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Trends and Outliers

TIBCO Spotfire's Business Intelligence Blog

02/10
2014

Building Intelligence into Manufacturing with Data Analytics

Many manufacturers have invested heavily in information technology systems to make decisions and execute plant operations. But these same firms often find that they’re missing key pieces of information needed to detect problems before they negatively affect operations.

images Building Intelligence into Manufacturing with Data AnalyticsHowever, there are ways that manufacturers can build intelligence into their plant operations, according to a new report from Aberdeen Group that details the ways top performing companies successfully adopt big data analytics.

“Technology has given us the capability to access information as never before, so it’s ironic that manufacturing data continues to be trapped,” the report notes.

And companies that implement enterprise systems to manage functional areas and safety controls to increase efficiencies, instead end up with “data silos, duplicate databases and wasted resources.”

Big data sources such as machine data and remote assets are exacerbating the situation, with 55% of the respondents to Aberdeen’s survey noting that the most challenging aspect they face in data collection is unstructured data.

The report details the top challenges for manufacturers to manage enterprise data:

  • Dataset structure is too complex for decision making – 55%
  • Data isn’t available when needed – 33%
  • Data includes hidden information – 29%
  • Users don’t trust the data – 25%

“Organizations that can centralize and consolidate their systems are more equipped to unlock the value of their data and, as a result, get buy-in from their users,” according to the report. “When approached effectively, manufacturing data can help companies grow and support the vision for a more knowledgeable, effective organization.”

Aberdeen advises manufacturers to take three steps to infuse intelligence into their operations:

1. Consolidate manufacturing data from all areas including finance, engineering and supply chain to transform the manufacturing system into a “system of record” for all manufacturing data.

2. Build an analytical culture.

“The most overlooked part of any manufacturing intelligence strategy is the analytics,” according to Aberdeen. “This is where all the data from all the systems comes together to be put in a format that decision makers can do what they are supposed to do, and it has to happen fast. In many cases, the information cycle has to happen faster than the operation cycle.”

According to Aberdeen:

  • 67% of the top performing companies can analyze large data volumes, compared to 28% of all other companies.
  • 50% of the best companies can perform inline/offline analysis embedded in design, operation and delivery processes for all user types, compared to 33% of all other companies.
  • 50% of the top performing companies can combine, and mine heterogeneous data sources, compared to 21% of other companies.

In addition, the companies that have analytical capabilities in place post better cost, yield, and inventory measures,

3. Enable mobile access to the intelligence generated by analytics.

Additionally, analytics is the key to building intelligence into manufacturing systems, according to Aberdeen.

“While it is important to have the right tools, it is even more important to hire the right people,” the report concludes. “Build an analytical culture through operations people that use analytics as second nature. Getting the right insight into current performance, from detailed operational metrics to high finance performance, can sometimes be more important than actual execution.”

Next Steps:

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