Tin tức

Hiệp định RCEP tạo dựng các mối quan hệ mới cho ngành dệt may

Dệt May, Tin tức / By

Ngành dệt may các nước RCEP tập trung phát triển sợi mới và sản xuất xanh, cũng như mở rộng thị trường liên quan đến Hiệp định Đối tác Kinh tế Toàn diện Khu vực.   Do xung đột Nga – Ukraine đang diễn ra khiến xuất khẩu hàng dệt may của Trung Quốc sang …

Hiệp định RCEP tạo dựng các mối quan hệ mới cho ngành dệt may Read More »

Doanh nghiệp dệt may: “Xanh hóa” hoặc tụt lại phía sau ?

Doanh nghiệp dệt may: “Xanh hóa” hoặc tụt lại phía sau ?

Dệt May, Tin tức / By

“Xanh hóa” chuỗi sản xuất trong ngành dệt may là xu thế toàn cầu mà doanh nghiệp bắt buộc phải triển khai để đạt mục tiêu phát triển bền vững. Đây là yêu cầu tất yếu đối với các nhà cung ứng nếu không muốn bị đẩy lại phía sau…   Mới đây, EU, thị …

Doanh nghiệp dệt may: “Xanh hóa” hoặc tụt lại phía sau ? Read More »

5 chỉ số để đo lường độ thành công của dự án ERP doanh nghiệp

5 chỉ số để đo lường độ thành công của dự án ERP doanh nghiệp

Kiến thức quản trị doanh nghiệp, Kiến thức về ERP, Tin tức / By

Hệ thống ERP là một loại giải pháp phần mềm quản lý đa chức năng, đa phòng ban giúp một doanh nghiệp có thể sử dụng để thu thập, lưu trữ, quản lý và phân tích dữ liệu từ hoạt động kinh doanh của mình. Hệ thống ERP (Enterprise Resource Planning Systems) là một loại …

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Rào cản trong chuyển đổi số của doanh nghiệp

Kiến thức quản trị doanh nghiệp, Tin tức / By

Chi phí đầu tư, ứng dụng công nghệ còn cao, khó khăn trong thay đổi thói quen là những rào cản hàng đầu trong chuyển đổi số hiện nay.    Rào càn trong chuyển đổi số của doanh nghiệp. Ảnh: Cục Phát triển doanh nghiệp (Bộ Kế hoạch và Đầu tư).   Thông tin này …

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Giải pháp ERP trên mây cho nhà máy – chọn tự xây hay thuê ngoài

Kiến thức về ERP, Tin tức / By

Trong bối cảnh mùa dịch, khách hàng tăng cường tích trữ, chủ động thu thập dữ liệu qua ERP là giải pháp giúp các nhà máy quản lý và phân tích dữ liệu từ hoạt động kinh doanh của mình 1. Tác động của chuỗi cung ứng tới doanh nghiệp sản xuất Việt Nam Ông …

Giải pháp ERP trên mây cho nhà máy – chọn tự xây hay thuê ngoài Read More »

