Strategies to Reap Operational Benefits from Smart Manufacturing or Industrial IoT

Valentijn de Leeuw
Vice President
ARC Advisory Group

Globally, the transformation of the manufacturing sector has begun. The emergence of the Industrial Internet of Things (IIoT), Industrie 4.0, Smart Manufacturing, and other new approaches and initiatives have the potential to uproot traditional ways of doing business. In a connected and collaborative environment the entire manufacturing and supply chain ecosystem becomes agile, flexible and performs better. The impact of these new technologies is being felt across all industries, including the downstream chemical industry.

Is Smart Manufacturing Or Industrial IoT a Smart Strategy?
Before the shale oil and gas boom in the US, and in Europe after the financial crisis of 2008, the industry GDP was declining. The European Union started issuing manufacturing competitiveness reports to guide policy makers to stimulate the economy. The studies found that manufacturing contributes over-proportionally to exports, a way of bringing liquidity to the region, and increases the resilience to crises and capacity to recover after them. Benchmarking with 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 in manufacturing is proven to stimulate manufacturing growth made the EU innovation program Horizon 2020 receive a focus on manufacturing. Both the program for the discrete and the 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, the most well-known being Germany's Industrie 4.0; however the UK's Catapult program and France's Industrie du Futur are also likely to create economic impact.

For the so-called innovation-driven economies as the World Economic Forum calls them, initiatives that impact product value are the most effective to boost growth; however, cost-related improvements in the domain of process and productivity innovation are also useful. All initiatives mentioned aim to affect these economic factors. Some initiatives, mostly government initiated, such as Horizon 2020 or Industrie 4.0, but also Smart Manufacturing Leadership Coalition (SMLC) are concerned with environmental footprint; and the European initiatives also have social sustainability goals, such as well-being at work, jobs, quality of life, etc. We do then conclude that these initiatives are a smart strategy for growth and societal well-being.

Other major initiatives
New information technologies have been applied to optimise individual unit processes in factories, but Smart Manufacturing (SM) systems that integrate manufacturing intelligence in real-time across an entire production operation are rare in large companies, and virtually non-existent in small and medium size organisations. The SMLC was founded as an industry initiative in the US to overcome the costs and risks associated with commercialisation of Smart Manufacturing(SM) systems, primarily oriented towards the process industries. A few years later, the Industrial Internet Consortium was founded to accelerate the development, adoption and widespread use of interconnected machines and devices and intelligent analytics. Also China and India started their initiatives.

To create clarity we make the following distinction between smart manufacturing and Industrial IoT: Smart Manufacturing is more encompassing and includes all methodologies, processes and technologies that substantially improves the outcome of manufacturing, be it in the form of product value, quantity or quality, or in the form of productivity or reduced environmental footprint. There are two main sources of improvement: advanced manufacturing that involves improvements in fundamentals, such as physics, or chemistry, such as photonics, or chemical nanostructures - engineering science, such as modular production technology and intensification, additive manufacturing or advanced forming. The second group is related to IT, communication or automation related technologies, among which we find internet enabled applications, often referred to as Industrial IoT. For example, advanced or model-based process control, often applied in refining and petrochemicals, could be applied to a larger degree in smaller chemical processes. Industrial Data Analytics have a great potential for the industry.

Application Examples
Current manufacturing processes and technologies can be augmented with smart manufacturing or Industrial IoT, and create incremental value quickly. In Europe, ThyssenKrupp is one of the integrated companies that consistently implements Industrie 4.0 across domains and operations. The company was able to increase the throughput of a plant producing intermediate products (transforming steel slabs into rolled steel), by applying pull manufacturing and coordinating manufacturing and logistics with real-time information. While pull manufacturing is not new, it a great opportunity for many industry sectors to apply it. Fortunately Industrie 4.0 creates momentum to do this.

In specialty and performance chemicals as well as life sciences, the NAMUR organisation is well known for their vision and standards, mostly in automation and IT for manufacturing. In supply chain coordination, the contribution by Dr Poetter from Bayer Technology Services in 2013, anticipated the use of wireless and/ or internet connectivity to faster order raw materials to variable production orders.

A supply chain operating network such as Elemica could perfectly play its role in such a scenario to make the link between supplier and manufacturer to find the raw material that could arrive earliest at the production site, when the supplier has IoT connectivity with its logistics network. The latter could then directly coordinate via Industrial IoT connectivity with the manufacturer to reserve a docking station and create an unloading appointment.

In the near future, modular production technology - miniature chemicals plants in a container - will make supply chains much more agile than today's . But to plan and optimise them, they become also more complex. Manufacturing capacity can then be very fast and flexibly scaled up and down , and production units can 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. As a result of these developments, ARC Advisory Group expects that supply chains will become far more agile, dynamic and complex over the coming years . Not only will the number of permutations of possible routings become orders of larger magnitude, also the tighter supply chain network integration will cause important supply and demand volatility that should be damped with high quality supply chain coordination and professional operation of supply chain operation networks (SCON). New developments in discovery, predictive and prescriptive analytics applied to supply chain network regulation and optimisation are very promising to assist the operators. As these can operate in-memory and in the cloud, they take out 'latency' of previousgeneration applications and can compensate for supply chain volatility.

Industrial data analytics, in combination with the internet is particularly powerful. When data scientists add their complex event processing techniques and statistical methods to process structured (eg, timeseries) data and unstructured data such as operator logs, to the modelling techniques based fundamental and engineering science, great results can be obtained in the domains of production, quality, energy or assets. Production analytics at Dow or Sabic UK have proven to create several millions of dollars in operating efficiency. Other companies use quality analytics to monitor maintenance needs of quality testing techniques, or to determine the useful life span of rotating equipment before maintenance, resulting in improved uptime, increased first-quality production and major cost reductions.

Engineering humans into the system
Industrial IoT, supply chain optimisation and analytics can help the operators best, when they allow them to focus on problem solving by providing easily interpretable analyses within context and free them from repetitive tasks. Then the operator can work at his best, and assess, delegate, interpret, judge and decide with consciousness and skill. As each decision involves emotional processing in the brain, for example when it concerns ethics, we will continue to need operators as part of our systems.

ARC recommends users to take time for planning the future and set radical improvement targets in product, process and supply chain performance, as Industrial IoT solutions have shown to be able to provide those. These goals should be aligned with the users' strategies, and a roadmap should include quick wins - as incremental solutions are available today - as well as a long term plan. Since the smart manufacturing landscape changes quickly these roadmaps must be updated regularly.