Author: Tronserve admin
Wednesday 28th July 2021 12:29 PM
Making Co-Innovation Count in the Chemical Industry
When AmSty, which manufactures polystyrene and styrene monomer, reported this spring that it is pairing with Agilyx on a partnership that will completely recycle post-consumer polystyrene and turn it into new products, Brad Crocker, president and CEO of Texas-based AmSty, hailed the venture and the circular recycling process that underpins it as an action “toward a new future for plastics recycling.”
To take that step, the joint venture, Regenyx, will be using a process developed by Oregon-based Agilyx to change used polystyrene products including foam cups, foam packaging and single-use picnic items back to their original liquid form, styrene monomer. AmSty then will use that styrene monomer and, in collaboration with its supply chain partners, work to develop new polystyrene products that offer “a favorable environmental profile without any degradation of quality or value.”
For the chemicals business, ventures like these offer a telling glimpse of what’s possible — in relation to new business opportunities and revenue streams, and in terms of environmentally friendlier “cradle-to-cradle” approaches to chemical production and usage — when chemical companies and other entities along the supply chain come together to co-innovate.
Fostering the kind of supply-chain collaboration that co-innovation ventures like Regenyx need to succeed requires three key ingredients:
1. A win-win business proposition, just where each of the stakeholders stands to gain from co-innovating.
2. A strong, trusting relationship among stakeholders. For legitimate competitive reasons, companies sometimes are resistant to share proprietary data. Co-innovation, nevertheless, is in most cases predicated on companies developing enough mutual trust to share data in the name of pursuing important (and potentially profitable) new concepts such as the circular use process dubbed “PolyUsable” that Regenyx will utilize.
3. The ideal connected digital tools make it possible for chemical manufacturers and their partners to mutually develop products. A common digital platform for sharing the data needed to develop, fine-tune and find a market for co-created products within a chemicals ecosystem is a remarkable starting point. By linking intelligent digital tools — machine learning, artificial intelligence — to that platform, the manufacturer obtains the capability to analyze data from an array of inputs (their own and the customer’s) to inform development of custom formulations designed specifically for recycling/reuse, and to predict how those formulations would perform throughout their lifecycles.
Having these three elements in place open doors for chemical manufacturers and their partners along the supply chain to provide, explore and exploit new co-innovation opportunities in a wide range of chemical markets. Here are numerous areas within the chemical business where co-innovation looks particularly viable:
In the Circular Economy.
On one level, contributing in the Circular Economy, where materials are repeatedly looped back into the value chain for recycling and re-use, is a response to the increasing ecological, resource and regulatory compliance challenges that today experience chemical manufacturers in markets around the world. On another level, it also provides fertile territory for manufacturers and their partners to improve new businesses and revenue streams around co-created products and processes optimized for recycling and re-use.
The AmSty-Agilyx joint venture is an example of companies exploring co-innovation opportunities in the Circular Economy. A cooperation between the auto manufacturer Audi and Umicorp, a multinational company that develops materials technology and recycling processes, is another. Together the two companies are progressing in their effort to develop a closed loop for recycling and reusing beneficial materials like cobalt, nickel and copper from high-voltage batteries used in electric cars, with establishment of a raw materials trading “bank” to support the effort.
Digital technologies are the best enabler for ventures like these, giving them the ability to instantly and collaboratively analyze and scale up processes and products, taking into account not only environmental and sustainability factors, but also regulatory and safety requirements. With predictive tools driven by machine learning and AI, a chemical manufacturer and its co-innovation partners can quickly and perfectly forecast how particular formulations will perform, not only in terms of quality but also in terms of carbon footprint and environmental impact. They can use those same digital tools to adjust formulations as needed to meet performance and environmental standards and expectations.
In precision agriculture.
Cooperation between fertilizer and crop protectant producers, seed providers and farmers can lead to promising new co-innovation opportunities to develop particular customized agrochemicals and formulations that are developed to assist farmers maximise crop yield and profitability. By feeding data on soil composition, weather conditions and other factors into a digital platform with machine learning capabilities, agrochemical manufacturers and their partners can expediently co-create recipes customised to targeted farming customers, specified agricultural regions or specific crops, with the ultimate goal to maximize farm output while minimizing overall environmental impact. In this context, innovative environment, health and safety (EHS) management tools with advanced prediction and simulation capabilities can quickly and properly analyse new formulations for compliance and for quality.
In specialty chemical markets: coatings, resins, lubricants, etc.
Let's say a specialty coatings manufacturer is approached by an automotive manufacturer who wants a unique variation on a coating for certain parts. In the place of running labor- and time-intensive laboratory trials to try to generate a workable formulation for the customer’s application, the chemical company uses its internal intellectual property as well as external (e.g. patent) knowledge databases and applies machine learning technology to anticipate formulation performance. Such an approach can significantly reduce development efforts and time to market. In addition, the paint manufacturer can capture customer production parameters (temperature, viscosity, flow rates, etc.) via sensors, again use machine learning on a digital platform to correlate these parameters with the quality of the painted semi-finished goods, then offer a new service or “business outcome,” such as first-pass-quality painted semi-finished goods, instead of tons or volume of material.
This style of outcome-oriented service creates the coating manufacturer another way to strengthen its relationship with a valued customer, while developing new revenue streams in the process. By upgrading their digital capabilities, chemical companies turn themselves into intelligent enterprises.
This article is originally posted on manufacturing.net