Food and beverage manufacturers use big data analytics to optimize processes, improve efficiency, increase quality, and ship products on schedule. By analyzing real-time sensor data from manufacturing equipment, manufacturers can identify patterns and relationships to optimize factors like yields. Manufacturers also use big data analytics to analyze supplier and logistics data to optimize supply chains and logistics. The key takeaway is that by quantifying risks using big data tools, food and beverage companies can make more intelligent business decisions.

1. HOW BIG DATA IS
TRANSFORMING THE FOOD
AND BEVERAGE INDUSTRY

2. Find new business insights by
combining data from
multiple outside streams.
TABLE OF CONTENTS
Introduction
What is Big Data?
Challenges with Data Today
When to Know if You Need Big Data Analytics?
How It Works
Benefits in the Food and Beverage Industry
Examples
Tool Box: Big Data Analytics Platforms

3. INTRODUCTION
In order to keep pace with consumers’
fickle buying habits, food and beverage
companies need to begin by gathering
all the different data streams into one
system. Analytical capabilities then can
transform this data into meaningful
intelligence that can inform
management decisions. Those
decisions will boost sales and improve
overall bottom-line performance.

4. WHAT IS BIG DATA?

5. BIG DATA ANALYTICS
REFERS TO THE
STRATEGY OF
ANALYZING LARGE
WHAT IS BIG DATA ANALYTICS
WHAT IS BIG DATA?
This big data is gathered from a wide
variety of streams, including
sensors,
devices,
networks,
videos,
digital images,
sensors,
sales transaction records.
The aim in analyzing all this data together
is to uncover patterns and connections that
might otherwise be invisible, and that
might provide valuable insights about the
users who created it. Through this insight,
businesses may be able to gain an edge
over their rivals and make superior
business decisions.

6. WHAT IS BIG DATA? VOLUME
How much data are you dealing with?
With big data, you’ll have to process high
volumes of low-density, unstructured data.
This can be data of unknown value, such
as your SENSOR-ENABLED EQUIPMENT.
1
VARIETY
If you are receiving data from multiple
sources, such as in your supply chain, then
this is considered high variety.
2
VELOCITY
How quickly is the data streaming in? This is
usually always the case for the foodservice
industry.
3
terabytes
petabytes
realtime or
near-realtime
social networks
blog posts
logs
sensors, etc.
VOLUME
VARIETY
VELOCITY

7. TOP CHALLENGES
WITH DATA TODAY

8. DIFFICULT TO FIND INSIGHTS
The huge volumes of raw, streaming data from all different sources
make it extremely difficult to pull out insights needed to become more
efficient.

9. BIG DATA CHALLENGES
“Data silos are the reason you have to number
crunch to produce a monthly sales report.
They’re the reason that C-level decisions are
made at a snail’s pace. They’re the reason your
sales and marketing teams simply don’t get
along. They’re the reason that your customers
are looking elsewhere to take their business
because they don’t feel
their needs are being met and a smaller, more
nimble company, is offering something
better, according to
B2C.com.
DATA SILOS
This is when an organization stores its
information in silos, whether in individual
departments, regional offices, different
channels and sometimes even in different
management levels within an organization

10. The data doesn't reside in a database. Documents, photos,
audio, videos and other unstructured data can be difficult to
search and analyze.
UNSTRUCTURED DATA
BIG DATA CHALLENGES

11. BIG DATA CHALLENGES
Social Media
Sensor Data
Temperature Data
Quantity Data
Warehouse Data
Tons more real-time data streams
STEMS FROM MULTIPLE
SOURCES

12. HOW IT WORKS

13. DATA LAYERS
SENSORS
EFFICIENCY
FEEDS
DEVICES
VIDEOS
MACHINE DATA
ANALYZING YOUR BIG DATA
FOR INSIGHTS

14. FROM RAW DATA TO OPERATIONAL
INSIGHTS
PRODUCTION LINE
DIGITAL MODEL
PREDICTIVE ANALYTICS
AUTOMATED ROOT - CAUSE ANALYSIS
SIMPLE & ACCURATE INSIGHTS
FOR OPERATION TEAMS
IMAGE COURTESY Seebo.com

15. HOW IT WORKS
INTEGRATE
Big data brings together data from many
disparate sources and applications.
Traditional data integration mechanisms, such as
ETL (extract, transform, and load) generally aren’t up
to the task.
It requires new strategies and technologies to
analyze big data sets at terabyte, or even petabyte,
scale.
During integration, you need to bring in the data,
process it, and make sure it’s formatted and
available in a form that your business analysts can
get started with.

16. HOW IT WORKS
MANAGE
Big data requires storage. Your storage
solution can be in the cloud, on premises,
or both. You can store your data in any
form you want and bring your desired
processing requirements and necessary
process engines to those data sets on an
on-demand basis. Many people choose
their storage solution according to where
their data is currently residing. The cloud is
gradually gaining popularity because it
supports your current compute
requirements and enables you to spin
up resources as needed.

17. HOW IT WORKS
ANALYZE
Your investment in big data pays off when
you analyze and act on your data. Get new
clarity with a visual analysis of your varied
data sets. Explore the data further to make
new discoveries. Build data models with
machine learning and artificial intelligence.
Put your data to work.

