Faced with new demands and new possibilities, what can we expect from the industry in the coming decades? We show you.
The industry expands the barrier of plausible creations every day, and within these new possibilities, it is necessary to establish what will be the really relevant differences for the future. From error prevention to result estimation, these are the four pillars of the industry for the future.
ESG
ESG ( Environmental, Social and Governance) reports are reports made by industries in order to disclose some of their practices and get investors. The topics covered are related to the environment, social and governance issues, and some relevant points to be addressed are the following:
- Environmental: the involvement of companies within the issues of combating environmental damage. Within the creation of ESG, documentation of the measures taken regarding the company's environmental impact is required.
- Social: how relationships develop within the company in consideration of its employees and customers. The measures taken to ensure that workspaces are suitable for all individuals involved.
- Governance: regulations above company representatives, the means of mutual supervision for internal and external company matters.
The creation of the ESG was aimed at questioning industries about their non-profit contributions. The charge has become increasingly present, to the point of generating discussions above the creation of standardised ESG reporting by industries.
Machine Performance
Within large industries, full functionality of all its parts is essential for a positive outcome, but with the high demand and attention required, this can be hard work and unrewarding.
The digitalisation of performance data is the way that several companies have found to have greater control of their industries. Through the monitoring of machinery, it is possible to make adaptations to more specific and functional methods. Furthermore, the prevention of possible errors and problems can be carried out quickly, avoiding material and time losses.
Monitoring machine performance is one of the steps to performance improvement, but this comes with more strategies, see below.
Predictive Maintenance
Predictive maintenance is a data integration resource that predicts the need for maintenance in order to gain control over the machine and improve its service life. This type of maintenance differs from preventive maintenance in that it uses real-time data, whereas the other is based on historical and statistical data.
As previously mentioned, predictive maintenance is based on real-time data, achieved from condition monitoring. This happens continuously during the machinery process, and can be carried out periodically, remotely or online.
Before joining this resource, it is necessary to make some observations in relation to the demands of the industry - such as evaluations on the machines, the system, integration, etc. - and study the results of the implementation, as well as work and interpret this data.
Some of the assignments about predictive maintenance are:
- Minimisation of time lost to unexpected problems;
- Minimising time to repair;
- Maximising machine safety and reliability.
Product Quality
"The totality of requirements and characteristics of a product or service that establish its ability to satisfy certain needs."
- American Society for Quality (ASQ)
With just these changes in place, not only is there a notable improvement in overall productivity, but the bottom line also shows improvement. There are still points to note with a focus on product quality.
The quality of the product is determined by several factors. The evaluation criteria must be relative to the object of study, but in short, there are some points to be taken into consideration in all cases.
Physical integrity is the starting point for any assessment. The product must be in full usable condition. In a situation where the product does not prove to be effective in this aspect, other criteria begin to suffer, such as performance. From an affected performance, the consumer's expectation is broken and the product's reliability is tarnished, generating, in the long run, a consistency problem.
Within the new reality of the industry and its relationship with Big Data, product quality management is intrinsically tied to the acquired data. And it is through the implementation of better and smarter strategies, that the production and quality of products is improved.
Conclusion
The four pillars of development for the future of industries open up many opportunities for system improvements, and consequently, improvements in overall and net output. Still, there are the charges for all the changes and they must be studied and applied within the delimitations of the industry.