The topic of data analytics is as much hyped as it is questioned – the spectrum of opinion ranges from “data as the new oil of the economy” to “analytics conclusions are not 100% reliable” and all the nuances in between. Each one is true in its own way. To help you better understand this topic, I gathered some recurring questions that production experts usually raise, and discussed them with our manufacturing analytics engineers, IT experts, and data scientists.
There is no best level or function. Where to start a project depends on several factors:
Is the concept of data analytics already well understood at your company? If not, the best way to start is to hold a data analytics orientation workshop for both experts and management. The goal of this workshop is to impart a basic understanding of the possibilities of analytics and to identify potential use cases.
Does management have a (deep) understanding of the technical process? If not, a requirements workshop with production site experts may be your first choice. The output is usually a cause and effect diagram (see below). Typically, our data analytics team (comprising a mix of IT, manufacturing, and data science experts) asks in-depth questions, such as: “Do you want to differentiate between rework and scrap?” In the best case, it can be very helpful to involve management to achieve serious buy-in.
Has the problem that data analytics is expected to solve been defined concretely? If so, you may opt first for an analytics tool. The problem can be as concrete as “EoL testing effort is too high and needs to be reduced.” An analytics team can then immediately start working with the production site experts to determine whether an existing test-time reduction tool can be applied or how it may need to be customized or expanded.
Thanks to a UX study conducted by Bosch, we know that there are three types of plant experts that need to be addressed, each one in a completely different way – e.g. more on a business level, more on a technical level, or more on a data level.
The skeptical type needs evidence of the benefits data analytics will deliver. To convince the skeptic, we need an excellent understanding of the ROI mechanisms, and we must be able to validate these mechanisms quickly with results, focusing on output, quality, and costs.
The open-minded type is interested in new ways to optimize things. Emphasis should be placed on explaining which methods are used, why certain algorithms are selected, and why the resulting prediction model is ready for application to live data.
The believer has usually already had exposure to data analytics and believes it can make a difference for the business. The best way to start is to immediately apply CRISP-DM (cross-industry standard process for data mining, see diagram below) with him or her and the team for the data analytics project.
In our experience, orientation workshops have generally proven to be very useful in obtaining the buy-in of all stakeholders. We usually start with the business question and then iterate by understanding the technical process and constraints relating to that question.