As the manufacturing industry continues to adopt digital solutions, cloud-based data platforms are becoming a game-changer in manufacturing data management. In the past, companies struggled to manage the vast amounts of data generated by their production processes, leading to inefficiencies, errors, and lost opportunities. Lucky for them, this time is over.
With the rise of cloud-based data platforms, manufacturers can offer an efficient and cost-effective way to manage and store vast amounts of data generated from various processes. Now they can collect them not only from IoT devices and machines in the production lines but also acquire information from supply chain management, quality control, and even end-customer service satisfaction. Needless to say, everything happens in a real-time manner.
Data Platforms based on cloud computing technology are transforming how manufacturers collect, do data analysis or data storage, and use this data. Enabling them to monetize it, make the right decisions, and increase OEE.
All that technology changes also the way they think and influences decision-making and business development. Because these platforms are very often equipped with advanced analytics tools – Artificial Intelligence (AI)/Machine Learning (ML) algorithms and data visualization features (BI), which enable manufacturers to gain actionable insights and predictive capabilities in real time.
In this article, I will delve into:
- topic on how cloud-based data platforms are revolutionizing manufacturing data management,
- I will explore some of the Data Platforms’ key benefits to the manufacturing industry,
- we will also discuss vital elements of such platforms,
- and last but not least: how they can fit into the organization’s Data Strategy.
Why Data Platforms are crucial for manufacturing
The manufacturing industry is undergoing a significant digital transformation and appropriate Data Strategy is playing a crucial role in this evolution. In today’s fast-paced business environment data is the new currency and managing it effectively can be the difference between success and failure.
Cloud-based Data Platforms offer a scalable and cost-effective way to store, manage, and do data analysis of the data that was generated from various manufacturing processes. With the increasing complexity of data traditional on-premise solutions are simply no longer adequate to handle the sheer volume of information generated by modern manufacturing facilities.
Such platforms provide manufacturers with real-time access to critical data from all around the world, enabling them to make informed decisions quickly and be agile. Moreover, data platforms facilitate collaboration between different departments and stakeholders within a manufacturing organization. With secure access to data – a crucial part of Data Governance, teams can work together seamlessly and make decisions based on shared insights, leading to increased efficiency, productivity and profitability.
How to define proper Data Strategy
Data Strategy is basically a comprehensive plan that outlines how an organization will ingest, persist, manage, analyze, and use data to achieve its business objectives. Or in other words – it defines a company-wide approach to data monetization.
And guess what is the resulting object that arises from Data Strategy implementation?
It’s a Data Platform!
For that reason defining a proper Data Strategy is very important in the process of Modern Data Platform design, and it involves several key steps:
- Definition of business goal: identify the role of data in achieving an organization’s business objectives – a super important step that is often forgotten,
- Determination of the scope of data: define types of data that are relevant and the sources from which they are collected,
- Definition of data governance policies: guidelines for managing data throughout its lifecycle. This includes data quality, data security, data privacy and compliance,
- Choose technology: what sort of hardware/software should be used to implement the modern data platform? This has a crucial impact on modern data platform design and architecture,
- Development of data management plan: define how data will be collected, stored, managed or analyzed.
Different business goals
Depending on the business goal manufacturing companies want to achieve their data strategy might differ; thus underlying technology can vary.
- If the goal is to implement real-time analytics to improve quality control, then the modern data platform will require a robust and scalable infrastructure capable of processing large amounts of data in real-time.
- On the other hand, if the goal is to improve supply chain management – the modern data platform will need to be integrated with various IT systems and databases to provide a comprehensive view of the entire supply chain.
What are cloud-based data platforms and how do they work?
Data platforms offer businesses a powerful solution by establishing a centralized repository that effortlessly integrates data from various sources and IT systems. This seamless integration ensures that individuals within the organization can conveniently access all the essential data in a secure and unified location. By streamlining data management, these platforms provide businesses with a comprehensive and efficient means of harnessing their valuable information.
At the heart of these platforms lies data governance, a critical element that safeguards sensitive manufacturing data throughout its lifecycle and ensures compliance with data privacy regulations. The concept of data governance revolves around granting appropriate data access to the right individuals at the right time. To achieve this, a structured approach to data management is essential, encompassing the establishment of data policies and procedures, identification of data stewards and owners, implementation of data quality standards, and deployment of robust data security measures.
The flow of data through these platforms begins with data ingestion, encompassing both raw and pre-aggregated data. This data is then stored in a centralized location (it can be for example: Data Lake, Data Warehouse or Data Lakehouse – depending on given realm and requirements), allowing diverse stakeholders such as production managers, data analysts, and even machine learning algorithms to access it.
Once data is aggregated within the data platform, manufacturing companies can unlock the potential to leverage and monetize their data.
By gaining access to historical asset’s data and insights into their behavior, factories can enhance their production processes, optimize supply chain management, and develop effective risk mitigation strategies. Such data-driven decision-making holds particular significance in the manufacturing industry, as even small efficiency improvements can yield substantial cost savings and enhance overall competitiveness.
