Professor Demirel is a cofounder at Tandem Repeat Technologies and Huck Endowed Chair Professor at Penn State.
Data is increasingly considered a new form of capital that drives economic growth in the digital age. This is because data has become more valuable for various activities.
For example, it can be used for personalization, tailoring products and services to individual users, which makes them more relevant and engaging. Data can also be used for predictive analytics, allowing businesses to make decisions based on anticipated future trends and behaviors. Data can even be used to automate tasks and processes, leading to increased efficiency and cost savings.
I will focus on how the growth of data and the decline in manufacturing will push innovation in the manufacturing industry.
Decline In Manufacturing
Based on data published by the World Bank and the OECD, the value added by the manufacturing sector to the global Gross Domestic Product (GDP) has declined from 20% at the turn of the century to 16% over the past two decades. This decline highlights a significant shift in the global economy, and policymakers, investors and business leaders need to take note of this trend and its implications for future growth and development.
Globalization has caused manufacturing production to shift to countries with lower labor costs, such as China and other Southeast Asian nations. This shift has made it difficult for developed countries to compete in traditional manufacturing industries as they can now produce goods as cheaply as their overseas counterparts. However, the decline in Asian manufacturing indicates that globalization is not the main reason for this decline.
Automation has significantly reduced the need for human labor in manufacturing. As machines become more sophisticated and able to perform more tasks, fewer workers are required to produce the same goods. This has resulted in job losses in the manufacturing sector, particularly among lower-skill workers. Additionally, there has been a shift toward service-based economies in many developed nations as the demand for services has grown. This has decreased demand for manufacturing goods as consumers increasingly purchase services rather than physical products.
Growth Of Data In Manufacturing
The manufacturing industry can benefit significantly from using data in several ways, including increased efficiency, productivity and innovation. For instance, sensors embedded in machinery can collect data, which can be analyzed to predict potential failures and schedule maintenance proactively. This helps to minimize downtime and prevent costly equipment breakdowns.
Monitoring data from manufacturing processes can help identify defects and deviations from quality standards in real time, which ensures consistent product quality and reduces waste. Supply chain management can also be optimized to reduce costs, improve delivery times and enhance customer satisfaction by integrating data on inventory levels, supplier performance and customer demand.
Examining customer feedback, usage patterns and field performance data informs product design and development, leading to the creation of products that align more closely with customer needs and market trends. In addition, the analysis of production process data can identify bottlenecks and inefficiencies, which enables process optimization that increases productivity and reduces costs.
Data that is shared among suppliers, manufacturers and customers can enhance collaboration, streamline processes and improve supply chain visibility. Additionally, data can be used to track and monitor energy consumption, resource utilization and environmental impact. This enables manufacturers to reduce their ecological footprint and adopt sustainable practices.
How Data Lowers Manufacturing Costs
There are several ways in which data can contribute to the cost reduction of a product. Improved efficiency, lower purchasing expenses and enhanced product quality are some of the most notable avenues by which this can be achieved. By utilizing data, companies can optimize their production processes, reduce waste and streamline their supply chain, while supply chain optimization can help companies reduce transportation and logistics costs by finding more efficient routes and modes of transport.
Leveraging data can be beneficial for businesses looking to remain competitive while improving their bottom line.
The Role Of Data In Biomanufacturing
In the evolving landscape of industrial processes, the transformative power of data extends beyond its role in reducing manufacturing costs. As we delve into specialized domains such as biomanufacturing, the significance of data becomes even more pronounced. The optimization of biomanufacturing processes, a complex and intricate stage in product development, relies heavily on leveraging vast biological datasets. Let’s explore the nuanced role of data in the realm of biomanufacturing and how it shapes the trajectory of innovation in this field.
Optimizing biomanufacturing is often the most time-consuming and expensive step in developing a bioproduct. Companies must explore a vast biological data space by rationally designing and screening for a desired product. Numerous parameters influence production, including the choice of host strain, initiation and termination signals for transcription and translation, the coding sequence itself and the number of copies, orientations and insertion locations for the expression cassette. Data also plays a key role in fermentation processes.
Cost can be minimized by optimizing the process for key performance indicators such as biomass titer, feedstock uptake and downstream purification yield. As a result, the design possibilities for a bioproduct are vast, consisting of billions of combinations of these factors. I believe data will play an essential and significant role in the optimization of new bioproducts for the progress and success of biomanufacturing.
Data has become a central part of all types of manufacturing and its optimization. However, training this data into smart algorithms such as machine learning or artificial intelligence can be expensive and complex. It requires advanced algorithms and a lot of energy. To strengthen advanced manufacturing, we need to find ways to lower the cost of data storage and machine learning tools.
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