For those in high volume, low margin industries such as food and beverage or automotive parts manufacturing, preventative maintenance may be wrongly seen as unattainable, due to perceived high upfront costs. The digital universe is doubling in size every two years, growing from 4.4 trillion gigabytes in 2013 to 44 trillion in 2020. Enterprises have responsibility for around 85 per cent of the data held in this digital universe, according to the EMC Digital Universe with research and Analysis by IDC report. Manufacturing plants are just one type of enterprise contributing to this data production, by choosing to collect process data in order to monitor machinery status. Here, Claudia Jarret, US country manager at automation parts supplier EU Automation, explains how plant engineers can make the most of this data and ensure their maintenance schedule benefits their plant. Manufacturers, faced with the constant pressure to keep up with customer demand while remaining flexible, are acutely aware of the threat of plant downtime. Caused by a multitude of factors, including machine failure, downtime can cost manufacturers up to $260,000 per hour, according to research by analyst firm Aberdeen Research. Therefore, preventative maintenance, where equipment is maintained before it breaks, is essential, rather than reactive maintenance, which focuses on fixing equipment following a breakdown. But for those in high volume, low margin industries such as food and beverage or automotive parts manufacturing, preventative maintenance may be wrongly seen as unattainable, due to perceived high upfront costs. 1. Prevention The days of waiting for a system to fail due to overheating motors or leaking heat transfer systems for example, are thankfully behind us. Plant managers now plan regular inspections, upgrades and troubleshooting to avoid breakdowns. These simple steps are one way in which preventative maintenance can be carried out. But as Industry 4.0 technologies such as condition monitoring become more accessible, there are even smarter, more accurate and less time-consuming methods available. Many manufacturing plants now use predictive maintenance, a sophisticated form of preventative maintenance, to establish how well equipment is functioning and accurately predict failures before they occur. Not only does this reduce the risk of downtime-causing breakdowns, but it can also mean any necessary replacement parts can be ordered and are in stock in case of equipment failures. In addition, monitoring the health of equipment can allow for gradual tweaks, where a huge overhaul would previously have been required for improvements. Alterations can improve the efficiency of processes, ultimately reducing costs by eliminating inefficient elements and increasing equipment lifespan through maintenance or replacement of wearing parts. 2. Where to start The first step a plant manager should take when implementing any preventative maintenance schedule is to gather as much data as possible. Crucially, this should include an accurate estimate of the real cost of downtime to the particular plant in question. Collating data collected by smart sensors, sales figures and retrofitted systems should allow you to take into account loss of sales, wasted product and the cost of emergency repairs, which can provide a useful baseline figure. Using their experience of the plant, maintenance engineers should also be involved to produce an ideal plan for the plant, including areas they believe will require higher levels of maintenance, such as older equipment or machinery that is inherently prone to breakdowns such as motors, turbines, or other moving parts. This plan can then be used alongside the budget to produce a realistic maintenance plan, including what level of preventative maintenance can be employed. For some manufacturers, this will mean regular equipment checks, such as once a month for a section of plant, or daily for more volatile machinery. 3. Data If true predictive maintenance is chosen, data such as temperature, pressure and vibration is collected by sensors and will be continually integrated, stored, and analyzed. The next question for manufacturers is how to make the most of these valuable, but large, data sets. Once the data has been collected, the next step to make the most of its potential is to begin analysis. One popular option for data analysis is a cloud analytics service. Here, raw data is transmitted to the cloud, where it can both be stored and analyzed for trends that can predict an event including a breakdown. Many services also incorporate an alert system and warnings of impending breakdowns can be sent via a web portal, app, e-mail, or text message to relevant personnel. Other manufacturers, perhaps concerned about cybersecurity, the long-term stability of data stored in the cloud, or lag between data collection and analysis, will choose to undertake the analysis of raw data in-house. Although this is often significantly more resource-heavy, it does give plant managers complete control over their data. 4. Obsolescence Regardless of which system is implemented, predictive maintenance can be used to manage obsolescence, in addition to reducing downtime and improving process efficiency. This means parts can be ordered at the correct time to suit the maintenance schedule. Traditionally, manufacturers would have to keep a stock of parts that may need replacing, which take up valuable space on the plant floor that can instead be used for operations. Spare parts can also be bulky and produce a health and safety hazard, including trip or fire hazards, if stored on the plant floor. Instead, manufacturers can choose to order in replacement parts only when necessary, maximizing the space and resources they have available to them. With suppliers such as EU Automation able to ship obsolescent parts worldwide within 24 hours, there is now no need to store parts on site, or to suffer extended periods of downtime waiting for them to arrive. Preventative maintenance is essential for manufacturers to reduce downtime and the vast amounts of data now being produced by plants can be effectively used for predictive maintenance. When predictive maintenance is employed, the data collected can also be used for other purposes, such as increasing process efficiency and ensuring replacement parts are ordered in plenty of time. Therefore, proper collection and analysis of this data is essential to maximize its potential and this will only become more important each year as the quantity of data increases.

Improving Maintenance in Manufacturing

Kiến thức bảo trì, Tin bảo trì, Tin tức / By

For those in high volume, low margin industries such as food and beverage or automotive parts manufacturing, preventative maintenance may be wrongly seen as unattainable, due to perceived high upfront costs. The digital universe is doubling in size every two years, growing from 4.4 trillion gigabytes in 2013 to 44 trillion in 2020. Enterprises have …

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The smart move to more efficient and safer maintenance operations

Kiến thức bảo trì, Tin tức, Uncategorized / By

Regardless of their size or the industry segment in which they operate, manufacturing companies are constantly striving to increase efficiencies within the various processes they employ. Arguably, maintenance is one of the primary areas in which companies are continually seeking ways to lower costs and cut the time taken to perform essential tasks. With many …

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