The pursuit of sustainable enterprise growth has accompanied the rising interest in optimizing the use of global resources. Companies know that the proper management of global resources today isn’t strictly a question of ethics, but can also have a very real impact on building competitiveness. Sustainable growth is becoming a leading trend in the business world, one that can be supported by AI and advanced data analysis.
In business, basing decisions on reliable, real data can be a key means to achieving sustainable growth. AI has made it possible to monitor and analyze huge amounts of data, from various sources, more or less in real time. This provides a full view of the current situation and enables flexible action.
In the pursuit of sustainable resource use, advanced algorithms can promote better decision-making and process optimization in numerous key areas including not only supply chains, transport, and production processes, but also building value for society and the environment.
Balanced supply chains and transport
Lack of control over the extensive global supply chains takes a toll on the environment and sustainable trade, and also costs companies money. Fortunately, AI has it possible to monitor and optimize, on an ongoing basis, all aspects of the acquisition and transport of resources. For its part, deepsense.ai helps its clients optimize their supply chains in a number of ways, including providing systems to help minimize empty mileage in road transport. Machine learning models analyze the location of transport loads by selecting the most optimal route, maximizing the load capacity of trucks and minimizing the number of kilometers they travel. AI not only increases transport efficiency, but also helps companies reduce their carbon footprint.
Optimizing production resources
The optimal use of production resources is becoming another key aspect of sustainability. It offers measurable benefits in the form of higher operational efficiency. Factories that have adopted a zero-waste policy – completely eliminating waste from production lines or their use in subsequent production phases – meet the demand for resource optimization in its most extensive form. But a sustainable approach to production doesn’t have to be so revolutionary in scope. The deepsense.ai team works with clients on various aspects of production and resource optimization through intelligent quality monitoring. One area of focus is real-time monitoring of production lines using a combination of computer vision technology and advanced data analysis. The systems analyze products for defects and impurities, appropriate shape and distribution and a range of other anomalies. This helps companies identify and minimize losses in the early stages of production while maximizing their resource use.
Boosting employee safety is yet another important aspect of sustainable production. In response to this challenge, deepsense.ai created a system for monitoring the safety of factory employees based on the analysis of camera images. Trained neural networks analyze the situation on an ongoing basis, identifying potential threats such as violations of security rules or the sudden emergence of new threats in high-danger zones. When a threat is detected, the system automatically sends alerts and suggests specific preventive actions.
Building value for society and the environment
Another way companies engage in sustainable development is by also means building a company’s value by contributing to society. Here advanced data analysis can be used in a number of ways. An interesting example is a challenge deepsense.ai took up in 2016 as part of an analytical competition announced by the US National Institute of Justice. The task involved predicting the location of possible crimes in the city of Portland, Oregon, by identifying crime hotspots – small areas with the highest predicted crime rate. Our team’s results were impressive enough to take first place.
AI-based systems can also be employed to help protect the environment. The image recognition-based early warning system deepsense.ai created for detecting potential forest fires is a good example of such a system. The machine learning model automatically recognizes smoke in photos from cameras placed in the forest and provides predictions about the locations of possible fires. The solution not only increases the accuracy of the early warning system for fire hazards, but also enables a much faster reaction from fire departments, potentially eliminating the threat at an early stage.
The use of advanced data analysis and artificial intelligence can help companies and other organizations maximize their achievement of sustainable development goals, build competitive advantage and create a better, safer world. The COVID-19 pandemic has highlighted how the lack of a sustainable approach can disrupt business continuity. The ideas that define sustainable development have gained in importance and will become a permanent feature of companies’ strategies. However, in the pursuit of optimization through the use of the latest technologies, striking a balance between the outcomes we anticipate and the environmental and economic costs of their implementation is crucial. Ever aware of the importance of this balance, deepsense.ai produces solutions that draw on the latest technologies and the optimal and agile model training process.
The article was published in the Polish edition of the MIT Sloan Management Review, in November 2021.