Several trends in the food and beverage industry are leading to challenges for manufacturers that can be best addressed with data analytics. With growing digitalization, more companies have access to the kinds of data that can transform their processes to meet the latest consumer demands as well as to shorten time to market, reduce costs, and shrink health and safety risks.
Throughout the evolution of manufacturing, many industries have gradually shifted away from batch process to continuous process manufacturing as production technologies matured. This has included industries such as chemical, petroleum, steel, automobile, consumer goods and food manufacturing.
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.
Making the perfect bar of chocolate is not just about mixing the right amount of sugar and cocoa, or adjusting the process for product quality. Another factor that must be taken into account to optimize both taste and profits is the grinding time of the cocoa beans. Let’s take a look at how data analytics can be used to elevate both, and to find the right combination of ingredients and process to support the business goals — an important factor in the food and beverage industry.
In production, your media will pass several different refinement steps. To really understand and be assured about a good progression and state of the production, all of these processing steps need to be monitored continuously. With SIMCA® and SIMCA®-online, both part of the Umetrics® Suite of Data Analytics Solutions, you can confidently monitor and control every step of your process. The web clients allow you to access manufacturing data anytime, anywhere.
In life science biopharma manufacturing, demonstrating consistent, repeatable processes is essential both for regulatory compliance and product quality. Being able to create data-driven, performance-based objectives, and aligning the process control strategies with compliance and business performance objectives, allows companies to take their data analysis to the next level: the level at which it becomes meaningful for the company’s bottom line.
In bioprocessing today, a shift is happening that takes the ability to monitor, optimize and control processes to the next level. Whereas in the past manufacturers aspired to measure data in order to find out why a bioprocess action happened (using descriptive and diagnostic analytics), today we are able to use predictive analytics to determine what will happen in a bioprocess based on specific process data measured in real-time. This migration “up the food chain” to a higher level of data analytics requires automation, ongoing process monitoring and the ability to make adjustments in real-time.
In pharmaceutical and other industries that rely on spectroscopy and multivariate calibration for quality control of manufacturing processes, optimizing the analysis of spectral data is imperative. Using a tool that is specifically designed with spectral analytics in mind can make the job faster, easier and more reliable.
For manufacturing companies, process control is essential— even for those producing low-cost items such as small plastic parts. That’s because even when units are small and inexpensive, the cost of defects becomes exponentially higher when they reach the next manufacturing step at another plant.
In chemical manufacturing, the process involved in creating a breakthrough product often takes several years — with ongoing tests that may be based on trial and error as much as specifically applied knowledge. One area of development in the specialty chemicals market involves the creation of new new additives called plasticizers that can help resins or polymers retain a more supple or flexible nature.
