We looked at how important it is for organizations to practice sustainability across business functions in our previous article. While sustainability has become a hot topic in boardrooms, organizations have put sustainability on the back burner as they grapple with macroeconomic challenges like supply chain shortages and rising global costs. Today, only one in two businesses are achieving their environmental goals, and less than 4 out of 10 are effective against governance and social goals - Forbes Survey. Despite the hype, companies have yet to begin their sustainability journeys and don’t realize that by focusing on sustainability, they can improve their all-important bottom and top line as well as their green line simultaneously.
While organizations have started to invest in tracking and reporting, they struggle to operationalize and execute their sustainability strategy. This is attributed to operational data spanning a complex landscape of systems, people, and processes.
Sustainability goals need to be fundamental aspects of a company’s overarching business strategy and respective KPIs. For instance, carbon emissions can be tracked in a similar manner as their business counterparts. To do this, businesses need to examine their supply chain and its underlying processes, as it reaches every part of an organization and is essential to operationalising sustainability goals. Doing this requires the right tools. Yet companies are being held back by a failure to harness existing data and break up organizational silos, as well as a failure to analyze operational inefficiencies, emission hotspots and supply chain risks in real-time and in the economic context of business execution.
By amalgamating data and leveraging process mining, organizations can ensure their sustainability vision ushers green-line value - set a precedent and be the industry benchmark.
A carbon reduction opportunity lies in optimizing logistics, but pinpointing sources of emissions remains a challenge because organizations are unable to unify data and leverage insights to deploy countermeasures.
The intelligence module based on process mining, having an array of AI and ML algorithms enables companies to calculate emissions from their shipments in real-time with industry-approved reporting standards. Automation of carbon emission reporting by using shipment data in an organization’s IT system is now seamless. This enables customers to identify where they have the highest carbon-saving potential in their logistics chain and pinpoint the exact actions required to reduce their carbon footprint.
In procurement, supplier selection is vital. The best organizations are one step ahead of the curve. But, modern-day procurement has a new factor to consider in their complex decision-making, which is: “Are my suppliers sustainable?” It can be challenging to evaluate sustainability metrics and select optimal ones to meet regulatory and customer requirements. Factoring supplier sustainability ratings into procurement processes can make sustainability a priority in purchasing decisions.
Consolidating supplier systems and overlaying process mining on its data points can help organizations track supplier ratings and compliance with international sustainability standards and regulations. Sustainable sourcing is achieved by deprioritization of high-risk suppliers and boosting high-rated suppliers while continuously working with suppliers to improve their performance. Data-driven management based on supplier sustainability rating can boost sustainable spending. Furthermore, by rewarding the right suppliers, sustainable action is incentivized throughout the supply chain.
Historically, organizations do not typically change their fuel sources, as it is not clear how choosing alternatives can save costs. There is a business opportunity in choosing renewable or recyclable sources but a lack of insight into the measurable value and the potential trade-offs stifles interest in alternative sources.
With process mining, a bird's eye view of the logistics network can be established which can aid in evaluating the impact of fuel-switching strategies on energy consumption, emissions, and cost. This aids in the identification of opportunities for fuel switching to sustainable options that align with ESG goals, such as transitioning from fossil fuels to renewable energy sources while also ensuring business continuity.
Organizations generate a substantial amount of waste and the modern-day conundrum is its management and its heavy implications on cost. The end-to-end process of waste generation and disposal is mandated to conform to regulations, and the onus is on organizations to establish a circular economy. The lack of transparency into waste management is a cause for concern as it can lead to legal ramifications due to non-compliance.
An intertwined, all-encompassing process data model can tap into digital ecosystems with flow sensors to understand the flow of waste. Having transparency on the quantities and types of waste produced at different stages, organisations can further implement targeted measures for its reduction. This increases recycling rates, minimizes contamination, and upkeeps waste protocol thresholds.
As processes determine how businesses run, they can be the vehicles for operational and even systemic change. Once they are analyzed and improved with intelligence and data execution, it becomes possible to prioritize sustainability in every operational decision and balance business and sustainability goals.
Transforming how processes run is a fast, minimally disruptive, and minimally capital-intensive path to better sustainability and business outcomes - simultaneously.
Editor's note: This article was originally published on the Nasscom community website and is reprinted here with permission. Siddharth Ravishankar, process mining consultant at Celonis, co-authored this article.