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Conversion Cost Reduction: Using AI in Stone Processing
Conversion Costs Reduction: AI is being used across industries to reduce overhead costs and save companies money through automating specific parts of processes and tasks. In the dimension stone industry, AI can help to improve the recovery rate, reduce the waste generation, and optimize the production process.
According to a study by Springer, the dimension stone industry faces several significant challenges, such as low efficiency, high production costs, and many wastes generation and resource loss during extraction and processing phases. The study found that about 51% of all the material extracted from dimension stone quarries turns into waste during the extraction operation, and roughly 41% of the material that arrives at dimension stone processing plants turns into waste during the processing operation. Therefore, the overall recovery rate of the dimension stone production process is approximately 29%, signifying the significant volumes of resources lost and the large amounts of waste generated.
A report by McKinsey suggested that the tech-enabled cost-reduction approach uses automation, artificial intelligence (AI), and other technologies to find new opportunities in such areas as capacity reallocation, spending effectiveness, and accounts receivable. A report by BCG stated that AI can reduce producers’ conversion costs by up to 20%, with up to 70% of the cost reduction resulting from higher workforce productivity. AI can also help to develop and produce innovative products tailored to specific customers and to deliver these with a much shorter lead time.
Conversion Costs Reduction by Improving recovery rate:
The recovery rate is the ratio of the final product volume to the initial raw material volume. It reflects how much of the valuable material is recovered from the extraction and processing operations. The higher the recovery rate, the lower the production costs and the environmental impacts. However, the dimension stone industry has a very low recovery rate, which is estimated to be around 29%. This means that about 71% of the material extracted from quarries and processed in plants is lost as waste.
AI can help to improve the recovery rate by using advanced technologies such as laser cutting, water jet cutting, diamond wire sawing, and multi-blade gang sawing. These technologies can cut the stones with high precision, accuracy, and speed, reducing the material loss and increasing the product quality. AI can also help to apply optimization methods such as linear programming, genetic algorithms, and artificial neural networks to find the optimal cutting patterns and parameters that maximize the recovery rate.
Vendors who supply linear programming, genetic algorithms, and artificial neural networks software used in natural stone processing industry are:
- DDL: DDL is a software for stone suppliers, planners and architects. The core feature is dry laying stone digitally and collaborating between project managers in an BIM-ready environment. Saving up to 20% on production cost through process optimizations and digital cutting tickets. The web-based software includes warehouse management with live inventory, project and customer management as well as an easy-to-use offer interface. Moreover, with this service you can synchronize your inventory with your website to make it accessible to customers and increase your sales. You can visit their website here.
- Siemens: Siemens offers solutions for natural stone processing machines that meet the highest requirements for precision, reliability and robustness. They provide a scalable portfolio of hardware and software that can help machine builders find the optimal solution for every customer requirement. They also offer digital enterprise solutions that support the entire value chain of natural stone processing, from design to service. You can visit their website here.
- LINDO Systems: LINDO Systems develops software tools for optimization modeling. They offer solvers and a featured environment for linear programming, nonlinear programming, integer programming and global optimization models. Their products include Lindo API, LINGO, and What’s Best for Excel. You can visit their website here.
Conversion Costs Reduction by Reducing waste generation:
Costs Reduction in waste generation is the amount of material that is discarded or disposed of during the extraction and processing operations. It reflects how much of the natural resources are wasted and how much of the environmental pollution is caused by the production process. The lower the waste generation, the higher the resource efficiency and the environmental sustainability. However, the dimension stone industry has a very high waste generation, which is estimated to be around 51% during extraction and 41% during processing. This means that about half of the material extracted from quarries and more than a third of the material processed in plants is turned into waste.
AI can help in cost reduction in reducing the waste generation by using data analytics, machine learning, computer vision, and sensors to monitor and control the production process. These technologies can collect and analyze data from various sources such as machines, equipment, materials, environment, etc., to detect and prevent faults, errors, defects, damages, etc., that can cause waste generation. AI can also help to implement waste management practices such as recycling, reusing, and valorizing to minimize the environmental impacts of waste disposal.
Some vendors who supply solutions for AI to implement waste management practices such as recycling, reusing, and valorizing to minimize the environmental impacts of waste disposal are:
- Kabadiwalla Connect: Kabadiwalla Connect is a social enterprise that uses AI to connect waste collectors with recycling facilities and empower the informal sector to decentralize waste management. Their solution includes a point of sale device for scrap shops, a mobile app for waste pickers, and a web platform for recyclers. They also use data analytics and machine learning to track and trace materials, optimize logistics, and provide certification for recycled products. You can visit their website here.
- Recykal: Recykal is a cloud-based platform that enables digital transactions and traceability across the waste value chain. Their solution connects waste generators, aggregators, processors, and recyclers through a marketplace app. They also use AI to provide data-driven insights, analytics, and reports on waste management performance, compliance, and sustainability. You can visit their website [here].
- ZenRobotics: ZenRobotics is a company that develops AI-powered robots for sorting and recycling waste. Their solution uses computer vision, machine learning, and sensor fusion to identify and separate different types of materials from mixed waste streams. They also use AI to optimize the robot’s movements, speed, and accuracy.
Optimize the production process:
The production process is the sequence of activities that transform raw materials into final products. It reflects how efficiently and effectively the production system operates to meet customer demands and expectations. The higher the production process optimization, the higher the customer satisfaction and loyalty. However, the dimension stone industry has a very complex and variable production process, which involves many factors such as material characteristics, market trends, customer preferences, design specifications, quality standards, etc., that can affect the production outcomes.
AI can help to optimize the production process by using natural language processing (NLP), recommender systems, and deep learning to understand customer needs and preferences, to offer personalized products and services, and to deliver them with a shorter lead time. AI can also help to develop and produce innovative products that are tailored to specific customer segments and niches, creating new revenue streams and competitive advantages.
vendors who supply solutions for AI to optimize the production process by using natural language processing (NLP), recommender systems, and deep learning to understand customer needs and preferences, are:
- DDL:
- Siemens:
- LINDO Systems:
Conclusion
AI is a powerful tool that can help to improve the performance and productivity of the dimension stone industry, by improving the recovery rate, reducing the waste generation, and optimizing the production process. AI can also help to create new value propositions and business models for the dimension stone industry, by offering customized and innovative products and services. AI can thus help to reduce the conversion costs, increase the customer satisfaction, and enhance the environmental sustainability of the dimension stone industry.