Seamless integration with IT Systems
Pure manufacturing data (OT) alone often fails to provide a comprehensive view of production processes. The involvement of multiple IT systems, not only on the shop floor but also across various departments of industrial companies, such as customer relationship management (CRM), enterprise resource planning (ERP), and a wide array of supply chain management systems, adds complexity.
Cloud technology inherently supports seamless integration among diverse IT systems, simplifying data access and correlation for businesses. Moreover – data platforms can easily adapt to new systems as they are implemented. This agility allows businesses to swiftly integrate new systems into their manufacturing operations without disrupting existing data flows.
By facilitating this integration, data silos are dismantled, empowering businesses to gain a deeper understanding of the interrelationships between different processes and unveiling optimization opportunities. As a result, companies can leverage their data effectively to drive improvements across their operations and enhance overall performance.
On-prem vs Cloud-based data platforms
One of the primary drawbacks of on-premises Big Data platforms is the cost factor. Building and maintaining the necessary infrastructure entails substantial upfront capital expenditure and ongoing operational expenses. These include expenses related to hardware, software, data center space, cooling, power, and round-the-clock support from the IT department.
Scalability is another limitation of traditional, non-cloud data platforms. They possess a finite capacity and can only handle a certain volume of data. As data volumes expand, businesses are required to add more servers or upgrade existing hardware, which can be both time-consuming and costly. Scaling the platform up or down also demands a significant amount of time.
When it comes to Cloud, the situation is totally different – you can scale the whole infrastructure automatically up or down just in time, when it’s needed, almost instantly, which simultaneously is also super cost-effective. When it comes to money cloud computing also eliminates the investment needed for hardware and software resources as well as maintaining it.
It’s on the vendor’s shoulder to keep everything up and running – isn’t that awesome?
Surely, at first glance when you start to add up subscription costs of different cloud services, it might not look cheap, but in the end – when you take into consideration all the costs related to the on-premise approach – the cloud computing almost always wins.
Data Visualization and Business Intelligence
Crucial components of any effective data management strategy, data visualization and business intelligence (BI) are fundamental aspects integrated within the data platform. These tools play a vital role in transforming raw data into actionable insights, especially for manufacturing companies dealing with the complexity of analyzing pure operational technology (OT) data.
Cloud computing providers offer a wide array of data visualization and BI capabilities, empowering manufacturers to gain comprehensive insights into their operations. With customizable dashboards and interactive visualizations, users can effortlessly analyze data from multiple sources, enabling quick identification of correlations, trends, and patterns. Additionally, these tools facilitate the generation of comprehensive reports that provide a holistic view of the entire organization, including performance comparisons between different plants and shifts. By leveraging these visualizations and BI capabilities, manufacturing companies can derive valuable insights that drive informed decision-making and optimize their operations.
Advanced Analytics and Machine Learning
AI is now the biggest global trend, not only in the IT world, so no surprise that cloud providers are rushing to expand their portfolio with more and more data analytics capabilities that can help manufacturers analyze their data in real time and extract insights.
In the manufacturing industry machine learning algorithms can be used for a wide range of applications. For example, predictive maintenance is a popular use case where AI powered by historical OT data is used to predict when equipment is likely to fail so that maintenance can be scheduled before a breakdown occurs. This can help manufacturers avoid costly downtime and improve their OEE and other KPIs.
Another use case is anomaly detection, where advanced analytics is used to identify unusual patterns or behavior in manufacturing data. This can help manufacturers quickly identify and address issues that could impact product quality or safety.
Furthermore, ML models can be used to optimize production processes and end-product quality just by analyzing underlying IIoT (Industrial Internet of Things) data.
Last but not least – industrial companies can leverage AI and ML for non-OT-related use cases like accurate demand forecasting for their products, optimization of production schedules or supply chain/logistics improvements.
However – none of those business goals could be achieved without a proper data platform underneath. Leaning towards a Cloud-based approach, manufacturers can benefit even more with the usage of dedicated, out-of-the-box AI-based cloud services.
Future trends and innovations
The world of manufacturing is constantly evolving, and the need for more sophisticated and effective data management solutions is growing along with it. As such, cloud-based data platforms for manufacturing are likely to continue to evolve in the coming years, incorporating new technologies and features to meet the demands of the industry.
One trend that is likely to continue is the use of even more AI to derive deeper insights from OT data. Cloud services providers are going to release more tools for autonomous and automatic manufacturing process optimization that could be easily integrated into business once the cloud-based data platform is in place.
This could include features such as automated data processing and analysis, real-time anomaly detection, and predictive maintenance based on already trained machine learning models, so no additional effort from data scientists will be needed to adjust solutions to customer realms.
Another area of innovation is likely to be in the field of data security and privacy. With the increasing amount of sensitive data being generated by manufacturing operations, it is essential that data platforms are designed with robust security and privacy features to ensure that data is protected throughout its lifecycle.
Let’s be honest – proper implementation of data governance is not easy, and surely there is room for improvement and simplification on the side of the cloud provider.
Adopting Data Platform for manufacturing is a strategic imperative for businesses looking to thrive in the Industry 4.0 era and can give them a competitive edge in the data-driven manufacturing landscape.
Classical on-premise big data platforms have limitations that cloud-based data platforms can overcome, and coupled together with BI and AI capabilities are the best choice when it comes to technology for holistic data management.