Following the 2008 financial crisis (and prior to the shale oil and gas boom in the U.S.), we experienced a period of declining industry GDP. In response, the European Union (EU) started issuing manufacturing competitiveness reports to guide policymakers to stimulate the economy. The studies found that manufacturing contributes over-proportionally to exports, providing a way to bring liquidity to the region, speed recovery, and increase resilience to future crises. Benchmarking against countries such as Germany with a higher-than-average industry GDP demonstrated that the average level of industry GDP could be increased.
Finally, the fact that innovation is a proven way to stimulate manufacturing growth made manufacturing the focus of the EU’s Horizon 2020 innovation programs. The programs for both the discrete and process industries are set up as private-public partnerships to increase ownership by industry and multiply the public investment. Europe’s strategy inspired the member countries to set up their own programs in line with national needs. Germany's Industry 4.0 is by far the best known of these, but the UK’s High Value Manufacturing (HVM) Catapult program and France’s Industry of the Future are also likely to create economic impacts.
For the so-called innovation-driven economies as labeled by the World Economic Forum, initiatives that impact product value are most effective at boosting growth. However, cost-related improvements in process and productivity innovation are also useful.
Government initiatives such as Horizon 2020, Industry 4.0, and to a lesser extent the U.S.-based Smart Manufacturing Leadership Coalition (SMLC) are concerned with reducing manufacturing’s environmental footprint. The European initiatives also have social sustainability goals, such as well-being at work, job creation and quality of life. In general, these initiatives all represent smart strategies for growth, environmental conservation and well-being.
Smart manufacturing or Industrial Internet of Things?
So what’s the difference between the terms “smart manufacturing” and Industrial Internet of Things (IIoT)? Smart manufacturing is more encompassing and includes all methodologies, processes and technologies needed to substantially improve the outcome of manufacturing, be it in the form of product value, quantity or quality; productivity; or reduced environmental footprint. There are two main sources of improvement:
- Advanced manufacturing, which involves improvements in fundamental science or engineering. Examples include scientific advances such as photonics or chemical nanostructures, or engineering improvements such as modular production technology, additive manufacturing or advanced forming.
- Smart manufacturing, which includes information, communication or automation technologies applied to production processes and assembled into smart manufacturing systems.
Among these technologies we not only find the connected devices, systems, applications and diagnostics that we refer to as IIoT, but also established technologies that have potential in new domains and industrial sectors. For example, autonomous production optimization applied in the process industries could make its way into discrete manufacturing, and lean or pull manufacturing could be introduced into the process industries.
Current manufacturing processes and technologies can be augmented with smart manufacturing or IIoT to quickly create incremental value. In Europe, one of the integrated steel companies consistently implements Industry 4.0 across domains and operations. The company was able to increase the throughput of a plant by applying pull manufacturing and coordinating manufacturing and logistics with real-time information. Though pull manufacturing is not new, there is great opportunity to apply it in new industry sectors. Industry 4.0 creates momentum to do this.
In seven demonstration projects running for more than five years at several major chemical, polymer and life sciences companies, the EU-sponsored F3 Factory project demonstrated its ability to reduce both capex and opex significantly—in the 20-40 percent range. This was accomplished by simplifying and accelerating plant construction, skipping upscaling, and improving sustainability dramatically. The solution was to create smaller, modular units with proportionally high production capacity.
These miniaturized, highly efficient chemical, pharmaceutical or polymer plants in a container will make supply chains much more agile. But planning and optimizing them is a complex procedure. With these modular production units, manufacturing capacity can be quickly scaled up or down to increase flexibility. Production units can also be shipped to sites close to raw material production or consumers. Early examples today are on-site production of liquefied air and dangerous gases as feedstock for downstream production. A very recent development in methane cracking that produces hydrogen and carbon without producing carbon dioxide is planned to be industrialized using modular production technologies. This would provide CO2-free energy from fossil fuels at industrial scale for a competitive price.
In another example, a large food and beverage company created highly standardized, modularized packaging lines. The company employed the ISA 88-based PackML standard to dramatically reduce engineering and integration efforts when implementing new machines. Other companies are looking to this example to learn how to set up modularized automation to realize modular, skid-mounted “plug-and-produce” production units.
In another example of applying advanced manufacturing, the new Singapore plant of a large pharmaceutical company decreased the time to produce its active ingredient from 12 months to just six hours by transforming a batch process into a continuous process.
Humans, of course, are the key element in any manufacturing strategy. At their best, advanced manufacturing technologies, applications and approaches can free plant personnel at all levels from repetitive tasks and provide appropriate tools; timely, in-context information; and easily interpreted analytics to help solve problems and entirely avoid or minimize the impact of issues.
>>Valentijn de Leeuw is vice president of consulting at ARC Advisory Group’s European organization. His experience includes knowledge of unit processes, simulation and modeling, and business practices using application software designed for manufacturing operations. He has a Ph.D. in technical sciences from Delft University of Technology in cooperation with École Nationale Supérieure des Mines de Paris and IFP, and a masters in chemistry from Utrecht State University, The Netherlands.