Abstract
Digitalization in the form of Big Data and Digital Twin inspired applications are hot topics in today's bio-manufacturing organizations. As a result, many organizations are diverting resources (personnel and equipment) to these applications. In this manuscript, a targeted survey was conducted amongst individuals from the Danish biotech industry to understand the current state and perceived future obstacles in implementing digitalization concepts in biotech production processes. The survey consisted of 13 questions related to the current level of application of 1) Big Data analytics and 2) Digital Twins, as well as obstacles to expanding these applications. Overall, 33 individuals responded to the survey, a group spanning from bio-chemical to biopharmaceutical production. Over 73% of the respondents indicated that their organization has an enterprise-wide level plan for digitalization, it can be concluded that the digitalization drive in the Danish biotech industry is well underway. However, only 30% of the respondents reported a well-established business case for the digitalization applications in their organization. This is a strong indication that the value proposition for digitalization applications is somewhat ambiguous. Further, it was reported that digital twin applications (58%) were more widely used than Big Data analytic tools (37%). On top of the lack of a business case, organizational readiness was identified as a critical hurdle that needs to be overcome for both Digital Twin and Big Data applications. Infrastructure was another key hurdle for implementation, with only 6% of the respondents stating that their production processes were 100% covered by advanced process analytical technologies.
Highlights
The economic value proposition of Industry 4.0 concepts and related technologies versus the traditional engineering approach to production improvements is the exploitation of information rather than the implementation of traditional "steel and concrete” solutions to realize suchTowards Digitalization in Bio-Manufacturing Operations improvements
The terms digitalization and Industry 4.0 are used interchangeably as different organizations use either or both these words to describe similar programs that primarily rely on Digital Twins or Big Data-based tools
From a purely economic point of view, these results show that most organizations could not see an immediate and well-defined economic benefit from investing in digitalization/Industry 4.0
Summary
The economic value proposition of Industry 4.0 concepts and related technologies versus the traditional engineering approach to production improvements (be it capacity, quality, resource utilization, environmental footprint, or other relevant attributes) is the exploitation of information rather than the implementation of traditional "steel and concrete” solutions to realize such. *. | (Continued) Survey for the industrial perspectives on big data and digital twins 8–Do you use advanced data-driven or model-based methodologies during the early-stage design, feed, detailed design, plant upgrading, and/or testing of your production processes? The process industries have decades of experience collecting process data from a vast number of sensors (Venkatasubramanian, 2019; Udugama et al, 2020), which is significantly longer than other manufacturing domains Sectors such as fine chemicals, refining, and polymer production enjoyed this “data rich” environment where Real Time Optimization and Model Predictive Control practices have been carried out pervasively (Bauer and Craig, 2008). To elucidate the industrial perspective on the current state and future plans for Big Data and Digital Twin-based solutions in bio-manufacturing, a survey was developed and distributed (digitally) by e-mail to known industrial practitioners in Denmark. Interchangeably as different organizations use either or both these words to describe similar programs that primarily rely on Digital Twins or Big Data-based tools
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