The Use of Data in Industry 4.0
by Renato Azevedo Sant Anna – Consultor de Transformação Digital & Insights | Regional Partner & Mentor at FasterCapital
Industrial organizations have seen the volume of data generated in the production process and subsequently by the products that are produced grow every day due to the collection of information from the environment through sensors that are incorporated in order to diagnose possible errors and the current conditions of operations and in order to promote the preventive and predictive maintenance.
For example: a modern car is a true computer on wheels, with some more advanced models having the function of assisted steering, that is, semi-autonomous, performing parking and collision detection functions, they are equipped with several sensors that generate such a volume information at every moment, which at the end of a day can reach the order of several hundred megabytes.
The question is to decide whether this data will be stored for later analysis or not, which, if the answer is positive, would require the collection, processing and later sending of this information so that they can be aggregated in order to allow the identification of patterns in the data for eventual anomaly detections.
This information, when analyzed with advanced analytical systems of Artificial Intelligence, allows insights to be generated and supports the decision-making process in a data-driven manner, providing recommendations that once implemented are able to provide process improvements, which in addition to improving the results achieved, provide more efficiency and cost reduction.
Unlike data that permeates the Internet, data from industrial systems tend to be structured, that is, they follow a pre-established rule that allows their treatment without much changes by advanced analytical systems in an easier way. With exceptions for industrial systems that for example make use of computer vision, and therefore, they need to deal with data coming from sensors that capture images of real objects and people and that due to the chaotic dynamics of reality can have great variability, presenting with irregular and complex shapes.
3 V’s of Big Data applied to Industry
The very initial concept applied to the Big Data of the 3V’s applies to the data generated by the machinery used in modern industrial parks, since more and more sensors and embedded digital systems with their respective logs exist in all their diversity.
The first component of Big Data’s 3Vs in Industry, is about volume, to designate the great increase in the amount of data that is generated that has grown exponentially, the second component being variability, as there are data sources generated by several different sources and each one using its own standards many times, which increases the difficulty in processing this data, and lastly the speed factor, that machines in production generate data in real time that need to have a special treatment in their monitoring, so that it can occur the appropriate processing to extrat the value of the data.
With the introduction of digital systems such as PLCs (programmable logic controllers) and SCADA systems (supervisory control and data acquisition systems) in industries, for the measurement and control of industrial processes with their various sensors and actuators that make up the value chain ecosystem, the expression that “the data is the new oil” started to make a lot of sense, as it became possible to have a lot of indicators that enable better informed decision-making about the production and all the machinery that it supports, which will be necessary to meet the demand that also varies, due to the seasonality of several regions with their respective consumer markets.
As well as, more and more the products manufactured themselves, when equipped with embedded digital systems, as in the example mentioned above of vehicles that have a high technological component, with sensors that capture information in the form of records with timestamp and that even allow remote support by the manufacturer for diagnosing errors in the form of reports.
Industry 4.0 and the concept of “Smart Factory”
The big factor that made the so-called “Industry 4.0” viable was the affordable cost of technologies that before were restricted only to large industrial conglomerates due to their high cost until then.
The advancement of technology has the characteristic that, once its R&D (Research & Development) cost is covered, and given the increase in scale of its adoption, there is a decrease of its unit cost, which ultimately allows the massification of its use.
More recently, there was the emergence of the concept of “Smart Factory” that precisely designates the use of data for the management of the entire value chain, making use of the interoperability and integration of different systems, IIoT, computer vision and Artificial Intelligence, in a way optimized to maximize the results of the manufacturing unit, with greater visibility of the operation for decision-making in near real time, covering the entire supply chain and the logistics necessary to meet the orders that go into production.
The availability of real decision-making “cockpits” through cloud systems, where through web access, you can access panels with dashboards containing information on various industry indicators, allowing an incredible agility gain in industrial operations in order to adapt to changes that present in all their complexity.
Within a “Smart Factory”, each new order request entering production requires that the entire chain of events be reprocessed in order to adapt and synchronize all stages of the supply chain to meet the new demand in the expected time.
In other words, it means that “batches” of orders when they go into production, each with its own customization characteristics, trigger an integrated process of different systems responsible for planning processes and scheduling, which are ERP (Enterprise Resource Planning) and MES (Manufacturing Execution Systems), in order to provide the necessary resources to adaptively and dynamically attend the queued order requests to be executed in the shortest possible time and always seeking to optimize the allocation of resources to maximize results.
Industry Data Governance
And in this complex chain of events that are interdependent, each with its degree of importance and dependencies, an error at some stage of a process, has the potential to cause major operational problems.
That is why, for everything to work in an integrated way, the different systems must have the necessary data validated, so that there are no errors during the execution of an order request to go into production, which would lead to a standstill with the delay of the entire production, and would cause losses for the organization.
Thus, the correct treatment of all this information in all its variability and complexity, with the creation of Data Governance policies, making use of the standardization of Data Quality techniques and the formatting of the data, is mandatory so that this information can be considered consistent to serve as input for both the manufacturing process and the decision-making process, in order to extract the value in the form of actionable insights.
