To operate effectively, businesses need business intelligence (BI) insights. Key measures can help manufacturers address the sustainability challenges associated with an increased use of BI.
In the era of digitisation and smart factories, data has proliferated from IoT and sensor points all along the supply chain and factory floor, and data analysis and insight has become an essential tool for business leaders.
Efficient data collection and analysis are the bedrock of modern manufacturing and enable manufacturers to remain competitive and agile. The ability to interpret data from various sources and derive actionable insights is crucial. This includes understanding data trends, forecasting demand, and optimising inventory levels, along with continuous monitoring and quality control throughout the production process in order to improve efficiency, productivity and decision-making. It’s no exaggeration to say that Business Intelligence (BI) is an essential tool for modern manufacturers.
By combining Enterprise Resource Planning (ERP) and BI systems, businesses can leverage the benefits of both systems and gain complete visibility of the organisation. This can help businesses increase their agility, improve their oversight, and achieve their goals.
Like many advancements, the AI-driven developments in BI have an associated cost. More data means more energy is required to process that data and turn it into valuable insights.
Advances comes at a cost
Data centres are notoriously energy-hungry, and they are proliferating globally to meet the demand for the intensive computing power needed for AI. The International Energy Agency estimates that data centre electricity usage is set to double globally by 2026, in part because of the rise of power-intensive workloads such as AI.
In tandem, sustainability is increasingly becoming an important concern for consumers. Manufacturers must look at ways to harness the power of smart factory technology while mitigating the impact of digitisation advances. By implementing key measures, manufacturers can mitigate the sustainability challenges that arise from an increased use of BI.
Key considerations
Data optimisation is a significant factor in making business intelligence more sustainable. Fundamentally, high-quality data reduces the energy used to process data, and less energy means a lower carbon footprint. Producing high-quality data requires standardising data to move across a company’s systems in a seamless process so that it doesn’t need to be reconfigured or converted.
Most companies use multiple systems, such as ERP, CRM, eCommerce, Payroll and others, and these must be connected to standardise and unify data so that less energy is required to glean insights. Fortunately, many ERP systems standardise data, but this is probably the most difficult challenge for manufacturers.
Choosing cloud-based services
Cloud ERP Software-as-a-Service (SaaS) democratises access to essential resources like computing power, data storage, scalability, and crucially, it boosts affordability because it eliminates the need for expensive on-site hardware and the infrastructure and manpower to manage and maintain it.
SaaS or public cloud is the most energy-efficient option for manufacturers, although some may choose other cloud options such as private or hybrid cloud, depending on their specific requirements. SaaS ekes out maximum efficiency through the economies of scale that are achieved through multi-tenant environments.
The scalability of cloud-based ERP empowers companies to flexibly adjust to changing workloads, ensuring optimal resource utilisation and operational performance. Subscription-based pricing models further offer manufacturers the flexibility to scale up or down in response to market demands for both cost-effectiveness and responsible power usage.
Increasingly, cloud service providers themselves are recognising the importance of sustainability measures, with many opting to use renewable energy options and improving data centre efficiency through better cooling systems.
Harnessing AI in BI
Generative AI significantly enhances the data analysis capabilities of ERP platforms, with the capacity to analyse vast amounts of data that business systems generate and identify patterns that enable businesses to identify best practices and AI-powered recommendations to gain faster insights that power transformation and continuous innovation. Tools like predictive analytics and customer sentiment analysis are critically important to manufacturers. However, as generative AI becomes more commonplace emissions will increase. Using AI is unavoidable, so the question is how best to use it to get the best results for the business.
Niche AI can assist manufacturers to manage their sustainability metrics. Niche AI is trained on certain tasks and is more energy-efficient because of this. Unlike general AI, which can handle a wide range of activities, niche AI focuses on specialised areas to meet the unique needs of their field, which makes them highly effective and efficient.
We will see sustainability becoming more and more prominent in both customer preferences and in mandated or regulatory ESG reporting for companies. Approaching business intelligence with an eye on sustainability measures will enable manufacturers to plan for the future while harnessing the immense business value from the technology available today.
Mark Wilson is the CEO of SYSPRO EMEA and APAC. An experienced managing director in the technology space, Mark defines and leads SYSPRO’s strategies at a regional level.
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