.svg)
Build scalable, secure apps that drive innovation and growth.
Thank You !
Back
As the recently released Corporate Climate Responsibility Monitor 2022 indicates, leading global organizations will be closely monitored for their success and efforts towards meeting their stated sustainability goals. They are not only on the hook for their carbon footprint but also how they engage with their suppliers and customers to ensure their Scope 2 and 3 emissions footprints are reduced as well.
As companies firm up their plans to achieve ambitious 2030 and 2050 sustainability goals, procurement teams are increasingly focusing on choosing the right energy sources (electricity, fuel, water, etc.), using recyclable materials, cultivating supply chain transparency and more. Other key efforts include tracing sustainability measures across the organization, tracking current emission levels with deep granularity and assessing whether implemented changes are having the desired positive impact.
While these goals serve as the North star, the steps to meeting them are less clear. Typically, implementing such changes can be year-long or more projects as they require changing many legacy systems and software, investing in new equipment and empowering employees to work differently. The challenge, then, is not just deploying a more environmentally friendly operations model but doing so in a way that makes business sense.
At a high level, there are two steps procurement organizations should take to accelerate their sustainability plans: (1) gain insight into historical data, and (2) generate accurate forecast data and engage suppliers with a data-driven approach. With these capabilities, teams can make better decisions that conserve resources and facilitate ambitious sustainability goals.
One example for how implementing a data and analytics strategy can benefit sustainability initiatives is through carbon credits. Companies operating in the European Union must observe emission caps based on the carbon credits system, which allows companies to purchase permits that account for emissions. Carbon credit pricing fluctuates with the market and can become more or less (but usually more) expensive. Procurement teams need to find the right emission buying strategy while simultaneously discovering ways to reduce their carbon footprint overall.
Increasingly, teams are buying recyclable materials to make eco-friendly products and investing in renewable energy – both strategies that contribute to sustainability goals and can help offset the carbon credit costs. But for some materials, the renewable or recyclable option may not be cost-efficiently available. Procurement teams must determine where that point lies, and strategically determine when to purchase carbon credits for the remaining products.
A client with 15 manufacturing sites across the EU has an annual carbon credit budget between $100M and $130M USD to manage across the footprint. The client’s procurement team sought to create a more efficient purchasing strategy that considered the next five years, but they lacked insight into the historical data necessary to predict future emissions. Without access to accurate and enriched data to inform predictive forecasting models, the team could not respond quickly to market fluctuations and capture the best prices.
One carbon credit is equal to one ton of carbon dioxide, and the prices fluctuate with the market. The company plans its credit purchases for years into the future, making it desirable to accurately forecast whether the current rate will be better than the rate in the next year. If they produce more emissions than credits purchased, the organization will face costly fines and higher rates. If they purchase more credits than needed, they will need to trade them back and potentially lose the money.
The client knew they needed insights into their current and future emissions to successfully manage their carbon credit strategy. Predictive models and forecasting capabilities are quickly becoming must-haves in any competitive business strategy. The technology can rapidly analyze years of data to inform key decisions in a way that is nearly impossible and inefficient for a human to do manually. However, there are common obstacles to implementing these tools, such as poor data quality.
Enriched data and web-based solution offers a consolidated view across the emissions footprint for enhanced decision making
The client initially had raw data that was not suitable for predictive modeling. The Aligned Automation team enriched the client’s internal data by selecting and labeling key features, integrated it with external market data and applied enablers such as predictive modeling. This enabled useful insights, allowing the procurement team to quickly simulate and forecast strategies based on different parameters.
The team connected this data to a purposely designed web-based app that collects important data points across 15 manufacturing plants. This information is collected at each installation level within the plants and across Level 1 and Level 2 sub-suppliers. With access to all the data in a single view, the category manager can simulate various scenarios and price points across years to study its impact to the strategy and make more informed purchasing decisions. In the first year, this enhanced data-driven strategy and digital solution is on track to uncover more than $14M USD.
At Aligned Automation, we combine cutting edge procurement knowledge with dedicated technical experts who care about delivering sustainability outcomes with efficiency. From enriching and connecting disparate data to pursuing better future options, we work alongside procurement teams to execute ambitious business goals.