18. BENEFITS FOR THE FOOD & BEV
INDUSTRY

19. HOW BIG DATA ANALYTICS IS USED FOR THE FOOD
AND BEVERAGE INDUSTRY
collecting and analyzing sensor data such as vibration and temperature to monitor asset
health can significantly reduce machine downtime. This allows companies to identify and
fix potential component faults before the machine fails while maximizing the efficiency of
maintenance teams by allowing them to focus their time on degrading assets.
CONDITION MONITORING &
PREDICTIVE MAINTENANCE
collecting and analyzing data from the use of cameras, vision systems and other
inspection equipment can be used to monitor shape and color of produce to ensure
it meets standards while reducing food waste.
PRODUCT SORTING
By collecting information on food (including flavor compounds), and analyzing online
recipes has enabled artificial intelligence solutions to create new recipes and
combinations.
CREATING NEW RECIPES
Monitoring production line variables such as pressure, temperature and other metrics
of machine performance, and allow machines to adjust to required minimum weight
tolerances while ensuring minimum product weights are met.
IMPROVE MEASUREMENT
Combining machine vision with artificial intelligence can improve the quality and safety
of food to identify and remove sub-par or defective ingredients through the identification
of visual anomalies.
IMPROVE FOOD SAFETY
Through added sensing, operators can monitor the amount of food debris remaining
in the machine and optimize the cleaning process accordingly – reducing cleaning
time, energy and water consumption.
MACHINE CLEANING

20. HOW BIG DATA ANALYTICS IS USED FOR THE FOOD
AND BEVERAGE INDUSTRY
PREDICTIVE MAINTENANCE -
FORECASTING
Machine data is securely streamed from
equipment sensors to a central repository using
industrial data protocols and gateways. IoT
behavior analytics are applied to predict failures
before they arise.
Implementing predictive maintenance typically
starts with rule-based alerts until sufficient data
is collected, at which time machine-learning
algorithms can be applied to identify complex
behavior patterns and anomalies.

21. HOW BIG DATA ANALYTICS IS USED FOR THE FOOD
AND BEVERAGE INDUSTRY
IMPROVE
MEASUREMENTS
Monitor production line variables such
as pressure, temperature and other
metrics of machine performance, and allow
machines to adjust to required minimum
weight tolerances while ensuring minimum
product weights are met.

22. HOW BIG DATA ANALYTICS IS USED FOR THE FOOD
AND BEVERAGE INDUSTRY
PRODUCT
SORTING
Use of cameras, vision systems and other
inspection equipment can be used to
MONITOR SHAPE AND COLOR OF PRODUCE
to ensure it meets standards while reducing
food waste.

23. HOW BIG DATA ANALYTICS IS USED FOR THE FOOD
AND BEVERAGE INDUSTRY
IMPROVE FOOD
SAFETY
Combining machine vision with artificial intelligence can improve the
quality and safety of food to IDENTIFY AND REMOVE SUB-PAR OR
DEFECTIVE INGREDIENTS through the identification of visual
anomalies.

24. HOW BIG DATA ANALYTICS IS USED FOR THE FOOD
AND BEVERAGE INDUSTRY
CREATING NEW
RECIPES
By collecting information on food (including
flavor compounds), and analyzing online
recipes has enabled ARTIFICIAL
INTELLIGENCE SOLUTIONS TO CREATE
NEW RECIPES AND COMBINATIONS.

25. EXAMPLES

26. EXAMPLE
A Fortune 500 food supplier needed to contain its
growing expenses and educate its business units
on the hidden costs of workplace injuries. By analyzing
available data and partnering with outside experts,
the company was able to assess the situation, develop
incentive plans, and ultimately save more than
$500,000. A decision that may once have been based
on guesstimates was instead driven by metrics,
harnessing the power of data.

27. EXAMPLE
Ben and Jerry’s recognized the need for dairy-free ice
cream options and are capitalizing on this under-served
segment of the market with the release of seven
non-dairy flavors, much to the delight of vegan
and lactose intolerant ice cream lovers everywhere.
Listening to what your customers want will help you to
retain them and build customer loyalty, reducing the
risk of losing them to specialty brands or other
companies.
ACCORDING TO A QUANTZIG REPORT

28. BIG DATA ANALYTICS
TOOLS

29. BIG DATA ANALYTICS
TOOLS

30. SUMMARY
Food and Beverage Manufacturers use big data
analytics to identify patterns and relationships
among discrete process steps and inputs, and then
optimize the factors that improve Efficiency.
By using big data analytics on a growing flow
of real-time sensor and machine data,
manufacturers can improve yields, increase
quality and ship products on Schedule.
Manufacturers optimize supply chains and
logistics with big data analytics by analyzing
supplier and logistics tracking data with cost
and historical logistics data.
TAKEAWAY
By using big data-based tools to quantify their risk, food and beverage companies
are assigning values to the unknown, forecasting the extent of potential financial damage,
and assessing possible mitigation opportunities to manage their exposures. In short,
they’re using data and analytics to drive intelligent business decisions and change.

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