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.
식품 진열대, 냉장고, 양쪽 끝 진열대, 선반, 진열 상자 등, 소매 부문에서 PoS(판매 시점) 장비의 세계는 그 요구 사항만큼이나 다양합니다. 소매 시설에는 마감은 말할 필요도 없고, 소매 공간에 맞춰 다양한 크기와 구성으로 각 구내 공간의 특성과 관리 대상 식품 및 음료의 유형에 따라 설계된 맞춤형 장비가 필요합니다. 각 주문의 시간 제약에 맞춰 이 모든 것을 구성하고 생산을 정밀하게 제어 및 조정해야 합니다.
판금 부문의 변화는 공장 자체에서 시작하는데, 공장에서 나오는 데이터를 모두 함께 시각화할 경우 생산 용량을 예측하고 문제를 예방하거나 해결 방법을 찾아낼 수 있게 된 덕분에, 모든 기계와 구역을 실시간으로 연결하여 더욱 생산성이 높아지고 있습니다. 이런 최적화는 MES(Manufacturing Execution System)라는 고급 소프트웨어를 사용해야만 달성할 수 있습니다. MES는 모든 기계와 호환되는 프로그램으로, 스마트 팩토리를 현실로 만들어 줍니다.