The Future of the Chemicals and Plastics Industry: Emerging Technologies, The Great Reset, Sustainability, and Post COVID-19 (Part 1)

 

Hello and welcome to this blog post about the future of the chemical and in particular the plastics industry. I do not have a crystal ball to predict the future, however we can envision it based on the current megatrends. This is my attempt to do so and since this is a broad topic, I split the post in two major parts:

Part 1: the present time plus 3 years (2021 and post COVID-19 phase) and

Part 2: the near future: 2025 – 2035

Let us get started with the first part.

Emerging technologies

In their latest foresight report, Lux research [1] presented 12 emerging technologies which will have great potential to strive after the COVID-19 Pandemic time. Among the 12 technologies, five are in particular important for the chemical and plastics industry:

-       Advanced plastics recycling

-       Materials informatics (the use of machine learning in materials development)

-       Additive manufacturing (3D printing)  

-       Synthetic biology

-       Digital sales platforms

Advanced plastics recycling is an important cornerstone for the plastics industry to become a circular economy. Plastic waste will be a viable feedstock available in a decentralized way. The commoditization of customization is achieved by digital material platforms which decrease inefficiencies and minimize the difference between a commodity and a specialty business.

Growth is important, however qualitative growth is much more relevant than growth over consuming more things.

Emerging Technologies to watch in 2021 and beyond (The Future of Chemical and Plastics Industry).

The Great Reset

In the year 2020, Klaus Schwab, founder of the World Economic Forum, presented the three core components of the Great Reset [2, 3]. He is referring to the post COVID-19 time which offers an opportunity to "reset and reshape" the world. The idea is to align the world more with the United Nations 2030 Sustainable Development Goals (SDGs).

What are the three core components [2]?

1.    Stakeholder Economy: aim is to create conditions for a stakeholder capitalism. This includes the policy improvement on taxes, regulations, fiscal policies, and trade to obtain fairer outcomes for stake- and shareholders.

2.    Building green urban infrastructures and creating incentives for businesses to improve their environmental, social, and governance (ESG) metrics.

3.    4th Industrial Revolution: how to use the innovations of the 4th Industrial Revolution for the public good.  

Globally our population is growing. The middle classes are expanding too and more people will escape poverty, which is very positive for all of us. The demand in chemical and materials is expected to quadruple by 2050 and to fulfill the Paris climate agreement, the chemical industry needs to shift to a net-zero emission industry. Reaching such aims is facilitated by using low carbon emitting technologies in the production plants.

Solar electrification of chemical operations is ongoing. For example, specialty material producer Solvay is with 81.4 MW installed capacity among the top ten companies in the US for solar adoption [4].

Top 10 Players in the US using solar energy to power their plants [4].

Sustainability

With the Green New Deal agenda on the start in the European Union, the focus on the UN Sustainable development goals (SDGs) will further increase [5].

Pressure on the chemical and plastics industry will increase to accelerate the transition towards circular business and operation models. Together with a reduction of the environmental footprint of the operations to protect biodiversity on a global scale. Downside may be more regulations set by the EU and directing certain sectors into the wrong direction [6]. Furthermore, the Green New Deal agenda is in contradiction to the Jan Tinbergen Rule of Thumb which states that a political instrument cannot efficiently achieve two goals at the same time [7, 8].

Conclusions first part

The rebound of the economy has already started, especially in Asia. Worldwide companies try to catch up and profit of the rebound. The COVID-19 pandemic accelerated the digitalization of chemical business operations. Furthermore, investment companies such as BlackRock increased the focus on sustainable investing. This means that capital will flow more and more to companies which try to solve the world’s biggest challenges. The combination of the steps shown above will allow the plastics industry to be part of this trend too. 

In the second part we discuss a possible transformation of the plastics industry in terms of business models and how it can keep up with the growth of tech companies in the long run.

