Manufacturing Methodologies

Manufacturing methodologies include:
Lean manufacturing

A lean organization understands customer value and focuses its key processes to continuously increase it. The ultimate goal is to provide perfect value to the customer through a perfect value creation process that has zero waste.

To accomplish this, lean thinking changes the focus of management from optimizing separate technologies, assets, and vertical departments to optimizing the flow of products and services through entire value streams that flow horizontally across technologies, assets, and departments to customers.

Eliminating waste along entire value streams, instead of at isolated points, creates processes that need less human effort, less space, less capital, and less time to make products and services at far less costs and with much fewer defects, compared with traditional business systems. Companies are able to respond to changing customer desires with high variety, high quality, low cost, and with very fast throughput times. Also, information management becomes much simpler and more accurate.

AI Weak and Strong

Capabilities currently classified as weak AI include successfully understanding human speech, competing at a high level in strategic game systems (such as chess and Go), self-driving cars, intelligent routing in content delivery networks, and interpreting complex data. Done through mainly calculus involving prediction problems, extrapolation based on feeding quality data.

The central problems (or goals) of AI research include reasoning, knowledge, planning, learning, natural language processing (communication), perception and the ability to move and manipulate objects.

General intelligence is among the field’s long-term goals. Approaches include statistical methods, computational intelligence, and traditional symbolic AI. Many tools are used in AI, including versions of search and mathematical optimization, logic, methods based on probability and economics. The AI field draws upon computer science, mathematics, psychology, linguistics, philosophy, neuroscience and artificial psychology.

From a deep learning perspective, even neuroevolution approaches has its limits for a strong AI, no matter how much the population of networks grow to attain the objective the acknowledgment of the solution has to have an external “decidant.”

“That is, evolution in this case is not just deciding the architecture and weights, but also the rules that guide how and when particular weights change.” from Neuroevolution: A different kind of deep learning by O.Stanley

In the context of strong AI, I really think that we need a kind of “attractors” framework as a “loosely” decidant role (aka. “conscience”).

“Strong AI is related to the field of consciousness, sentience, mind. We are on the way to general intelligence with every AI field evolving (from natural language processing to cognitive computing, social intelligence, planning, learning, perception etc.) The rules that govern the universe are already there, our mind works under the patterns within the universes pattern. To realize the strong AI we need to hook the machine into this flow of our as-such universe of small-world networks*. Consciousness is not programmable, it is a result of the sensory, when hooked to the flow, it can be seen as a result of the “strange attractors” created into the flow. A strong AI will exist when all the sensory inputs will have a proper cognitive processing, conscience will arise de facto from the space where the inputs exists but under a proactive threshold. The triggering occurs under strange attractors influenza, thus the conscience manifests itself.” (Definition of Strong AI by Essential-Works, C.Stefan 04.2017).

* See: Watts and Strogatz model & Barabási–Albert model.
* Restricted Boltzmann Machine and Self-Organization systems#.
* Active inference#.

IIoT Platforms

GE’s Predix, Siemen’s MindSphere, and the recently announced Honeywell Sentience are likely to be on any short list of industrial cloud platforms. But they aren’t the only ones in this space. Cisco’s Jasper, IBM’s Watson IoT, Meshify, Uptake, and at least 20 others are competing to manage all those billions of sensors that are expected to encompass the Industrial Internet of Things (IIoT).

Sample providers: Amazon AWS, AT&T M2X, Bosch IoT, Carriots, Cumulocity, GE Predix, IBM Watson IoT, Google Cloud IoT Core, Intel IoT, Cisco Jasper, Losant IoT, Microsoft Azure, PTC ThingWorx (connected to Windchill/PDMLink), SAP Hana Cloud,, C3IoT, Uptake, Amplia IoT, XMPRO, Meshify, TempoIQ, Bitstew Systems, Siemens MindSphere, AirVantage, Honeywell Sentience, Schneider Electric’s Ecostruxure, Alibaba Cloud will roll out its big-data service, called “MaxCompute”, and Parker Hannifin’s Voice of the Machine IoT platform.

GE Predix: is a platform-as-a-service (PaaS) specifically designed for industrial data and analytics. It can capture and analyze the unique volume, velocity and variety of machine data within a highly secure, industrial-strength cloud environment. GE Predix is designed to handle data types that consumer cloud services are not built to handle.

Siemens MindSphere is an open platform, based on the SAP HANA (PaaS) cloud, which allows developers to build, extend, and operate cloud-based applications. OEMs and application developers can access the platform via open interfaces and use it for services and analysis such as the online monitoring of globally distributed machine tools, industrial robots, or industrial equipment such as compressors and pumps. MindSphere also allows customers to create digital models of their factories with real data from the production process.

Honeywell Sentience is the recently announced cloud infrastructure by Honeywell Process Solutions. It is a secure, scalable, standards-based “One Honeywell” IoT platform, that will be able to accelerate time-to-market of connected solutions, lower the cost-to-market, and enable new innovative SaaS business models. It will have the ability to run global security standards embedded throughout the solution and make applications that are plug & play and scalable.

C3 IoT is a PaaS that enables organizations to leverage data – telemetry from sensors and devices, data from diverse enterprise information systems, and data from external sources (such as social media, weather, traffic, and commodity prices) – and employ advanced analytics and machine learning at scale, in real time, to capture business insights for improved operations, enhanced customer engagement, and differentiated products and services. C3 IoT is led by Silicon Valley entrepreneur Thomas Siebel. It has closed deals with the U.S. State Department and the French utility ENGIE SA, based on C3 IoT’s focus on machine-generated data.

Uptake: is a predictive analytics SaaS platform provider that offers industrial companies the ability to optimize performance, reduce asset failures, and enhance safety. Uptake integrates data science and workflow connectivity to provide high-value solutions using massive data sets. In 2015, it entered into a partnership with heavy construction equipment manufacturer Caterpillar to jointly develop an end-to-end platform for predictive diagnostics in order to help Caterpillar customers monitor and optimize their fleets more effectively.

Meshify is an Industrial IoT platform for tracking, monitoring, analyzing devices. The Meshify suite of tools provides all the features needed to deploy, monitor, control, and analyze the results of an IoT solution. Despite being a young technology business, it has a growing portfolio of clients with industrial-oriented companies, including Henry Pump, Sierra Resources, Stallion Oilfield Services, Gems Sensors & Controls and MistAway Systems.