Algist Bruggeman NV produces yeast for large-scale bakeries and home bakers. The company lacked insight into its fermentation process as its sensor data collection process was manual. Production data was committed to paper, making it difficult to compare batches, aggregate production parameters or detect anomalies. Factry.IO’s data historian, built on InfluxDB, has helped the company collect process data, enabling it to gain more insight into its production process and provide predictive maintenance. In this webinar, learn about Algist Bruggeman NV’s business outcomes and the technical setup of linking time series data with ERP, planning and quality data for operational improvement.

1. by factry Operational intelligence made easy
by factry Operational intelligence made easy
InfluxData webinar
Data collection and integration
Using OPC-UA to Extract IIoT Time Series Data from PLC and
SCADA Systems
Confidential

2. Improving data integrity, gaining
more insight in the production
process and making better yeast.

3. Agenda
Introducing Ivo - Algist Bruggeman
Introducing Frederik - Factry
Situation and challenge
Solution and outcomes
Unexpected Benefits
Q&A

4. ● Project Manager
Automation
● Responsible for
automation and
electrical maintenance
● Project lead for MES &
Historian project
About Ivo Lemmens

5. About Algist Bruggeman

6. Algist Bruggeman supplies fresh, liquid and dried yeast to
industrial, semi-artisanal and artisanal bakeries, as well as
to the wine, beer and pharma industry.
Algist Bruggeman is part of Lesaffre Group, a key global
player in fermentation for more than a century, with a 2
billion EUR turnover and established on all continents,
counting 10.000+ employees and more than 85
nationalities.
Its Belgian site employs about 170 people and has an
annual turnover of over 120 million euros.

7. ● Raw Materials
● Yeast fermentation
● Processing
⇒ Fresh, liquid, dry yeast
Production Process

10. A bit of history
● https://github.com/coussej/n
ode-opcua-logger
● Started with InfluxDB v1.0.x
in 2016
● Begin with temperature
logging for food safety and
then gradually expanded data
collection on site.
● ~50 PLCs, 4000 tags, ~1Hz
resolution

11. ● Co-founder and
business developer
● Bioscience engineering
degree
● Developed large parts of
Algist Bruggeman MES
application
About me

12. About Factry

13. The Digital Factory
We look at the world of industrial
automation through the eyes of an
IT company.
Extract process data to IT level as
soon as possible.
Because at that level, the fastest
progress is being made.
OT
IT
DIGITAL
FACTORY

14. Factry Historian
Collect data from
production equipment
Store it in a time-series
database
Visualize it with web-based
visualization tools

15. What problems does this solve?
Look back
(Near)
real-time
Use as basis
to predict

16. Join our team!
https://www.factry.io/jobs

17. The situation and challenge

18. The challenge
- Why?
● How did this specific
fermentation perform?
● Does a specific fermentation
perform differently on
fermentor X than on
fermentor Y?
● How well does a certain
fermentation parameter
follow its reference curve?

19. ● Fermentations are followed
up on paper
Valuable information ends up in a
folder
● Some data is registered, but
isolated in a specific system
Cumbersome to answer these
questions
Data is not readily
available

20. The problem: different data sources
● Recipe management in Excel
● Fermentation progress on paper
● Lab results in LIMS software
● …

21. Answering the 3
questions is hard!
Why?
Data is not linked. The human
needs to bring everything
together.
PLANNING
PRODUCTION LOG
HISTORIAN LOGBOOK
TRACEABILITY
LAB DATA
SHIFT
PLANNING
REPORTING
AFHAALLIJST
OTHER...
ERP

22. So let’s bring this data together
When have we succeeded?
● Human has become a user, not the person linking data
● The 3 questions can be answered in reasonable timeframe

23. Take a holistic approach
● Before fermentation
○ Planning
○ Recipe management
● During fermentation
○ Dispatching
○ Process data collection with Factry Historian in InfluxDB
○ Automation of fermentation sheet completion
● After fermentation
○ Linking lab results
○ Reporting

24. Step 1: get batch information
● Planning of orders
○ Retrieve data from planning software & ERP via B2MML
■ Business 2 Manufacturing Markup Language
■ https://github.com/factrylabs/go-b2mml
● Information:
○ Start- and end times of upcoming batches
○ Equipment these batches will run on
○ BatchID, recipeID and recipe version

25. FACTRY MES
ERP DATA
Closing
the loop

26. Step 2: get recipe data
● Recipe data contains all reference values
○ Material feed
○ Critical process parameters
● Used for:
○ Providing set points to the PLCs that will control the production
process
○ Providing reference values to compare with the actuals recorded
during the production process

27. FACTRY MES
ERP DATA
RECIPE DATA
Closing
the loop

28. Now we know:
● When we will produce
● What we will produce
● How to identify what we’re
going to produce
● Where we will produce
(fermenter)
What’s missing?
● The actuals!

