Data Analytics and how to make the best decisions for your plant
by Lantek
Digital Transformation
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Data on operations, processes, logistics, personal data, financial data... Data, data and more data. Too much data. So much data, in terms of quantity and variety, that it can be overwhelming. What is the best way to collect and organize them so that they make sense? To know what to do with them, so that they boost the productivity and efficiency of the plant?
Alberto Martínez, CEO of Lantek
In addition to the other improvements proposed by Industry 4.0, Data Analytics is essential to exploiting the value of the information. Now, when I speak of value, and pardon the repetition, it is, in fact, derived from the data itself. According to a report by Forbes Insights and EY, based on the opinion of 1,500 executives from large companies worldwide, 66% of companies with a well-defined advanced analytical strategy improve their operating margins and profits by more than 15%. It is clear, then, that on the road to digitization, a significant growth opportunity is provided by the effective analysis of the information that we generate in our companies.
The challenge is, therefore, to be able to know how to structure all of this information so as to optimize processes, identify areas for operational improvement, and strengthen the relationship with customers; in short, to enhance business growth. Aided by specific software and other technological tools, data analysts extract, select, process, analyze and organize data in order to establish patterns, trends, partnerships, and follow-ups that help us to be able to make the best decisions possible at all times, using the most disruptive business model possible, in terms of production schedules, maintenance, processes, inventory management... all of this in real time, automatically, which in turn results in greater cost reduction.
Now, if that described above were not enough in itself, Data Analytics facilitates decisions made in advance. Said in plain English, it will no longer be necessary to have to send e-mails to the different departments so that they communicate some type of data to us that, if not located, would take a great deal of time to find. With digitization, we are able to be aware, for example, before the assembly has concluded, of whether the result is to be optimal, we can predict scenarios that could delay production and repair them, and even the machines themselves are capable of resolving potential problems.
Conclusion: data analysis helps to avoid missing any details involved with the production process.
How do we translate what the data tells us? As a starting point, let us use the analysis that they perform in the technological consultancy firm Principa, using the four types of Data Analytics:
What is happening? This is the most basic of interpretations. We visualize all of the data in order to carry out a descriptive analysis of the business, its products, and its customers.
Why is it happening? A second step of descriptive data analysis is to apply diagnostic tools to determine whether there are potential problems, so that they may be resolved.
What is most likely to happen? Here, the possibilities of Data Analytics in terms of prediction are significant. The probability of occurrence of some incidence, which we can in turn resolve before it occurs. This ability to predict allows for better decisions to be made.
What do I need to do? The last step is from the prescriptive model, in which an analysis is performed to determine what has happened, why it has happened, and what could happen, in order to make decisions together.
However, Data Analytics is merely one of the cornerstones of digital transformation. If we add the possibility of learning from the data (Machine Learning) and creation of an Artificial Intelligence, as well as to connect our plant with sensors (Internet of Things), or upload all our Big Data to the Cloud, then we will have indeed reached the apex of Industry 4.0.
Regardless of the degree of digitization of a plant, it is essential to be familiar with each of its processes by using data, and thus gain a competitive advantage, which increases profitability and helps us to generate disruptive business models. For those who are not able to take adopt to digital transformation on their own, this new Revolution favors collaborative environments. There is no excuse for us all not to transform and to become more competitive. Choose the leading partner in your industry.
우리는 산업 분야에서 공장 수익성 인식 경쟁력을 유지하기 위하여 앞으로 더 중요해질 새로운 디지털 에코 시스템의 원격 작업을 일상 생활과 통합하고, 더 효율적인 사용을 위하여 인공지능 기술과 센서화를 이용해야 합니다. 따라서 생산 효율성을 측정하는 KPI를 도입하여 시정 조치를 할 수 있도록 하는 것이 절대적으로 중요합니다.
클라우드, 분석, 빅 데이터, 딥 러닝, 인공지능, 증강 현실… 팬데믹 이전부터 이미 사용되던 이러한 개념과 기타 개념들이 이제는 우리에게 도움이 된다는 목적, 특히 우리에게 영향을 미치는 영역에서 업계가 안전하고 효과적인 방식으로 디지털화하여 프로세스를 최적화하는 데 도움이 된다는 유일한 목적을 가진 콘텐츠로 채워졌습니다.
비즈니스 인텔리전스 시스템은 점점 더 우리가 다루는 데이터를 더욱 나은 방식으로 실시간으로 사용할 수 있도록 하기 위한 도구로서 일상적인 비즈니스 현실의 일부가 되어가고 있습니다. 우리는 이를 통해 더 나은 결정을 내리고 오류를 방지하고 새로운 니즈를 예측하고 새로운 비즈니스 모델을 개발할 수 있습니다.