Of the three ESG factors, “social” criteria represent some of the most pressing challenges in modern supply chain management. There is a growing focus on social issues among stakeholders, in part due to increased consumer awareness and the subsequent rise of conscious consumerism.
The supply chain is a key area of focus for evaluating and improving a company's social impact. An estimated 16 million people experience modern slavery in corporate supply chains, and the value chain is intimately connected to working conditions, wages and other job characteristics tied to wellbeing.
In addition to stakeholder influences, the regulatory landscape is shaping the direction of future social disclosures. Mandatory reporting requirements on modern slavery put legal pressure on companies to account for and reduce forced labor in their supply chains. The GRI is also adding human rights issues to its voluntary reporting framework, challenging companies to increase transparency surrounding their working conditions.
However, even with these strong incentives, many corporations are lagging in comprehensive social metric data capture and reporting.
The Need for Intelligent Supply Chains
The greatest challenges to measuring and managing ESG performance revolve around data, according to a survey of senior executives. The vast majority of these respondents (92%) believe the solution is an investment in technology.
Qualitative processes, including interviews and audits, have traditionally been the methods of choice for investigating supplier integrity. However, these approaches are easily circumvented by bad actors. Investigative journalists and NGOs have also uncovered abuses in these processes, leaving many companies with damaged reputations.
If reliable and accurate data capture is the biggest hurdle for managing social issues in supply chains, technology is the greatest opportunity.
Advances in technology now allow for data-driven elements in sustainable supply chain management to verify pay, safety, health and other social conditions. For example, the first step in addressing modern slavery is to obtain verifiable and trusted data as close to the first-mile labor source as possible.
Companies that want to begin measuring social performance in the value chain may not currently be equipped with the necessary tools. Fortunately, businesses can incorporate available emerging technological solutions into supply chain management to facilitate the capture and reporting of social data.
Technologies for Driving Social Insights
Technology is critical for supply chain management professionals to gain tangible and actionable information from data. Blockchain, digital twins and artificial intelligence/machine learning (AI/ML) can be combined to digitize the value chain and foster intelligent supply chain management.
Blockchain. This shared digital ledger that records transactions — or "blocks” — is a robust tool for supply chains. Blockchains can improve system resiliency and maintain system integrity due to their decentralized and tamper-proof nature.
Today, blockchain technology can track, trace and audit supply chain transactions in real-time. Transactions between workers and suppliers can be verified by comparing them to the supplier's actual payroll, procurement and other transaction and operations data. Businesses can also leverage digital wallets via blockchain to automate payment data and ensure adequate wages are being paid.
In addition, blockchain can function in conjunction with low-tech solutions to create new data types. Mobility devices such as flip phones can be used to verify a worker's identity, actual exchanges, agreed-to payment amounts, etc. This data can then be recorded in the blockchain ledger.
These blockchain applications facilitate the capture of accurate, verifiable data companies can utilize to monitor social factors, such as the working conditions in a factory, more efficiently. This technology is vital for ensuring the source, quality and integrity of data and enhancing ESG reporting.
Digital twins. A digital twin is a virtual representation of a physical object or process. The concept originated in Product Lifecycle Management two decades ago as a way to establish a comprehensive digital footprint for products, where design and manufacture data is collected and stored throughout the entire lifecycle.
In the context of the value chain, a digital twin is essentially a composite view of all the individual products' digital twins produced by that value chain. This view can be confined to a single brand manufacturer or expanded to include an entire industry to visualize the data in an end-to-end supply chain.
Digital twin data drives predictive analytics and AI/ML intelligence to project future design needs. Value chain digital twins can further drive decision intelligence by informing network design decisions guided by ESG metrics, KPIs and targets.
The digital twin concept is foundational to transforming value chains into sustainable operating models. Establishing a digital twin model of the full value chain and integrating it with social data from the blockchain ledger enables visibility into material social risks and opportunities.
Artificial intelligence/machine learning. AI encompasses smart machines that can perform tasks that typically require human intelligence. ML, a discipline within AI, is a powerful tool in supply chain management.
AI/ML algorithms can process large volumes of complex data encoded in the blockchain and digital twins to capture insights. The technology can detect anomalies or variances in the data that are key indicators or red flags. For example, AI/ML can flag potential undisclosed subcontracting or worker rights issues according to the factory production capacity data.
The algorithms also have the capacity to learn, either with assistance or on their own. AI/ML systems can be taught to identify patterns not visible to the eye, alerting supply chain managers to previously undetected potential human rights violations.
AI/ML takes the guesswork out of sustainable supply chain management and improves efficiency. The technology can be leveraged to automatically notify actors to make corrective actions, simultaneously saving resources and holding suppliers accountable.
As the issue of supply chain transparency and traceability grows in major economies around the world, so does the importance of enabling technology for transformation. The proposed technological solutions for measuring social performance — blockchain, digital twins and AI/ML — build on each other to create an intelligent system that can handle the complexity of modern global supply chains.
These tools are particularly suitable for implementing a company’s roadmap to enhance ESG performance and meet social targets. They allow companies to blend new data types with existing operational data to track modern slavery indictors and other social metrics.
Rethinking how we acquire and use social data is the first step to achieving meaningful progress in reducing the social impacts of corporate value chains.
Teresa Russell is a sustainability and ESG consultant at SCERTIFY.
Read more of SupplyChainBrain's 2022 Supply Chain ESG Guide here.
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