In today’s competitive manufacturing landscape, maximizing return on investment (ROI) is a top priority for manufacturing organizations. One effective strategy to achieve this goal is by focusing on improving key performance indicators (KPIs). By tracking and optimizing KPIs, manufacturers can enhance operational efficiency, reduce costs, and increase profitability. Defining the main KPIs that are important to your industry will improve ROI and implement a continuous improvement culture.

Today, manufacturers can gain immediate insights into their manufacturing data to improve KPIs, including: 

  • AI Analytics dashboard
  • Prediction of failures
  • Manufacturing KPIs
  • Product quality problems
  • Root cause analysis down
    to the component level
  • Cross correlations between processes

 

Below are the 7 main steps to achieving ROI by enhancing manufacturing KPIs.

1.Identify Relevant KPIs: The first step in the process is to identify the KPIs that directly impact ROI in the manufacturing environment, for example:

A- First Pass Yield. 

B-Retest rate. 

C-Scrap/Rework rates.

D-On-time delivery

 

2.Identify Relevant for overall equipment effectiveness (OEE), such as: 

A-Meantime between failures (MTBF).

B-Defective parts per mission opportunities (DPMO). 

C-Production cycle time.

 

3.Cross correlations between processes: Before initiating any improvement, it is crucial to establish baseline metrics for the identified KPIs. This provides a starting point for comparison and allows you to measure the effectiveness of your efforts. Accurate and consistent data collection is essential during this stage to ensure reliable benchmarks.

 

4.Analyze Root Causes: To improve manufacturing KPIs, it is vital to identify the root causes of underperformance. AI-powered root cause analysis is a game changer in  improving quality and problem-solving. It uses smart algorithms that show  exactly what is causing quality issues and alerting it to the team. By automating Root Cause Analysis, the technology effectively     overcomes  quality and yield challenges Increasing productivity and KPIs.

Statistical process control (SPC) mainly delivers Key Performance Indicators such as Cp/Cpk, first Pass yield and other statistical analysis related to the process, while AI root cause technology uses anomaly detection and automatically identifies correlations between different processes and product parameters. 

 

AI-Powered Root Cause Analysis will deliver:

  • Machine learning 
  • Anomaly detection of quality and yield problems
  • Prediction of failures
  • Cross correlations between process and parameters
  • Automatic alerts 
  • Pattern recognition for automated data mapping

5.Implement Continuous Improvement Initiatives: Once the root causes are identified, implement continuous improvement initiatives to address them. These initiatives may include process optimization, equipment upgrades, employee training, or automation implementation. Involve cross-functional teams and encourage a culture of innovation and collaboration to drive these improvements effectively.

 

7.Monitor and Measure Progress: Regularly monitor and measure the progress towards your KPI goals. Use visual management tools as manufacturing dashboards, and real-time data analytics to track performance and identify trends. Regular review meetings with relevant stakeholders will ensure everyone is aware of progress and can provide feedback or suggest further improvement opportunities.

 

7. Continuous Review on product quality: Quality improvement often means lower recall and warranty costs, and that’s where the real value lies, measured not only in higher revenues, but also in operational efficiency and innovation.

With a dedicated focus on improving KPIs, manufacturers can position themselves for long-term success in a competitive marketplace. 

This is an example of a ROI calculator from QualityLine AI Analytics

Manufacturing analytics ROI

Artificial intelligence technology is an important capability offered by QualityLine’s solution that is applied to automatically unify multiple manufacturing data sources into one database to identify product and process errors in order to deliver detailed insights into your manufacturing process. The AI analytics creates a total end-to-end process control, improving quality and problem-solving. Customers of QualityLine typically achieve a return on investment within four to seven months.