Developing a framework and a system for data collection

Developing a framework and a system for data collection is not just about reporting metrics for the purposes of ESG but also enables an enterprise to execute a cohesive digital transformation strategy.

It is a well-known fact that sustainable practices are not just about doing the right thing for the environment; they also make good business sense. However, how good is "good" here? What kind of impact can an enterprise expect from data collection and reporting? The real value comes only when the insights are actively applied across business functions, creating a closed-loop system of improvements and iterations.

The Higg Index, a widely used framework, has a specific goal to provide a standardized framework for assessing and improving the practices related to sustainability for an industry. Now there are metrics such as the Higg Materials Sustainability Index (MSI) that can be used to evaluate the sustainability of materials used in products. The mechanism for data collection hence becomes critical.

Traditional sustainability metrics include carbon footprint, energy consumption and resource usage efficiency, among others. Collecting this data requires a comprehensive IoT platform—but is that its only role? Or can it offer even more?

This is where enterprises can potentially unlock new opportunities, realizing significant collateral benefits—and in some cases, additional business advantages—across various functions.

Sustainability Efforts in Supply Chains

Sustainability efforts in supply chains typically focus on tracking suppliers and ensuring responsible sourcing. However, enterprises can extend these efforts to unlock additional benefits, such as:

• Enhancing Material Quality Tracking: Moving beyond supplier and material tracking to monitor the quality of raw materials, which directly impacts the production quality of manufactured goods.
• Improving Traceability: Establishing visibility from raw materials to finished products, enabling better management of recalls, root cause analysis and mitigating risks to customer experience and brand reputation.
• Preventing Pilferage And Counterfeiting: Protecting assets from counterfeiting, which, according to an OECD report, costs the global economy up to $509 billion annually.

This could be implemented through a product lifecycle management (PLM) platform, which provides comprehensive workflows for tracking suppliers, designs, raw materials and finished goods, essentially covering the complete value chain.

Traditional Sustainability Metrics and IoT Platform

Traditional sustainability metrics include waste management, energy consumption, carbon footprint and resource usage. An ideal technology enabler for this is a standardized IoT platform with various workflows to gain additional value:

• Deploying Additional Sensors: Monitoring water leaks, detecting carbon monoxide and improving safety and security in facilities such as buildings, malls and airports.
• Enabling Data-Sharing For Risk Management: Utilizing collected data to support other industries, such as insurance companies, in optimizing premiums and risk assessment.
• Enhancing Asset Management: Improving efficiency in managing data centers, cellular towers and other critical infrastructure (the U. S. alone has more than 5,000 data centers and is growing annually at more than 9%).
• Estate And Resource Planning: Better planning and design by leveraging insights from other data centers to optimize designs and improve operational efficiency.

This can be implemented through a cohesive IoT platform that integrates diverse elements, including sensors for data collection, telemetry for data transmission and cloud-based processing for analysis.

Sustainability in Manufacturing

Sustainability in manufacturing involves tracking plant efficiency, energy consumption, waste reduction, greenhouse gas emissions and compliance with regulatory requirements. This potentially provides an avenue to manage other aspects such as the quality of produced goods.

Enterprises can also leverage data for:

• Ensuring Traceability: Monitoring materials from raw input to final production to improve accountability and compliance.
• Enabling Real-Time Quality Monitoring: Implementing systems that provide continuous oversight of production quality to reduce defects and waste.

This can be implemented through manufacturing execution systems (MES), which help manage the quality of designs. These systems are primarily focused on production management rather than collecting enterprise-level metrics or data.

Many enterprises have already implemented some of these programs as part of their sustainability initiatives, such as Coca-Cola (using IoT to manage bottling plants specifically to achieve sustainability goals), Schneider Electric and many more.

According to McKinsey, "While more than 95 percent of S&P 500 companies issue a sustainability report, very few fully integrate environmental, social, and governance (ESG) into their equity stories." This gap between sustainability goals and an implementation strategy can actually make it difficult for stakeholders to understand how a firm is acting on these priorities, as reported by McKinsey.

This clearly shows the need of the hour—a strategy where systems come together and work in a harmonious fashion to benefit the enterprise, serve its corporate needs and align with business priorities.

The idea is not to take a leap of faith but to catapult with convincing data and proof of value.

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