The data challenge
Delivering net zero goals and embedding circularity in manufacturing are huge data challenges.
Measuring, tracking, and reporting transparently on the carbon footprints of trillions of items sold to billions of people, produced across millions of locations from tens of thousands of components is not easy. Currently, in many manufacturing organizations, the use phase or end- of- life data is assumption-based. Generic data sets for carbon dioxide emissions don’t accurately represent the supply chain or allow for variations in product specifications. Combined scope 3 emissions by derivatives or parts are restricted and limited, as at that level, the data is mostly aggregated. Scope 1, 2, and 3 impacts are reported with little or no mention to other environmental impact.
An automotive story
Understanding the data complexities and other factors that hamper intelligent decision making is the first step.
Let’s take an example of an automaker that manufactures 450,000 units in a fiscal. This translates to a minimum 2,000 unique derivatives or parts, about 700 materials, (classified as grades of steel, aluminium, various plastics, rubber, glass, and textile) and over 5,000 suppliers requiring multiple modes of transportation and deliveries, both ad hoc and planned. The manufacturer has multiple manufacturing sites across continents with a similar number of varied production lines.
Besides these complexities, there is a need to track changes in car composition, battery traceability, recycled content, employee, and community welfare, and greenhouse gas (GHG) emissions too. Data, in these cases, is fragmented across multiple functions giving a point- of- time view at best for a limited area. Multiple sites use multiple systems with each managed independently and with a focus on compliance. There is a distinctive lack of intelligence to drive improvement and efficiencies.
The visibility conundrum
The conventional approach to any solution is a form of bottom-up data analysis where we start with a single, defined product that is the subject of the study.
The approach, where materials and processes used to manufacture a product are systematically mapped back through the value chain to quantify their environmental impact, is nonetheless a complex task. It requires specialist knowledge and sophisticated software tools. It is usually time-consuming, expensive, and not scalable across an organization, which deals with multiple products.
Every department within a manufacturing unit needs visibility into the material – whether it be to source, manufacture, transport, recycle, or simply report on it. With growing regulatory requirements, commitment to Science Based Targets initiative (SBTi) goals, and governments looking to control sustainability footprints, there is a need to bring a cohesive view of enterprise data.
This integrated view will allow for scalable and autonomous reporting, proactive monitoring, and reduction of emissions. It will also conduct a what-if analysis into the future with simulations to measure the impact on production balance, stock, procurement, GHG emissions, and carbon intensity. Without that cohesive view, all these advantages are lost to the decision makers.