Capitalizing on Big Data: Driving Operational Improvement with Advanced Analytics
Data is a powerful asset that successful organizations use to gain new business insights and improve operations with less time and lower investment.
To effectively gain value from data, manufacturing organizations need to develop strategies to optimize existing data and collect more data from multiple sources. However, increasing data volumes from the Internet of Things (IoT) poses challenges for manufacturers that lack the systems and skills to manage and analyze large, disparate data sets.
Open source big data technologies such as Hadoop provides a scalable enterprise data management and analytics platform for driving operational improvement with advanced analytics.
The Need for Closed Loop Feedback Systems
Industries operate based on cohesive, interactive information. For example, production depends on demand and inventory, and is supported by supply chain systems for seamless performance. Manufacturing industries, in particular, require these production processes to align properly for business to gain meaningful insights.
Machine maintenance is a vital component for production that requires constant attention to control overhead costs. As technology advancements translate to better automated machines and infrastructure, equipment management and monitoring at department and industry levels need to take priority.
To effectively optimize performance, business needs a central repository to maintain dynamic information on changing requirement parameters and generate ad-hoc business insights. Additionally, business needs to ensure proper maintenance of infrastructure and machineries, which necessitates the implementation of near real-time monitoring systems to enable corrective measures in time to take preventative action.
The Hadoop big data ecosystem provides a viable framework for manufacturing industries to close feedback loops on equipment monitoring systems and enhance business precision.
Hadoop Architecture for Manufacturing
Complexity and cost are the primary barriers for organizations to gain valuable insights from traditional data processing and data management techniques. Proprietary legacy systems are not designed to manage and efficiently process data from a variety of data sources intensified by IoT expansion.
By leveraging the power of distributed processing (or massively parallel processing – MPP), Hadoop can handle large volumes of structured, unstructured and semi-structured data more efficiently than the traditional Enterprise Data Warehouse (EDW) approach.
The common misconception about Hadoop is that it is a replacement for legacy systems. However, when implemented properly, Hadoop can add value to traditional data warehouses. Hadoop can integrate all enterprise data sources within an Enterprise Data Hub (EDH) and make that data available with EDWs for advanced analytics on manufacturing industrial data to gain valuable insights on subject areas such as product, process, customer, inventory and supply chain.
Since its early days of adoption at enterprise organizations, Hadoop and its ecosystem of open source tools has helped solve many traditional enterprise challenges. The Hadoop EDH framework gives businesses greater access to data - both in terms of the size and number of data sets, and the time in which they can access the data.
Manufacturing industries can use the EDH approach for efficient data processing and data management.
Real-Time and Predictive Analytics: Boon to Industrial Big Data Owners
The Hadoop EDH provides organizations an edge on time and cost, as well as process optimizations. These benefits are enhanced by advanced capabilities such as real-time source integration, data science and predictive information on the Hadoop ecosystem. Manufacturing industries can effectively manage real-time sources of data like machine sensors and log data with new and evolving real-time technologies. These cutting-edge tools provide real-time data processing power that can address the need for real-time machine monitoring and feedback reporting.
Real-time data sources can also be integrated with the Hadoop ecosystem, where the data can be maintained as history for generating periodic batch insights as well as for integrating it with other important industrial information available on the EDH. When utilized as such, this implementation can give stakeholders 360 degree views of the complete industrial landscape.
Data on EDH can also be leveraged to generate predictions on production, supply, customer satisfaction and infrastructure durability. With these predictive inferences, along with other analytical reports generated on EDH, manufacturing companies can stay ahead of market requirements.
Big Data Process Flow to Close Business Feedback Loops
Big data makes it possible to consolidate complex manufacturing processes - supply chain management, production and demand management, inventory management and infrastructure/machinery maintenance – into a single architecture design.
To leverage this big data architecture for optimizing production, EDH needs to be configured to ingest the data sets in Hadoop in a way that partitions them as separate subject areas for the ease of management and integration as required. These data sets can be efficiently processed using Hadoop’s distributed processing to generate batch reports that can be delivered to stakeholders. These periodic batch reports can provide end-to-end reporting on daily or weekly production, inventory, supply chain needs and other critical processes. In addition, stakeholders and end users can have a business layer exposed to them where they can use the EDH data to generate on-demand reports specific to situations or needs.
The data on the Hadoop file system can also be leveraged by Data Scientists to execute machine learning algorithms, connectors and APIs that are available in multiple big data technology packages like Mahout and Spark. These reports can draw from a variety of machine learning, statistical and predictive analytics techniques on data sets to predict future industrial needs and raise risk flags in advance.
The machine learning tools and techniques within the open source Apache framework are best employed when the speed and latency of the data are of a primary concern. However, for more rigorous analytical work, the big data ecosystem can be leveraged through cutting-edge open platforms, such as the R language, to enable the manufacturing organization to remain on the forefront of data science. This secondary approach is more time or resource consuming from a processing perspective, but is essential to ingrate within the big data stack for organizations trying to develop and sustain a competitive advantage through analytics.
Real-time monitoring is another chief concern for business. Real-time big data processing platforms such as Storm and Spark can be integrated into the enterprise architecture to process machine logs and sensor data in near real-time. These platforms can generate and send performance feedbacks to remote machine monitoring systems and to stakeholders. When pushed onto the Hadoop file system, the data can be used to keep machine performance histories, which can be a very valuable source for tracking a machine’s durability and for conducting the maintenance evaluation.
Process Flow Diagram
With big data implementation methodologies such as those outlined above, a new era of innovation knocks at the doorstep of manufacturing industries. The possibilities created by open source big data technology can empower businesses to take data-driven decisions for better quality deliveries, improved internal management and quality control.
As the age of legacy platforms is being succeeded by an ever evolving big data technology stack, CIOs are taking note of the advantages. With proper vision and long-term strategic planning, the Hadoop-based EDH can provide businesses with a 360 view of their manufacturing operation and advanced analytics capabilities to transform how they run their venture and leverage their data sets to their best advantage.
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