Sales and Fulfillment
Mastering carbon footprint analytics: The new data-driven approach
Logistics service providers and manufacturing supply chains are often seeking the help of carbon footprint analytics to reduce their overall impact on the environment and comply with prevailing global and local regulations. In this effort, the importance of data-driven insights in understanding an organisation’s carbon footprints cannot be overstated.
A recent study conducted by a government organisation stated that supply chains account for more than 90% of greenhouse gas (GHG) emissions worldwide. While this figure seems very concerning, it provides an opportunity for companies to rethink their approach to sustainability. Leveraging accurate data plays a critical role in achieving sustainability by enabling organisations to track their progress, make informed decisions, identify pain points, and manage their resource usage. Organisations from all domains are now more willing than ever to adopt data-driven carbon footprint analytics solutions to meet their long-term sustainability goals.
Challenges in traditional carbon data collection
Considering the wide range of logistics and manufacturing operations, coupled with the dynamic nature of global markets, collecting reliable carbon data is painstaking for many organisations. This leaves many companies struggling with inconsistencies and gaps in data collection. Accurate and real-time data is the foundation of effective carbon management, and collecting relevant and high-quality data can be a significant challenge.
Maintaining data reliability
Manual data collection is wrought with inefficiencies. From human error to inaccurate and outdated data, manual data collection has significant challenges. Companies are then left relying on external sources to provide them emissions data, which is not always reliable.
Scattered data streams
Data gathered from third-party sources is often not cohesive and organised. Integrating disparate data sources results in a waste of time and renders carbon management strategies ineffective.
Lack of real-time data
Traditional data collection techniques often result in operational lag due to delayed data collection and analysis. Most industrial operations require real-time communication of on-ground data to formulate effective decarbonisation strategies to reduce overall emissions.
Benefits of modern data-driven carbon footprint analysis
The global trend towards sustainability has led companies to increase their reliance on data-driven insights to reduce their carbon footprint while boosting their economic performance.
Cost efficiency
By pinpointing inefficiencies, data-driven analysis can help companies identify opportunities to reduce waste, optimise energy use, and ultimately save money while reducing their carbon footprint.
Improved reporting and transparency
Accurate data supports better reporting, whether for regulatory compliance, investor relations, or public communication. It builds trust with stakeholders by providing transparent and verifiable information on your sustainability efforts.
Proactive risk management
Understanding carbon emissions across the supply chain helps businesses anticipate regulatory changes, market shifts, or supply chain disruptions related to climate change. This foresight allows companies to adapt and mitigate risks before they become critical issues.
Enhancing asset lifecycle management
Accurate sustainability data allows companies to enhance asset lifecycle management and identify areas for improvement. This could include analysing production schedules to avoid peak energy usage times, improving the energy efficiency of the equipment, and updating equipment for more energy-efficient models.
Long-term sustainability
Data-driven analysis provides a continuous feedback loop, helping companies track their progress over time. This ongoing monitoring ensures that sustainability initiatives remain on course and adapt as necessary to achieve long-term goals.
Ensuring data quality: Best practices
Maintaining high-quality data is crucial for accurate carbon footprint analysis. Poor data quality can lead to misleading results, undermining your sustainability efforts. Here are some best practices to ensure your data is reliable:
Regular audits: Conduct periodic reviews of your data to check for accuracy and completeness. Audits help identify errors or inconsistencies early, allowing you to correct them before they affect your analysis.
Use verified data sources: Whenever possible, source data from verified and reputable sources. This includes using government databases, industry standards, or third-party certified information.
Standardise data collection methods: Ensure that your data is collected in a consistent format across all departments and locations. This might involve using standard units of measurement or unified data entry protocols.
Train the team: Equip your team with the necessary skills and knowledge to collect and manage data effectively. This could include training on the tools and methods used in your carbon footprint analysis.
How can Infosys BPM help with a carbon-zero logistics world?
Infosys BPM has leveraged the power of data analytics and artificial intelligence to develop solutions to reduce an enterprise’s carbon footprint across the supply chain. Our carbon footprint analytics solution provides a comprehensive overview of carbon emissions and costs across an enterprise.