Thanks for reading and #findoutaboutplastics

Greetings,

Herwig Juster

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Literature

[1] https://www.luxresearchinc.com/hubfs/2020%20Executive%20Summaries/1%20-%202020%20Executive%20Summaries%20-%20Press%20Versions/Foresight%202021%20Executive%20Summary%20-%20press.pdf

[2] https://www.weforum.org/agenda/2020/06/now-is-the-time-for-a-great-reset/

[3] https://www.weforum.org/agenda/2020/08/building-blocks-of-the-great-reset/

[4] https://solarmeansbusiness.com/

[5] https://www.nytimes.com/2019/02/21/climate/green-new-deal-questions-answers.html

[6] https://www.welt.de/wirtschaft/article226970437/Folgen-des-Green-Deal-Top-Oekonomen-warnen-vor-den-Gefahren-des-Klima-Primats.html

[7] https://www.investopedia.com/terms/j/jan-tinbergen.asp

[8] https://www.thehindu.com/opinion/op-ed/in-economics-what-is-tinbergen-rule/article24332615.ece#:~:text=This%20refers%20to%20a%20rule,precludes%20the%20achievement%20of%20others.

[9] https://www.blackrock.com/us/individual/investment-ideas/sustainable-investingIdea collection

“Data is the new plastic” - Data Algorithms in….Plastics Injection Moulding

Hello and welcome back to my “data is the new plastic” blog post series.

In this post I will scrutinize how plastics processing companies, in particular injection moulding operations increase efficiency using algorithms. The main question evolves around which algorithms can we use to improve our processing operations and which data we have to provide to make these operations successful?

“The goal is to turn data into information, and information into insight.” – Carly Fiorina, former executive, president, and chair of Hewlett-Packard Co.

Since decades, quality departments collected data from injection moulding machines for a production run and ensure the defined set of quality levels. Once quality is fulfilled, data will be stored and is available in case of complains. There is much more potential in the data which is stored over time. Algorithms (Wikipedia definition: “a process or set of rules to be followed in calculations or other problem-solving operations, especially by a computer”) enable the analysis of large data sets. They allow a descriptive or predictive (future) look at your production facility. Moulders are under constant cost pressure, especially in the Automotive industry. Therefore, applying data analytics might bring you the 2-3% improvement overall needed to keep efficiency innovation alive and competition away. Looking from a broader angle, every task in business operations has a potential digital component. Average business tasks will disappear into the cloud.

Based on the DataIkuBlog post , I made a table to give you an overview on algorithms used in data mining:

Following a list of scientific publication topics on how algorithms can be applied in plastics processing is shown:

1) Taguchi method: optimization of injection moulding machine parameters and quality characteristics [1-3].

2) Artificial Neural Networks: process modelling, parameter optimization and quality production [4-8].

3) Fuzzy logic: predict flash of mixed materials [9].

4) Generic Algorithms in injection moulding: optimize the parameters of the process [10].

5) Response Surface Methodology: create a non-linear model of the process which is used to control process [11-12].

6) Rule based expert systems and case based reason techniques: design plastic injection processes [13-14].

7) Linear Regression Models: predict production [15].

8) Support Vector Machines: quality monitoring of the process [16]

Now a quick reality check: how does it look like in industrial practice?

Most of the algorithms listed before are not yet implemented completely in real industrial environment, however current innovation speed together with the Open Platform Communication (OPC) standard (Euromap 77) is changing this. Data will be more accessible and turned into valuable information used to improve efficiency, predictions and decisions.

A positive outlook for processors, or putting it all together:

Analyzing your business data will lead to a massive productivity advantage and as such more revenue for your business. Extra capital can be used then to expand other fields of the Industrial Internet of Things. Over this route, new digital business models are generated over which you can attract more customers. Platform business models, especially B-2-B platforms (“Platform as a Service”) show here an enormous potential in plastics industry. They need to be open and independent. Currently more and more B-2-C platforms are sweeping over and interest in B-2-B platforms is increasing. At the end, new business models should be explored together by using deep technology (artificial intelligence to just name one). Plastics companies should at least in a theoretical experiment think how a platform could look like. It could look like this:

Example of platform economy in plastics industry

There will be main customers such as OEMs and Tier-1s on one hand and all kind of plastics business such as tooling, moulding and service providers on the other. The latter can function in a complementary manner. For example, when a part from a moulding company is ordered, a simulation and a suggestion for optimal material selection can be provided by the first when possible or by a complementary external party connected to the main platform (comprehensive global scale). In this way, customer service will be stronger than ever. Once again, OPC will be the enabler of such services.  

