How to Navigate the Complexities of Data-Driven Manufacturing?

Data driven manufacturing in action

Picture this - an equipment manufacturing facility where machines work in perfect harmony, each contributing to a complex and seamless production process. This isn't just any typical industrial setting, it's a showcase of the evolution of modern industrial practices.

As more and more companies hop on the data-driven manufacturing solutions train, they're starting to see the huge potential that data and analytics bring. From improving operational excellence to achieving sustainable business success, the possibilities seem endless.

But tapping into this potential isn't as easy as flipping a switch. It requires a solid grasp of two key things: the industrial data itself and the challenges that come with managing that data.

In this blog, we will delve into these aspects, shedding light on the various levels of industrial data and the obstacles that industries face in making use of this data.

Peeling back the layers: A deep dive into industrial data

Data is the raw material that, when processed and analyzed, can yield valuable insights for improving operations, enhancing efficiency, and driving growth. Let us break down this data into different levels to better understand its nature and significance.

Level 0: Process
At the ground level, we have process data, which includes sensor data and actuator data. Sensor data is gathered from various sensors measuring parameters like temperature, pressure, flow rate, and more. Actuator data, on the other hand, comes from actuators and control valves, indicating their current positions and statuses. Together, these data types provide a real-time snapshot of the ongoing processes.

Level 1: Control
The next level is control, which involves control setpoints, control system status, and safety system data. Control setpoints represent the desired values for process variables, while control system status indicates the operational status of the control system hardware and logic solvers. Safety system data comes from safety instrumented systems (SIS) ensuring safe plant operations.

Level 2: Supervisory
At the supervisory level, we deal with supervisory control data, alarm and event data, and historical process data. Supervisory control data comes from the supervisory control layer responsible for higher-level control and coordination. Alarm and event data show abnormal conditions and events, while historical process data logs past process values for analysis, optimization, and troubleshooting.

Level 3: Manufacturing operations
The manufacturing operations level encompasses a wide range of data types, including maintenance data, inventory data, quality data, energy consumption data, emissions data, sustainability data, safety data, and production data. These data types provide a comprehensive view of the manufacturing operations, from maintenance activities and inventory status to energy efficiency and production planning.

Level 4: Business planning
At the highest level, business planning, we find enterprise resource planning (ERP) data and financial data. ERP data relates to business planning, procurement, and resource allocation, while financial data involves financial transactions, budgets, and cost analysis. These data types are crucial for strategic decision-making and business planning.

Across all these levels, we have real-time data, historical data, machine and equipment data, and environmental data. Real-time data is crucial for immediate control and safety decisions, while historical data is used for trend analysis, predictive maintenance, and compliance reporting. Machine and equipment data provides information about the performance and health of individual machines and equipment, and environmental data relates to weather conditions that may influence plant operations.

By understanding these different levels and types of industrial data, businesses can better utilize the power of data analytics to drive their operations and achieve their strategic goals. Doing it alone can be difficult and time-consuming. But having a strategic data analytics consulting partner can help you in navigating these data complexities and easily revealing valuable business intelligence.

Unpacking the obstacles: Challenges in industrial data utilization

While the potential of industrial data is immense, it is not without its challenges. These hurdles can often complicate the process of data analysis and utilization. Let's take a look at some of these challenges:

  • Data complexity and heterogeneity
    Industrial data is often complex and heterogeneous, coming from various sources and in different formats. This diversity can make it difficult to integrate and analyze the data effectively. It requires sophisticated tools and techniques to handle this complexity and heterogeneity, turning raw data into actionable insights.
  • Data security and privacy
    As with any form of digital data, industrial data is subject to security and privacy concerns. Protecting sensitive information from breaches and ensuring compliance with privacy regulations is paramount. This challenge is even more significant given the increasing prevalence of cyber threats in the industrial sector.
  • Data quality and reliability
    The value of data driven manufacturing insights is only as good as the quality and reliability of the data itself. Inaccurate, incomplete, or outdated data can lead to misguided decisions and strategies. Ensuring data quality and reliability is a constant challenge that requires rigorous data validation and cleaning processes.
  • Legacy systems and silos
    Many industries still rely on legacy systems and have data siloed in disparate systems. These outdated systems and data silos can hinder data integration and accessibility, making it difficult to leverage data effectively. Overcoming this challenge often requires significant digital transformation efforts.
  • Data volume and velocity
    The sheer volume of industrial data, coupled with the speed at which it is generated, can be overwhelming. This high data volume and velocity require robust data storage, processing, and analytics capabilities.

However, with the right strategies, technologies, and expertise, industries can overcome these hurdles and unlock the full potential of their data.

For an illustrative example, let's take a look at how a leading instrument engineering company transformed its data acquisition systems for streamlined industrial monitoring and analytics.

The problem
The instrument engineering company faced challenges with its diverse range of data acquisition systems (DAQ). Each device or sensor had its unique technology and data acquisition platform, leading to scattered product portfolios, disconnected sales processes, and low customer perception. Their end customers, primarily large industrial enterprises, had to navigate multiple platforms when using various devices/products. This complexity resulted in significant effort and resources to maintain these platforms, slow time-to-market, and a lack of standard functionality across platforms.

The solution
Saviant collaborated with the Instrument Engineering company to develop a comprehensive condition monitoring platform equipped with smart analytics and AI capabilities. This platform captures data from various sources in real-time, processes it, and delivers it to different applications via APIs. The solution provided a unified sensor-to-software platform, offering standardization across platforms, platform independence (compatible with Windows, Linux, MacOS, etc.), scalability, and enhanced security.

The impact
The modern platform introduced by Saviant enabled a genuine plug-and-measure capability for customers across different products and platforms. It streamlined the data acquisition process, ensuring seamless integration and communication between devices and applications. This transformation led to improved customer experience, reduced maintenance efforts, and a more efficient and standardized approach to data acquisition and analysis.

Final thoughts: Reflecting on the impact of data driven manufacturing and analytics in the Industrial landscape

In the end, the rise of data-driven manufacturing and smart industrial solutions goes beyond mere data collection and analysis. It's about truly grasping the data, recognizing its challenges, and harnessing advanced analytics like AI-ML to derive meaningful outcomes. These outcomes empower businesses to make informed decisions, enhance operations, and achieve lasting success. As we progress, the influence of data driven manufacturing and analytics on the industrial landscape will only continue to grow, shaping the future of industry in ways we can only begin to imagine.

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