Opportunities for AI in Manufacturing

          Introduction

          Every industry has been disrupted with the emergence of Artificial Intelligence (AI) tools and platforms. The AI-related noise in the media is overwhelming. Every day there is news of companies announcing new AI features and offerings. For an enterprise, much of this still remains marketing buzz. Most of the AI initiatives are still under experimentation with limited proven outcomes.

          However, the market pressures in industries such as manufacturing, as well as the deluge of available data and supply chain instabilities, is causing adoption of these technologies at a faster pace. Thus to address the market pressures, the companies are actively investigating and deploying AI-based interventions to address these pain points. The manufacturing industry value chain is poised to create new levels of planning, execution precision and efficiency with the help of AI and Data Science.

          Examples below illustrate key use cases for Artificial Intelligence that demonstrated an ability to deliver business outcomes.

          Predictive Maintenance

          One of the most critical applications of AI in manufacturing is predictive maintenance. Predictive maintenance uses machine learning algorithms to predict when equipment is likely to fail. Advance predictions allow manufacturers to take proactive measures to prevent machine downtime and avoid costly repairs. In addition for original equipment manufacturers, this capability allows to upsell service contracts and grow after-sale business.

          Key Metrics:

  • Downtime: Predictive maintenance can significantly reduce machine downtime by predicting when maintenance is needed and taking proactive measures to prevent machine failure. This can result in an uptime increase of up to 20%.[1]
  • Maintenance Costs: By preventing machine failure, predictive maintenance can reduce maintenance costs by up to 10%.
  • Equipment Life: Predictive maintenance can also extend the life of equipment by identifying potential problems before they become serious, resulting in a potential improvement of up to 25% in equipment life.
  • Revenue Upsell: In cases of predictive maintenance for customer-connected products, the insights allow to upsell additional service solutions and offerings. The actual benefits will differ from customer to customer.

          What is needed:
          Data Collection systems, Data Management infrastructure, Data Preprocessing, Predictive Maintenance Models, Integration with Field Service and Sales Systems.

          Quality Control

          Quality control is an essential aspect of manufacturing, and AI can help in this regard by automating the inspection process. AI-powered cameras and sensors can identify defects in real-time, and alert the appropriate personnel. This type of AI-driven automation increases the accuracy of the inspection process and speeds up the production process.

          Key Metrics:

  • Defect Detection: AI-powered cameras and sensors can detect defects in real-time, resulting in a potential improvement of up to 90% in defect detection accuracy.[2]
  • Inspection Time: Automating the inspection process with AI can speed up the production process and reduce inspection time by up to 50%.
  • Product Quality: By reducing defects and improving the accuracy of the inspection process, AI-powered quality control can result in a potential improvement of up to 20% in product quality.

          If you are interested in a more detail example, read an article from Dr. Ilya Kalagin here https://ctech-labs.com/ai-for-defect-detection/

          What is needed:
Real-time Video or Image Capture systems, Data Processing, Data Annotation, Machine Learning Algorithms, Integration with Manufacturing Execution Systems.

          Supply Chain Optimization

          AI can also play a critical role in optimizing the manufacturing supply chain. Machine learning algorithms can analyze data from multiple sources, such as sales, production, and delivery data, to identify patterns and make predictions about future demand. This allows manufacturers to better manage their inventory levels, reduce waste, and improve delivery times.

          Key Metrics:

  • Demand Forecasting: By predicting future demand, AI-powered supply chain optimization can help manufacturers better manage their inventory levels, resulting in a potential improvement of up to 50% in forecast accuracy.[3]
  • Delivery Time: Improved inventory management can also result in a reduction in delivery times and decrease in excess inventory 20% to 50%.
  • Waste Reduction: By reducing excess inventory and improving delivery times, AI-powered supply chain optimization can result in a significant reduction of waste associated with excess inventory.

          What is needed:

          Integration with inventory management and transactional systems, Data Lake for data management and processing, Integration with Salesforce Forecasting systems, Optimization Algorithms, Analytics and collaboration platforms.

          Process Automation

          AI is already commonly applied in manufacturing as robotic process automation. AI-powered robots can perform repetitive tasks faster and with greater accuracy than humans. Sample examples of process automation may include deep integration of sales with complex order management, fulfillment and billing processes.

          Key Metrics:

  • Productivity: AI-powered bots can perform repetitive tasks faster and with greater accuracy than humans, resulting in a potential improvement of up to 50% of productivity gains.[4]
  • Human Error: Automating processes with AI can reduce the potential for human error, resulting in a potential improvement of up to 70%.[5]

          What is needed:
RPA toolset, Integration Middleware, Workflow Management Systems, Analytics and Process Mining Applications to mine opportunities and inefficiencies.

          Sales Acceleration

          AI can also be used to support sales acceleration in manufacturing companies. Machine learning algorithms can analyze vast amounts of data to identify patterns and make predictions about individual customers or prospects; as well about a portfolio of customers in aggregate. Sales and Sales Operations professionals can get prescribed guidance on how to navigate complex selling relationships; which products and opportunities to prioritize, as well as map high-risk customers that require urgent attention..

          Key Metrics

  • Cross-sell: AI-powered decision making applied to whitespace prediction and deal prioritization can drive up to 25% percent revenue uplift.[6]
  • Customer Churn: Being able to proactively identify and manage customer in-need, can significantly decrease potential customer attrition.

          What is needed:
          Master Data Management, Data Lake or Customer 360 platform, Integration with existing Sales and Service Systems, Data Analytics, Prediction Algorithms, Integration with Workflow Management.

          Conclusion

          Artificial Intelligence has the potential to transform the manufacturing industry and bring about significant benefits. Predictive maintenance, quality control, supply chain optimization, process automation, and sales acceleration are just some of the areas where AI can be applied in the manufacturing industry to deliver business outcomes. Incorporating these capabilities into the operations, manufacturing companies can increase efficiency, improve product quality, and stay ahead of the competition. For organizations initiating such a journey, it is worth evaluating investments into low code integration platforms, data management and workflow solutions as well as machine learning platforms.

[1]https://www2.deloitte.com/content/dam/Deloitte/de/Documents/deloitte-analytics/Deloitte_Predictive-Maintenance_PositionPaper.pdf

[2]https://www.mckinsey.com/~/media/mckinsey/industries/semiconductors/our%20insights/smartening%20up%20with%20artificial%20intelligence/smartening-up-with-artificial-intelligence.ashx

[3]https://www.mckinsey.com/~/media/mckinsey/industries/semiconductors/our%20insights/smartening%20up%20with%20artificial%20intelligence/smartening-up-with-artificial-intelligence.ashx

[4]https://technologypartners.net/blog/2019/12/how-can-robotic-process-automation-increase-employee-productivity/

[5] https://forum.uipath.com/t/use-cases-of-rpa-facilitating-it-process-transformation/510658

[6] https://www.bain.com/client-results/insuranceco-harnesses-the-power-of-ai-to-boost-cross-selling/