Data analytics as interface between IT and plastics engineering will enable companies and customers to strive their ventures. On the other hand, neglecting data analytics can be fatal for business. First changes need to be done now.

“If we have data, let’s look at data. If all we have are opinions, let’s go with mine.” – Jim Barksdale, former Netscape CEO

“Without data you're just another person with an opinion.” - W. Edwards Deming

Thanks for reading and till next time!

Herwig Juster

New to my Findoutaboutplastics Blog – check out the start here section.

Literature:

[1] Chung-Feng J. K., Te-Li S, “Optimization of multiple qualitycharacteristics for polyether ether ketone injection molding process”,Fibers and Polymers, Dec 2006, Volume 7, Issue 4, pp 404-413.

[2] Chung-Feng J. K., Te-Li S., “Optimization of Injection Molding Processing Parameters for LCD Light-Guide Plates”, Journal of Materials Engineering and Performance, Oct 2007, Volume 16, Issue 5, pp 539-548.

[3] Oktem, Tuncay Erzurumlu, Ibrahim Uzman, “Application of Taguchi optimization technique in determining plastic injection molding process parameters for a thin-shell part”, Materials & Design, Volume 28, Issue 4, 2007, Pages 1271–1278.

[4] Jie-Ren S., “Optimization of injection molding process for contour distortions of polypropylene composite components by a radial basis neural network”, The International Journal of Advanced Manufacturing Technology, April 2008, Volume 36, Issue 11-12, pp 1091-1103.

[5] Sadeghi B.H.M., “A BP-neural network predictor model for plastic injection molding process” Journal of Materials Processing Technology, Volume 103, Issue 3, 17 July 2000, Pages 411–416.

[6] Ozcelik B., Erzurumlu T.,” Comparison of the warpage optimization in the plastic injection molding using ANOVA, neural network model and genetic algorithm”, Journal of Materials Processing Technology, Volume 171, Issue 3, 1 February 2006, Pages 437–445.

[7] Shi F., Lou Z.L., Zhang Y.Q., F. Shi, Lu J.G., “Optimisation of Plastic Injection Moulding Process with Soft Computing”, The International Journal of Advanced Manufacturing Technology, June 2003, Volume 21, Issue 9, pp 656-661.

[8] Chen W.C, Tai P.H., Wang M.W, Deng W.J., Chen C.T. “A neural network-based approach for dynamic quality prediction in a plastic injection molding process”. Expert Systems with Applications, Volume 35, Issue 3, October 2008, Pages 843–849.

[9] Zhu J., Chen J.C., “Fuzzy neural network-based in-process mixed material-caused flash prediction (FNN-IPMFP) in injection molding operations” The International Journal of Advanced Manufacturing Technology, June 2006, Volume 29, Issue 3-4, pp 308-316.

[10] Shi F., Lou Z.L., Zhang Y.Q, Lu J.G, “Optimisation of Plastic Injection Moulding Process with Soft Computing” The International Journal of Advanced Manufacturing Technology. June 2003, Volume 21, Issue 9, pp 656-661.

[11] Mathivanan D., Parthasarathy N.S. “Prediction of sink depths using nonlinear modeling of injection molding variables“, The International Journal of Advanced Manufacturing Technology, August 2009, Volume 43, Issue 7-8, pp 654-663.

[12] Mathivanan D., Parthasarathy N.S., “Sink-mark minimization in injection molding through response surface regression modeling and genetic algorithm” The International Journal of Advanced Manufacturing Technology, December 2009, Volume 45, Issue 9-10, pp 867-874.

[13] Kwong C.K., Smith G.F., “A computational system for process design of injection moulding: Combining a blackboard-based expert system and a case-based reasoning approach” The International Journal of Advanced Manufacturing Technology, 1998, Volume 14, Issue 5, pp 350-357.

