Implementing AI for Manufacturing

Implementing AI in Manufacturing: an Introduction

AI is a transformative technology

Like the steam engine, the assembly line and electricity before it, AI is a transformative technology. It offers not incremental improvement in existing processes, but a fundamental change in the way companies run their production lines, innovate their products and designs and optimize yield. By enhancing human capabilities, providing real-time insights, and making use of big data generated by connected systems and platforms, AI is key in the effort towards fully automated production. It is now widely accepted that every industry will be impacted dramatically by Artificial Intelligence (AI) within the next five years. Industries and companies that have embraced this technological revolution are already seeing huge benefits that translate into a significant competitive edge.  

The potential of Artificial Intelligence in manufacturing  

For manufacturers, AI promises to be a game-changer at every level of the value chain. In fact, the AI transformation in manufacturing is so profound that it’s already termed “Industry 4.0”, to denote the revolutionary essence of the connection of physical industrial assets with digital insights, leading to the digitizing of the entire chain of production. 

Accenture and Frontier Economics estimate that by 2035, AI-powered technologies could add an additional 3.8 trillion dollars GVA to the manufacturing sector, an increase of almost 45% compared to business as usual. That’s because in industries such as automotive, semiconductors & electronics, heavy industry and consumer products, AI is already being successfully used for direct automation, predictive maintenance, defect detection, yield optimization and other use cases, significantly lowering operational costs, increasing efficiency, improving quality control, and enabling faster decision making. For manufacturers, this highlights the significance of AI in manufacturing factories: AI leads to productivity gains across the business, as well as to increased consumer demand resulting from the availability of higher-quality AI-enhanced products and services.  

AI in manufacturing market

In the industrial sector, AI application is increasingly supported by the ongoing adoption of the Internet of Things (IoT), the growing network of connected devices and sensors. Production lines, machines, vehicles, and devices generate enormous amounts of data. AI enables the use of such data for highly value-adding tasks such as predictive maintenance and performance optimization at unprecedented accuracy and speed. The combination of IoT and AI is already ushering in the next wave of performance improvements, especially in the industrial sector. 

How will AI change manufacturing? 

Manufacturers across verticals harness the power of AI to create and maintain a competitive edge by augmenting human capabilities, expediting processes and creating greater efficiency. While there are dozens of use cases and opportunities for the introduction of AI into the manufacturing floor, it’s crucial to think about AI holistically, so that solutions don’t remain siloed.  

Manufacturers focus on adding AI solutions to their core production processes: product development, engineering, assembly and quality testing. Below are three of the most prevalent AI use cases in manufacturing, and any one of them represents a good place to start.   

Predictive Maintenance (PdM) 

The main promise of predictive maintenance is to prevent unexpected outages, breakdowns, and equipment failures, and to allow convenient scheduling of corrective maintenance based not on averages but on the equipment’s condition in real time. Predictive maintenance relies on the ability of AI algorithms to autonomously baseline the machine’s condition, and through continuous monitoring alert stakeholders about any degradation, as well as forecast the optimal maintenance schedule so that downtime can be planned to coincide with slower periods. This proactive rather than reactive approach to maintenance results in substantial cost savings derived from minimizing downtime, maximizing equipment lifespan, optimizing employee productivity, and reducing spare part inventory. Indeed, McKinsey postulates that in manufacturing, the greatest value from AI can be created through predictive maintenance and estimates this value at $0.5T to $0.7T across the world’s businesses.  

PdM improves reliability while reducing maintenance frequency & costs

Defect Detection 

The early detection of faults or defects and the removal of the elements that may produce them are essential to improve product quality and reduce the negative economic impact caused by discarding defective products. Compared to traditional QA procedures, AI systems for defect detection hold the promise of higher accuracy and precision. They are able to detect defects that are otherwise impossible to uncover, and do so earlier in the process so that defunct products, product returns, and wasted materials and resources are reduced. At the core, defect detection uses computer vision capabilities, such as image recognition, classification, anomaly detection, object detection and semantic segmentation to facilitate rapid detection of quality degraded objects in any industrial context. Computer-vision-based approaches for defect detection are already widely deployed across manufacturing domains – including assembly lines, electronics and nanotechnology – providing increased ROI on QA efforts. 

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Yield Optimization 

Manufacturers in every field are constantly struggling to improve their business operations and scale their throughput. In manufacturing, increasingly complex production lines and supply chains require a proactive, integrated, systems-level approach to optimizing yields. As opposed to non-automated process optimization procedures and methodologies, AI systems rely on big data and real time insights to enable manufacturers to make rapid, data-driven decisions, optimize manufacturing processes, minimize operational costs, and improve the way they serve their customers. Manufacturing optimization solutions study each part of the manufacturing process in detail, detect the exact points in time at which productivity drops, analyze all performance parameters to understand the cause for inefficiency, and provide solutions to solve issues. 

AI in manufacturers deployment

Challenges of AI adoption 

While manufacturing is often considered to be at the forefront of the application of new technologies, according to McKinsey’s The state of AI in 2020 survey, AI impact on manufacturing is still low across all use cases. Only 15% of manufacturers have deployed AI for yield optimization, 12% for predictive maintenance, and 21% for product/feature optimization. This is probably because the process of introducing AI is highly complex, time consuming, and capital intensive, and requires a comprehensive, systematic approach if it is to prove successful. 

