In industries ranging from biopharmaceuticals to chemicals, executives in today’s manufacturing marketplace face ever-increasing pressures to grow profit margins, reduce time to market and optimize processes across all aspects of their business. Everything from constraints in the supply of raw materials to multiple steps in a manufacturing process can affect productivity—making process optimization an amorphous target.
Big data analytics can help manufacturers improve processes, reduce waste and maintain quality standards.
That’s why more and more manufacturers are turning to big data to offer a systematic and specific approach to diagnosing and correcting process issues. In fact, a 2016 study found that 67 percent of manufacturing executives planned to invest in big data analytics, even in the face of pressure to reduce costs. Why? Because data analytics has proven to be a fundamental component in finding solutions to problems that lead to downtime and lost revenue.
What is big data analytics?
Big data refers to extremely large or complex data sets that may be analyzed using advanced statistical methods and tools designed to reveal patterns, trends, and associations, especially relating to processes, behavior and interactions. Advanced data analytics uses high-level methods and tools to focus on uncovering dependencies, identifying cause and effect, and projecting future trends, events, and behaviors.
One of these statistical methods is multivariate data analytics (MVDA), which provides a way to analyze data with more than one variable at time. This gives manufacturers the ability to perform advanced statistical models such as ‘what-if’ calculations, pinpoint where processes deviate, and future-proof various aspects of their operations.
How is big data being used by manufacturers?
Big data is being used to achieve productivity and efficiency gains and uncover new insights that drive innovation. Using data analytics, manufacturers can discover new information and identify patterns that enable them to improve processes, increase supply chain efficiency and identify variables that affect production quality, volume or consistency.
Data analytics helps manufacturers uncover defects in processes, speed product development and improve (or maintain) product quality, as well as reduce variation in quality. For example:
- Process manufacturers are using predictive analytics tools to assess the makeup of chemicals, minerals and other raw materials to make sure they meet production requirements.
- Manufacturers in biopharmaceuticals are using advanced data analytics to significantly increase production for biologics such as vaccines, without incurring additional capital expenditures.
- Chemical manufacturers use advanced data analytics to compare and measure the effect of various production inputs, such as coolant pressure, temperature or carbon dioxide flow on yield – often finding surprising and unexpected dependencies that are impacting output.
- Companies such as a precious-metals mine are able to gain insights from fragmented production data across multiple processes to find correlations between specific variables (such as oxidation and grinding) on the final output quality (grade of ore).
- Pharmaceutical manufacturers use data analytics to verify that processes—especially those created in batches – conform to standards that will define the proper characteristics. Read about one such manufacturer that used MVDA control batch-to-batch process variability.
- Manufacturers use big data to manage the long-term operational health of production equipment, using predictive analytics to reduce unscheduled down-time, and prevent breakdowns for apparatus using the “internet of things.”
- Biologics and pharma manufacturers run real-time analytical models on the properties of the raw materials that go into formulas and recipes and make adjustments to ensure a consistent output based on variations in the raw materials.
- Companies use predictive analytics to project future variations and implement automatic controls for batch processes with various types of raw ingredients that can be affected by contamination, temperature, time or other parameters.
- Manufacturers use data analytics to predict product lifespan (or customer demand for products) based on sales cycles, repeat orders, maintenance schedules or other factors.
In addition, manufacturers are also applying big data analytics across their processes to their supply chains, to improve product scheduling and sales forecasting, reduce costs, develop new propositions and monitor machine usage and reliability.
Some other ways companies today are using big data and data analytics:
- Reduce investment in working capital and stock
- Improve the accuracy of cash flow forecasting
- Embed a culture of data-driven decision making
- Predict customer product preferences
- Optimize logistics and distribution
Making use of big data
The first step in making use of your data is to gather it all into one central place. You may have vast amounts of process data that is under-utilized for improving operations and applied only for managing ongoing processes. It’s time to think about changing that.
Next you’ll need to invest in systems and skill sets that allow you to optimize your existing process information. That could mean hiring data analysts who can help you uncover patterns and draw actionable insights from your information, as well as purchasing new software and/or hardware.
Here’s a look at some of the tools manufacturers need to optimize data usage:
Data storage — Like many manufacturers, you may have an assortment of different data storage tools in place to gather information about your equipment, raw material inputs, manufacturing processes and production output. Collecting this into one location is the first step in making use of big data.
Data cleansing — It’s likely your data is collected in a number of formats coming from a variety of sources. In order to use it, you need a way to transform unrefined information into a readable, unified dataset. Data cleanup tools use mathematical approaches to reconcile inconsistencies and standardize the data so it can be used by different applications and systems.
Data mining —Having access to your information is necessary in order for it to be useful. Data discovery tools provide access to information in a useable format when it’s needed.
Data mapping —Data mapping tools help you identify dependencies and pinpoint potential bottlenecks. These tools may also help you identify access points and monitor data security.
Data analysis — In order to gain insight, you must be able to identify patterns, measure impact and predict outcomes based on your data. Data analytics tools help you put your data to practical use in improving processes and production outcomes.
Data monitoring — Monitoring tools help you automate quality assurance and ensure consistency in production processes.
Data visualization — Gaining a clear picture of what your data means typically requires: pictures. Graphs and charts provide visualization of your data in a way that makes it easier to understand.
Data forecasting — Get foresights into the production outcome with a specific setting on the machines.
By incorporating these tools into your manufacturing process, you can optimize production, streamline supply chains and generate productivity and efficiency gains.
Find out more
Want to know more about data analytics tools can help improve your manufacturing processes?
Read one of our case studies.