The 4 pillars of sustainability
To answer the previous question, it is necessary to implement sustainable industrial processes, which are based on four fundamental pillars:
- Management of the waste produced
- Eco-friendly use of chemicals
- Reduced energy consumption
- Supply Chain Integration
How can we achieve these goals?
The answer lies in data analytics, a tool that allows you to collect, process and analyze large volumes of information from various sources, such as sensors, machines, control systems, etc. Data analytics helps us better understand how industrial processes work, identify opportunities for improvement, optimize performance and quality, and make evidence-based decisions.
Let’s look at some examples of how data analytics can contribute to sustainability in industrial manufacturing processes:
Management of the waste produced
Allows us to monitor and control the flows of materials and waste, as well as their final destination. In this way, we can reduce waste generation, increase recycling and reuse, and comply with environmental regulations. In addition, we can detect and prevent potential leaks, spills or pollutant emissions, and mitigate their effects.
Eco-friendly use of chemicals
With data analytics, we can optimize the use of chemicals, both in the manufacture and maintenance of equipment. We can adjust dosages, times and application conditions, to avoid waste and the risk of poisoning or corrosion. We can also assess the environmental impact of chemicals, and look for greener or biodegradable alternatives.
Reduction of energy consumption
Measure and manage the energy consumption of industrial processes, as well as their efficiency and carbon footprint, it’s possible with data analytics. We can identify pain points, sources of loss or waste, and opportunities for savings or improvement. We can also implement energy efficiency measures, such as the use of renewable energy, cogeneration, smart lighting, demand control…
Supply chain integration
Data analytics allows us to coordinate and synchronize procurement, production, and distribution processes, for greater efficiency and flexibility. We can predict demand, plan production, optimize inventory, reduce lead times, improve quality and customer satisfaction, and foster collaboration among stakeholders. All this, with a lower environmental and social impact.
As we can see, data analytics is an indispensable ally to achieve sustainability in industrial manufacturing processes. It not only allows us to improve the economic and competitive performance of companies, but also to contribute to sustainable development and the well-being of people and the planet.