制造行業的企業往往擁有并運行大量的工業設備，所有這些設備都需要監控和維護。對于現有的部署，工業物聯網使得基于更精確數據的制造過程中決策的改進成為可能。它還可以用來提高生產質量和正常運行時間，因為從網絡上的設備和傳感器收集的數據可以實現對生產設備的實時和預測性維護。
工業物聯網背后的主要思想是使機器在制定決策時比人類更智能、更高效。這依賴于準確、一致地捕獲和傳遞數據。信立科技多年從事工業物聯網的核心產品無線傳感器，在數據的抓取和傳輸上技術成熟，開發了系列網關、測控、傳感裝置，并應用到各個制造業中，為合作伙伴帶來了高效率的生產決策基礎。
在大數據支撐下，機器學習，系統可以被訓練以發現潛在的模式，這些模式將對未來的失敗給出指示。如果結果是有關的，他們可以立即調查。這些信息以前需要花費數周的時間才能發現，并依賴于每個技術熟練的專業人員的可用性。現在，使用實時數據可以幫助那些擁有合適技能的人在多個地點監控更多的機器，從而更快地做出維護決策。反過來，制造業效率可以大大加快。
除了預測性維護以外，機器學習在工業性能方面也有很大的改進。要做到這一點，企業需要了解所有可用的數據，對數據進行量化，并根據收到的信息提供有關如何最好地進行制造過程的深入見解。機器學習可以用來演示如何提高性能，并在同一級別的投資提供更快的結果。
在制造行業中，時間就是金錢。隨著工業物聯網戰略的實施，從單獨的傳感器到可用的分析和自動化，制造商可以圍繞整體效率和成本做出更好的業務決策。工業物聯網戰略在可持續和綠色生產實踐以及供應鏈可追溯性方面也具有很大的潛力。
隨著“循環經濟”在企業的應用，從產品到包裝的所有要素都應該被重復使用，并在整個價值鏈中循環利用，工業物聯網的實施對其追蹤結果至關重要。
Manufacturing enterprises often own and operate a large number of industrial equipment, all of which need to be monitored and maintained. For existing deployments, the Industrial Internet of Things makes it possible to improve decision-making in manufacturing processes based on more accurate data. It can also be used to improve production quality and uptime, as data collected from devices and sensors on the network enables real-time and predictive maintenance of production equipment.
The main idea behind the industrial Internet of things is to make machines more intelligent and efficient than human beings in making decisions. This depends on accurate and consistent capture and transmission of data. Xinli Technologies has been engaged in wireless sensor which is the core product of industrial Internet of Things for many years. It has developed a series of gateways, measurement and control, sensor devices and applied them to various manufacturing industries. It has brought high-efficiency production decision-making basis for partners.
Supported by large data, machine learning, systems can be trained to discover potential patterns that indicate future failures. If the results are relevant, they can investigate immediately. It took weeks to discover this information and depended on the availability of each skilled professional. Now, using real-time data can help those with the right skills monitor more machines in multiple locations, making maintenance decisions faster. In turn, manufacturing efficiency can be greatly accelerated.
In addition to predictive maintenance, machine learning has also improved in terms of industrial performance. To do this, companies need to understand all the available data, quantify the data, and provide in-depth insights into how best to conduct the manufacturing process based on the information received. Machine learning can be used to demonstrate how to improve performance and provide faster results at the same level of investment.
In manufacturing, time is money. With the implementation of the industrial Internet of Things strategy, from individual sensors to available analysis and automation, manufacturers can make better business decisions around overall efficiency and cost. The industrial Internet of Things strategy also has great potential for sustainable and green production practices and supply chain traceability.
With the application of "circular economy" in enterprises, all elements from products to packaging should be reused and recycled throughout the value chain. The implementation of the Industrial Internet of Things (IOT) is crucial to its tracking results.
