Food & Beverage 4.0: Putting Pieces of the Process Puzzle Together
How F&B Can Use Advanced Analytics to Maintain Precise Process Records
Currently, the Food & Beverage industry is facing some tough challenges, especially with ever-evolving consumer behavior and expectations and strict government regulations. Both of these challenges are strongly correlated to product traceability which is considered a pivotal challenge for the industry, not just for managing production records but also for generating revenue.
Spoiler alert: consumers want and expect high quality products, so maintaining precise process records is crucial to meeting quality assurance to give consumers what they want. And the Food & Beverage industry can do this with the help of advanced analytics.
Food & Beverage 4.0, Digital Transformation & Advanced Analytics
Ahhh digital transformation… one of Industry 4.0’s biggest buzz words. If you’re associated with the process industry, you will hear the word just about every day and probably many times a day. But do you actually know what it means? Well, for starters, the main objective of digital transformation is to establish smart factories in order to reach the highest levels of operational efficiency and excellence. This allows companies to stay competitive and profitable, today and in the future. Digitalization in the Food & Beverage industry is coming about just like it is with other process industries, and with many of the same sets of challenges. A simple example is switching from paper base workflows to digital workflows – as in moving from manual, paper-based shift logging to shift logging with digital solutions.
But in a nutshell, it’s the shift towards new, advanced technologies that includes the transition to fully digital processes on all levels, and that includes adopting and using things like advanced analytics – self-service analytics in particular.
See the Bigger Picture by Combining Time-Series & Context Data
Not all F&B companies are the same. Some operate batch processes so they need to analyze cycle times and batch qualities to keep process performance in check. Others might operate continuous processes so they’re focused on achieving operational excellence by reducing costs and improving yields. And of course, process experts want to keep ideal operating zones in check along with their equipment. They surely want to save operating and maintenance costs and improve performance. Self-service analytics fully leverages their time-series data to do all of this, but takes it step further by combining time-series data with process context data, making the data available for analysis and collaboration of the whole team.
Normally, process context data sources come from third-party systems that are external and reside in their own data silos. With solutions like TrendMiner’s ContextHub, you can search and filter relational data across various sources to uncover the information you’re looking for, and even save it as a view to start an analysis or share with others. At once, you can get a grip on a large time window with many different types of information. Now information is centralized, sharable, and accessible for everyone to see and, more importantly, it provides process experts the ability to maintain precise records and see the bigger picture.
Eliminate Data Silos to Provide a One Production Data Source for the Entire Team
Besides providing the capability to integrate contextual process data with time-series data, using self-service analytics eliminates data silos. Typically process records are stored in their own data silos held by different teams. For instance, the lab samples would be with the lab technicians and process experts may not have easy access to it. The maintenance information would be stored somewhere in SAP, so when process experts see a dip in a sensor, they might not know the reason for this dip.
But what would happen if that maintenance information was readily available for the entire team to monitor and visualize? The process experts could see that a sensor went down due to maintenance. This type of insight can be provided for each piece of equipment because context information can be added – thus eliminating, you guessed it, data silos.
Self-Service Analytics in Action
To understand how self-service analytics can be used to integrate production metadata with time-series data and eliminate data silos, check out our webinar on demand: Advanced Analytics for the Food & Beverage Industry.
In this webinar, Rob Azevedo, TrendMiner Product Manager demonstrates a use case involving beer brewing. He shows how to use self-service analytics to combine time-series data and context data to develop precise process records and gain insights into quality assurance.
Have a look at the information that was added for this beer brewing case:
- Mash Mill: The production steps showing when the malt mill was actively milling.
- Batch ID: The information linking the batch to the production.
- Mash Ton: The production information including alarms about pH limits, process anomalies, and planned maintenance.
- Fermenter: The fermenting steps along with lab samples, batch ID and beer measurement results including the alcohol percentage, the bitterness unit, and pH balance.
The first thing Rob did was to create a production overview of all the equipment involved in the brewing process. The brewing time-series data from each asset was plotted in its own swim lane, and the respective context items (metadata) for each asset were displayed in a chart next to this plot.
You can add any context information you want to provide insight into the process, information like production records, quality samples, and maintenance periods. This data helps you follow the process and tells you more about what is going on.
A Holistic View of Production
The Food & Beverage industry can use self-service analytics to fully leverage and analyze production and context data for data-driven insights. Personnel will be able to maintain precise process records which will contribute to quality assurance. Essentially self-service analytics provides one data source for a holistic view of production. What you get is a much more efficient and collaborative approach to monitoring and controlling your processes and running your plants.