更接近成为世界上最数字化的装瓶业务
技术
- 网络安全和隐私 - 数据库安全
- 网络安全和隐私 - 身份认证管理
适用行业
- 包装
适用功能
- 商业运营
用例
- 网络安全
服务
- 网络安全服务
客户
可口可乐欧洲太平洋合作伙伴
关于客户
Coca-Cola Europacific Partners于 2021 年 5 月由 Coca-Cola European Partners 收购装瓶公司 Coca-Cola Amatil 成立。根据净收入，该实体是世界上最大的独立可口可乐装瓶商。除了可口可乐饮料，品牌还包括健怡可乐、芬达和雪碧，以及种类繁多的补水和即饮咖啡、茶和果汁。
挑战
虽然数字化正在提高效率并显着增加客户参与度，但它也带来了一些挑战，尤其是网络攻击的风险越来越大。
这促使该公司的澳大利亚、太平洋和印度尼西亚业务部 (CCEP API) 制定了一个为期三年的路线图，用于开发和实施增强的安全措施。该计划的一个关键要素是改进现有的特权访问管理流程，并加强对提升凭证的使用的监督和控制。
解决方案
特权访问管理对于降低风险至关重要
加强对 PCI DSS（支付卡行业数据安全标准）等标准的合规性和对 NIST（美国国家标准与技术研究院）框架的遵守也是变革的重要推动力。
为了选择最佳的特权访问管理解决方案，CCEP API 将利益相关者聚集在一起，以确保在选择新解决方案后跨业务协调并简化采用。
CCEP API 推出了 CyberArk Privileged Access Manager Self-Hosted——一种混合本地和云解决方案，利用 Microsoft Azure 和其他云服务，如亚马逊网络服务 (AWS) 和谷歌云平台。
CCEP API 在 CyberArk 的支持下管理实施，以帮助确保遵循 CyberArk 蓝图方法定义的最佳实践指南。
运营影响
Case Study missing?
Start adding your own!
Register with your work email and create a new case study profile for your business.
相关案例.
Case Study
IoT Data Analytics Case Study - Packaging Films Manufacturer
The company manufactures packaging films on made to order or configure to order basis. Every order has a different set of requirements from the product characteristics perspective and hence requires machine’s settings to be adjusted accordingly. If the film quality does not meet the required standards, the degraded quality impacts customer delivery causes customer dissatisfaction and results in lower margins. The biggest challenge was to identify the real root cause and devise a remedy for that.
Case Study
Zenon the Ideal Basis for An Ergonomic HMI
KHS develops and produces machines and equipment for filling and packaging in the drinks industry. Because drinks manufacturing, filling and packaging consist of a number of highly complex processes, the user-friendly and intuitive operation of equipment is increasingly gaining in significance. In order to design these processes as simple as possible for the user, KHS decided to introduce a uniform, transparent and standardized solution to the company. The HMI interface should meet the requirement for people with different qualifications and enable them to work on a standard platform.
Case Study
Sparks Dynamics Assists Atlas Container Secure a $15,000 BGE Energy Rebate
The ReMASTER Compressed Air Monitoring system was installed in 2015. This system is capable of monitoring compressed air system parameters on a continuous basis and transferring that information to a cloud server which can be accessed by Atlas Container personnel, Industrial Diagnostics and Sparks Dynamics. This information was collected into a database which can be exported to an Excel spreadsheet or displayed graphically using Sparks Dynamics ViewMaster Software. The average annual compressed air electricity expense was estimated to be approximately $116,000. This is based on an incremental $/KWh electric rate of $.091 per KWh and an estimated compressed air energy consumption of 1,279,200 KWH. The implementation phase of Energy Conservation Measures (ECMs) for the Compressed Air System included: • Identification and repair of compressed air leaks • Understanding of compressed air usage per manufacturing machine and installation of shut off valves when the machines are no longer in production mode • Identification of misapplications of compressed air to include blow offs, venturis, and cooling scenarios • Understand system pressure requirements and potential installation of point of use pressure regulation.
Case Study
Automated Pallet Labeling Solution for SPR Packaging
SPR Packaging, an American supplier of packaging solutions, was in search of an automated pallet labeling solution that could meet their immediate and future needs. They aimed to equip their lines with automatic printer applicators, but also required a solution that could interface with their accounting software. The challenge was to find a system that could read a 2D code on pallets at the stretch wrapper, track the pallet, and flag any pallets with unread barcodes for inspection. The pallets could be single or double stacked, and the system needed to be able to differentiate between the two. SPR Packaging sought a system integrator with extensive experience in advanced printing and tracking solutions to provide a complete traceability system.
Case Study
Mondi Implements Statistics-Based Health Monitoring and Predictive Maintenance
The extrusion and other machines at Mondi’s plant are large and complex, measuring up to 50 meters long and 15 meters high. Each machine is controlled by up to five programmable logic controllers (PLCs), which log temperature, pressure, velocity, and other performance parameters from the machine’s sensors. Each machine records 300–400 parameter values every minute, generating 7 gigabytes of data daily.Mondi faced several challenges in using this data for predictive maintenance. First, the plant personnel had limited experience with statistical analysis and machine learning. They needed to evaluate a variety of machine learning approaches to identify which produced the most accurate results for their data. They also needed to develop an application that presented the results clearly and immediately to machine operators. Lastly, they needed to package this application for continuous use in a production environment.
Case Study
Industry 4.0 at ALPLA: Enhancing Factory Efficiency with IoT
ALPLA, a global leader in packaging solutions, faced several challenges as the complexity of their production machinery increased. The need for highly trained specialists in each factory led to higher personnel costs, difficulties in recruiting experienced talent at each location, and costly personnel turnover. Furthermore, less experienced operators running the machines sub-optimally impacted resource consumption and overall equipment effectiveness (OEE). ALPLA also faced the challenge of monitoring visual inspection systems in every line of their plants, which was almost impossible to do manually. In 2016, ALPLA decided to use data from the 900 different types of embedded sensors in each factory to address these issues. However, their initial choice of SQL Server as the data store for the sensor data proved inadequate, as it was unable to cope with their data requirements.
