If we were to rate the scale of an industry on the basis of moving parts and components, the manufacturing sector would be at the top of the rankings.

From being one of the largest contributors of employment to being a hotbed for machine and component innovation, the manufacturers have one of the most dynamic operational working models in place for their daily routine. With so many moving parts and physical infrastructure collaborating in a near-unlimited fashion, manufacturers also fall prey to efficiency losses owing to a gradual decline in the effectiveness of equipment deployed at a manufacturing facility. The industry relies on measuring Overall Equipment Effectiveness (OEE) as a measure for productivity. The higher the OEE, the greater will be the overall efficiency levels of the manufacturing organization.

A higher score for OEE is desirable to remain competitive and sustainable in challenging market conditions. But to maintain good scores, manufacturers have to find ways to reduce the ‘6 Big Losses’ that often contributes to inefficiencies leading to lower throughput. They are

  • Equipment Failure
  • Setup and Adjustments
  • Idling and Minor Stops
  • Reduced Speed
  • Process Defects
  • Reduced Yield

It is very unlikely that any manufacturer would want to know what each of these losses is and how it occurs. What they would really want to know is to learn about solutions that will help reduce or eliminate the 6 big losses and help realize better OEE.

What if we told you that the ingredients required for a solution to these 6 losses are already available in your business? We are talking about data that is freely flowing within your manufacturing organization. Ranging from equipment performance data to workforce and labor productivity information, there is large pools of data that can help decision-makers take steps to eliminate the 6 big losses and gain good rankings for their OEE.

So how can data become a manufacturing business’s best friend in this scenario? The answer lies in implementing data analytics at the core of your manufacturing business.

By enabling a data-oriented decision-making framework, key business performance metrics can be evaluated on factual insights thereby allowing better organizational control over the 6 big losses which can be classified under 3 major focus areas- availability, performance, and quality.

Let us have a deeper look into how data analytics help manufacturers identify and rectify losses and improve their OEE significantly.

Predict Failure

The best remedy for improving OEE for manufacturers is to act in advance and prevent the causes for degradation of equipment performance. For proactive monitoring and maintenance of manufacturing infrastructure, data analytics can turn into a valuable asset. By leveraging utilization and historic operational logs of devices, analysts can uncover hidden behavior of equipment components that have the probability of leading to failure scenarios in near future. Using this insight, manufacturers can pro-actively run maintenance programs and upgrade initiatives to ensure that the identified candidates for failure are well covered for fail-proof reliability.

Enable Better Performance

To enhance performance, it is important to have control over minor stops and idling equipment. By enabling automated data collection of the whole manufacturing ecosystem and co-relating it with minor stops, manufacturers can find linkages between the stoppages and the actual behavior of an equipment or business process that displays the problem. By analyzing running data over a period of time, it becomes easier to spot a chronic minor stop and take remedial actions to safeguard the performance commitments of machinery.

Eliminate Defects

Data-driven analytical processing can help manufacturers to further dive deep into quality issues and discover suspicious operational or business workflows. A quick round of analytics on operational data can yield results such as the processes where defects are common and have higher chances of impacting overall yield and output. The information thus received can be used to strategically prioritize corrective measures for modules or equipment and ensure that they are defect-free in subsequent manufacturing cycles.

By tracking the right metrics through analytical observation, manufacturers can gain insights into the root causes that lead to the 6 big losses. From predicting its next arrival to ensuring that the losses are minimal and are of inconsequential value, analytics can redefine the way manufacturers embark upon their success journeys. By systematically working to drive your business forward based on data insights, OEE can be strategically improved in parallel. 

By bringing in analytics, information already generated across the operational ecosystem of manufacturing, businesses can be transformed into insights that dictate decision-making at leadership or management levels. These decisions, when put into action, can bring in desired results and ROI for manufacturers and help keep their business sustainable and profitable. 

Get in touch with us to learn more about creating a strategic roadmap for implementing analytics with best practices customized for different facets of a manufacturing business.