In the past decades, manufacturers have been increasingly taking advantage of big data and advanced analytics with a purpose to reduce costs and process flaws, to be more time-efficient and to improve product quality and yield. Nowadays, due to the extreme complexity of production activities directly affecting input and output, time has come for a more granular approach to access and improve business operations. With literally every industry undergoing a digital transformation, modern companies heavily rely on operational data marts to deliver their services and directly interface with their clients. Operational data mart is a type of strategic database, which includes internal control and operational environment information such as data on the company's workforce, direct competitors, creditors, suppliers and information on customers.
By means of applying statistics and diverse mathematical tools to business data, operational data helps to decode complex manufacturing processes. As a result, one can take a deep dive into historical process data, identify patterns and relationships among discrete process steps and inputs, optimizing the factors that prove to have the greatest effect on yield. The vast majority of global manufacturers now have lots of real-time shop-floor data at their disposal. In other words, they obtain a wider access to previously isolated data sets, aggregating them, and analyzing them to reveal important insights.
Competing for new opportunities and growing quicker than competitors requires capturing a huge amount of data, as well as the insights it provides. All manufacturers are pursuing greater speed, scale and simplicity across every area of their operations. Operational data often proves to be a true catalyst making competing against time and customers’ continually increasing expectations of consistent manufacturing quality much more simple.
The ways to get the most of your operational data
Create a Data Lake
Data lakes facilitate keeping all your operational data nicely organized and secured (e.g., service requests, configuration data, change records, resource consumption history, etc.). They provide the basis for discovering patterns that could otherwise not be detected by correlating data from different sources. Thus, collecting historical data across numerous servers allows one to predict server outages by spotting servers that are misbehaving. Similarly, analyzing endless change records enables a specialist to more accurately assess the risk of changes, which often is classified incorrectly by IT personnel.
Establish Comprehensive Monitoring and Event Management, Feeding into the Data Lake
While implementing APIs for the data lake is foundational to growing content from different sources, the main conduit is the event management system. It is very important to reduce the proportion of events that turn into incidents that require attention. By fine-tuning event correlation mechanisms, nearly 90% of events can be filtered out.
Set a Continuous Feedback Loop
Every action leaves a trace in the data lake, which should be routinely analyzed for improvement opportunities. It is vital to make this an ongoing iterative process.
Make Data and Insights Visible to Create a Data-Driven Culture
Through dashboards and self-service analytics, you can make your operational data marts the foundation of any conversation between stakeholders. These insights from dashboards increasingly have a business perspective. Thus, you can group information pertaining to IT resources for the business service they support, so that IT issues are made visible in a business context. You can use dashboards to create transparency and to drive an efficient data-based culture. Adopting a data-driven continuous improvement approach has a profound impact on operational performance. You have to address all aspects of manufacturing processes and organizational structure to make a data-driven transformation highly sustainable throughout the whole company.
Attain Higher Levels of Compliance by Receiving Data Directly from Any Machine on the Shop Floor in Real-time
Taking into account the rapid advances in PLC-based monitoring and Machine to Machine (M2M) interfaces, today, one can capture real-time data on metrics and Key Performance Indicators (KPIs) of interest. Capture item number, manufacturing number, work order details, lot numbers, date, time and additional KPIs to make traceability one of the strongest aspects of your company’s manufacturing operation.
Prolongate the Life of Equipment via Real-time Operational Data Use
Real-time monitoring enables one to predict when maintenance or repair are needed. By providing an entirely new series of insights into how manufacturing equipment and machinery lifespans function not only prolongs the machine durability, but also drives up Return on Invested Capital and augment overall business results.
By and large, today’s manufacturing companies operate within cutthroat, rapidly evolving business environments. Breaking down information silos facilitate the data flow across departments, feeding performance management technology solutions with financial and operational data for more sophisticated data insights. Recently, organizations massively start taking advantage of Internet of Things (IoT) and innovative IT technologies and obtain a unique opportunity to capture data that has eluded manufacturers for years.