Supply Chains and Sustainability

Figure: AI and sustainable supply chains (created with the help of Copilot ppt / © M. Semadeni)

Introduction

Circular economy and resource efficiency are promising ways to achieve a more sustainable economy. The key is to conserve resources to become less dependent on the supply of natural resources or, in sharper terms, to minimize the global exploitation of natural resources for the growth of a unidirectional value chain.

The mechanisms of the circular economy can create value by considering waste as a secondary resource. To adapt the business model to a circular economy, supply chains must be understood in detail and based on data. On the other hand, production and consumption must be adapted to aspects of circularity, such as product design and waste prevention.

An assessment of circularity requires a high level of data availability with secure/verified data on the products, from the elementary raw material to materials and their composition, the design (individual or components), the product life cycle (including possible repair and upcycling), and the secondary raw material (side streams/rejects/recycling). This only works through the digitalization of supply chains and accessible, forgery-proof and product-specific data.

The extent to which Distributed Ledger Technology (DLT) or blockchain technology could be a solution is discussed in the book "Blockchain, IoT, and AI Technologies for Supply Chain Management" by V. Grover et al., Apress, 2024. A brief overview of the topic ‘supply chains and sustainability’ is given below.

Data-Based Supply Chain Management

Supply chains encompass the flow of goods and services from their creation to delivery to the end customer. Creation includes the provision or availability of raw materials, additional materials, intermediate products, resources, their timely delivery, and processing into products. Depending on the business model, delivery to the end customer can be linked to additional services such as maintenance, repair, replacement, and return of products.

Supply chain management focuses on optimizing the chain from creation to delivery or, depending on the business model, to the return, reuse, recycling, or disposal of products. When it comes to optimization, cost efficiency and safety are top priorities, with operational, waste management, customer-oriented, and competitive aspects playing important roles, which can also have opposing impacts or efficiency potentials. Supply chain management must integrate a multitude of processes and stakeholders and maintain interfaces with purchasing, operations, logistics, and marketing. If a sustainability strategy is pursued in addition to a more classic optimization strategy, other aspects in the areas of environment, society, and management must be considered. The key is to set up a professional data management to analyze efficiency potential and improvement measures to gradually promote a more sustainable supply chain.

Data and information management affects many areas of supply chain design, including the integration of new technologies such as AI-based data analysis, supplier networks or platforms, new ways of working with suppliers and customers in relation to the circular economy, and the automation of warehouse processes, inventory management across the entire supply chain, as well as logistics and transport. Managing a sea of data with a continuously changing data floods and ebbs seems almost impossible; but with the introduction of AI applications for data processing, it may be possible.

If you look at the supply chain from the perspective of the composition of a product and assess how sustainable and circular it is or could become, secure data must be available that would be collected, migrated, and carried over every step in the supply chain from raw materials to the product. But what data is this and how can suppliers provide it securely across the entire supply chain?

Perhaps the data could be uploaded to supply chain networks using QR codes and product design information (PDI), and/or distributed ledgers/blockchains could make all the enormously growing data sets along the way through the supply chain available to all users and manage them securely. This could ensure traceability for a product, for example, where the various parts or proportions of the product composition and the materials from raw materials, additives, and auxiliary materials come from. The data sets could also contain how much energy was used along the supply chain from raw material to product (part of the life cycle analysis of a product) and how much greenhouse gases (GHG) were emitted in the process. This would allow supply chains to be compared in terms of their energy and GHG efficiency. Other questions would be how much waste and what type of waste was generated along the supply chain and whether this waste was introduced into a cycle as secondary 'raw materials'/resources, or was incinerated and landfilled. The data sets could also contain information on certifications or provide a comprehensible list of measures implemented to improve resource efficiency and to protect the environment, climate, and biodiversity.

This could simplify the difficult and time-consuming search for trustworthy data sources for downstream data applicants/users and ensure a uniform data standard. However, the need for trusted partners to collect and validate data sets across the entire supply chain remains for each link in the chain. They would have to be able to check the comparability of the input values based on standardized data collection. Often, in supply chain analyses, expenditure-based surveys are likely to be biased, and activity-based surveys are simply too complex. Differences between standards of scientific data and corporate data must also be taken into account, with the latter often based on estimates. The more measurements are carried out to collect actual data instead of estimates, the greater the deviations in the initial/reference state (reference or baseline) can arise.

