Manufacturing Data Analytics

When it comes to successful manufacturing, it depends on how your business strives hard to make the best use of analytics tools and simplify operations. Previously, it implied analyzing a single process, testing the same, as well as re-testing ideas that might work. If it worked, implementation was possible. Now, all of this means much waste of your time for several months. Such obsolete methods will put your manufacturing unit at risk. That is why you need manufacturing data analytics and dashboards to streamline your business operations.

Manufacturing data is complicated and challenging to collate, examine, and use beneficially. Then, fret not. Here are five best tips to achieve success when it comes to your manufacturing data analytics:

1. Capture quality data

It is not easy to collate and understand manufacturing data because you need quality information to implement your project fruitfully. The typical issues are sample sizes so small that makes it challenging to draw inferences from such data.

Again, the manufacturer may lack standard operating processes that help them to build data that is useful and consistent. Let us explain this point with the help of a suitable example. For instance, if your standard operating process cannot assure that the readings are consistent, the data gathering method would introduce additional variables, thus creating obstacles for the data analysis process, making it challenging and sometimes, impossible.

The greatest challenge is that most of these problems do not surface until someone is working in the middle of a particular project. That is why you need to create project targets. This way, the data analyst team can figure out whether the data collated is quality information or not for examining the issue in its original scope.

You also need to engage your resources including software sellers, consultants, as well as internal resources from the other facility to use their skills on the standard data problems for every defined use case.

2. Use appropriate data

Usually, manufacturers are keen on addressing particular issues like controlling scrap, predictive maintenance, or minimizing downtime, but without using appropriate data to solve these issues. It leads to disappointments for project supporters as well as data analysts.

These days, you have data analytics solutions to address such problems. Then, before that you need to mull over two essential aspects:

Create a data resources catalog to figure out what sort of analytics and related business processes you can apply immediately. You need to understand precisely what info or data you have in your arsenal. Start working on your current data so that you can develop the potential for useful insights into actions that make a difference.

Identify the future condition of your business operations as well as the relevant use cases to help you realize this vision. Next, you can figure out the data needs and describe a roadmap for bridging the gaps in data to facilitate your future state.

The greatest mistake many manufactures do is that they do not focus on a vision for the long-term, or for that matter, the targets for creating data potentials so that they can build a competitive advantage in the industry.

3. Paying heed to your data will help you improve

Usually, manufacturers are keener on solving a particular issue, which is a narrow approach. Such a method restrains scalability, flexibility, and eventually, the project’s efficiency or effectiveness. As the approach isn’t meant to improve across numerous use cases, machines, or plants, the effort and time spent is a complete waste.

When it comes to accomplished manufacturers, they do not follow this narrow approach but pay more heed to create a data platform that will let them transform the way the manufacturing unit function. When you make certain to make data models adaptable and generally applicable, it will let the project address numerous use cases and widen potentials through the organization.

4. Don’t allow your analysts to work on manual data creation

Most data analysts cite that most of their time is gone, 80 percent of their productive time, on cleansing and mixing data. The rest 20 percent is spent on evaluation and creating useful insights. Now, this approach is not acceptable when your organization has a few data scientists and your business requires including data-oriented decision making into operational procedures, which is persisting to pick up the pace.

That is why you must focus on data modeling as well as, contextualization tools to help in automating the data cleaning and mixing process. When you create a data-modeling platform in real-time, it would become the basis and essential requirement for manufacturing businesses in the subsequent decade.

5. Make certain to achieve actionable results

The greatest challenge for data analysts is making certain data examinations and insights achieve fruitful results. Many times, useful insights end up as tools or slideware for experts, and this does not affect the business so much. You may address this problem in two ways:

    1. Display the results that are related to a user. Create self-sufficient dashboards or for that matter, user interfaces for various audiences, who would use that data. Understand what line managers, operators, and factory managers need to learn and offer the data analysis to every audience in a clear way, which is not disordered and ambiguous with the things users find unrelated.
    2. Make certain that change management resources play a part in putting into operation the output as well as drive buy-in with neighboring factory operational teams. Most of the time, efforts do not see fruition because no person is there to create opportunities for cultural and operational changes detected by the data and business intelligence analytics. You can assure this by integrating change management resources in an analytics project.

Final words

The path to implanting effective data-oriented manufacturing projects is full of challenges, drawbacks, and obstacles. Many business and technical aspects can create a roadblock for success. At Cognilytic, we provide you with the best manufacturing data analytics tools to help you use the right data, analyze, and change them into actionable insights for successful business operations. With analytics and dashboards, you can ensure a more detailed understanding of how your manufacturing process functions and simplify it more.

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