Three stages to achieving sustainable life sciences manufacturing

  • November 08, 2023

The idea of creating customer value through focused environmental sustainability practices has fast emerged as the new mantra of stakeholder capitalism.

A recent study published by the Journal of Cleaner Production in 2019 affirms that the life sciences and pharmaceutical industries collectively generate more than 48 metric tons of carbon dioxide-equivalent emissions — with the top 15 of those companies accounting for 35.35 metric tons of carbon dioxide-equivalent emissions. These include both Scope 1 (direct emissions) and Scope 2 (indirect emissions) resulting from byproducts released through complex manufacturing processes.

Estimates by the British Pharmaceutical Association suggest that about 70% - 80% of the pharmaceutical industry's carbon footprint is generated within therapeutics' manufacturing processes. This implies a strong need to implement sustainable manufacturing practices within the pharmaceutical and life sciences industries. Given the capital-intensive nature of the sector, the primary challenge for sustainable practices will be geared towards driving change management.

An important driver in the adoption of sustainability practices in the life sciences industry has been the establishment of funds to mobilize the concept of “circular bioeconomy” across industries in the European Union. The circular bioeconomy concept emphasizes sustainably creating products through biological resources.

However, life sciences companies face several challenges in their path to transform into sustainable circular bioeconomy-centered organizations, especially when choosing between batch-process manufacturing versus continuous-process manufacturing. This is further exacerbated by the complex supply chain and manufacturing ecosystem issues that life sciences have to deal with daily.

Digital transformation of manufacturing processes offers a systematic approach to address the many challenges associated with sustainable manufacturing in the life sciences industry. Digital technologies make it easier to integrate sustainability protocols within life sciences manufacturing organizations while enabling the effective tracking and management of resources and identification of suitable alternatives. By helping reach sustainability metrics such as Environmental, Social and Governance (ESG) scores, digital technologies create long-lasting value for life sciences companies.

For sustainability to be achieved, it's important that end–to-end visibility of the manufacturing process be adopted as a best practice across the industry as well as implementing digitally driven sustainable transformation that facilitates measurement and control. The design of sustainable manufacturing processes in life sciences companies through digital technologies may be achieved through three key stages:

Stage 1: Evaluate the manufacturing workflow for cost leakages that negatively impact sustainable practices.
Unlike traditional digitization approaches of workflows in life sciences, the case for digital transformation towards supporting sustainable manufacturing practices requires a critical assessment of potential aspects within the workflow that can impact the efforts of a life sciences organization to drive sustainability. Early in 2015, the EPA announced two hazardous waste regulations: the management standards for hazardous waste pharmaceuticals and hazardous waste generation improvements.

Manufacturing units of life sciences companies tend to generate a significant amount of effluents such as solvents, cell culture waste and bioreactor waste as byproducts of the therapy manufacturing process. Given the complexity of global manufacturing operations, investment in digital technologies for sustainability helps the manufacturing process identify high-yielding opportunities for cost saving and waste reduction.

Digital technology consulting companies such as NTT DATA offer a systematic evaluation framework for sustainability operations. The sustainability evaluation framework developed by NTT DATA helps life sciences companies evaluate the manufacturing process and determine intervention points for implementation of digital technologies to integrate sustainability approaches in the manufacturing process of the life sciences enterprise.

Stage 2: Invest in a strong data ecosystem that incorporates sustainability metrics and facilitates real-time visibility of manufacturing workflows.
After the initial assessment of manufacturing workflows, life sciences companies must invest in a connected data ecosystem supported by a data warehouse that incorporates ESG data sources alongside internal and external data sources on manufacturing workflows. This can be enabled through sustainability-centered data warehouses such as Snowflake.

The integration of the data across these databases would facilitate the training of a modular AI and analytics layer. The AI and analytics layer facilitates constant monitoring of data streams through collected data from sensors across the manufacturing workflows. An important enabler of real-time visibility would be to enable edge computing across complex bioprocessing instrumentation, which would provide a constant stream of data that can be analyzed through a federated data ecosystem.

Further mapping the process flows with ESG benchmarks will facilitate the identification of specific leakages in the manufacturing workflow and help align the metrics for manufacturing output such as overall equipment efficiency with sustainability indicators.

Stage 3: Integrate systems that facilitate the automation of process flows and predict for potential aberrations.
The final aspect in enabling sustainable manufacturing practices is to implement connected IoT platforms to capture real-time data from solutions and integrate within a digital twin of the manufacturing plant.

Considering that many parameters need to be tracked around the sustainable manufacturing ecosystem, the first step is to align the process automation points through a simulation of the production process using a universal data model. This enables the capture of all possible limiters and potential sources of wastage and optimizes for rate-limiting steps across the manufacturing workflow.

The second step is to integrate the automation process flows with AI/ML models to create and implement predictive models that would evaluate the potential for waste generation across each step of the manufacturing process.

The third step is to integrate the automated process flows with the digital twin of the life sciences manufacturing plant. This would thereby facilitate identification of effluent byproduct release patterns through analytical models that capture data from multiple manufacturing parameters onsite in real time.

Go digital to achieve your sustainability goals

Implementing environmentally sustainable manufacturing practices in the life sciences workflow requires a systematic evaluation of opportunities for sustainability-oriented process improvements and deploying supportive digital technologies to facilitate the desired transformation for reducing the carbon footprint.

As life sciences companies embark on the journey of sustainability, it's essential that they engage with the right digital solutions partner to navigate this complex process. The sustainability practice at NTT DATA offers a wide range of solutions and services geared toward helping life sciences companies maximize value in an environmentally sustainable manner, thereby integrating environmental sustainability as a strategy.

Contact us now to learn how we can help your organization accelerate its digital transformation to achieve its sustainability goals.

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Bhuvaneashwar Subramanian

Bhuvaneashwar Subramanian has more than 20 years of experience as a thought leader in the healthcare and life sciences. He has published extensively, including peer-reviewed academic articles on cloud computing in life sciences, digital health and nanobiotechnology commercialization. Bhuvaneashwar is a qualified biotechnologist and holds a master’s degree in molecular and human genetics from Banaras Hindu University India, a diploma in molecular biology from the Hungarian Academy of Sciences and an MBA from Edith Cowan University Australia.


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