toggle

Customer stories

Improving manufacturing productivity through conveyor operations analysis

Manufacturing company reduces product rejection by 18% addressing operational inefficiencies through data-driven solutions.

12%

Increase in manufacturing efficiency

18%

Reduced reduction rates

Location

US

Industry

Manufacturing

Employees

300+

About client

Our client is a fast-growing FMCG company with manufacturing plants and inventories all over the US. Their manufacturing plants are equipped with modern equipment for generating, packing, and moving, and produce 200 to 300 tons of goods daily in a semi-automated manner.

Challenges

One of the key goals of our client, as they meet the day’s manufacturing demands, is reducing rejection costs and productivity issues. That’s when they noticed specific issues related to conveyor that affected their major operations.

Some of the issues with conveyors and the impact it caused:

Issues leading to rejections: They noticed an increase in rejection rates due to issues like misalignment or damage while assembling, un-synced movement causing wrong packaging and labeling, seized rollers, missing parts, etc.

Unable to perform root-cause analysis: When quality mismatches or jamming issues occur, they couldn’t instantly perform root-cause or correlation analysis due to lack of data or visualization tools.

Poor visibility into conveyor performance: Since they run many conveyors, it’s hard to monitor each conveyor’s performance separately. They were unable to predict which equipment requires prioritized care, which could have prevented unexpected downtime and maintenance issues.

Solution from datakulture

With help from datakulture, the client implemented a conveyor operations dashboard, that helped them visualize key metrics in near-real-time.

Aggregating data from sources – operations data and equipment data from IoT sources (temperature, speed, etc.), we transferred them into charts and visuals. The visuals reflected the data like passed vs. rejected items, conveyor efficiencies, hourly rejection rate, conveyor throughput per hour, rejection reasons, etc.

The impact our solution brought:

  • 12% increase in conveyor efficiency due to the real-time monitoring of equipment and proactive maintenance.

  • 18% reduction in rejection rates due to easy and in-depth monitoring of conveyor issues that led to instant fixing and damage control. Being able to narrow down and find the exact reason for rejections improved their quality and brought uniformity to goods manufactured.

  • Received triggers and alerts whenever any critical metric dropped below the threshold value. This helped them stay alert and fix issues that needed attention without periodic monitoring.

  • The operations team of the unit could monitor and find the variations in performance and throughput on a day, so they can align resources effectively and make the most of productive hours.

Conclusion

By correlating data from manufacturing, quality control, and conveyor operations, they were able to find how one issue impacts the other. They could minimize conveyor operational faults like seized rollers, spillage, fast or slow movements, mistracking and mispackaging, etc., reducing cost, material, and productivity wastage. Relying on current and historical data, their operations team gained necessary insights about their performance trends, making data-driven decisions, and improving key manufacturing metrics.

Let's build your data culture together

Talk to a datakulture consultant today.

Click to

Get in touch