At a time when climate change is forcing individuals to rethink their way of life and consumption, companies are also asserting their policies regarding social and environmental responsibility (CSR), with the primary goal of systematically taking into account the challenges of sustainable development in their strategy. To help them, they can rely on new technologies and their own data, provided they can and know how to use it.
The digitization of companies has led to a multiplication of data subject to a real paradox: on the one hand, they represent a performance lever, and on the other hand, the management of their volume is a real challenge.
Poorly managed, they have a direct impact on the company’s performance and its operational processes. These can suffer from breakages and malfunctions that ‘traditional’ methods unfortunately cannot detect, let alone fix. Events which in a supply chain can lead to unnecessary consumption of resources, an indirect increase in costs and CO2 emissions.
AI is at the heart of detecting inefficiencies
Technology now makes it possible to identify an organization’s hot spots based on a reliable overview of its processes. However, the analysis and optimization of these cannot now be carried out without an approach based on artificial intelligence (AI). The challenge here is not to introduce new IT systems or overload the IT infrastructure, but to make better use of existing systems and technologies in the service of business performance. Create transparency around internal processes and provide tangible remedial and improvement actions. So far, such an approach has failed mainly due to manual data collection or insufficient data availability.
AI – at the heart of a process mining technology – focuses on viable business data to scan each characteristic step of an internal process and detect any malfunctions. In this approach, data from common business computer systems such as SAP, Oracle Where Sales force collected, analyzed and mapped. In the event of failure in a business process (redundancy, inefficiency, etc.), technology ensures its conversion into a healthy, simple, efficient and manageable asset. This method promotes the development and management of positive indicators necessary for the sustainability of the company, whose greatest potential for improvement lies at the level of production, material management and logistics.
Data for the benefit of a positive ecological impact
Excess stock, production waste, routing errors, etc. may be due to compromised processes, miscalculations, quality deficiencies or bottlenecks. Such waste generates additional costs and a negative impact on the company’s carbon footprint. In this context, it will seek to detect unnecessary consumption of resources and reduce them where necessary. In particular, the introduction of AI-driven technology at the heart of its operational processes will enable the company to optimize the planning and management of its production. The use of machine learning algorithms will facilitate the ranking of different variants of a process according to their similarity, allowing it to transfer and apply the results of other production processes and ultimately to reduce its waste.
Processes capture everything a company does, from product design to manufacturing, distribution and order fulfillment. By analyzing them, processing them and improving them with the help of intelligent technological solutions, it is now possible to fundamentally change the way a company is run.
Any operational decision, and not just for compliance with the various regulatory requirements, can thus be made by seeking to prioritize a final goal of sustainability.
Only by carefully x-raying their internal processes and streamlining them, via AI-supported technologies, will companies be able to engage in a long-term sustainability process.
Tribune written by Fadi Naffah, Vice President, Managing Director France MEA – Celonis
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