The first step in establishing a solid AI business case is separating the hype around AI from its actual capabilities in a specific, real-world context and adopting a realistic view of AI’s capabilities and limitations. So the starting point needs to be a sufficient, high-level grasp of how AI works, how it differs from conventional technological approaches, and what it takes to get started with AI in a specific business context.  

Even then, building an actual business case is much easier said than done. Much of the information is imperfect, the initial results are often unclear in the early stages, and often there is also in-house reluctance towards the adoption of transformational technologies.  

What helps is a pragmatic prioritization of potential use cases for AI-enhanced products and processes, based on two major dimensions. The first is the technical feasibility and complexity of the required AI solution. The second is the overall impact potential of the solution, derived from estimates of the financial baseline and optimization potential. 

To make things even more complex, successful AI adoption doesn’t end with implementation. AI systems that interact with the real world need ongoing maintenance in order to stay on course and provide the stability and robustness that guarantee positive ROI for the long run. So the challenges of a successful AI implementation are fourfold: 

  • Building, training and implementing a customized solution for your environment. Every AI use case is unique. Even in the same vertical or operational area, every manufacturer uses specific data to achieve specific goals according to a specific business logic. For the AI solution to provide a perfect fit for the data, usability, and business needs, all these specifications need to be translated and built into the model. The customized solution is not an end in itself, but rather a means to prove value from AI. ROI can be created only with a customized solution because mainstream solutions do not meet the specific needs and constraints of the business and therefore will not be effective. 
  • Achieving stability and robustnessIn production, the AI solution is required to cope with extreme data scenarios including noisy, unstructured, or small data sets, 24/7. Integrated technologies and AI expertise are key to achieving this kind of robustness and stability, that are the true test of effective AI. 
  • Staying the course: maintenance, updates, improvements. While achieving production can be extremely difficult, it is only the first half the battle. The second half of the battle is maintaining production to ensure that the model does not go off the rails as data and circumstances change. Maintaining AI in production requires data and model version control, updates, optimization of human machine-interaction, monitoring the robustness and generalization of the model, and on-going input noise detection and correlation. On-going maintenance can be an extremely challenging and expensive aspect of AI solutions.  
  • Towards full AI transformation: adding new use cases. Companies in manufacturing that adopt AI incrementally see more value faster compared to companies that opt for organization-wide AI adoption. However, it’s essential to maintain continuity in the AI deployment approach in order to prevent the creation of a patchwork of siloed use-case-based AI solutions that can’t perform holistically.  

Fastest time to value with AI 

As opposed to previous technological pivots of recent years, AI implementation in production is still at a stage where neither the build nor most buy options provide working solutions for companies seeking AI transformation. That’s because, on the one hand, every AI problem, scenario and use case is completely unique, requiring minute customization. For most companies and use cases, this rules out most off the shelf verticalized or use case based solutions that lack the specificity required to achieve significant business and ROI impact in the long term.  

On the other hand, AI is an innovative technology that demands cutting-edge domain expertise to build. Enterprises attempting to build AI solutions in-house often opt for establishing an AI center of excellence, amassing huge costs and an extremely long time to value on their AI projects, which, depending on scope, take anything between one and three years to achieve — if success in production does indeed ensue. This AI in production Catch-22 is at the core of the current massive failure rate for AI deployment, demonstrated through Venturebeat’s findings that a massive 87% of AI projects in enterprises never even reach the production stage.  

This is especially true since enterprises need to implement a variety of different AI solutions throughout their organizations in order to remain competitive in an AI-transforming ecosystem. When trying to achieve this goal with off the shelf AI platforms, a messy patchwork of solutions is created. When building in house, either more investment is needed in man-hours, or projects are further delayed. That’s because AI is a general term that denotes a variety of technologies and approaches, each demanding unique expertise. At a manufacturing enterprise, for example, conquering the defect detection problem with AI computer vision has nothing to do with Machine Learning of time-series data that’s required for product line anomaly detection.  

Further increasing the complexity, each of these AI solutions require independent maintenance and they are threaded together with separate and often unstable APIs, that do not strategically interact with each other or use the company data in a holistic and synergistic manner. In the long run, this dramatically decelerates the AI transformation process. 

Accelerate AI transformation with BeyondMinds 

BeyondMinds has built the first enterprise AI platform that is universally applicable and easily adaptable across manufacturing environments. We deliver hyper-customized, production-ready AI solutions as a service, enabling manufacturers to scale their AI transformation and rapidly create ROI from AI. While many other AI solutions can take months or years to successfully reach production, if ever, the BeyondMinds platform enables companies to achieve production-grade AI within weeks. BeyondMinds AI solutions are:  

Customized. Every company’s unique use cases and specifications are taken into account in every BeyondMinds solution built on the platform, ensuring that it will provide a perfect fit for data, usability, and business needs.  

Production-grade. The BeyondMinds platform is designed as a robust, end-to-end state of the art AI platform — that goes far beyond a simple trained model, and together with each custom built solution, provides a complete working system in production. 

AI-as-a-Service (SaaS). BeyondMinds’ solutions are provided as a service (AI as a Service) to ensure continuous improvement and increasing value overtime, enabling manufacturers to build, deploy, run and maintain their AI-based technologies fast. 

BeyondMinds’ mission is to create AI partnerships that enable the world’s most sophisticated companies to benefit quickly and enduringly from AI’s benefits, while freeing themselves of the risks and burdens of its development and ongoing maintenance. 

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