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Ice cream perfection from cow to cone
Technology Category
- Analytics & Modeling - Predictive Analytics
- Functional Applications - Manufacturing Execution Systems (MES)
Applicable Industries
- Food & Beverage
Applicable Functions
- Quality Assurance
Use Cases
- Manufacturing System Automation
- Predictive Quality Analytics
Services
- Software Design & Engineering Services
- System Integration
The Challenge
Ben & Jerry’s maintains the strictest standards of product quality to ensure its customers get the full flavor experience each time they enjoy a pint or cone of Vermont’s finest ice cream. This attention to detail can be seen from “cow to cone,” as the company says, meaning that each step of its supply chain – from suppliers and distributors to manufacturing operations – must comply with the company’s three-part mission statement, which emphasizes product quality, economic reward, and a commitment to the community. Focusing on its manufacturing operations, Ben & Jerry’s maintains quality procedures for key performance indicators (KPIs) that ensure consistent product quality for every pint produced. To track quantitative data, the ice cream manufacturer had previously been using a paper-based system, which was proving to be cumbersome for operators and data administrators alike. Operators would take individual readings and calculate an average of those readings to plot on a paper chart. Quality assurance personnel would then perform manual calculations to compute trends and create reports. This system was not only slow and inflexible, but also costly in terms of man hours required for calculation and analysis. Ben & Jerry’s needed a fast and reliable way to collect and analyze the vital quality data of its super-premium ice cream products.
About The Customer
Ben & Jerry’s is a Burlington, Vermont-based corporation, and a wholly-owned subsidiary of Unilever. The company produces a wide variety of super-premium ice cream and ice cream novelties, using high-quality ingredients including milk and cream from family farmers who do not treat their cows with the synthetic hormone rBGH, eggs from hens on Certified Humane cage-free farms, and brownies from Greyston Bakery, a social enterprise in Yonkers, New York. Founded in 1978 by Ben Cohen and Jerry Greenfield, the company now has just over 500 employees and nearly 700 Scoop Shops worldwide, with manufacturing operations at facilities in Waterbury and St. Albans, Vermont, as well as some production lines at the Unilever plant in Henderson, Nevada. Offering beloved flavours such as Phish Food, Cherry Garcia, Chunky Monkey, and more, Ben & Jerry’s products are distributed in over 35 countries in supermarkets, grocery stores, convenience stores, franchise Ben & Jerry’s Scoop Shops, restaurants, and other venues.
The Solution
Ben & Jerry’s decided to deploy the InfinityQS ProFicient enterprise quality hub to streamline its quality control procedures. Powered by a Statistical Process Control (SPC) analysis engine, the system easily automates data collection and integration from terminals on the shop floor, while its real-time monitoring and analysis functions enable the quality department to acquire Manufacturing Intelligence by tracking variability across each production line. Because each line has a different run capability, Ben & Jerry’s created run charts within ProFicient based on Six Sigma data collected on the plant floor to determine variations specific to each individual line. By measuring, monitoring, and controlling four main product attributes—weight, volume, air addition, and inclusion amounts— as the pints come off the production lines, quality teams can work with production to quickly make adjustments in real time as products or processes approach specification limits. Quality teams can also compare the data acquired through visual cut-ups—a process where a pint is cut into quadrants to ensure the proper amount of inclusions appear in each serving—to the run capability data in ProFicient to identify the source of any variability in inclusion or variegate distribution and volume as identified during the cut-up.
Operational Impact
Quantitative Benefit
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