Four concrete steps to more sustain(profi)table operations

At Nortal, we have seen numerous customer cases where sustainability and profitability – luckily – are two sides of the same transformational coin. In today’s technological environment, this transformation revolves around data. With a few attainable data steps, you can achieve two things: transparency and action. 

Greener practices are not just mandated by law but also offer substantial financial incentives from the government. Therefore, transparency has become a must. With Nortal, our customers have harnessed their data and gained noticeable tax benefits in fields where, e.g., the use of sustainable fuel is compensated – if there’s reliable data to prove it. The most important outcome from data, however, is action: what business decisions you make, which actions you take, and do you find the equilibrium where responsible action, in the long run, goes parallel with your business’s short-run prosperity. 

Our earlier article from May -24 explored how data and digital technologies can enable sustainability, especially in industries like energy and metals. In the most recent writing, we showcased our customers who have successfully made their business operations data-driven. This article will introduce four concrete steps to help you achieve the transparency needed to drive more sustainably profitable operations. 

Your journey towards more transparent, sustainable, and profitable operations begins by structuring your group’s data into identifiable, traceable, and combinable entities that your businesses can utilize. You will first want to identify the business domains whose data correlates with your sustainability metrics. Good first suspects are, e.g., procurement from the viewpoint of supply chain transparency, production from the sense of reducing waste, and HR to decrease the number of accidents and close calls. Data owners from these functions should help organize and translate data into business terms. This makes data a valuable resource for everyone in your organization, allowing for better decision-making and more efficient operations. 

The actions in this first step may already reveal invaluable insights to steer your business. This was the case with one of Nortal’s customers, a Finnish pioneer of consumer online services. Translating existing data into business language revealed a case of misuse in their online system for end-users. Eliminating this single fraud case yielded savings of several hundreds of thousands for the customer. 

Integrating Operational Technology (OT), i.e., the hardware and software systems that monitor your physical processes and devices, with Information Technology (IT) ensures that operational data is always at your fingertips. It’s like having a real-time pulse on every aspect of your operations. Thus, this is your second step. 

A great example of using data from operational systems is the Finnish textile service company Lindström. Lindström commissioned Nortal to help automate a process related to tailoring their rental clothes to each customer’s needs. All the systems supporting the daily production business were interconnected in the project, allowing the information to flow automatically. The new, automated process yielded productivity improvements that surpassed Lindström’s expectations and shortened the garment manufacturing lead time from weeks to one day. On top of the productivity leaps and cost reductions, such data usage breakthroughs usually also diminish operations’ environmental footprint by reducing waste, energy consumption, or emissions. 

By fully integrating AI and Machine Learning (ML) capabilities with your data, you unlock the power of predictive analytics, which helps optimize operations and reduce waste. As your third step, AI-driven analysis can become the super-intelligent assistant that tirelessly identifies patterns and trends that human analysis might miss, leading to more sustainable and profitable business practices. 

An example of AI in action in Nortal’s customer base is Europe’s leading stainless steel producer, Outokumpu. We built a surface defect detection system that helps Outokumpu understand defects and root causes like never before. Based on AI and ML, the system evaluates steel coils to the degree of detail and consistency that no human eye can. Customers get the required quality, production flows can be optimized, and coils with problems are repaired or rerouted early. Defects identified earlier mean lower costs in storage, rejected materials, and customer claims, and root cause identification enables long-term optimization. 

As in this case, the KPIs leading in the discussion are often financial. However, improving the quality and reducing waste in energy-intensive fields like steel production will undoubtedly also positively impact the environmental metrics.

If data is today’s fuel, then innovation is your business’s data-powered engine. Enriching business operations with unique components invented on data and AI can, at its best, differentiate you from competitors. An essential fourth step and an enabler for harnessing data for innovation is encouraging your IT department to adopt a composable architecture framework instead of building rigid, point-to-point integrations between your systems. Composable architecture allows the creation and rebuilding of systems as needed, just like Lego blocks. This caters to flexibility in adapting to changes quickly and continuously innovating new ways of doing things.  

Continuous innovation, in turn, ensures long-term sustainability and profitability, as with our customer, food service provider Compass Group. Compass Group’s operational and financial steering is based on the data model built by Nortal. The model boosts creativity in Compass Group’s business domains and helps automate processes. Compass Group has, for example, increased the quality and accuracy of food production and significantly reduced manual routine work, such as complying with sustainability reporting requirements for work and product safety. 

Fast forward to 2069, where we will hopefully find ourselves at the culmination of decades of persistent efforts to create a more sustainable and greener world. By then, we should have witnessed the successful integration of AI and data analytics into every facet of our lives, enabling us to make informed decisions that benefit both people and the planet. 

The extent to which those decisions have helped us slow down or even stop the current concerning developments depends on our ability to tap into the possibilities of today’s technology.