Software AG > Case Studies > Bega Cheese butters up supply chain with IoT

Bega Cheese butters up supply chain with IoT

Customer Company Size

Large Corporate

Region

Pacific

Country

Australia

Product

Cumulocity IoT

Tech Stack

IoT
Real-time data analytics
Machine Learning

Implementation Scale

Enterprise-wide Deployment

Impact Metrics

Cost Savings
Productivity Improvements
Customer Satisfaction

Technology Category

Platform as a Service (PaaS) - Connectivity Platforms
Analytics & Modeling - Real Time Analytics

Applicable Industries

Food & Beverage

Applicable Functions

Logistics & Transportation
Quality Assurance

Use Cases

Supply Chain Visibility
Predictive Quality Analytics
Real-Time Location System (RTLS)

Services

System Integration
Software Design & Engineering Services

About The Customer

Bega Cheese is an Australian diversified food company and one of the largest dairy producers in the country. It was originally founded as a dairy cooperative in 1899 with a number of its shares still held by Bega's farmer-suppliers. Headquartered in Bega, and with manufacturing sites in New South Wales, Queensland and Victoria, over half of Bega Cheese's revenue (as of 2019) comes from its spreads, dairy consumer packaged goods and other grocery products. Bega Cheese is a master at making cheese; its flagship Bega brand holds 15.7% of the Australian retail cheese market. But it has other iconic brands, too, such as the world- famous Vegemite spread, Picky Picky peanuts and Zoosh French onion dip. And it is not just Australians that like Bega; about a third of its revenues (as of 2019) came from exports of dairy products to around 40 countries around the world.

The Challenge

Bega Cheese, a major player in the Australian dairy industry, faced challenges in its supply chain. The process was almost entirely manual, with farmers having to test milk quality and check vat levels themselves, then phone in the data to Bega Cheese. If there was extra production, Bega Cheese would have to rush to get tankers there quickly—if they were available. Milk tankers could sometimes deliver too-warm milk—or shaken-up milk (which changes the quality). The supply chain had to be much faster, and much more efficient. The company wanted to improve the efficiency of its supply chain, increasing efficiency and improving visibility to address these unique challenges.

The Solution

Bega Cheese turned to the Internet of Things for a solution. They partnered with Swinburne University of Technology, which—with its IoT lab and Industry 4.0 initiative—was an ideal choice for Bega. Swinburne did a lot of the heavy lifting to get the project off the ground, which included successfully applying for a research grant from the federal government’s Cooperative Research Centres Projects (CRC-P). The project to optimise the productivity and competitiveness of Australia’s dairy industry, is in collaboration with Bega Cheese and three Australian milk suppliers initially—then scaling to 100+ suppliers. NBioT was selected as the network technology type to be used with new sensors. Then, what the project needed was an IoT platform that could start small, start quickly and then scale smoothly to include Bega Cheese’s 100+ suppliers. That is where Software AG was brought in—to scale up the 2.5-year project with its Cumulocity IoT platform.

Operational Impact

Storage vats can be measured automatically to see how full they are. If full, the IoT platform can trigger a transport request to collect the milk from the farm, saving the farmer the trouble of phoning in.
Vats can also be monitored for temperature and frequency of cleaning to ensure a low bacteria count.
Trucks can be monitored for bacteria count in their tanks and for temperature of the cargo. Bega Cheese can use real-time data to make sure the milk trucks take the best route—and avoid stopping at farms where there is not enough milk for pick-up.
Bega Cheese will know when the trucks will arrive at the factory depot, for better production scheduling.
Using real-time and historical data, Bega Cheese will be able to predict higher milk production with machine learning models; this will help it to schedule more pickups automatically and to plan in advance for increased production capacity.

Quantitative Benefit