The manufacturing industry is in an unenviable position. We face a relentless onslaught of cost pressures, supply chain volatility and disruptive technologies like 3D printing and IoT. The industry needs to repeatedly optimize processes, improve efficiency and improve overall equipment effectiveness.
At the identical time, there’s this huge wave of sustainability and energy transition. Manufacturers are encouraged to cut back their carbon footprint, adopt circular economy practices and usually change into more environmentally friendly.
And manufacturers are under pressure to always innovate while maintaining stability and security. An inaccurate AI prediction in a marketing campaign is a minor annoyance, but an inaccurate AI prediction on a factory floor will be fatal.
Technology and disruption are nothing latest for manufacturers, however the primary problem is that what works well in theory often fails in practice. For example, as a manufacturer, we create a knowledge base, but nobody can find anything without spending hours searching and browsing the content. Or we create an information lake that quickly degenerates into an information swamp. Or we keep adding applications, so our technical debt continues to extend. But we’re unable to modernize our applications because logic developed over time stays hidden there.
The solution lies in generative AI
Let's explore a few of the features or use cases where we see essentially the most appeal:
1. Summary
Summary stays the first use case for generative AI (Gen AI) technology. When combined with search and multimodal interaction, Gen AI is an incredible assistant. Manufacturers use the summary in other ways.
You can use it to develop a greater way for operators to quickly and effectively retrieve the proper information from the extensive archive of operations manuals, SOPs, logbooks, past incidents and more. This allows employees to higher think about their tasks and make progress without unnecessary delays.
IBM® has Gen AI accelerators focused on manufacturing for this purpose. Additionally, these accelerators are pre-integrated with various cloud AI services and recommend the most effective LLM (Large Language Model) for his or her domain.
The summary also helps in harsh operating environments. If the machine or system fails, maintenance engineers can use Gen-AI to quickly diagnose problems based on the upkeep manual and an evaluation of the method parameters.
2. Contextual data understanding
Data systems often cause major problems in manufacturing firms. They are sometimes different, isolated and multimodal. Various initiatives to create a knowledge graph of those systems have been only partially successful as a result of the depth of legacy knowledge, incomplete documentation, and technical debt gathered over many years.
IBM has developed an AI-powered system Knowledge discovery system Leverage generative AI to realize latest insights and speed up data-driven decisions with contextualized industrial data. IBM has also developed an accelerator for context-sensitive feature engineering in the economic sector. This provides real-time insight into process conditions (normal/abnormal), alleviates common process roadblocks, and detects and predicts golden batch.
IBM has developed a Workforce Advisor that leverages summarization and contextual data understanding with intent recognition and multimodal interaction. This allows operators and plant engineers to quickly address an issue area. Users can ask questions via voice, text and pointing, and the Gen AI advisor processes them and provides a solution while being aware of the context. This reduces users' cognitive load by helping them perform root cause evaluation faster, reducing their effort and time.
3. Coding support
Gen AI also helps with coding, including code documentation, code modernization, and code development. As an example of how Gen AI helps with IT modernization, consider the Water Corporation use case. Water Corporation has launched Watson Code Assistant, powered by IBM's Gen AI capabilities, to ease the transition to a cloud-based SAP infrastructure.
This tool accelerated code development through the use of AI-generated recommendations based on natural language input, significantly reducing deployment times and manual work. With Watson Code Assistant, Water Corporation was capable of reduce development effort and associated costs by 30% while maintaining code quality and transparency.
4. Asset management
Gen AI has the ability to rework wealth management.
Generative AI can create baseline models for assets. When we’d like to predict multiple KPIs for a similar process or have a fleet of comparable assets. It is best to develop a basic model of the asset and refine it several times.
Gen AI also can train for predictive maintenance. Foundation models are very useful when failure data is scarce. Traditional AI models require many labels to make sure adequate accuracy. However, with baseline models, we are able to pre-train models without labels and fine-tune with the limited labels.
Additionally, generative AI can provide technician support and training. Manufacturers can use genetic AI technologies to create a training simulator for the operators and technicians. In addition, genetic AI technologies can provide guidance throughout the repair process and generate the most effective repair procedure.
Build latest digital capabilities with generative AI
IBM believes the agility, flexibility and scalability offered by generative AI technologies will significantly speed up digitalization initiatives within the manufacturing industry.
Generative AI strengthens firms on the strategic core of their business. Within two years, foundational models will power a few third of AI in corporate environments.
In IBM's early work using baseline models, time to value is as much as 70% faster than a standard AI approach. Generative AI makes other AI and analytics technologies more usable, helping manufacturing firms see the worth of their investments.
Build latest digital capabilities with generative AI
