Highlights of the Case Study:
| Particulars | Description |
| Client | A leading manufacturing company wanted to deploy digital twin analytics to make manufacturing more flexible and efficient. |
| Business Challenge | The client wanted to use our digital twin analytics to create an IoT sensor-enabled digital copy of all its critical equipment to predict any equipment anomalies and maintain its assembly line flow.  |
| Impact | Quantzig’s data analytics and predictive maintenance solutions-based digital twins analytics helped optimize IoT deployment for maximum production efficiency. This, in turn, helped the client track equipment health to ensure the meeting of production targets. |
Game-Changing Solutions for Manufacturers
Equipment failures result in unnecessary delays and an increase in cost, leading to poor outcomes for the business. Quantzig’s digital twin analytics can help manufacturers address these issues by providing real-time insights into machine health. An IoT-enabled virtual simulation of the equipment thus created helps to avoid breakdowns and provides real-time insights for predictive maintenance. Â
Quantzig’s comprehensive predictive maintenance solutions and advanced technologies enable businesses to analyze data from various sources, including machines, sensors, and maintenance logs, to reduce costs, maximize machine uptime, and enhance product quality. Digital twin evaluates this real-time data from machine sensors to deliver desired outcomes. 
The Challenges of the Manufacturing Client
A leading manufacturing company wanted to deploy digital twin analytics to make its manufacturing process more flexible and efficient. The client was struggling to meet its production targets due to unplanned downtime. The client wanted to use our digital twin technology to create an IoT sensor-enabled digital copy of all its critical equipment to predict any equipment anomalies and maintain its assembly line flow. The client wanted to address the following issues that were arising due to frequent and unexpected equipment failures: Â
- Lack of visibility on the condition of critical equipment across all facilities 
- Achieved only 70% of its production target due to increased downtime 
- Increase in operating costs 
- Failure to meet the demand  
- Lost market share to competitors 
Quantzig’s digital twin technology has helped multiple manufacturing organizations realize a superior and more flexible manufacturing process.
Digital Twins Technology for the Manufacturing Industry
Quantzig’s team set to resolve the issue at the equipment level by collecting real-time data along with historical sensor data and maintenance data, such as temperature and motor speed, to create a digital twin. Furthermore, IoT sensors fed the information to a digital twin software at each location that created a virtual equipment model on the cloud. Such models allowed the client to monitor physical assets remotely and collect data on them for future reference. 
At the advanced operational level, our team equipped the digital twin software with ML-based algorithms trained on data collected by sensors. This helped detect abnormal equipment behavior proactively and suggest corrective action before equipment failure. These interventions helped the client optimize equipment use and meet production targets to cater to market demand and thus strive to gain a larger market share.
Request a Demo of this Case Study.
Impact Analysis of Quantzig’s Digital Twins Solution
Quantzig’s data analytics and predictive maintenance solutions enabled digital twins to optimize an IoT deployment for maximum production efficiency. This helped our client track equipment health and identify potential failures by capturing real-time data and supplementing it with historical data on machine failure and maintenance. Consequently, our client was able to prevent production stoppages. Quantzig’s digital twin technology helped our client achieve the following benefits: 
- 100% achievement of production target 
- 25% reduction in operating costs
- 54% increase in profit margins
- 100% Achievement of production target 100%
- 54% Increase in profit margins 54%
- 25% Reduction in operating costs 25%
Key Outcomes
The implementation of the digital twin solution enabled the client to increase the profitability of its production units by reducing downtime and ensuring maximum resource utilization. Quantzig’s intervention allowed the client to address issues that led to failure in meeting targets and preventing the client from its growth and expansion goals. The positive impact of this collaboration led the client to extend its contract with us and seek our help in creating a virtual model of the entire manufacturing process on the cloud. 
A Broad Perspective on the role of Data Analytics Solutions in the Manufacturing Sector:
Digital twin technology complements other Industry 4.0 technologies in various industries, helping in the reduction of waste and batch changeover times, optimization of product quality, improvement in traceability, and more. For instance, in manufacturing, the digital twin is a digital reflection of the as-configured physical product enhanced by real-time equipment data based on an accurate outline of the production process and equipment. In addition, having a digital twin enables digital diagnosis and troubleshooting, thereby eliminating the need for physical limitations of expert engineers at the site.  This technology is also at the core of customer interactive supply chains.    
Key Takeaways
- Enable real-time visibility on critical equipment across all its facilities
- Enable predictive maintenance and thus prevent downtime
- Ensure timely delivery of products
- Implement maximum utilization of resources
- Enhance productivity and, thus, achieve higher profit margins
- Cater to market demand and, therefore, carve out a more significant market share
Request a Demo of this Case Study.
Related Articles:
Machine Learning Solutions Optimized Manufacturing Line for a Major US-Listed Copper Manufacturer
Manufacturing Analytics Solutions Enabled F&B Manufacturer Stabilize Profit Margins
