Digital Transformation and Data Analytics in Process Industries

The value of data and data analytics in digital transformation and smart manufacturing

Data is a key pillar for DX as every interaction in the digital world generates data. So, along with exponential increases in connectivity and devices and IT/OT convergence, there has been a corresponding increase in data volume and new tools and mechanisms have been created to analyze, visualize, and interpret this enormous volume of what some refer to as: big data.

 

How can (big) data and data analytics help manufacturers?  

Big data has been a common theme in the data and analytics arena for a while. Generally, it is a term that describes the large volume of data—both structured and unstructured—that inundates a business on a day-to-day basis. According to Gartner, big data is a greater variety of data that arrives in increasing volumes and with ever-higher velocity.

Data has the potential to make manufacturing smart(er); data-driven—as opposed to event driven. In the operations and production functions of an industrial organization, using data and digital technology (DX) for strategic problem-solving and business innovation, is what is called smart manufacturing. 

In contrast to traditional manufacturing, smart manufacturing uses internet-connected technology, such as wireless sensors and smart devices, to provide real-time data processing and analytics. This, in turn, allows for the near-immediate output of information, such as information on material availability and inventory, or predictive maintenance. Organization can then use this data to improve their plant’s performance. 

Unfortunately, manufacturers aren’t perfect, generally they use less than 5% of their plants’ operational data due to poor data management, legacy applications, and static strategies. One way for them to derive meaningful insights from the other 95% of their data is to better make use of advanced big data analytics. Thanks to new predictive, preventive, or even prescriptive data modeling techniques from data science, new insights can be derived by combining data from IT and OT streams. It’s one of the reasons why even the most conservative manufacturers increasingly rely on data and analytics to provide actionable insights into how to improve their businesses’ operational effectiveness and new product development. 
 

What insights do big data and advanced analytics provide? 

There is a close relationship between the use of advanced analytics, meaning the computational use of statistics to understand a business’s data to make better (smarter) decisions, and a successful digital transformation.

 The use of data to enhance production and operations isn’t new; many manufacturers already use data or data analytics for industrial applications. However, they are often only providing old answers to old questions, such as how to reduce downtime by 4% to decrease costs by 2%. True smart manufacturing with big data analytics will create new answers to new questions. Advanced analytics can even uncover new insights into the operations of their plants. They can gain insight into everything from major inventory management problems to a specific machine’s effectiveness. Incredibly, enterprises can use big data to uncover and solve hidden operational problems, too.  

There are different data science strategies for utilizing big data. Maybe the simplest form is called descriptive data analytics, meant to understand changes in their business through the analysis of historical data. As a next step, diagnostic data analytics examine data to determine why specific events occur though root-cause analysis. For maximum corporate benefit, process manufacturers can turn to business analytics insights from practices such as predictive data analytics, which determines data patterns to predict future outcomes, and prescriptive data analytics, which provides recommendations for the best next steps to drive desired results.

What are the key enabling technologies for data analytics and data management to help manufacturers digitally transform their companies?

Numerous technologies, such as Industrial Internet of Things (IIoT) devices, AI, Machine learning, digital twins, 5G, robotics, and cloud can help process manufacturers sense and provide data for applications to analyze and manage in order to digitally transform their businesses. As process manufacturers move to combine information technology (IT) with operational technology (OT) to improve tools and techniques, IIoT works to collect massive amounts of data from a plant’s industrial data sources and assets. 
Businesses now need complex algorithms to help them interpret the big data they collect in their plants and use it to optimize value chains and improve product quality. 

What are examples of successful data analytics in manufacturing?

Several studies have shown that organizations make the best decisions when armed with data and tools to gather insight:
•    15–20% increase in ROI can be achieved by introducing big data to enterprises’ business analytics.
•    According to research by Deloitte, high performing organizations are 4-5 times more likely to have fully deployed advanced analytics/visualization, 10x more likely to have fully deployed RPA solutions, have fully deployed predictive analytics capabilities (12% vs. 0% for others), and are 18x more likely to have fully deployed AI/cognitive capabilities.
•    A research project conducted by Towards Data Science used data analytics to tweak the efficiency of air-conditioning units in factories. In some cases, air conditioning accounts for some 50 percent of a building’s energy consumption and 10 percent of all global electricity usage. By measuring and analyzing data from air-con units, a reinforcement learning algorithm was able to optimize their air-conditioning or 25 percent energy savings across the boards. Manufacturing data can be similarly analyzed to uncover anomalies or inconsistencies that drive process refinement

 

The role of data management and data architecture in DX

The increasing data-driven operations in manufacturing, also brings new challenges and requirements to data management, integration, and data architecture. The goal of data management is to collect, store, and analyze information to ease the digital transition by organizing both structured data and unstructured data to facilitate interpretation and assist in daily decision-making. But proper data management requires a sufficient data architecture of a manufacturing organization, especially if that organization pursues digital transformation. The right approach to data therefore captures the entire value-creation process, from data capture to value delivery, by way of various software applications and their augmented offerings.

What is the role of data management? 

Data management and orchestration services span the entire set of tools and frameworks that enable cataloging, organizing, managing, and processing data for delivery to specific analytic applications. These services help analytical applications automate the different steps of data pipeline development from source to consumption. These include extract-transform-load operations, data transformations, and data modeling to provide a fully abstracted but unified environment for delivering large volumes of data for individual analytics applications in the form they expect.

