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IT pros may find IoT, blockchain and AI difficult to conceptualize as individual technologies, but together, the three can generate data, improve analysis and establish trust.

Consider organic systems as a metaphor for how these technological systems will interrelate. An organism has a central nervous system; IoT systems use sensors to collect and "feel" information about their environments. An organism's brain processes information and detects patterns; AI plays this role in technological systems. Finally, an organism has memory or some ability to store information; distributed ledger technology (DLT) represents shared and immutable record-keeping in technological systems.

These technologies will influence each other's trajectories, as shown in Figure 1 below.

Blockchain, AI and IoT yet to converge as holy trinity

The scope and architectural designs of any one of these technologies -- never mind all three -- are truly vast and advancing daily. Each suffers from various limitations, such as lack of interoperability, processing speed for high-volume or mission-critical applications, and different standards. Each offers new design paradigms in privacy and security contexts. Each faces significant legal, political, cultural and ethical challenges relative to incumbent models. Not one individually, nor all three, is a silver bullet for capitalizing on data or data-driven economies, but all are subjects of extreme hype and promise. Nonetheless, there are important caveats that help ground an understanding.

Figure 1. IoT data funnels to AI to improve analysis and feed information to run applications. IoT and AI data can become immutable in a blockchain.

Much depends on the problem. Whether in a business, health or societal context, do not blindly pursue these technologies because they sound innovative. Instead, evaluate the criteria, capabilities and constituencies needed to solve an actual problem. Look beyond organizations to capitalize on ecosystem-based technologies.

Not everything will be registered to a blockchain. It's unlikely any blockchain will be able or need to capture all data emanating from IoT devices or elsewhere. But there are plenty of device transactions or interactions that warrant an immutable record, including when a car passes through a tollbooth or when perishable cargo reaches a distribution center. What are the events that multiple constituencies in the network need to agree on?

IoT is just part of the big data iceberg. Sensor and device data are just a fraction of the myriad data feeds powering AI. What's essential is data quality. When it comes to blockchain, however, there are important limitations to current DLT architectures that make that distinction even more important. For example, billions of sensors in an agricultural environment or 1 million cars in a city might cause issues with the scale or connectivity required for real-time reconciliation on a blockchain.

See the bigger picture of blockchain, AI and IoT convergence

Smart homes, cars and cities depict how an AI, blockchain and IoT convergence might underpin real-world uses.

This infographic illustrates the AI, blockchain and IoT convergence potential for checks and balances in an accelerated world with evermore distributed compute. Blockchain helps make AI and IoT more accountable. Software and hardware intelligence enhance IoT and blockchain applications and process automation. As connectivity and algorithms infuse every aspect of business and society, the need for accountability, security, access to data and, most importantly, trust is only growing.

Source : https://internetofthingsagenda.techtarget.com/tip/AI-blockchain-and-IoT-convergence-improves-daily-applications

• 46% of manufacturers agree that implementing and using data analytics is necessary to stay competitive and grow their businesses.
• 69% of manufacturing decision-makers believe analytics will be crucial for business success in 2020, with 15% considering it crucial today.
• 32% of manufacturers see the potential for analytics to improve supply chain performance and increase revenue. Manufacturers perceive data analytics as a key component of successful sourcing, procurement, and production strategies.  

• Manufacturing Cycle Time – One of the most popular metrics in manufacturing, Cycle Time quantifies the amount of elapsed time from when an order is placed until the product is manufactured and entered into finished goods inventory. Cycle times vary by segment of the manufacturing industry, size of manufacturing operation, global location and relative stability of supply chains supporting operations. Real-time integration, applying Six Sigma to know process bottlenecks, and re-engineering systems to be more customer-focused improve this metrics’ performance. Cycle Time is a predictor of the future of manufacturing as this metric captures improvement made across systems and processes immediately. 
• Supplier Inbound Quality Levels - Measuring the dimensions of how effective a given supplier is at consistently meeting a high level of product quality and on-time delivery is valuable in orchestrating a stable supply chain. Inbound quality levels often vary from one shipment to the next, so it’s helpful to have Statistical Process Control (SPC) charts that quantify and show the trends of quality levels over time. Nearly all manufacturers are relying on Six Sigma programs to troubleshoot specific trouble spots and problem areas of suppliers who may have wide variations in product quality in a given period. This metric is often used for ranking which suppliers are the most valuable to a factory and production network as well. 
• Production Yield Rates By Product, Process, and Plant Location – Yield rates reflect how efficient a machine or entire process is in transforming raw materials into finished products. Manufacturers rely on automated and manually-based approaches to capture this metric, with the latest generation of industrial machinery capable of producing its yield rate levels over time. Process-related manufacturers rely on this metric to manage every production run they do. Microprocessors, semiconductors, and integrated circuit manufacturers are continually monitoring yield rates to determine how they are progressing against plans and goals. Greater real-time integration, improved quality management systems, and greater supply chain quality and compliance all have a positive impact on yield rates. It’s one of the key measures of production yield as it reflects how well orchestrated entire production processes are.    
• Perfect Order Performance – Perfect order performance measures how effective a manufacturer is at delivering complete, accurate, damage-free orders to customers on time. The equation that defines the perfect order Index (POI) or perfect order performance is the (Percent of orders delivered on time) * (Percent of orders complete) * (Percent of orders damage free) * (Percent of orders with accurate documentation) * 100. The majority of manufacturers are attaining a perfect order performance level of 90% or higher, according to the The American Productivity and Quality Center (APQC). The more complex the product lines, configuration options, including build-to-order, configure-to-order, and engineer-to-order, the more challenging it is to attain a high, perfect order level. Greater analytics and insights gained from real-time integration and monitoring help complex manufacturers attained higher perfect order levels over time.    
• Return Material Authorization (RMA) Rate as % Of Manufacturing – The purpose of this metric is to define the percentage of products shipped to customers that are returned due to defective parts or not otherwise meeting their requirements. RMAs are a good leading indicator of potential quality problems. RMAs are also a good measure of how well integrated PLM, ERP and CRM systems, resulting in fewer product errors.