DS-SEP24-PG58+59_Layout 1 19/09/2024 11:38 Page 1


FEATURE


SUSTAINABILITY IN MANUFACTURING


USING THE POWER OF DATA-D PROMOTE INNOVATION AND SUS


Ori Yudilevich, CPO of MaterialsZone, explains how data and AI have the power to usher in a new era of innovation and sustainability


S


low innovation cycles, competitive pressure, and the increased importance of ESG (Environmental, Social, Governance)


compliance present significant challenges in today’s manufacturing industry. In this environment, data-driven decision-making has emerged as a crucial strategy for overcoming these obstacles. Outdated systems have long plagued the


manufacturing industry. Organisational knowledge loss due to employee turnover and siloed data causing inaccessibility and lack of collaboration are two examples illustrating the urgency of implementing robust data infrastructure. Manufacturers must embrace technology that aggregates and utilises data to enable informed decisions and significantly boost innovation, competitiveness, and ESG goal achievement. Fortunately, technological advancements


leveraging data analytics and AI have empowered the industry, and making data- driven decisions is easier than ever. Data analytics and AI hold the power to usher in a


new era of innovation and sustainability.


OVERCOMING SLOW INNOVATION CYCLES As manufacturers face mounting pressures to keep up with global competition and evolving market demands, they must overcome slow innovation cycles, continually innovate, improve efficiency, and adapt to changing conditions. Data-driven decision-making can significantly speed up innovation processes and empower manufacturers to collect and analyse vast amounts of data to identify market trends and consumer needs more accurately. Predictive analytics aids with product design, adaptive


Below: A plot in MaterialsZone’s platform visualising how a project reaches its target in just a few iterations using MaterialsZone’s AI-guided experimentation capabilities


pricing, and demand forecasting, giving companies a sense of control and confidence to stay ahead of the curve. AI and machine learning enables rapid


prototyping and testing, significantly reducing the amount of resources typically needed and time to market for new products. R&D teams can conduct virtual experiments to simulate and evaluate product formulations, variations, and designs faster than with traditional methods. Machine learning algorithms are used to analyse historical data to identify patterns and predict outcomes, allowing researchers to focus on the most promising formulations and experiments. AI-driven tools automate routine tasks such


as data collection, analysis, and reporting. This automation speeds up the research process, ensures higher accuracy and consistency, and allows professionals to focus on strategic work, reducing the likelihood of human error. Mars, an American multinational manufacturer


of consumer goods, successfully utilised digital twin technology by creating a digital replica of their manufacturing supply chain. They collected data and produced a simulation, allowing company leaders to make informed decisions to improve the process. The use of data analytics in digital twin technology also allows for real-time monitoring and predictive maintenance, reducing downtime and improving efficiency. Data analysis and AI technologies streamline


A correlation matrix displaying the effect of the composition of a material or product on its properties, such as mechanical properties


58 DESIGN SOLUTIONS SEPTEMBER 2024


the entire product development cycle, from initial research to final production, enabling companies to bring innovative products to market faster and more efficiently than ever before. This speed and efficiency are critical for many industries where trends and


Page 1  |  Page 2  |  Page 3  |  Page 4  |  Page 5  |  Page 6  |  Page 7  |  Page 8  |  Page 9  |  Page 10  |  Page 11  |  Page 12  |  Page 13  |  Page 14  |  Page 15  |  Page 16  |  Page 17  |  Page 18  |  Page 19  |  Page 20  |  Page 21  |  Page 22  |  Page 23  |  Page 24  |  Page 25  |  Page 26  |  Page 27  |  Page 28  |  Page 29  |  Page 30  |  Page 31  |  Page 32  |  Page 33  |  Page 34  |  Page 35  |  Page 36  |  Page 37  |  Page 38  |  Page 39  |  Page 40  |  Page 41  |  Page 42  |  Page 43  |  Page 44  |  Page 45  |  Page 46  |  Page 47  |  Page 48  |  Page 49  |  Page 50  |  Page 51  |  Page 52  |  Page 53  |  Page 54  |  Page 55  |  Page 56  |  Page 57  |  Page 58  |  Page 59  |  Page 60  |  Page 61  |  Page 62  |  Page 63  |  Page 64