Manufacturing Yield Improvement With Data Science

Manufacturing yield improvement leads to a direct reduction in costs and increase in revenues and is, therefore, a crucial consideration for industries. While many yield metrics, including first-pass yield, rolled throughput yield, and first-time yield, are applicable in different contexts, essentially, yield metrics measure what fraction of units input into a process come out as useful, sellable outputs.  

Manufacturing Yield Can Fluctuate Wildly Across Different Batches of the Same Product in Certain Industries

Manufacturing operations can suffer from variability in yield due to variations in raw materials, environmental disturbances, machine calibrations, or inherent randomness in steel, biopharmaceutical, chemical, or mining processes. The impact of yield variability can be substantial, with scrap rates as high as 20% in some cases. The modern manufacturing operations are often complex and can involve hundreds of production parameters. Associating the impact of a specific production parameter on the yield of a particular batch is quite challenging.  

An operator in a modern manufacturing floor often needs to track dozens of production parameters 

Manufacturing yield can be increased by reducing rework or scrapping of defective parts due to low production quality. Please check our blog on product quality analytics to learn how data science methods are successfully applied to improve product quality. However, even after resolving issues with product quality, potential gains can still be made in improving manufacturing yield by optimizing manufacturing process operations. 

Explainable Manufacturing for Manufacturing Yield Improvement

To provide visibility and understanding into the multiple parameters that make up the modern manufacturing process, Aimpoint Digital has developed an Explainable Manufacturing Platform. The Explainable Manufacturing Platform is composed of the following six steps: 

1. Data Load: Flexibly access data from multiple sources such as data lakes and data warehouses across all major cloud providers
2. Data Manipulation: Initial cleanup to improve the quality of data at hand 
3. Data Sampling: Select random samples from the data to speed up insight generation without sacrificing the quality of the insights 
4. Data Wrangling: Carry out null value analysis, feature engineering, variable encoding, standardization, cardinality analysis, and segmentation by product and process type 
5. Model Development: Build machine learning and inference analysis models that characterize the relationship between production parameters and their impact on the yield of various products  
6. Visualization: Visualize results through intuitive dashboards that are easily consumable by various stakeholders to drive action 

Explainable Manufacturing in Action

Problem 

A major North American steel manufacturing client needed visibility into how the multitude of parameters associated with each stage of the steel-making process influenced processing times at each stage and how they interacted to impact the manufacturing yields. Ultimately, the client was interested in how to manipulate operating parameters to drive tangible results in improving the manufacturing yield at its plants. 

Aimpoint Digitals’ Explainable Manufacturing Platform led to significant yield improvements for a steel manufacturer. 

Aimpoint Digitals’ Explainable Manufacturing Platform led to significant yield improvements for a steel manufacturer. 

Solution 

Using the explainable manufacturing framework described in the earlier section, our team built ~800 probability distributions across each different individual process segmented across the different types of steel grade that our client was producing. For each of the models, there were >150 production parameters that were of interest to the client. We placed a special emphasis on interpretable results to ensure that the insights would be consumable for a non-technical audience. At the same time, we automated all processes to ensure those insights were continuously generated and updated with minimal intervention. The twin focus on interpretability and automation ensured that our modeling results would lead to actionable insights from plant operators. 

Value Generated 

The platform was adopted and integrated into the steel manufacturing processes at our client site and led to significant improvements in manufacturing yield at multiple production steps. For example, for a certain grade of steel, the caster processing time was reduced by 10%. The client reaped significant financial benefits because of this work.  

Improve Your Manufacturing Yield With Aimpoint Digital 

The Explainable Manufacturing Platform developed by Aimpoint Digital provides exciting opportunities for all manufacturing industries to improve their manufacturing yield. Our team at Aimpoint Digital combines data science expertise with domain knowledge of various industries such as steel, chemical, and biopharmaceutical to provide a potent combination capable of solving a multitude of problems facing the manufacturing industry. 

Contact us through the form below to get started with optimizing your manufacturing yield today.