製(zhi)造(zao)商(shang)有(you)海(hai)量(liang)的(de)數(shu)據(ju)，但(dan)往(wang)往(wang)沒(mei)有(you)正(zheng)確(que)的(de)工(gong)具(ju)來(lai)開(kai)發(fa)它(ta)。這(zhe)裏(li)有(you)極(ji)大(da)的(de)潛(qian)力(li)可(ke)挖(wa)。但(dan)如(ru)果(guo)你(ni)沒(mei)有(you)這(zhe)些(xie)工(gong)具(ju)，你(ni)該(gai)從(cong)哪(na)裏(li)開(kai)始(shi)
？遵(zun)循(xun)這(zhe)六(liu)個(ge)步(bu)驟(zhou)
，開(kai)始(shi)從(cong)你(ni)的(de)數(shu)據(ju)中(zhong)獲(huo)得(de)盡(jin)可(ke)能(neng)多(duo)的(de)價(jia)值(zhi)。

1. 數據整合

zaizhizaoyezhong，xinchuanganqicaijidekeyongshujujizeng，erchuantongdeshujuxitongzaichulihezhenghezhexiexinxiyuxianyoulaiyuanfangmiancunzaikunnan。nideyewuliuchengyilaiyuqingchu、可靠的數據，從而帶來你在運營效率

、客戶滿意度、財務業績等方麵所期望的結果。
建立合適的基礎設施來協調和集中來自任何數量或源類型的數據
，以確保在整個組織中使用通用定義，同時節省大量開發時間。

2. 數據治理

數(shu)據(ju)治(zhi)理(li)是(shi)成(cheng)功(gong)的(de)數(shu)據(ju)管(guan)理(li)的(de)一(yi)個(ge)主(zhu)要(yao)組(zu)成(cheng)部(bu)分(fen)。這(zhe)是(shi)一(yi)個(ge)持(chi)續(xu)的(de)過(guo)程(cheng)，用(yong)於(yu)確(que)定(ding)哪(na)些(xie)數(shu)據(ju)對(dui)你(ni)的(de)業(ye)務(wu)至(zhi)關(guan)重(zhong)要(yao)，並(bing)確(que)保(bao)它(ta)保(bao)持(chi)正(zheng)確(que)的(de)質(zhi)量(liang)水(shui)平(ping)。關(guan)鍵(jian)是(shi)要(yao)為(wei)你(ni)的(de)企(qi)業(ye)確(que)定(ding)正(zheng)確(que)類(lei)型(xing)的(de)治(zhi)理(li)框(kuang)架(jia)，並(bing)定(ding)義(yi)員(yuan)工(gong)需(xu)要(yao)遵(zun)循(xun)的(de)流(liu)程(cheng)。

生產、運營和業務對成功的看法都略有不同。你需要調整和管理你的數據，以確保他們目標一致。

3. 分析

shujukeshihuashininenggouyishijiaoshangxiyinrendegeshiliulanshuju，bingdechuduiqiyechenggongzhiguanzhongyaodejielun。tongguocongwanquanbutongdelaiyuanhuoqushuju
，duiqijinxingzhuanhuan
，bingjiangqixianshizaizuizhongyonghukeyikandaohelijiedeyibiaobanzhong，nikeyishenrufenxizhongyaodeKPI和指標。借助易於訪問的高級分析，找出差距和根本原因，並揭示趨勢。

4. 利益相關者權利

利li益yi相xiang關guan者zhe的de認ren同tong和he持chi續xu支zhi持chi對dui於yu數shu據ju項xiang目mu的de成cheng功gong至zhi關guan重zhong要yao。確que保bao自zi動dong化hua並bing在zai整zheng個ge組zu織zhi內nei分fen享xiang見jian解jie，讓rang每mei個ge人ren隨sui時shi隨sui地di都dou能neng看kan到dao事shi情qing的de進jin展zhan。

