“Should I start selecting an MES right now in the middle of the crisis? Or would it be better to wait until the situation calms down and it becomes clearer where things are headed?”
Let’s take a look at the whole thing from a DATA perspective.
The focus here is primarily on the fact that an MES first creates a meaningful (production) data foundation, enabling further digitization projects to optimize production.
What does that mean?
The MES helps you control and monitor your production. That is its purpose and design: to capture and process large amounts of production data in the right context. Control and monitoring through the MES are based on numbers, data, facts (NDF). It is therefore crucial that you can rely on all incoming data. Only then can evaluations, monitoring, or the control and orchestration of various production workflows function properly. Perhaps you know the IT saying: “Garbage in, garbage out.” In other words, if you input poor-quality data into the system, you will get poor results. And then you fail in your goal to manage and monitor production more effectively and purposefully.
This includes clean master data as well as knowledge of which sources are connected to your MES and how they provide data.
Typical sources include:
Your ERP system
Your machines
Sensors
Measurement programs
QA applications
Reporting systems
Logistics applications
etc.
Each of these data sources has its own data structure, timing (how fast, which, and how much data is sent), and other peculiarities to consider during integration. Only if the integration works correctly can you ensure your MES is supplied with clean, trustworthy data.
Use the introduction of a new MES as an opportunity for data cleanup, correction, and consolidation. Define which system is the master for certain data, and consider organizational responsibilities: who is responsible for which master data?
See the MES implementation project — and even the selection process for a new system — as a chance to bring order to your data. Believe me, making these corrections later in operational use is extremely difficult. As a result, you may face unclear data, unreliable information for decisions, interface problems between systems, and so on — exactly what you wanted to improve in the first place.
Hand on heart: how trustworthy is a system that outputs wrong or “bad” data? Hard to trust, right? But that is exactly the point — in the future, the system will make certain decisions, and we must be able to trust those decisions. Therefore, it is important to approach data from the beginning with the right mindset — which, as you can imagine, also requires time and resources.
Another thought on the topic of data — simply because it is so important.
You’ve surely heard the slogan:
Data is the new oil.
That is certainly true, but first, it requires the “data” mindset in your company and a fair amount of work. To extract valuable insights from data, certain prerequisites and time are needed.
Time to first build a meaningful data foundation. Production applications such as Statistical Process Control (SPC) only work if a statistically significant amount of data can be analyzed and the data is valid. This applies to any further usage — whether detecting deviations, faulty machine conditions, or automatically evaluating and categorizing measurement results.
To further increase production flexibility in the future (e.g., more product variety or customized customer orders and batch sizes), the growing data foundation must above all be clean: accurate, valid, and stored in the right context. The effort to achieve this should not be underestimated. A major step is starting the MES with clean master data from all connected sources.
It is also worth clearly defining data responsibilities within the organization! Which system is the master for which master data, and which department is responsible for ensuring its completeness and accuracy? Thinking further about the “data” mindset, you will also need data scientists in your organization in the future, who will work with this data and generate new ideas and insights. It is therefore crucial to treat data as a raw material from the beginning, applying the same care and seriousness as you would for a valuable supplier batch. If you have to purchase the batch at a high cost, you would naturally conduct a precise quality check before using it in production — and the same applies to your data.
You see, any effort to establish a clean data landscape is worthwhile — from the very start:
With the MES implementation, you start digital production with clean data (and defined responsibilities).
Clean data = clean work via the MES = clean reporting — you work better in production and receive accurate production reports and information.
Based on your ever-growing production data, you can gain further valuable insights into production processes, logistics, measurement handling, and deviations.
This knowledge feeds back into optimized production, improved measurement strategies, and better logistics — wherever potential exists. Success depends largely on data quality and is a prerequisite for future projects.
Conclusion:
Production digitization generates huge amounts of data. However, it is crucial that this data is clean and reliable for your company. This requires time and the establishment of a “data” mindset. Another reason to engage with MES selection and production digitization sooner rather than later. Even if you are uncertain, you can start analyzing and, if necessary, correcting the currently used data for potential future MES use.
What is your opinion? Take data seriously and start immediately, or wait and see how the situation develops?
What is your path to production digitization?
Do you want to get started now and have plenty of questions? Take advantage of the opportunity and schedule a free strategy session!
Simply click the blue button below this blog post. I look forward to speaking with you!
