{"id":2782,"date":"2020-07-16T05:23:53","date_gmt":"2020-07-16T03:23:53","guid":{"rendered":"https:\/\/staging.blogs.yokogawa.de\/chemical-pharma\/uncategorized\/single-source-of-truth-the-times-come-for-industry-4-0\/"},"modified":"2022-06-17T09:28:49","modified_gmt":"2022-06-17T07:28:49","slug":"single-source-of-truth-the-times-come-for-industry-4-0","status":"publish","type":"post","link":"https:\/\/www.yokogawa.com\/eu\/blog\/chemical-pharma\/en\/single-source-of-truth-the-times-come-for-industry-4-0\/","title":{"rendered":"Single source of truth \u2013 the time\u2019s come for Industry 4.0!"},"content":{"rendered":"

.Whoever has had to grapple with the field of data and its evaluation is sure to have experienced some nerve fraying moments. Chafing at the bit with your head full of creative ideas, you\u2019re bound to come up against a brick wall sooner or later. Some data are available, other information is there too \u2013 but for the next batch you first need to submit five applications to actually get anywhere and, in the worst case, you\u2019ll be handed a collection of loose sheets… That initial contentment after having overcome the first hurdle will soon disappear once you\u2019re confronted with the issue of data quality \u2013 a challenge that may well leave you tearing your hair out.<\/p>\n

It certainly rings true as a rule of thumb: about 70 % to 80 % of the time spent on data analysis is devoted to data preparation. Of course, some of the steps are coordinated specially to the relevant analysis method. But a lot of things can be arranged in advance so the data can be used directly. Once merged reliably and cleaned, the data from all data sources should be free of redundancy and unique with a correct time stamp. Plus it needs to be up-to-date. Because this is the only way to implement insights form data analysis without a huge amount of extra work in realtime-based applications.<\/p>\n

Single source of truth truly a milestone<\/h2>\n

I\u2019m now concerned with the whole business when it comes to data derived directly \u2013 in the broadest sense \u2013 from the process. Innumerable data results across the entire production process from purchasing the raw materials through to selling the products, with various interrelations linking this data in one way or another. So it\u2019s not without reason that a central claim of digitalisation is the single source of truth.<\/p>\n

This concept of data retention enables all departments to access a central data source that is continually maintained to make sure it contains only consistent and redundancy-free data. Such a data repository prevents errors by using different data principles, but above all it\u2019s vital in enabling certain evaluations. Only in this way can a company\u2019s profit be mapped consistently over the entire value chain and across all levels. In fact, it can be regarded as indispensable for realising Industry 4.0 projects successfully.<\/p>\n

How the whole thing works<\/h2>\n
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Single Source of Truth<\/figcaption><\/figure>\n

As an employee who loves data, you\u2019re sure to say \u201cAt last!\u201d. Rubbing your hands with glee on your way to the next department to ask for information. Only to discover: They\u2019re stewing in their own juice there, an unwelcome surprise for you \u201cDamn, nothing\u2019s actually working!\u201d.<\/p>\n

In response to the question whether someone\u2019s demonstrated how the whole thing works. You hear: \u201cYeah, someone came \u2013 he showed us some features by clicking around wildly. But we didn\u2019t really understand everything.\u201d Already somewhat disillusioned, you knock on the next department with your list. Only to be turned away briskly at the door. \u201cWhat do you want from us? This hasn\u2019t got anything to do with you. We\u2019re already looking into the matter.\u201d<\/p>\n

Sweeping systematic transformation to Industry 4.0<\/h2>\n

This or other examples occur time and again within the context of digitalisation. Digital transformation actual entails (among other things) continuous further development and networking. Not only technically but also in terms of personal growth. Usually such a transformation is not so easy to master. And without a clear strategy or focussing on the company itself with all its individual circumstances, it\u2019s sure to fail very quickly. That makes the change difficult especially for small and medium-sized companies who don\u2019t have specialist staff. So-called maturity models provide support here. Based on questionnaires, companies can be classified in regard to the digitalisation\u2019s status quo. In both technical as well as organisational and cultural terms, depending on the provider. What\u2019s more, general recommendations for action or suggestions towards a customised roadmap can be derived form this.<\/p>\n

After a quick look in the Internet you\u2019ll soon discern a whole series of maturity models available with various focal points. On the one hand, it\u2019s certainly great how you can carry out a complete series of tests immediately online. But choosing the right tests can be very difficult owing to the sheer number on offer. A topic the VDI Guideline Committee STEPS has been tackling. STEPS = Systematic transformation and evaluation of production systems. Because only in this way do projects like those described above stand a real chance.<\/p>\n

Structured rating system<\/h2>\n

From quick check to individual roadmap \u2013 our team has closely scrutinised the maturity models, categorising them by various criteria. A structured rating system means the guideline will help you when choosing the model suitable for your needs. Have I piqued your curiosity? We\u2019re currently adding the finishing touches to the guideline \u2013 then it\u2019s hopefully ready for the greenprint. I\u2019ll let you know when the time comes. Perhaps including a good suggestion for a maturity assessment.<\/p>\n

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Keep Standard Operating Procedures (SOP) up to date – secure implicit knowledge<\/a><\/p><\/blockquote>\n