67% of manufacturers have implemented pilots or are engaged in advanced industrial analytics projects (LNS, 2022). This isn’t surprising, given that industrial analytics can provide the biggest impact in the shortest amount of time. However, just because this potentially high impact initiative is widely adopted doesn’t mean there aren’t challenges.

LNS Research recently released an Analytics That Matter report centered around the top 3 reasons industrial analytics fail. Number 1 on that list was data quality issues. The author isn’t surprised by this, given that 70-80% of analytics and data science teams’ time is spent on data preparation and cleaning.

Aside from cleanliness, core data requirements include that the data is in the required format, has the right context (meta-data), and is actually available when they need it. Again, even if a manufacturer has a data science team, they’re likely missing at least ⅓ of these requirements.

Required Formatting

Required formatting is extremely specific to job role, line, product and more. This is why it’s important to have a mechanism to easily build out reports to enable many people without the use of IT, solutions integrators, or data engineers. In addition, true advanced industrial analytics solutions should enable deep dive drill down capabilities, in the right format for specific problems.

Contextual Data

Contextual data (sometimes called intersectional data or meta-data) is the linking of siloed sources of data with time-series data. For example, let’s say you wanted to compare the run speed of a line, with quality measurements, and any variances from shifts, in the last week because there was more output, and fewer defects. If you had data infrastructure set up to bring all those siloed data sources together, this would be a short query. Unfortunately, this has been challenging for manufacturing companies to have because the matching of data across different processes, production, and business systems has been very difficult without the right partners. This means drilling down into problems, or surfacing product-line combination opportunities, and understanding the impact of these, is extremely resource intensive and expensive. Fortunately, Oden automatically cleans, enriches, and transforms raw data from machines into contextual reports, making it incredibly fast and efficient to get the insights you and your team needs.

Insights for the Right People at the Right Time

Accessibility and availability is key to maximizing the impact of data. Delays waiting for other teams, technical resources, or for data to be cleansed for review could mean thousands of pounds of scrap produced, and utilization decreases. What most manufacturers experience when trying to enable various teams with better data and insights is that there is too much irrelevant or complicated data served to stakeholders, the way data is presented is complicated and difficult to understand, or the data is untrustworthy.

Why is This so Hard?

So why do these data challenges happen? Creating a common data model that will enable data cleanliness, accessibility, and formatting is extremely expensive. Requiring legacy systems to “speak the same data language” is a huge undertaking that many teams don’t have time for in their day to day firefighting. In addition, there are many ongoing costs, including data engineering, maintenance, and hosting to consider. If your manufacturing company doesn’t have a large IT resource, and is under $10 billion in annual revenue, it’s unlikely that it will ever be cost effective to build this out.

And trying to build advanced industrial analytics on their existing tools such as their machine control and ERP systems, or with their solutions integrators isn’t the effective path forward. The amount of configuration, custom work, and ongoing maintenance quickly spirals out of control, and although it may create some early wins, often doesn’t enable true continuous improvement. This is a complicated problem that truly requires a specialized partner.

Advanced Industrial Analytics with 90% Fewer Resources

Rather than trying to use legacy or installed systems for advanced industrial analytics projects, consider a best-in-class solution. These challenges can be solved with 90% fewer launch and maintenance costs, and your plant can see results in less than 90 days.

Fortunately, there’s a turnkey solution. Oden software works with SCADA, machine control, ERP, and other production systems to get your team the actionable intelligence they need to lower costs, improve quality, and reduce waste. Having Oden in your plant is like having business intelligence, IT, and Data Science teams working 24/7 across factories to improve OEE.

At the core is a data infrastructure that uniquely merges, cleans, and enriches all production data sources automatically for easy, prescriptive, and actionable intelligence. All of this means trustworthy, high quality, and contextual data for the team members who need it. 

Learn more about Oden here

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