background image for GxP Lifeline
GxP Lifeline

Unlocking the Power of Your Manufacturing Execution System

How can you gain the full potential of your manufacturing data? To stay current with trends and gain a competitive edge in the market, manufacturing organizations collect a lot of data from various sources, such as the supply chain, manufacturing operations management, and key stakeholders. For this level of data gathering, companies need more storage capacity. They also need data storage technology that allows for rapid search and retrieval. To store and manage large volumes of data, companies striving to stay competitive are employing a data lake architecture

Data lakes provide a repository for storing vast amounts of data in structured, semi-structured, and unstructured data forms. They allow for secure and efficient storage of raw data without fixed limitations, which is helpful for gaining manufacturing insights. This differs from legacy storage architectures where data was stored in an external data bucket. To analyze the data, it needed to be copied to a repository where it could be retrieved for that purpose.

The Value of a Data Lake Architecture

A data lake provides a robust platform where data can be safely stored and easily retrieved to make accurate, data-informed decisions for manufacturing operations management. For example, knowing actual cycle times and how long it takes to execute a batch record or device history record (DHR) makes the production schedule complete and more accurate. You can more easily identify how to change processes to improve production and optimize lead times.

Advanced Manufacturing Data Analytics

A manufacturing data analysis software solution gives you greater functionality from your data assets. You can do a deeper dive into your data as well as mix and match data to create your own collections to reflect exactly what you need to see. Advanced analytics involves autonomous or semiautonomous examination of data. Autonomous data analysis involves the use of artificial intelligence (AI) algorithms to augment human judgements and decisions. You can gain more useful manufacturing insights, reduce the time necessary to investigate production issues, and generate recommendations for actions.

A big part of advanced analytics is natural language processing (NLP), which provides additional insights from text provided by end users. Other advantages of manufacturing data analysis software include:

  • More efficient organization – Easily gather, organize, and share the specific information that people need in real time.
  • More visibility – Gain a complete, at-a-glance view of your entire manufacturing operation.
  • Performance measurement – Organize, correlate, and track existing key performance indicators (KPIs), and easily create KPIs to track your most important metrics.
  • Improve risk management – A high-level of accuracy of data analysis enables businesses to reduce their risk of costly mistakes, i.e., data entry errors, illegible entries, or missing documents.
  • Anticipate problems and opportunities – Advanced analytics uses statistical models to reveal potential problems on the business' current trajectory, or to identify new opportunities, so stakeholders can quickly change course and achieve better outcomes.

For instance, with the functionality that anticipates problems and opportunities, when the manufacturing execution system (MES) solution detects an anomaly, it’s marked on a plot (line chart) with additional details. The analysis runs in the background and provides results that are sorted based on the number of detected outliers.

You can use this approach to see if there is a trend, such as particular anomalies. For anomaly detection, you can think of it as a presentation of a manufacturing operations management KPI plotted over time, using the sequence that was recorded. As soon as you gather a statistical analysis and determine that it could be considered an anomaly, you can record and store the information and render it to end users.

Advanced Analytics in Batch Production

Another benefit of advanced analytics is that you can quickly identify batch production issues without the need for data exploration. The manufacturing data analysis software completes all the steps necessary to identify anomalies and then provides the analysis information. Other use cases include:

  • Step execution duration and outlier detection – See a specific case for a particular batch record or DHR if you’re experiencing longer than expected directional detection.
  • Pattern detection in production record exceptions – Lets you figure out if there’s a recipe or combination of parameters that can cause more record exceptions than in normal production.
  • Seasonality detection of rejections in production records – Enables you to determine if there’s a certain time of year when you experience more than the usual number of rejections.

An example would be if you opened a production record to see the backlogs of production records or production record execution problems. The idea is that you can use the data to assist in making decisions. To learn more about what data analysis software can do for your company, see the MasterControl Insights page.


David Jensen is a content marketing specialist at MasterControl, where he is responsible for researching and writing content for web pages, white papers, brochures, emails, blog posts, presentation materials and social media. He has over 25 years of experience producing instructional, marketing and public relations content for various technology-related industries and audiences. Jensen writes extensively about cybersecurity, data integrity, cloud computing and medical device manufacturing. He has published articles in various industry publications such as Medical Product Outsourcing (MPO) and Bio Utah. Jensen holds a bachelor’s degree in communications from Weber State University and a master’s degree in professional communication from Westminster College.

[ { "key": "fid#1", "value": ["GxP Lifeline Blog"] } ]