[14] Shelesh-Nezhad K., Siores E. “An intelligent system for plastic injection molding process design” Journal of Materials Processing Technology, Volume 63, Issues 1–3, January 1997, Pages 458–462.

[15] DasNeogi P., Cudney E., Adekpedjou A., “Comparing the Predictive Ability of T-Method and Cobb-Douglas Production Function for Warranty Data”, ASME 2009 International Mechanical Engineering Congress and Exposition (IMECE2009) , November 13–19, 2009 , Lake Buena Vista, Florida, USA.

[16] Ribeiro B., “Support vector machines for quality monitoring in a plastic injection molding process”, IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, Aug 2005, Volume 35, Issue 3, Pages 401 – 410.

[17] https://www.featuredcustomers.com/vendor/ge-digital/customers/toray-plastics-inc

“Data is the new plastic” - Data algorithms in ….. Plastic Materials Selection

Hello and welcome to this blog post entitled “data is the new plastic”.

“In God we trust, all others must bring data.” W. Edwards Deming

As data science evolves throughout the plastics industry, materials, moulds and machines information come closer together by means of digital services. Following, are the three main business interfaces where data science is playing or will play a crucial role:

1. Resin suppliers and their customers: Better decision making together with the customer with regards to material proposal and selection (optimal material at the right time for the right price).
2. Plastics processing: Increased efficiency and streamline processes. For example, Toray Plastics optimized their manufacturing operational performance by using GE Digital’s Big Data Analytics [9].
3. Product designers and tool makers: Better prediction of which applications will evolve due to touch points with customer (e.g. industrial marketing offers by GadflyZone [10]) and better product design using data and simulation like CFD and FEA

As data analytics and data mining operations find their way through the plastics business value chain, business operations such as marketing, sales, social selling and customer relationship management (CRM) evolve into new disciplines - Plastics Information Technology (PIT). Data-driven marketing actions ensure a better customer journey.



Figure 1: Data generated from polymeric materials, machines and moulds is the foundation for Plastics Information Technology.

In this post, I focus on point 1) from above and thus on data driven decision making on the optimal material for a certain application. I will not cover the conventional selection methods, since most of us are familiar with them. Especially for automotive industry, I would like to refer to Paul F. Kusy’s material selection guide [11]. Basically, we always have a material screening phase and a material comparing phase. In the first one, elimination of the obvious unsuitable materials takes place. The second phase involves a multi-level decision process which finishes with a material decision step.

Al-Oqla et. al. [8] explains that the design of an engineering part has three elements: specifying a shape, selecting a material, and choosing a proper manufacturing process. The “selecting the material” element will be supported more and more by algorithms.

Figure 2: Overview of the data science approach for polymeric materials selection.

There are three main groups of algorithms which can be applied to the screening phase of materials selection and comparison phase (Figure 2):

1) Artificial intelligence (AI) methods: AI is used for solving complex problems by implementing intelligence for processing unstructured knowledge. Looking back, an engineer could inform himself in engineering handbooks, scientific journals and own experience. As the amount of polymeric materials and their compounds are increasing, an efficient managing of engineering data is necessary. Here, AI methods can play a key role. AI can access over the network (and stored in the cloud) all kind of information and does not need to make mistakes to learn. Following are some methods used in AI for materials selection:

• Computer-aided materials selection systems [1]
• Knowledge-based systems [6]
• Case-based reasoning [2]
• Neural networks [3]

2) Genetic algorithms combined with neural network: Connecting the desired mechanical properties with the polymer properties is enabled by genetic algorithms and neural network. It was found that neural network reduces the time and costs for selecting the optimum polymeric material. Al-Oqla et al. [8] has shown this by applying genetic algorithms and neural network for selecting the optimum composite material for different applications. In this case, fuzzy oriented models were used to select the relationship between performance requirements and material properties.

3) Optimization: three optimization techniques are shown for enabling better materials selection using an optimization algorithm.

