Predictive Analytics to Cut Manufacturing Risk

For manufacturers to be successful in today’s volatile economic landscape, they must react quickly to challenging events and rely more often on managing operations on a predictive basis.

riskIn fact, 86% of the top performing manufacturers are using analytics to reduce risk and improve operating performance, compared to 38% of average performing companies and 26% of industry laggards, according to research from Aberdeen Group.

Companies that feed data from their risk management systems into analytics and dashboarding applications can “more easily get the ‘big picture’ view of that state of their manufacturing operations – where the biggest risks lie – and most importantly where they should focus their efforts,” the report notes.

The top risk to manufacturers in terms of impact on the business is the failure of critical assets, the study finds.

The best performing companies report 18% higher overall equipment effectiveness and 13% less unscheduled asset downtime compared to the lowest performing organizations, according to Aberdeen.

The top performing companies have the ability to automate the collection and sharing of data to support predictive decision making.

Top performing companies also:

  • Pinpoint the plant assets and production processes that are high risk, establish a threshold value to monitor the risk and notify employees if the value deviates
  • Develop company strategies to ensure that predetermined thresholds remain accurate
  • Prioritize identifying and fixing problem areas
  • Track improvements in risk management by comparing current performance against baseline measure

Too often manufacturers identify historical information in standard key performance indicators without incorporating predictive analytics so they can look forward, notes Rob Handfield, supply chain professor at N.C. State University’s Poole College of Management, in a recent blog post.

“Where the real insights began to occur is when information that was presented in the context of baseline and trends emerged, leading to quick identification of deviations, variations, and trending patterns that could quickly lead to team reviews and action items,” he notes. “It is this context of ‘what does good (or bad) look like?’ that leads to the most important insights.”

Additionally, manufacturers should be open to thinking about and identifying data other than what typically resides in a database, he notes in a separate post.

“One of the things that is important is being able to open up your ears and eyes to drive creative thinking into other types of data that might be available that are not currently in your standard database,” he says. “Is there proxy data that can be used to approximate other related variables that can provide good insights into the parameter of interest? Are there other pieces of data that might be directly related but correlate to what we are trying to solve? This could include public domain data or economic data.”

In the supply chain domain, there is a strong need for manufacturing analytics for collaborative forecasting, inventory management and production planning, Handfield notes.

“One of the big themes is the need to drive end to end integration – so that demand sensing algorithms can provide early warning of surges (or slowdowns) in demand, that can be tracked and driven back into the supply chain, beginning with improved warehouse and distribution management, transportation management, production planning, and supplier capacity and order collaboration,” he adds.

“People need to have metrics that drive transparency on events both in the short-term, but that also provide input into broader strategic decisions, such as longer-term needs for capacity, or even slowdowns that translate to postponing major capacity investments,” Handfield says.

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