下载PDF
Delmaine Fine Foods Finds Inventory Accuracy Boost with RF-SMART
技术
- 功能应用 - 仓库管理系统 (WMS)
- 网络与连接 - 射频识别
适用行业
- 食品与饮料
适用功能
- 仓库和库存管理
- 物流运输
用例
- 库存管理
- 仓库自动化
- 供应链可见性(SCV)
服务
- 系统集成
- 培训
挑战
Delmaine Fine Foods faced significant challenges in managing their complex warehousing requirements. Their entire product range is batch controlled, with products varying in shelf life from five days to two years. Some products require chilled storage and shipping, while others are ambient. Additionally, their customers dictate the required shelf life of products based on when they will be available to the end consumer. This complexity made it impractical to work in a conventional pick face environment, as it would require multiple batches in the pick face and order quantities ranging from a few cartons to multiple pallets. Delmaine needed their ERP software to provide precise instructions on what product to pick, which batch, and from where. Accurate inventory was crucial for this system to function effectively.
关于客户
Delmaine Fine Foods, founded in 1980, is a leading manufacturer, importer, and supplier of high-quality food products. Based in Auckland, New Zealand, the company serves supermarkets and the food industry both domestically and internationally. With a business worth $73 million and a workforce of 200 employees, Delmaine Fine Foods has consolidated its manufacturing and warehousing operations in Auckland. The company sought to enhance productivity and inventory accuracy through the implementation of advanced technologies. Delmaine Fine Foods is known for its diverse product range, which includes items with varying shelf lives and storage requirements, making inventory management a complex task.
解决方案
Delmaine Fine Foods implemented RF-SMART to enhance efficiency and inventory accuracy. The adoption of RF-SMART led to a significant cultural shift within the warehouse. Previously, inventory accuracy was a shared responsibility, with different staff members handling physical picking and data updates at different times. This approach meant that no individual was solely responsible for accuracy at any given time. With RF-SMART, the picker now updates the stock in real-time, making them entirely responsible for the accuracy and timeliness of the transaction. This change has led to greater ownership of the process among pickers, who have willingly embraced the new technology. The implementation of RF-SMART has resulted in a more streamlined and accurate inventory management process, with pickers taking full responsibility for their tasks.
运营影响
数量效益
相关案例.
Case Study
The Kellogg Company
Kellogg keeps a close eye on its trade spend, analyzing large volumes of data and running complex simulations to predict which promotional activities will be the most effective. Kellogg needed to decrease the trade spend but its traditional relational database on premises could not keep up with the pace of demand.
Case Study
HEINEKEN Uses the Cloud to Reach 10.5 Million Consumers
For 2012 campaign, the Bond promotion, it planned to launch the campaign at the same time everywhere on the planet. That created unprecedented challenges for HEINEKEN—nowhere more so than in its technology operation. The primary digital content for the campaign was a 100-megabyte movie that had to play flawlessly for millions of viewers worldwide. After all, Bond never fails. No one was going to tolerate a technology failure that might bruise his brand.Previously, HEINEKEN had supported digital media at its outsourced datacenter. But that datacenter lacked the computing resources HEINEKEN needed, and building them—especially to support peak traffic that would total millions of simultaneous hits—would have been both time-consuming and expensive. Nor would it have provided the geographic reach that HEINEKEN needed to minimize latency worldwide.
Case Study
Energy Management System at Sugar Industry
The company wanted to use the information from the system to claim under the renewable energy certificate scheme. The benefit to the company under the renewable energy certificates is Rs 75 million a year. To enable the above, an end-to-end solution for load monitoring, consumption monitoring, online data monitoring, automatic meter data acquisition which can be exported to SAP and other applications is required.
Case Study
Coca Cola Swaziland Conco Case Study
Coco Cola Swaziland, South Africa would like to find a solution that would enable the following results: - Reduce energy consumption by 20% in one year. - Formulate a series of strategic initiatives that would enlist the commitment of corporate management and create employee awareness while helping meet departmental targets and investing in tools that assist with energy management. - Formulate a series of tactical initiatives that would optimize energy usage on the shop floor. These would include charging forklifts and running cold rooms only during off-peak periods, running the dust extractors only during working hours and basing lights and air-conditioning on someone’s presence. - Increase visibility into the factory and other processes. - Enable limited, non-intrusive control functions for certain processes.
Case Study
Temperature Monitoring for Restaurant Food Storage
When it came to implementing a solution, Mr. Nesbitt had an idea of what functionality that he wanted. Although not mandated by Health Canada, Mr. Nesbitt wanted to ensure quality control issues met the highest possible standards as part of his commitment to top-of-class food services. This wish list included an easy-to use temperature-monitoring system that could provide a visible display of the temperatures of all of his refrigerators and freezers, including historical information so that he could review the performance of his equipment. It also had to provide alert notification (but email alerts and SMS text message alerts) to alert key staff in the event that a cooling system was exceeding pre-set warning limits.
Case Study
Coca-Cola Refreshments, U.S.
Coca-Cola Refreshments owns and manages Coca-Cola branded refrigerators in retail establishments. Legacy systems were used to locate equipment information by logging onto multiple servers which took up to 8 hours to update information on 30-40 units. The company had no overall visibility into equipment status or maintenance history.