• Mathematical programming [5]
• Computer simulation [7]: Simulation tools like for example Moldflow and Sigmasoft have an extensive database on materials which allow you to test materials of different costs in a virtual domain.  It is possible to couple the results to a finite element analysis to simulate the mechanical performance too.
• Genetic algorithm [2]

The result of your screening process will be two to three materials which can do the job. Following, is the second phase of the material selection. This itself is a multi-level decision making process to come to a definite outcome. There are the following methods available which are supported by algorithms:

• Multiple attribute decision making methods (MADM) [12]: Focus on making the decision based on the attributes of the material which can be expressed as exact numeric values, Boolean values or just as ranking (best-good-bad).
• Multiple objective decision-making methods (MODMM) [12]: Focus on making a material decision considering different desirable objectives. The design engineer has the objective to develop the lightest and/or cheapest solution considering the boundary conditions.
• Polymer processing decision solver [4]: The main element of this program is a decision matrix which has as input criteria and options. As a result, the user will get a weighting of the options with points. The options are the different polymers to be decided upon. 

Let’s start harvesting insights from your data generated by our plastics products and get a competitive edge by proposing the optimal material!

Thanks for reading and till next time!

Greetings, Herwig Juster

New to my Find Out About Plastics Blog – check out the start here section.

Literature:

[1] Jahan, A., Ismail, M. Y., Mustapha, F., & Sapuan, S. M. (2010a). Material selection based on ordinal data. Materials & Design, 31, 3180–3187.

[2] Amen, R., & Vomacka, P. (2001). Case-based reasoning as a tool for materials selection. Materials & Design, 22, 353–358.

[3] Goel, V., & Chen, J. (1996). Application of expert network for material selection in engineering design. Computers in Industry, 30, 87–101.

[4] H. Juster, Strategic decision-making in the product development of polymer components, Master thesis, University Leoben, Austria, 2015 

[5] A. Jahan, M.Y. Ismail, S.M. Sapuan, F. Mustapha, Material screening and choosing methods – A review, Materials & Design, Volume 31, Issue 2, 2010, Pages 696-705, ISSN 0261-3069, https://doi.org/10.1016/j.matdes.2009.08.013.(http://www.sciencedirect.com/science/article/pii/S0261306909004361)

[6] Sapuan, S. M. (2001). A knowledge-based system for materials selection in mechanical engineering design. Materials & Design, 22, 687–695.

[7] SIGMASOFT® Virtual Molding: The best Ally in Resin Selection

[8] AL-Oqla, F. M., & Sapuan, S. M. (2015a). Polymer selection approach for commonly and uncommonly used natural fibers under uncertainty environments. Journal of the Minerals Metals and Materials Society, 67(10), 2450–2463.

[9] https://www.featuredcustomers.com/vendor/ge-digital/customers/toray-plastics-inc

[10] http://gadflyzone.com/

[11] Kusy, P.F., Plastic Materials Selection Guide, 1976

[12] AL-Oqla, F. M., & Omar, A. A. (2015). An expert-based model for selecting the most suitable substrate material type for antenna circuits. International Journal of Electronics, 102, 1044–1055.

Shu-Ha-Ri: An essay on innovation in plastics industry

The new economy is disrupting one industry after the other. With its customers being disrupted the plastics industry is no exception. Industries are pushed to adapt and in many cases even shift their core competencies in order to survive. A case in point is the ongoing shift from combustion engine expertise to battery technology expertise in the automotive industry [1]. Every disruption brings along opportunities for implementing new plastic solutions.

What are the ways in which a business organization must change to take up hard problems and offer unique solutions in a VUCA (Volatile, Uncertain, Complex and Ambiguous) world? First, business development must be customer-focused, i.e., customer is the center for innovation. This means a paradigm shift from market push to customer pull is necessary. Secondly, innovation concepts must be pre-defined. Only in this way, organizations can work effectively toward it.

Shu-Ha-Ri is a Japanese concept used in martial arts [2]. In my opinion, this may be applied in the context of innovation as well and help us to gain ground in the new economy environment. Following, I will explain the concept of Shu-Ha-Ri at first through a cooking example [1, 2]. Thereafter, I will make the bridge from the mentioned example to the plastics industry.

