For the manufacturing industry, using data to improve operational efficiency and performance is particularly relevant as the use case can be applied to any kind of manufacturing production system, including computerized numerical control infrastructure, supply chain and warehouse systems, logistics and test systems, and so on.
While manufacturers have traditionally focused on historical descriptive and diagnostic metrics, they’re now starting to use advanced analytics, machine learning, and data science to measure performance improvements and develop proactive, predictive, and prescriptive recommendations.
This use case is focused on the data platform architecture required to ingest, store, manage, and gain insights from data produced by manufacturing execution systems (MESs), warehouse management systems (WHMSs), computerized maintenance management systems (CMMSs), and maintenance systems to measure the operational efficiency of equipment, lines, and plants as well as performance metrics.
By ingesting, curating, and analyzing data on production processes and performance, manufacturers can identify and eliminate bottlenecks and inefficiencies to optimize production schedules and increase output. Applying the same approach to data on product quality, manufacturers can identify patterns and the root causes of defects, helping them implement more-effective quality control measures. Additionally, by including data on energy consumption, manufacturers can identify areas where they can drive energy efficiency to reduce costs and improve sustainability.
The architecture presented here demonstrates how we can combine recommended Oracle components to build an analytics architecture that covers the entire data analytics lifecycle, from discovery through to action and measurement, and delivers the wide range of business benefits described above.
All four capabilities connect unidirectionally into the serving data store and cloud storage within the Persist, Curate, Create pillar.
Additionally, streaming ingest is connected to stream processing within the Analyze, Learn, Predict pillar.
These capabilities are connected within the pillar. Cloud storage is unidirectionally connected to the serving data store; it is also bidirectionally connected to batch processing.
Two capabilities connect into the Analyze, Learn, Predict pillar. The serving data store connects to both the analytics and visualization capability and also to the data products, APIs capability. Cloud storage connects to the machine learning capability.
The Measure, Act pillar captures how the data analysis may be used: by people and partners.
Peoples and Partners comprises Operational Efficiency (Processing times, Error rates, Resource utilization), Process Bottleneck Identification, Customer Lifetime Value, Market and Competitive Analysis, Performance Attribution.
The three central pillars—Ingest, Transform; Persist, Curate, Create; and Analyze, Learn, Predict—are supported by infrastructure, network, security, and IAM.
Connect, ingest, and transform data
Our solution is composed of three pillars, each supporting specific data platform capabilities. The first pillar provides the capability to connect, ingest, and transform data.
There are four main ways to inject data into an architecture to enable manufacturing organizations to enhance operational efficiency and performance.
Persist, process, and curate data
Data persistence and processing is built on three (optionally four) components. Some customers will use all of them, others a subset. Depending on the volumes and data types, data could be loaded into object storage or loaded directly into a structured relational database for persistent storage. When we anticipate applying data science capabilities, then data retrieved from data sources in its raw form (as an unprocessed native file or extract) is more typically captured and loaded from transactional systems into cloud storage.
Analyze data, predict, and act
The ability to analyze, predict, and act is facilitated by three technology approaches.
The multiple models created by combining data science with the patterns identified by machine learning can be applied to response and decisioning systems delivered by AI services.
The final yet critical component is data governance. This will be delivered by OCI Data Catalog, a free service providing data governance and metadata management (for both technical and business metadata) for all the data sources in the data platform ecosystem. OCI Data Catalog is also a critical component for queries from Oracle Autonomous Data Warehouse to OCI Object Storage as it provides a way to quickly locate data regardless of its storage method. This allows end users, developers, and data scientists to use a common access language (SQL) across all the persisted data stores in the architecture.
As the speed of business—and the level of competition—increases, legacy systems used to deliver critical operating data can’t keep up. These systems need a lot of manual intervention to collate, integrate, and create reports from fragmented and siloed data, and that means the information arrives too late to give the business the advantage it needs.
Making the best use of your production resources is critically important to optimizing your manufacturing operations. Every minute spent producing the wrong products or producing the right products inefficiently not only increases costs and waste but also keeps you from delivering what your customers need. Optimizing operations and improving performance can bring numerous benefits for manufacturers, including the following:
Learn how to make manufacturing operations safer using a data platform that helps you improve health and safety with advanced analytics.
Learn how to consolidate plant data more efficiently and get insights faster with Oracle Data Platform for manufacturing.
Learn how to optimize assets with a data platform that enables predictive maintenance with machine learning.
Oracle offers a Free Tier with no time limits on more than 20 services such as Autonomous Database, Arm Compute, and Storage, as well as US$300 in free credits to try additional cloud services. Get the details and sign up for your free account today.
Experience a wide range of OCI services through tutorials and hands-on labs. Whether you're a developer, admin, or analyst, we can help you see how OCI works. Many labs run on the Oracle Cloud Free Tier or an Oracle-provided free lab environment.
The labs in this workshop cover an introduction to Oracle Cloud Infrastructure (OCI) core services including virtual cloud networks (VCN) and compute and storage services.
Start OCI core services lab nowIn this workshop, you’ll go through the steps to get started using Oracle Autonomous Database.
Start Autonomous Database quick start lab nowThis lab walks you through uploading a spreadsheet into an Oracle Database table, and then creating an application based on this new table.
Start this lab nowIn this lab you’ll deploy web servers on two compute instances in Oracle Cloud Infrastructure (OCI), configured in High Availability mode by using a Load Balancer.
Start HA application lab nowSee how our architects and other customers deploy a wide range of workloads, from enterprise apps to HPC, from microservices to data lakes. Understand the best practices, hear from other customer architects in our Built & Deployed series, and even deploy many workloads with our "click to deploy" capability or do it yourself from our GitHub repo.
Oracle Cloud pricing is simple, with consistent low pricing worldwide, supporting a wide range of use cases. To estimate your low rate, check out the cost estimator and configure the services to suit your needs.
Interested in learning more about Oracle Cloud Infrastructure? Let one of our experts help.