29. Step 3: dispatching of batches
● Upcoming batches are synched from planning / ERP system
● Because we know the batchIDs, expected start- and end times, recipes…
● The operators can just press “Start” and the SCADA system and PLCs are
loaded with the recipe data.
Error-free link between planning and production: No more manual selecting of
a recipe or typing in a BatchID

30. SCADA
FACTRY MES
ERP DATA
RECIPE DATA
Closing
the loop
OPERATOR
INTERFACE
PLCs

31. Step 4: collecting process data
● High resolution data is gathered from different PLCs
○ Broader than just fermentation
● Dashboarding and process analysis
● Data source for storing what actually happened during production

32. ● Supported by newer PLCs directly, or via SCADA system OPC-UA server
● Collecting metrics:
○ Polled (typical for sensors: e.g. every 5 sec, every min)
○ Monitored (typical for states or valves: on-change)

34. Collect data from
production equipment
Store it in a time-series
database InfluxDB
Visualize it with web-based
visualization tools

35. Collectors
Talk industrial protocol (e.g.
OPC-UA) on one end and HTTP on
the other
Store-and-forward
● Local buffering
● Keep a local copy of
configuration

36. The importance of naming
● Hierarchical structure
● AREA.EQUIPMENT.SENSORID
● Benefits!

37. PLCs
FACTRY HISTORIAN
SCADA
FACTRY MES
ERP DATA
RECIPE DATA
Closing
the loop
OPERATOR
INTERFACE

38. Step 5: fermentation sheet
completion
● Sampling of process data at regular intervals to replace a paper
fermentation sheet.
● Operator is partially relieved of repetitive tasks
● Automatic marking of critical process parameters that deviate too much
from the expected values from the recipe

39. PLCs
FACTRY HISTORIAN
SCADA
FACTRY MES
ERP DATA
RECIPE DATA
Closing
the loop
OPERATOR
INTERFACE

40. Step 6: linking LIMS data
● Lab performs analyses for each Batch. This information is synched with
all centrally collected data.
○ Yield
○ Dry matter content
○ ...

41. Step 7: answering questions with
reporting!
● Give me all batches from February 2021 that followed recipe X and had a
dry matter content of at least Y.
● Show me how the reference curve of parameter X evolved for a specific
batch. And how does this compare with the reference values from all
batches that followed the same recipe?
● Or… show me the raw process data for batch X in Grafana.

42. PROCESS
ANALYSIS
PLCs
FACTRY HISTORIAN
SCADA
FACTRY MES
OPERATOR
INTERFACE
REPORTING
TOOLS
ERP DATA
RECIPE DATA
Closing
the loop

43. Open technologies
and standards
● Vendor independent
● Source code available
● Has been running in
production for years

44. The Digital Factory
We look at the world of industrial
automation through the eyes of an
IT company.
Extract process data to IT level as
soon as possible.
Because at that level, the biggest
progress is being made.
OT
IT
DIGITAL
FACTORY

45. Some unexpected benefits
1. MRP functionality:
○ Prediction of material and air usage
2. Real-time machine monitoring
○ Valve maintenance
3. Debugging of cold-room door (anomaly detection)
4. Monitoring of the MES and Historian

46. Some unexpected benefits
1. MRP functionality:
○ Prediction of material and air usage
2. Real-time machine monitoring
○ Valve maintenance
3. Debugging of cold-room door (anomaly detection)
4. Monitoring of the MES and Historian

47. MRP
We know:
● Upcoming production
And therefore:
● Expected material use
and compressor load
As well as
● Current tank levels

48. MRP functionality in Grafana

49. Some unexpected benefits
1. MRP functionality:
○ Prediction of material and air usage
2. Real-time machine monitoring
○ Valve maintenance
3. Debugging of cold-room door (anomaly detection)
4. Monitoring of the MES and Historian

50. Valve maintenance

51. Some unexpected benefits
1. MRP functionality:
○ Prediction of material and air usage
2. Real-time machine monitoring
○ Valve maintenance
3. Debugging of cold-room door (anomaly detection)
4. Monitoring of the MES and Historian

52. Anomaly detection

53. Some unexpected benefits
1. MRP functionality:
○ Prediction of material and air usage
2. Real-time machine monitoring
○ Valve maintenance
3. Debugging of cold-room door (anomaly detection)
4. Monitoring of the MES and Historian

54. Monitoring of the MES system

55. And finally, our advice
Just give it a try!
https://www.factry.io/blog/process-data-integration-open-source-or-proprietary-software/

56. Wrapping up
● We are now able to answer the 3 questions in a reasonable amount of
time
● The human has become a user of the information flow, not the
centerpiece
● Additional benefits because of a common data platform
● All of this with open protocols and open source software

57. Takeaways
1. Build a platform for your
process data, not a collection
of point solutions
2. Think of your naming
structure
3. Work iteratively with all
stakeholders
4. Data has a clear impact on
the business

58. Thank you!
Questions?
Further reading:
https://www.factry.io/blog
https://medium.com/factry
https://www.linkedin.com/company/factry.io
IVO LEMMENS - Algist Bruggeman
FREDERIK VAN LEECKWYCK - Factry
+32 474 88 85 73
frederik.vanleeckwyck@factry.io