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Big Data Analytics in the Food and Beverage Industry

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Dana Roth
Dana Roth

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.

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HOW BIG DATA IS TRANSFORMING THE FOOD AND BEVERAGE INDUSTRY
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
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.
WHAT IS BIG DATA?
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.
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
TOP CHALLENGES WITH DATA TODAY
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.
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
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
BIG DATA CHALLENGES Social Media Sensor Data Temperature Data Quantity Data Warehouse Data Tons more real-time data streams STEMS FROM MULTIPLE SOURCES
HOW IT WORKS
DATA LAYERS SENSORS EFFICIENCY FEEDS DEVICES VIDEOS MACHINE DATA ANALYZING YOUR BIG DATA FOR INSIGHTS
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
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.
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.
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.
BENEFITS FOR THE FOOD & BEV INDUSTRY
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
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.
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.
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.
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.
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.
EXAMPLES
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.
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
BIG DATA ANALYTICS TOOLS
BIG DATA ANALYTICS TOOLS
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.
CHD Expert – Americas 130 S. Jefferson Street Suite 250 Chicago, IL 60661 1-888-CHD-0154 CHD Expert – France 15 Rue Claude Tillier, 75012 Paris +33 1 73 73 42 00 CHD Expert – EMEA & Global Innovation Center 41 Montefiore St 6520112 Tel-Aviv 972 54-332-9690 www.chd-expert.com

More Related Content

Big Data Analytics in the Food and Beverage Industry

  • 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
  • 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
  • 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.
  • 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
  • 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.
  • 31. CHD Expert – Americas 130 S. Jefferson Street Suite 250 Chicago, IL 60661 1-888-CHD-0154 CHD Expert – France 15 Rue Claude Tillier, 75012 Paris +33 1 73 73 42 00 CHD Expert – EMEA & Global Innovation Center 41 Montefiore St 6520112 Tel-Aviv 972 54-332-9690 www.chd-expert.com