5. 變革管理

幾ji乎hu任ren何he重zhong大da的de技ji術shu或huo組zu織zhi創chuang新xin都dou需xu要yao對dui人ren們men的de工gong作zuo方fang式shi做zuo出chu同tong樣yang重zhong大da的de改gai變bian。為wei了le使shi項xiang目mu成cheng功gong並bing產chan生sheng預yu期qi的de價jia值zhi，需xu要yao積ji極ji地di管guan理li組zu織zhi變bian更geng。培pei訓xun
、啟用和支持您的團隊，以確保你擁有合適角色的合適用戶，從而確保成功部署。

6. 演進

隨著你的不斷成長而發展！基於從第一步到第五步學到的知識進行迭代。

成果

你能期望從這樣的數據倡議中看到什麼樣的結果？這裏有幾個例子。
　
       · 結合生產力和財務數據
，為生產經理顯示每條生產線的近乎實時的利潤產出，以幫助確定任何維護問題的優先級

· 將需求預測與生產計劃聯係起來，以確保供應得到優化
，並確保正確的生產計劃到位
，以限製低速SKU的過度生產

· 利用物聯網數據報告現場機器的健康狀況，主動降低維護成本，從而更好地分配現場技術人員

一旦你通過這些基本步驟建立了基礎
，你就可以繼續探索高級分析和人工智能的可能性。

作者：Raz Nistor，Keyrus公司數據科學和CPG主任

文章原文：

6 Steps to Maximizing Value from Manufacturing Data

Manufacturers have tons of data but often don't have the right tools to explore it. There's a wealth of potential that's just waiting to be unleashed. But if you don’t have those tools in place, where do you start? Follow these six steps to start getting the most value possible from your data.

1. Data integration

In manufacturing, there’s an explosion of available data from new sensor sources, and legacy data systems struggle to process and combine this information with existing sources. Your business processes depend on clean, reliable data to produce the results you expect in terms of operational efficiency, customer satisfaction, financial performance, and more.

Set up the right infrastructure to harmonize and centralize your data from any number or type of sources to ensure that common definitions are used throughout the organization while saving significant development time.

2. Data governance

Data governance is a major component of successful data management. It’s a continuous process for identifying which data is critical to your business and ensuring it stays at the right level of quality. The key is to identify the right type of governance framework for your enterprise and to define the processes employees need to follow.

Production, operations, and the business all look at success slightly differently. You’ll need to align and govern your data to make sure they’re all looking at the same picture.

3. Analytics

Data visualizations allow you to explore your data in a visually appealing format and draw conclusions that are critical to the success of your business. By taking data from disparate sources, transforming it, and displaying it in dashboards where end users can see and understand it, you can drill in and analyze important KPIs and metrics. Find gaps and root causes, and uncover trends with easily accessible advanced analytics.

4. Stakeholder access

Stakeholder buy-in and continuous support are critical for data projects to succeed. Make sure to automate and share insights across the organization and allow everyone to see where things stand, any day, at all times.

5. Change management

Almost any significant technical or organizational initiative requires equally significant changes to the way people work. That organizational change needs to be actively managed in order for the project to be successful and generate the expected value. Train, enable, and support your team to ensure you have the right users in the right roles to ensure successful deployment.

6. Evolution

Evolve as you continue to grow! Iterate based on learnings from steps one through five.

Results

What kind of results can you expect to see from a data initiative like this? Here are a few examples.

Combined productivity and finance data to display the near real-time profit output of each line on the floor for production managers to help prioritize any maintenance issues
Connected demand forecasts with production schedules to ensure supply was optimized and that the right manufacturing schedules were in place to limit the overproduction of low-velocity SKUs
Proactively reduced maintenance costs using IoT data to report health of machines in the field, which leads to better allocation of field techs

Once you’ve laid the foundation with these basic steps, you can move on to exploring the art of the possible with advanced analytics and artificial intelligence.

About The Author
Raz Nistor is director of Data Science & CPG at Keyrus.