How does a supplier build up capacity to properly handle complicated data sets, successfully incorporate AI support, and counteract the risk of misinterpretation by AI due to incomplete data sets, incorrect data, or low-quality data? Alternatively, this data management could be outsourced. Accordingly, a so-called data value chain could develop, a value chain for identifying, collecting, gathering, publishing, updating, and evaluating sustainability and nature-related data sets, which would be introduced into the distributed ledgers/blockchains (data miners). Central to the processing by AI-based systems are machine-readable data formats and a subsequent check of the consistency of aggregated data. In order to optimize costs, the frequency of updating data sets and their open access would have to be determined.

To enable the financing of such a data-value-chain system, incentives and legal requirements would have to be worked out. However, it would be crucial to convince customers of the benefits of general data accessibility, comparability, and standardization (indicators, metrics) for their own business processes. Financing would have to be based on properly documented business cases. However, publicly accessible data sets cannot be for free, as every financing requires a return.

Of course, the data value chain would also need to include third-party data auditing and training of data managers and auditors, e.g., on principles and processes. Which data should be included in defined business processes and released? How can data gaps be analyzed and closed together with other industries and partners? Depending on the materiality assessment (see below), different data may be more important for different companies or may even be missing from the data set.

Sustainable Supply Chain and Business Model Adaptations

When discussing sustainability, environmental and climate aspects are usually emphasized, which could potentially influence the business model more than other sustainability aspects. In addition to regulatory requirements regarding environmental pollution and climate change, there is an increasing demand from customers for more sustainable products and corresponding transparency about the origin of the materials and resources used. In the area of financing, verifiable transparency in terms of sustainability reporting, including sustainable supply chains, is increasingly required (e.g., Scope 3 in GHG accounting). This creates opportunities to stand out from the competition by adapting business models. More sustainable supply chains can help to gradually transform the economic system towards a 'green economy'.

The share of the supply chain (Scope 3) in the impact on the environment and society compared to the share of operations and supply (Scope 1 and 2) can be significantly higher, if not the main determining factor, e.g., in terms of GHG emissions. The demand for transparency of sustainable supply chains leads to extensive surveys of suppliers on data collection. If data and information were more easily accessible via a data value chain, the costs of obtaining information could be better estimated in relation to competitive advantages and efficiency potentials.

For many companies, sustainability and nature-related data and information are becoming increasingly indispensable for adjusting their risk profiles, optimizing business processes, and meeting reporting standards more easily by using data sets. Data-driven business models can therefore be an opportunity to shift the economy towards sustainability, for example, through the introduction of data-driven solutions for resource efficiency and CO2 emission reductions. This also increasingly applies to SMEs, making the establishment of a common platform for sustainability information particularly useful, especially regarding supply chains (Scope 3). Such a platform would be part of the data value chain and dependent on stakeholders and their released data and information. The scope of the data would need to cover the entire company, not just operations and production, and follow specified standards, such as the 'Sustainable Supplier Reporting Standard' (SSRS). Furthermore, long-term procurement relationships, openness, transparency, and conflict-resolution-oriented collaboration would be important for developing and operating a common sustainability platform.

The effort required to fully integrate sustainability into the business model is very high, especially for Scope 3. With a common platform or by promoting the data value chain for sustainability, efficiency could be increased, and costs saved. This also requires inexpensive or even free tools for SMEs, such as compatible software for data and information management. These tools should also be part of comprehensive business intelligence in the area of risk management. Financial figures should be able to be linked to sustainability or ESG costs, meaning that 'environmental' accountants are likely to play an increasingly important role.

Materiality Assessment

Companies communicate their sustainability stories through reports. However, these reports must be based on reliable and verifiable data. The numerous data requirements from various standards make implementing sustainability increasingly challenging. Therefore, it is crucial to set clear priorities when gathering data across different business areas. A materiality assessment can be conducted to identify and present the areas and processes with the most significant impacts on the environment and society in a comprehensible manner. It is essential to analyze both the impacts of the environment and society on the company and the impacts of the company on the environment and society (double materiality). To address the complexity of sustainability, companies must proceed pragmatically and step by step. This approach enables measurable progress within the company, rather than publishing reports with targets, intentions, or action plans only.

Marco Semadeni, Dr. sc. nat. ETH

January 23, 2025