa.com.