What is the role of data historians and data lakes in managing industry process data?   

Through a plant’s operations, process and asset data are aggregated, cleaned, and enriched. Most operations have accumulated years of time series data in various states of completeness; however, only a small portion is being used as a basis for operational decisions, one of the pillars of smart manufacturing. By processing, interpreting, and applying business logic to the process and asset data, digital application and services are established.

Today’s historians and data lakes are being used to store ever-increasing amounts of data originating from a much wider variety of sources, including control and monitoring, laboratory information management, and asset management systems. They have the potential to translate this into actionable insights to implement and improve equipment diagnostics, maintenance, safety, alarms, production, performance, and other process plant activities. However, if not managed well, most data lakes lack the essential features that prevent your data lake from turning into a data swamp. Historians are the critical point of integration between IT and OT, acting as an edge data collector to distribute consolidated OT information throughout the enterprise in various formats via company intranets, the Internet, and the cloud.

What is the role of data architecture?

The design of a data (and platform)-centric architecture is important. It manages data along its entire lifecycle from ingress (entry) to egress (exit), and is architected around the established sequence of activities and processes around data, namely, data enablement (getting data into the system), data curation (transforming, storing, and organizing data so acquired), and data utilization (consuming the raw, acquired or process data, in order to perform analytics and ML to derive insights), culminating in innovation.

Data architecture captures the tech stack and its interplay with existing systems and business processes. At the heart of this architecture is a platform stack or digital platform that provides various reusable services and joins together the various components of digital solutions and applications. This platform is regarded as the operating system for the cloud or as middleware for digital applications. The platform stack and its platform provide application enablement and facilitating quick and effective delivery of software as a service to end users.

What is the role of the cloud in data analytics and digital transformation?

The cloud is at the heart of any IT transformation. It has practically eliminated the need for on-premise IT data centers, server co-location, and traditional in-house IT resources. Process manufacturers can program IIoT sensors and smart devices to send the data they collect to the cloud or a digital platform. The cloud has achieved this through virtual provisioning and access to computing infrastructure—computing power, storage, and bandwidth.

In a cloud environment, large amounts of data are ingested across multiple sources and are available to support insightful decision making and application interoperability. The industrial automation architecture is evolving its technology stack towards a model that resembles IoT while retaining the needs of industrial operations. Delivering new levels of connectivity without compromising safety requires a domain-aware approach to equipment, assets, data, and application integration.

The cloud is already the infrastructure of choice for most business applications, especially outside the energy and chemical sector. However, it remains unexploited for most operational applications. The reason is that most valuable operational applications rely on a continuous feed of plant data which means they can never be isolated from the plant in a way that say an HR performance management system or capital budgeting system can. This is partially addressed with ‘edge devices’ living in the ‘fog’ between the real world of the plant and the virtual world of the cloud to bridge the gap, but there is still a potential pathway for a ‘bad actor’ to reach the plant even through an edge device.

As manufacturers take DX steps to become make their business more efficient and even autonomous, many are looking for new technologies to consolidate IT and OT data. Typically, a cloud based digital platform helps businesses more easily collect and analyze useful data. 

 

Data, data applications, and autonomous operations 

In process industries, digital transformation and smart manufacturing are often seen as a journey towards autonomous operations. This manufacturing state can be defined as assets and operations that have human like learning and adaptive capabilities that allow it to respond without operator interaction to situations within a secure bounded domain that were not pre-programmed or anticipated in the design and is responsible for all safety-critical functions. 

An autonomous plant or manufacturing enterprise will require sensing and digital infrastructure that spans the entire operation and integrates data, smart devices at the edge, bulletproof hardware and software to deliver the required level of flexibility, adaptability, and resilience. New technologies such as autonomous robots, additive manufacturing, artificial intelligence (AI), machine learning, augmented reality and 5G will allow for the required increased levels of automation, remote and unmanned operations. Putting humans out of harm’s way, a facility might entirely automate operations, maintenance, and incident management.

What is the role of industrial applications in realizing industrial autonomy? 

Obviously, smart applications or solutions play an important role in the journey towards industrial autonomy.  Simply put, applications are software programs built to solve specific business problems, such as asset management, production optimization, or health and safety. They are enabled by big data analytics and built on digital, cloud platforms, using services such as data, enterprise data management and orchestration, and logic builders and visualization available from the platform. Platform plumbing encourages applications to connect with each other and securely exchange or reuse data as needed. Once available on the platform, data can be reused by other applications, thereby eliminating data duplication throughout the lifecycle. 

So, what is possible with today’s process and asset data? With the availability of application packages and open interfaces, virtually any kind of analysis of plant data is now possible. An OT data foundation is fundamental to your digitalization ambitions. Data reconciliation, field mapping, and normalization are prerequisites for the effective use of analytics software and applications, such as advanced process control, real-time optimization, simulation, and AI.

 

How Yokogawa can help

In today's world, everything is increasingly interconnected in complex ways. Deriving smart insights from big data analytics is DX’s biggest asset. But it requires intelligently integrated technologies and data analytics solutions.

According to the system of systems (SoS) concept, multiple independently operating and managed systems coordinate together to achieve a purpose that lies beyond the capabilities of any single system. In such a world, Yokogawa will promote effective connections and create value through overall optimization driven by integration, autonomy, and digitalization and aspire to lead the way forward as an integrator in a world where entire societies function as an SoS.

Learn more about how Yokogawa is helping customers create value through DX . 

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