How do you manage data and analytics in manufacturing operations?

In the case of many manufacturing concerns within the US, their "data" predates computers and much is carried around by Joe the maintenance supervisor who remembers when that steam loop was installed because it was the same week as the MASH finale. He tries to keep the P&IDs up-to-date. The institutional knowledge in his head could fill a book, but it will probably just walk out the door with his gold watch. If you have a plant similar to what I described, one data goal should be to get all of the operational documentation audited, digitized, and as accurate as possible. It will only get worse. Believe me you will be relieved you did when planning that major turn-around or when that big upset occurs that threatens process and human safety.

Información en las compañías, sobre variables, procesos, indicadores, proyecciones, históricos, etc. creo que las hay y en abundancia. La base esta en definir que es lo que consideramos relevante dentro de ese mar de información analizar y que realmente sea necesario y este alineado con los objetivos estratégicos de la organización. Una vez establecido lo anterior, debemos validar la calidad de la información con la cual se dispone y ver que otras variaciones exógenas pueden afectar la interpretación de esos datos, en resumen depurar la información para que tenga validez y robustez en los analisis posteriores.

1. thorough validation, automated processes, and consistent collection. Integrity is maintained via oversight of data, access limitations, and verification. Reliability is ensured by quality tests, expert training, and monitoring. 2. Utilizing real time data for routine decisions is the goal of operational analytics. Numerous sources of company data are fed into technologies that evaluate the data and spot issues and possibilities. Teams use this useful information to guide their decision making. 3. Create systems that are self comprehend the supply side of your manufacturing chain and understand the demand side. Improve Your Understanding of the Utilization and Efficiency of Your Machine. Better product demand forecasts should be made.

There are two key points in getting data. 1) If the data is for you and you are monitoring something for the first time. Get data from everything. Over a reasonable amount of time, watch for your key indicators. You will find that one or two key pieces of data will provide you with the details you need to know if your process is successful or not. 2) If you need the data for an ROI or some other external process, then define what you need and why you need it. Then, look for that data.

Manage manufacturing data by centralizing storage, implementing real-time monitoring, utilizing predictive analytics, ensuring data security, integrating with ERP systems, providing employee training, focusing on continuous improvement, defining performance metrics, fostering collaboration, and staying adaptable to technological advancements.

This can help you avoid the terminology, “ Tribal Knowledge “. Tribal knowledge is the definition of a culture that has not adopted a data driven culture and does not see the sever importance of capturing data and assigning ownership.

This can help you avoid the terminology, “ Tribal Knowledge “. Tribal knowledge is the definition of a culture that has not adopted a data driven culture and does not see the sever importance of capturing data and assigning ownership.

A importância da implementação de uma estrutura de governança de dados para assegurar a qualidade e segurança das informações. Ao abordar elementos como políticas, padrões e responsabilidades, enfatiza a necessidade de uma abordagem abrangente. A ênfase na definição de fontes, formatos, propriedade e ciclo de vida dos dados demonstra uma visão sistemática na gestão da informação. Destacar a capacidade da estrutura em monitorar, auditar e resolver problemas ou riscos ressalta seu papel proativo na manutenção da integridade dos dados. A relação direta com a consistência, precisão e confiabilidade dos dados nas operações de fabricação sublinha a contribuição significativa da governança para a eficácia organizacional.

Data governance is very important to retain the credibility of your data. For example, while many people may be granted "Read" access to certain files, you will certainly want to limit the people that have "Write" access. I have also found that it is important to create lists of people who have access. For example: - Building keys, and alarm codes - File access - Module access in ERP Next, it is important to have a backup to all of your critical documents. In one of my previous positions, I once saw the entire company unable to use their computers because one person accessed something on the internet that they shouldn't have. Your backups should automatically run on a regular basis, and if possible be stored on separate servers.

In manufacturing operations, data management is crucial for process optimization. We employ robust data collection systems, such as Manufacturing Execution Systems (MES) and SCADA, to gather real-time production data. This includes variables like machine performance, quality metrics, and resource utilization. Advanced analytics tools like Statistical Process Control (SPC) and Six Sigma methodologies are then utilized to analyze this data, identifying trends, anomalies, and areas for improvement. Through continuous monitoring and analysis, we drive informed decision-making, optimize processes, and enhance overall efficiency and quality in manufacturing operations.

Some analysts often see that the company has not grown its Information Technology as quickly as they have grown their business. Adopting new and more comprehensive ERP systems can also help data integration a sit will allow several platforms to merge into one database. Having several is never a good business practice. Integrate!!

A função crucial de uma plataforma de integração de dados na coleta e unificação eficiente de informações provenientes de diversas fontes e formatos. A ênfase na automação e simplificação dos fluxos de trabalho ressalta a importância da eficiência operacional. Ao mencionar a redução de silos de dados e redundâncias, destaca-se a contribuição da plataforma na superação de obstáculos comuns à integração de dados. A melhoria da qualidade e integridade dos dados enfatiza o papel dessa ferramenta na garantia da confiabilidade das informações. A criação de uma única fonte de verdade e uma visão holística reforça a capacidade da plataforma em proporcionar uma compreensão abrangente dos dados para operações de fabricação.

It's important to be data-driven in manufacturing operations. Here's how I have done it in the past: 1. Send regular reports to your leadership team. I personally like to send a weekly report on: - Safety - Quality - Financial Review (Throughput, Credits, Returns) - Productivity - Inventory 2. Find a strategic way to tell your story. You will need to find the right graphs, and data that tell the story that you need to tell. Some of this may be directly available from ERP, and some you may have to "stitch" together from multiple sources. 3. Never stop improving. Use the data as a platform to discuss the positive changes you are making. Nothing is perfect, but more importantly, what are you doing about it?

It's seductive and a siren call, installing more instruments and a system to collect data. Most will fail to achieve even a fraction of their potential. How many of us are sure that if we walk into a control room that we will like the answer to "what is a good day for you"? How many operators know what are the 2-3 key things they are doing that have the most impact on a process. I would suggest spending more time developing a KPI tree that drives down to operator actions before automating more of a process. Once an operator understands how their actions influence a process, you are a large step closer to sustainable improvement.

The key here is "use" the data. It's easy to collect data and look at it when needed. The real key is using the data for continuous improvement. It's not data for the sake of data, it's data that we can use.

🎯 To fully leverage data, a culture that values data is essential. It's not just about data collection but also actively involving employees. 👍 Through this involvement, we can explain the value of data for informed decisions and continuous improvements, preparing for upcoming transformations. 👀 On the shop floor, there is often a lack of deeper understanding of KPIs among employees, which is regrettable. However, after an explanatory clarification, employees can best analyze new trends in KPIs and either reassure or confirm potential negative trends. This fosters motivation, critical thinking, and active support, precisely what operations managers need from their teams.

In manufacturing, it is very easy to fall in the trap of reacting to issues without enough information. This can then draw resources away from important topics, to attend to priorities (which may not be that urgent, ultimately). A data-driven culture will help alleviate this: - Frame the issue correctly - Have as much data available (serial; test/manufacturing logs; suspect population; etc) - Involve the right folks: avoid adding individuals to the chain from the beginning as it may just create noise - Summarize the issue and communicate, then.

Data collection process if involves people then it has to be simplified and made user friendly. There should be no room for manipulation. The culture and mindset matter most and people involved should be taken into confidence so that we get the correct information and that would give insights to make the right decision.