Shu ("protect", "obey"): This is the beginner’s stage. In this stage, you start cooking according to the recipe and you keep yourself strictly to the recipe. There are no modifications. Convenience food manufacturers and fast food services are in those categories as industrial examples.

Translation to the plastics industry: For instances, concerning injection moulding: the machine operator is acquainted with the machine and the handling of the moulding process. The focus is mainly on how to excel the task in a mechanistic manner, e.g. proper injection moulding of thermoplastic resins.

 

Ha ("detach", "digress"): At this level, you still follow the recipe for cooking, but you start adding your sense of flavor. This can mean that you add a bit more salt and pepper to better fit the taste of the whole meal.

Translation to the plastics industry: The above-described machine operator starts learning the underlying principles behind the injection moulding process and is able to combine knowledge from the three M’s in plastics processing: Material, Machine and Mould. As a result, he/she is able to set up and adjust the running process.

 

Ri ("leave", "separate"): This is basically reinventing the way you cook a certain meal or even one step further to invent complete new ways of making new meals. To put it simple, with Ri you are the rule and you are able to seek unique solutions to hard problems or re-invent the way we do things. Translation to the plastics industry: The machine operator starts creating his/her own ways of processing and incorporates that in his/her daily operations. 

Shu-Ha-Ri in plastics industry:

The basic idea is that people in a (plastics) technology organization have to go through the different stages of Shu-Ha-Ri to be able to achieve excellence in innovation. Their mind- and skills set need to reach the Ri-level.

For too long, the majority of big companies shaped their departments toward Shu. This resulted in process thinking, performance self-assessment and outsourcing of services. Several departments made themselves redundant, which is highly beneficial for commodity type of business. Reduction of costs is the major upside of such systems, while major downsides include brain and skill drain. Because core competencies were more and more outsourced to Tiers [2], organizations struggle now to bring Ri-type of innovations to the market.

Now, in the times of new economy, we need more Ri skillset to integrate software, hardware and design into excellent products. The expectation of business leadership to transform Shu level stuff (‘cooking according the recipe’) to Ri innovators (‘re-invent the way of cooking’) has several challenges. Take plastic resin formulation as an example: Changing a resin formulation will result in Shu- and Ha-type of innovation. The base feedstocks are well known and available mostly in huge quantities. Exchanging certain parts of the recipe can, thus save costs.   There is no new market, nor new product involved. Conversely, for Ri-innovation, the business outcome is not clearly defined, meaning that there is no business case where you can be 200% sure that the numbers will be hit. This means that people need to have more an entrepreneurial edge then a classic business economics background. In Shu and Ha, business cases make sense and are accurate, since experience exist (from R&D, procurement over to supply chain). This is the reason why this way of “innovation” is preferred, resulting in efficiency innovation (e.g. moving production to cheaper locations) and good enough downgrade innovations (e.g. hardcover book to paperback only).


I would like to end this post with an example in plastics business, which shows a Ri behavior:
The 3M Company implemented a three-step process [3], which ensured them fruitful innovations over the years. The designated functions of the steps are “Scouts,” “Entrepreneurs” and “Implementers”. The scouts are the project hunters. Their job is to identify hard problems which are worth solving. The Entrepreneurs help then to figure out how to capitalize on the opportunities. Those can be manufacturing experts, engineering and other functional experts. They carve out a functional prototype. Once all the product related investigations are done, Implementers take over. Their job is to get the new product ready for commercialization. Scouts, Entrepreneurs and Implementers have a strong Ri-mindset and are able to think outside the classic ways because they know that is the only way to solve hard problems! It is a step away of ‘what’s in it for me’ and focusing on the greater vision of improving everyone’s life.

Thank you for reading and successful innovating!
Greetings,

Herwig

P.S. New to my blog – check out the start here section

Literature:

 [1] Prof. Dr. Gunter Dueck – Der Prozess ist der Innovation ihr Tod, Podcast Markenrebell, June 2017

[2] https://martinfowler.com/bliki/ShuHaRi.html

[3] https://www.fastcompany.com/40437745/the-three-step-process-thats-kept-3m-innovative-for-decades