For example, in critical industrial processes, where eventual failure can produce catastrophic results, the security and reliability of data obtained by telemetry are determining factors for proper operation and within safe operating standards.
Thus, being able to identify possible risks in a preventive manner, using predictive maintenance through data is vital for the success of the organization. As well as allowing risk management in the best possible way, it avoids the potential loss of life due to possible lack of maintenance.
Data-driven decisions as a competitive advantage
According to an article by the Harvard Business Review by the authors (McAfee, A. and Brynjolfsson, E .; 2012) on the theme “Big Data: The Management Revolution”, it showed that companies that classified themselves as data-driven, performed better in terms of operational and financial results, being respectively 5% more productive and 6% more profitable than its competitors.
The same article (McAfee, A. and Brynjolfsson, E .; 2012) still interestingly mentions that the role of more senior people, who have knowledge about the business, tends to change, becoming more focused on which questions to be asked, so that the data can be used to show the most appropriate responses, through actionable insights.
In addition, data is essential as inputs in the collaborative processes of generating ideas and prototyping new products in the experimentation cycles of industries that use data as intangible assets in the organization’s value chain to obtain scalable results.
The advancement of the presence of mobile cellular systems and Wifi access points / routers in industrial environments has also contributed to the widespread use of data as a competitive differentiator for industrial operations, in a complex ecosystem that feeds back to itself, and which, by providing a superior degree of informational awareness allows greater visibility of all operations, allows greater agility in decisions and responsiveness in the actions to be performed, reducing the so-called time to value, that is, the time necessary for the generation of value, even in complex business environments.
The challenge of obtaining actionable insights into the data flows generated by all components of IIoT (Industrial Internet of Things), of the various devices on the edge that capture data through sensors, is one of the differentiating factors of performance over time, being fundamental the use of advanced AI analytical systems within a secure Cloud Computing environment, for once all information is collected, to process and through analysis, identify patterns in the data in order to generate recommendations for the business.
Obs.: This article is an English translated version of my 2021 original article in Portuguese.
References
- Article: McAfee, A. e Brynjolfsson, E.; 2012; Big Data: The Managemente Revolution – Harvard Business Review; October, 2012 – Link: Big Data: The Management Revolution (hbr.org)
- Site: What is smart factory? – Definition from WhatIs.com (techtarget.com)
Papers:
- Miguel Oliveira, Daniel Afonso; 2019; Industry Focused in Data Collection:How Industry 4.0 is Handled by Big Data. In Proceedings of 2019 2nd International Conference on Data Science and Information Technology (DSIT’19). Seoul, Republic of Korea, 7 pages. https://doi.org/10.1145/3352411.3352414 – Link: Industry Focused in Data Collection (acm.org)
- M. Berndtsson, D. Forsberg, D. Stein, and T. Svahn, “Becoming a data-driven organisation,” in 26th European Conference on Information Systems: Beyond Digitization – Facets of Socio-Technical Change, ECIS 2018, 2018, doi: 10.1007/978-3-662-60304-8. Link: Becoming a data-driven organisation (diva-portal.org)
- Shuai Zhao, Piotr Dziurzanski, Michal Przewozniczek, Marcin Komarnicki, and Leandro Soares Indrusiak. 2019. Cloud-based Dynamic Distributed Optimisation of Integrated Process Planning and Scheduling in Smart Factories. In Genetic and Evolutionary Computation Conference (GECCO ’19), July 13–17, 2019, Prague, Czech Republic. ACM, New York, NY, USA, 9 pages. https://doi. org/10.1145/3321707.3321826 – Link: Cloud-based dynamic distributed optimisation of integrated process planning and scheduling in smart factories (acm.org)
- KDD ’15: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining – August 2015 Pages 2311–2312 https://doi.org/10.1145/2783258.2789994 – Link: Data-Driven Product Innovation | Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
- ICISS 2019: Proceedings of the 2019 2nd International Conference on Information Science and Systems – March 2019 Pages 93–98 https://doi.org/10.1145/3322645.3322667 – Link: Data Analytics and BI Framework based on Collective Intelligence and the Industry 4.0 | Proceedings of the 2019 2nd International Conference on Information Science and Systems (acm.org)
- MobiCom ’15: Proceedings of the 21st Annual International Conference on Mobile Computing and Networking – September 2015 Pages 550–551 https://doi.org/10.1145/2789168.2802150 – Link: Big Data, IoT, …. Buzz Words for Academia or Reality for Industry? | Proceedings of the 21st Annual International Conference on Mobile Computing and Networking (acm.org)
- KDD ’16: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data MiningAugust 2016 Pages 2119–2120 https://doi.org/10.1145/2939672.2945385 – Link: IoT Big Data Stream Mining | Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
About the Author – Renato Azevedo Sant Anna
Digital Business & Insights Consultant, Thinker and Curator about the VUCA World, with a natural curiosity about the World and its complexity, multidisciplinary knowledge and the ability to produce actionable recommendations and insights about the competitive landscape.
Also a Mentor, Content producer (content writer), Instructor and Speaker on topics related to The Digital Era, Innovation, Entrepreneurship, Technology, Future of Work, Artificial Intelligence Applications for Business and Consumer Behavior on digital channels.
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