Environmental, Social, and Governance (ESG) goals and practices are becoming increasingly critical for all industries; including the manufacturing sector which has its own unique challenges, such as data management, sustainable manufacturing, and ethical and social practices. To advance an ESG journey, manufacturers must start with assessing their entire business in greater detail to then be able to devise the right strategy moving forward. Digital transformation will inevitably be part of this plan as it has the power to not only support but amplify ESG activities.
“No matter if you don’t do anything else… improvements in manufacturing’s digital transformation journey drives positive around ESG [environment, social & governance] and sustainability,” says Craig Coulter, sustainability leader for global advanced manufacturing & mobility at Ernst & Young.
With digital transformation being an enabler of innovation, how can manufacturers best prioritise ESG data governance practices? The progression of a manufacturer’s digital transformation can illuminate new pathways, but it also has the potential to increase the amount of data manufacturers must manage, making ESG data governance a top priority for any business. In particular, manufacturers face significant obstacles surrounding ESG data governance due to complexities in reporting, gathering, and standardising ESG metrics across operations. Poor governance can lead to certain risks developing across a business from compliance to investments, and operations, which together could prevent progress towards achieving ESG goals.
ESG data management + sustainable manufacturing = competitive industry leaders
ESG data management is vital for sustainable manufacturing, as it aids the ability to monitor, evaluate, and boost ESG activities. It supports manufacturers with global sustainability standards for continuing success and regulatory observance, but clean and superior data is key.
“Quality ESG data is an intrinsic business imperative. Forward-thinking organisations are shifting from traditional methods and are beginning to invest in cutting-edge ESG data management solutions. This differentiates and repositions them for competitive sustainability performance,” Marilyn Obaisa-Osula, Associate Director and Lead, ESG/Sustainability Services, KPMG.
As enterprises progressively acknowledge the value of sustainability, creating and prioritising robust ESG data governance frameworks is far more critical than ever especially given its importance in terms of attaining long-term environmental and socio-cultural goals.
The top 5 risks to manufacturers without proper ESG data governance
The manufacturing industry is under enormous pressure to ensure that ESG data governance is prioritised, particularly after manufacturing breaches have spiked with the highest share of attacks globally but also due to the amount of data the industry collects. In the United States alone, roughly five million persons were impacted by data breaches in the manufacturing industry.
Specific risks that have emerged due to poor ESG data management practices include:
1. Reputational damage:
Enterprises falling short of ESG standards including unethical practices within their frameworks risk damaging their brand value, reputation and paying large fines. One of the largest fines of USD $34.69 billion was handed down to Volkswagen for employing software that “falsified” data and helped evade emissions tests on its vehicles, whereas Australian auto giant Eagers Automotive, has recently suffered reputation harm after hackers published sensitive data online.
2. Increased operational and financial risks:
Not reaching ESG goals, inclusive of ESG data governance benchmarks, can provoke operational and commercial risks. As reported by Lexology, “In addition to regulatory enforcement, firms may also be subject to ESG-related litigation. For example, with respect to ESG investing, firms may be accused of making false, misleading, or exaggerated claims about their ESG records or practices, often summarised under the term ‘greenwashing’.”
3. Environmental harm:
A significant amount (one-fifth) of the world’s carbon emissions can be traced back to the manufacturing and production sectors. Manufacturing without proper ESG data governance can equate to substantial environmental damage by allowing unrestricted pollution and resource depletion, leading to the weakening of the supply chain’s sustainability capacity.
4. Compromised data quality and ineffectiveness:
About 70 per cent of industrial ransomware breaches suffered are by the manufacturing operations industry, and in the United States manufacturing is the second highest targeted in the country. Vigorous ESG data governance is required if manufacturers intend to avoid compromised data quality, which can result in breaches and inaccurate sustainability reports or metrics.
5. Social issues and governance challenges:
Manufacturers that do not ensure proper ESG data governance will risk promoting threats to social and ethical issues, such as labour abuses. These massive governance slipups expand through the supply chain, weakening ethical sourcing and fair-trade practices, possibly leading to regulatory penalties and loss of business partnerships.
Removing obstacles to enhance ESG data governance practices
To address these five primary concerns, manufacturers can future-proof their ESG data governance practices by embracing cutting-edge technologies like AI and blockchain for real-time data monitoring and transparent reporting.
Sustainability frameworks that can address ESG challenges and concerns, such as the Consumer Sustainability Industry Readiness Index (COSIRI), can also strengthen accuracy and accountability with a robust framework and tools to support manufacturers in their sustainability journeys.
Lastly, encouraging a culture of constant improvement and stakeholder engagement can aid manufacturers in altering business best practices, helping them to achieve goals, staying on top of evolving ESG standards and regulations, and preserving resilience spite shifting social and environmental expectations.
