Pillars of the Future of Industry
In the face of new demands and new possibilities, what can be expected from the industry in the coming decades? We’ll show you.
The tech industry expands the barrier of plausible creations every day, and within these new possibilities, it is necessary to establish what will be the differences that are really relevant for the future. From error prevention to estimation of results, these are the four pillars of the industry for the future.
ESG
ESG (Environmental, Social and Governance) are reports carried out by industries in order to show some of their practices and attract 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, it is necessary to document the measures taken in relation to the company’s environmental impact.
- Social: how relationships develop within the company in relation to its employees and customers. The measures taken to make the workspaces suitable for all the individuals involved.
- Governance: regulations above the representatives of the companies, the means of mutual supervision for internal and external affairs of the company.
The creation of ESG aimed to question industries about their non-profit contributions. The demand has become increasingly present, to the point of generating discussions above the creation of standardized ESG reports by industries.
Machine Performance
Within large industries, the 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 little reward.
The digitization 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. In addition, the prevention of any errors and problems can be carried out quickly, avoiding material and time losses.
Monitoring the performance of machines is one of the steps to improve performance, but it is accompanied by more strategies, see below.
Predictive Maintenance
Predictive maintenance is a data integration resource that predicts the need for maintenance, in order to have control over the machine and improve its useful life. This type of maintenance differs from preventive maintenance in that it uses real-time data, while the other is based on history and statistics.
As previously mentioned, predictive maintenance is based on real-time data, obtained from condition monitoring. This happens continuously during the process of the machines, and can be carried out in periodic, remote or online organization.
Before adhering to this resource, it is necessary to make some observations in relation to the demands of the industry – such as machine evaluations, system evaluations, integration, etc. – and study the results of the implementation, as well as work and interpret this data.
Some of the attributions on predictive maintenance are:
- Minimization of time lost to unexpected problems;
- Minimization of time for repair;
- Maximization of machine safety and reliability.
Product Quality
“The totality of the requirements and characteristics of a product or service that establish its ability to meet certain needs”.
– American Society for Quality (ASQ)
With just these changes in place, in addition to a noticeable improvement in overall productivity, the end results also show improvements. Still, there are points to be noted with a focus on the quality of the product.
Product quality is determined by a number of factors. The evaluation criteria must be relative to the object of study, but in short, there are some points to be taken into account in all cases.
Physical integrity is the starting point for any evaluation. The product must be in full condition of usefulness. In a situation where the product is not effective in this aspect, other criteria begin to be impaired, such as performance. From an affected performance, the consumer’s expectation is broken and the reliability of the product 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 linked to the data acquired. 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 improvement, and consequently, improvements in overall and net yield. Even so, there are demands in relation to all changes and these must be studied and applied within the boundaries of the industry.
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