Making Co-Innovation Count in the Chemical Industry

 

To try that step, the joint venture, Regenyx, will be using a process developed by Oregon-based Agilyx to convert 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 have to succeed requires three key ingredients:

 

1. A win-win business proposition, in which each of the stakeholders stands to gain from co-innovating.

 

2. A strong, trusting relationship among stakeholders. For legitimate competitive reasons, companies normally are cautious to share proprietary data. Co-innovation, however, 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 best connected digital tools to enable 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 an amazing starting point. By linking intelligent digital tools — machine learning, artificial intelligence — to that platform, the manufacturer acquires the capacity to examine data from a selection of inputs (their own and the customer’s) to inform development of custom formulations designed expressly 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 various areas within the chemical business where co-innovation looks particularly viable:

 

In the Circular Economy.

 

On one level, participating in the Circular Economy, where materials are frequently looped back into the value chain for recycling and re-use, is a response to the climbing ecological, resource and regulatory compliance concerns that today encounter chemical manufacturers in markets around the world. On another level, it also provides fertile territory for manufacturers and their partners to nurture 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 important 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 good enabler for ventures like these, giving them the capacity to swiftly and collaboratively analyze and scale up processes and products, taking into account not only environmental and sustainability factors, but likewise regulatory and safety requirements. With predictive tools driven by machine learning and AI,  a chemical manufacturer and its co-innovation partners can quickly and efficiently anticipate how specific 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.

 

Partnership between fertilizer and crop protectant producers, seed providers and farmers can lead to promising new co-innovation opportunities to develop specified customized agrochemicals and formulations that are manufactured to greatly help farmers maximize 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 easily co-create recipes tailored to specific farming customers, special 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 suddenly and successfully analyse new formulations for compliance and for quality.

 

In specialty chemical markets: coatings, resins, lubricants, etc.

 

Let's assume a specialty coatings manufacturer is approached by an automotive manufacturer who wants a unique variation on a coating for certain parts. Other than running labor- and time-intensive laboratory trials to try to develop a potential 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. Furthermore, 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 kind of outcome-oriented service offers the coating manufacturer another way to strengthen its relationship with a valued customer, while developing new revenue streams in the process. By bettering their digital capabilities, chemical companies turn themselves into intelligent enterprises.

 

This article is originally posted on tronserve.com