公司规模
Mid-size Company
地区
- Europe
国家
- Italy
产品
- SAP Business Planning and Consolidation
- SAP ERP
- SAP Customer Relationship Manager
- SAP Enterprise Portal
- SAP Mobile Platform
技术栈
- IBM iLOG Transportation Analyst
- SAP Software
实施规模
- Enterprise-wide Deployment
影响指标
- Productivity Improvements
- Customer Satisfaction
技术
- 功能应用 - 企业资源规划系统 (ERP)
适用行业
- 农业
- 可再生能源
适用功能
- 物流运输
- 采购
用例
- 供应链可见性(SCV)
- 库存管理
服务
- 系统集成
- 软件设计与工程服务
关于客户
COPROB 是意大利最大的甜菜加工商之一,年产糖约 284,000 吨(约占全国产量的 56%)。该公司拥有 310 名员工,与意大利北部的 5,700 家合作农场合作,销售额约为 3.35 亿欧元。除了糖加工外,COPROB 最近还扩展到可再生能源领域,使用甜菜生物质(糖生产的副产品)作为燃料源,并已与 Enel Green Power 签订了供应合同。这些合同要求 COPROB 供应特定数量的生物质(能源作物和农业残留物),这反过来意味着 COPROB 需要能够预测未来的糖加工操作以及这些操作可能产生的生物质数量。
挑战
COPROB 是意大利最大的甜菜加工商之一,该公司希望提高效率、收紧财务并拓展其新的生物质能源业务。该公司需要管理 5,700 个合作农场,面临着有效管理增长的挑战。该公司现有的系统基本上没有受到技术的影响,许多流程都是手动的,或者依赖于电子表格、本地技术解决方案和部门特定专业知识的组合。该公司需要生成足够高质量的数据,以实现支持新生物质业务所需的预测分析能力,并确保核心业务的持续成功。管理 5,700 个合作农场至关重要,因为了解每个农民种植的甜菜数量和作物预期会直接影响甜菜和生物质的供应。
解决方案
COPROB 聘请 IBM 全球商业服务部实施基于 SAP 软件的企业流程管理解决方案,提供敏捷、集成和集中的流程管理解决方案。IBM 团队设计并部署了 SAP 解决方案,以满足 COPROB 独特且苛刻的业务需求。该解决方案通过 IBM Ascendant 项目方法交付,并由 IBM 全球融资部提供支持。该解决方案支持改进田地、农场、农民和供应链合作伙伴的分层主数据管理,更好地了解预算和生产约束,根据不断变化的生产需求和产能约束加强对农业活动的控制,并优化收获期间的运输物流管理。卡车的调度使用 IBM ILOG Transportation Analyst 来处理,这是一个基于路线、容量和时间约束的车辆路线问题优化引擎。
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
Remote Monitoring & Predictive Maintenance App for a Solar Energy System
The maintenance & tracking of various modules was an overhead for the customer due to the huge labor costs involved. Being an advanced solar solutions provider, they wanted to ensure early detection of issues and provide the best-in-class customer experience. Hence they wanted to automate the whole process.
Case Study
Intelligent Farming with ThingWorx Analytics
Z Farms was facing three challenges: costly irrigation systems with water as a limited resource, narrow optimal ranges of soil moisture for growth with difficult maintenance and farm operators could not simply turn on irrigation systems like a faucet.
Case Study
Vestas: Turning Climate into Capital with Big Data
Making wind a reliable source of energy depends greatly on the placement of the wind turbines used to produce electricity. Turbulence is a significant factor as it strains turbine components, making them more likely to fail. Vestas wanted to pinpoint the optimal location for wind turbines to maximize power generation and reduce energy costs.
Case Study
Siemens Wind Power
Wind provides clean, renewable energy. The core concept is simple: wind turbines spin blades to generate power. However, today's systems are anything but simple. Modern wind turbines have blades that sweep a 120 meter circle, cost more than 1 million dollars and generate multiple megawatts of power. Each turbine may include up to 1,000 sensors and actuators – integrating strain gages, bearing monitors and power conditioning technology. The turbine can control blade speed and power generation by altering the blade pitch and power extraction. Controlling the turbine is a sophisticated job requiring many cooperating processors closing high-speed loops and implementing intelligent monitoring and optimization algorithms. But the real challenge is integrating these turbines so that they work together. A wind farm may include hundreds of turbines. They are often installed in difficult-to-access locations at sea. The farm must implement a fundamentally and truly distributed control system. Like all power systems, the goal of the farm is to match generation to load. A farm with hundreds of turbines must optimize that load by balancing the loading and generation across a wide geography. Wind, of course, is dynamic. Almost every picture of a wind farm shows a calm sea and a setting sun. But things get challenging when a storm goes through the wind farm. In a storm, the control system must decide how to take energy out of gusts to generate constant power. It must intelligently balance load across many turbines. And a critical consideration is the loading and potential damage to a half-billion-dollar installed asset. This is no environment for a slow or undependable control system. Reliability and performance are crucial.
Case Study
Remote Monitoring and Control for a Windmill Generator
As concerns over global warming continue to grow, green technologies are becoming increasingly popular. Wind turbine companies provide an excellent alternative to burning fossil fuels by harnessing kinetic energy from the wind and converting it into electricity. A typical wind farm may include over 80 wind turbines so efficient and reliable networks to manage and control these installations are imperative. Each wind turbine includes a generator and a variety of serial components such as a water cooler, high voltage transformer, ultrasonic wind sensors, yaw gear, blade bearing, pitch cylinder, and hub controller. All of these components are controlled by a PLC and communicate with the ground host. Due to the total integration of these devices into an Ethernet network, one of our customers in the wind turbine industry needed a serial-to-Ethernet solution that can operate reliably for years without interruption.