As consumers increasingly shop based on a brand’s sustainability practices, retail brands are making public commitments with respectful manufacturing practices and operations with the environment.
retail trade can reduce three levels of carbon emissions: direct emissions, indirect emissions – such as secondary produced electricity or steam – and emissions for which a company is indirectly responsible, both up and down its value chain – such as transport and distribution.
The latter are the most difficult to track due to the number and variety of data sources involved. They need to detect the emissions of all suppliers in the value chain, as well as the handling and delivery of the retailer’s products. Although this information is complex, it is likely to become more important as government entities and the public increasingly focus on this area in the coming years.
Let’s think of a distributor with a complex supply chain and a wide catalog of products. Measure the emissions for which they are indirectly responsible It is challenging, for the following reasons:
- Variation in the maturity of supplier measurements: A supply chain typically has hundreds, if not thousands, of suppliers, some of which can be small businesses No ability to measure emissions.
- The nature entangled supply chains: Identifying the various sources of emissions can be difficult when supply chains are deeply entangled. A large-scale data collection exercise is required to collect this information. This puts pressure on providers, who are constantly being asked similar but different questions by all of their customers, on an ongoing basis throughout the year.
- A large catalog of products: vendors potentially have tens of thousands of different entries in their catalogs, compounding the two challenges mentioned above.
- The lack of standard measurement formats in the sector: various certifications and methodologies are used to calculate emissions without there being an established standard.
- Manual and slow processes: the measurement of indirect emissions is a typically manual process that is carried out through spreadsheets and emails. This process makes it difficult to make agile business decisions, and in addition, the data may not have been recorded correctly.
Automation of data collection
Automating data collection facilitates more frequent reporting, ideally in near real time. Data must be collected from various sourcesincluding the following:
- Sensor data from the Internet of Things (IoT): Includes data from buildings (such as stores, warehouses, and office buildings) and vehicle fleets (such as delivery and supply chain trucks).
- Internal data collection from business applications as well as infrastructure – this helps capture data relating to direct and indirect scope emissions, thus helping a company understand the level of carbon emissions emitted from its data centers or cloud infrastructure, for example.
- Collecting external data from third-party data sources: The market is rich in technical solutions and platforms that help address this challenge, which is a data integration challenge for retailers. For example, AWS Data Exchange can provide additional data points for certain emissions metrics with a growing number of sustainability-focused data sets already available.
It is important to take a progressive approach. Initially, reporting requirements may only require high-level information, but as methodologies evolve, it will be important to design an extensible system that supports increasingly granular information needs in the future. For example, measurement today might involve tracking a store’s electricity bills, while tomorrow it might be necessary to assess the footprint of a single fridge within the store.
Organize data into domains
Once the data has been incorporated, it must be organized as independent domains that act as master data sources for an organization. For example, a “supplier” domain might contain all the data collected from suppliers, while a “product” domain brings together descriptions of the products sold, information related to the supply chain, and the carbon footprint involved in creating of each product.
Data from each domain can be exposed to other applications internally and consumed through APIs, with each domain using the appropriate technology for the type and structure of data stored.
data composition
Organizing data into domains means that users must be able to discover and compose it. An orchestration component must maintain a catalog of data, and consumers must be able to subscribe to the orchestration component to receive any data updates. Data access should be managed at this level to allow permission management for any data that may be sensitive.
data consumption
Finally, applications can be created to report carbon emissions. This component would aggregate emissions data for each product, supplier, manufacturer, product category, or other parameters based on the needs of the data viewer. Another application could be in charge of retrieving all the sustainability data related to a specific product; for example, revealing the carbon footprint of a t-shirt in response to a customer request.
Andre Nedelcoux, Head of Solutions Architecture for Retail, Amazon Web Services.
