04 8月 Predictive Manufacturing for Small and Medium Manufacturers
A leading initiative within many Industry 4.0 programs is the adoption of predictive manufacturing technologies and protocols. The evolution from reactive に preventive に predictive has accelerated over the past few years as artificial intelligence (AI) and machine learning (ML) technologies have evolved to provide more powerful analytics capabilities.
Initially, predictive manufacturing was being driven primarily by larger manufacturing enterprises, those operations better able to support the cost of legacy AI and ML solutions. However, with the development of new embedded AI technologies and solutions, small and medium manufacturing operations are now able to leverage the power of predictive manufacturing to attain new levels of operational performance and competitiveness.
Predictive Manufacturing – What is it?
Predictive analytics is the engine that drives predictive manufacturing. A holistic approach to predictive manufacturing will provide predictive capabilities in the following areas:
Predictive Maintenance: Eliminating the problem of no real-time visibility into heath status of mission-critical manufacturing assets.
- The ability to accurately predict when an asset will need maintenance. Predict potential problems instead of reacting to them.
- Reduce unexpected and unnecessary downtime of critical assets.
- Fully optimize the efficiency of production and maintenance personnel.
Predictive Process: AI and ML-enabled intelligent workflows that learn and evolve. Manufacturing processes that are automated and smart.
- The embedding of AI and ML algorithms that learn, train, and evolve.
- Critical processes are optimized to improve performance and reliability.
- Intelligent workflows that reduce the reliance on human intervention.
Predictive Security: Embedded AI-enabled security that provides proactive safeguards against today’s sophisticated cyber-threats.
- Self-learning security algorithms that evolve with changing threats.
- Proactive vs reactive cyber-threat mitigation.
- Reduction in security-induced downtimes and data integrity risks.
Endpoint AI Solutions for Small and Medium Manufacturers
In the past, cost had been a principle entry barrier for small and medium-sized manufacturers looking to leverage the benefits of predictive manufacturing. Those high costs were influenced by several factors, including: the cost of the AI/ML solution itself, the cost associated with extraneous support hardware, the cost of implementation, the price paid for cloud-based data storage and processing, and the inability to gradually scale up the size of the AI-enabled ecosystem.
New Endpoint AI solutions are providing a paradigm shift in the way that AI can be embedded and trained into a device or machine. This new AI “at the extreme edge” methodology allows a manufacturer to embed AI right at the asset level (typically onto the asset’s microcontroller (MCU)). This new technology provides several tangible business and operational advantages for operators in the mid-size manufacturing segment. Advantages that include the following:
Asset centricity and flexibility. Endpoint AI solutions can be embedded into, and customized for, an individual device or machine. Small and medium manufacturers can deploy on a small scale and then gradually expand to additional assets as their requirements expand.
Predictive asset insights. Many smaller manufacturing operations are heavily reliant on the performance of a small number of machines. Real-time insights into performance variables within those assets can mean the difference between operational excellence and sub-par performance. Endpoint AI solutions provide faster identification and remediation of asset performance issues resulting in higher uptimes and productivity.
Enhanced cyber-security. Endpoint AI provides the most cost-effective approach to protecting mission-critical assets from cyber-attack; a predictive approach to cyber threats that enables proactive threat recognition and mitigation. This reduces the risk for unauthorized asset access and theft of asset data.
Lower entry cost and quicker ROI. Endpoint AI effectively reduces the AI cost barrier for small and medium manufacturers. Lower costs and quicker ROI resulting from:
- Elimination of the need for expensive external support hardware
- The flexibility to deploy on a small scale (e.g. an individual machine) and then expand to a larger machine ecosystem
- Reduction/elimination of costly implementation services
- Local processing of data at the asset level eliminates costs associated with cloud data storage and processing
With the advent of Endpoint AI, predictive manufacturing will no longer be limited to the largest manufacturing operations. Small and mid-size enterprises can now implement AI solutions that will allow them to improve operational efficiencies, competitive positions, and bottom lines.