Leveraging Predictive Analytics in Manufacturing to Eradicate Defects Leveraging Predictive Analytics in Manufacturing to Eradicate Defects

Leveraging Predictive Analytics in Manufacturing to Eradicate Defects

Driving efficiency: The power of predictive manufacturing in defect prevention for a vehicle manufacturing company


The client is a globally renowned company in the commercial vehicle sector, celebrated for crafting high-quality trucks and buses designed for various industrial applications. Their extensive product line spans all eight classes, encompassing light, medium, and heavy-duty vehicles, specialized transport, and buses, catering to a diverse range of market needs. Their core focus is on innovation and sustainability, as they lead the charge in environmentally friendly technologies like electric and hybrid vehicles. With a presence spanning the globe, robust customer support, and an unwavering commitment to reliability, they have become the preferred choice for businesses worldwide, offering dependable and efficient transportation solutions across various industries. 


The Client’s Business Challenges: 

• The Client encountered difficulties in upholding quality standards for their crankshafts, leading to defect rates of one part per month. These defects resulted in material wastage and losses in opportunity costs. 

• Rapid expansion and inorganic growth created challenges in overseeing and aligning manufacturing performance across multiple sites. This was exacerbated by limited insight into global downtime on production lines and ineffective monitoring, follow-up, and escalation procedures. 

• Notification delays and prolonged resolution times negatively impacted manufacturing efficiency. 

• The situation was further complicated by using distinct tools and systems at each site, leading to varying measurement approaches. As a result, the client aimed to create a tool for monitoring downtime at both line and site levels. 

The client aimed to enhance production efficiency through two phases: 

• Regularly monitor critical production parameters. 

• Develop a model for predictive analytics in manufacturing to evaluate the likelihood of defects in the crankshaft grinding process. 


The Hexaware Solution: 

Technical Approach: 

As an integral part of the solution, Hexaware provided comprehensive support to the client in both project phases. During Phase 1, Hexaware identified crucial measurement and reporting parameters, including grinding force, speed, feed rate, lubricant usage, and more. Customized dashboards were created to routinely report these metrics to management and shop floor engineers. These dashboards also pinpointed timestamps and part numbers for defective components, laying the groundwork for Phase 2 (the development of an advanced predictive manufacturing analytics model for crankshaft production). 

• In Phase 1, this solution allowed the client to accumulate approximately 3 months of production data, involving 120 parts per day. This data is currently utilized for advanced analytics predictive modeling utilizing Azure AIML. It facilitates the identification of frequency deviations beyond set limits (prompting corrective actions), determination of optimal production parameters, and supports root-cause analysis and diagnostics. 

Business Coverage: 

The quality analytics-driven approach was adopted to achieve the goal of establishing a monitoring platform equipped with data-powered predictive capabilities, which encompassed the following: 

– Creating a data analytics platform for ongoing monitoring of machinery’s key performance indicators (KPIs) to reduce malfunctions. 

– Determining the optimal operational parameters for factory processes to enhance energy efficiency and overall performance. 

– Analyzing machinery data to construct predictive models capable of early defect detection. 

– Ensuring effective oversight across diverse manufacturing sites, with a stringent commitment to upholding quality standards. 

– Implementing timely incident reporting and escalation procedures to address issues in manufacturing units promptly, enabling real-time corrective actions.


Achieved 40% cost savings in enterprise transformation through custom application development. 

• Realized a 30% enhancement in uptime and operational efficiency across sites. 

• Attained a 25% reduction in operational expenses by implementing standardized benchmarking and more accurate downtime forecasting across sites. 

• Established complete oversight of manufacturing site operations, ensuring 100% visibility and control. 


Hexaware assisted the client in creating a new platform that utilized data analytics, introduced new features, and provided comprehensive insight into critical machine operating parameters for optimal performance. After implementation, the client experienced a significant reduction in manufacturing defects and gained complete control over their manufacturing operations management. This resulted in improved quality control and oversight. 

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