Artificial intelligence and its possible impact inside a proposed future state model for the process of a global design registration system(s) from onset to maturity

Mark Swartz, Don Elario, Kenneth Richards

Abstract:

Artificial intelligence as a technology has many implications on a real-world workflow routine or process where human cognition, data input or output exists. Initially as part of a system design plan with the totality of a global architecture you can have a set of advantages early that can allow for the best possible outcomes for Machine learning processes to be used. Today exists a global processed call the design registration process. Using AI in the future state model we can create a more efficient business that can globally support the size and scale of the entirety. We also will explain key areas where AI can help persons and roles have a more streamlined process with the addition of key AI cognitive based routines.

Background: The process/system would aid in creating and approving a unique Design that will contain a specific semiconductor or electronic material. This approval is for the design regulation and its accountability, both financially and the spirit in which the business intended, during the onset and until the registration expires. The fundamentals are a unique registration ID and global library where this information is cataloged and access by its partners. Today the process is a disconnected and un-standardized global routine that affects hundreds of supply chain professionals daily. In addition, it creates communication waste in the form of Emails, Calls, and subsequent follow-up and generates sentiment between entities and persons.

This process, while un-standardized, has survived and lives in various forms within each of the entities. Thus, we can assume it survives merely by the core benefits it creates, yet at the cost of not standardizing it.

We were introduced to Design Registration by ECIA and understood its current state and proposed future state through this organization’s prior work. These findings provided a means to establish an understanding of Where AI or What could AI provide as a benefit, Based on the future state.

The current demand creation process is one in which a distributor and/or manufacturer rep works with designers to include a supplier’s part in a design.

During this process a supplier acknowledges the distributors efforts by assigning a design registration number using the ECIA design registration form or supplier website form. The assembly is tracked to its manufacturing location and the purchase of the components through the distributor. The distributor is usually compensated for the design work through advantageous pricing of the components which enhances the distributor’s ability to win the production order at an acceptable margin.

The demand creation process is valuable for suppliers in that distributors and reps are working on their behalf to introduce new products and to include their parts in new designs. The process works successfully when the supply chain can track the assembly to the manufacturing location and fill the order for the designed in components. The process is completed when a fair return is paid to the distributor and rep and is reflected in the P&L for the designing organization as well as in the compensation for the rep

The design registration program is designed to compensate the distributor/rep for the time, resources, and effort required to influence customers to include certain parts in their products. The distributor will normally send Field Application Engineers (FAE) to the customer during the design phase and work with the design engineer to include the appropriate parts. Through the design registration program, manufacturers will compensate the distributor/rep for this effort.

Artificial intelligence as a technology has many implications on a system from the onset. Initially as part of a system design plan with the totality of a global architecture you can have a set of advantages early that can allow for the best possible outcomes for Machine learning processes to be used.

Our review of the future state model provides us a clear path as to a best practice currently as of August 2021. At the onset it seems clear to design architects to create an overall main objective mapping which would highlight the major channels of data, interfaces, security & governance considerations.   As part of this process, we were provided access to both the current state and future state goals that could contribute to a starting point by way of a System Design Document and Program Charter.

The future state system has a series of growth stages which when completed contain a global mesh of inner-woven subsystems operating within the constraints of each domain.

A domain could be considered a Country or Region which may have AI, or even Data Laws restrictions which must be adhered to as part of the total system.

Slide 1: Agenda

Slide 2: Problem and solution Chicken or Egg? – Designing a system

Slide 3: MVP (MoSCoW) – Review the core features

Slide 4: Domain Levels – Entities

Slide 5: Domains of Change – Processes, Organizations, Location, Data, Applications, Technologies

Slide 6: Starting point “Theory” as of August 2021

Slide 7: Neural Corp’s “Statement” or Thesis – Submittal to Working group

 

It is our hypothesis, with a few assumptions in place, that (1) AI can help automate and create a cost-efficient business model for the entire design registration process. (2) AI would, at the onset, streamline the cognition requirements of the persons responsible at every stage within the workflow or registration process. We will explain four key areas that would create the necessary system and data to maintain a Global, Open and Secure Design Registration Eco-System.

  1. Data Normalization
  2. Interface Automation
  • Support System(s)
  1. Data Governance

 

To begin we will lay out some key assumptions which would make the reality of the above statements accurate. The initial assumption is that adherence to data standards in both consumption, storage and dissemination are agreed to or minimally an AI based NLP-ETL system is established to server as a Data Gateway. This major mechanism would then serve as a boundary for all Countries, Regions, Territories or Entities where data security and portability have regulations to adhere too. This would solve the burden of operational expenses directly in staffing, HR, payroll, inner-system Security and streamline governing certifications. A secondary assumption would be that the system is Open and any Entity or participating role could become a “trading” partner, thus subscribing to the system itself. After having this in place the burden of sales, marketing and or promotion to gain attraction to joining would be decreased and provide a clearer barrier of entry for Manufacturers (a Prescriptive model) who would not necessarily join. Both of these assumptions would be a framework that would then allow a System or series of Systems to be built upon and operate in the manner most appropriate to the global community.

 

Data normalization as a practice would allow new partners without technology staff or investment be able to attach by way of 3rd party plugins or systems. The primary mission is to create a data ocean that is Data science level 3 compatible and maintains a level of security even at its core. The data consumed is validated and transmitted to silos for refinement and segmentation. Abstractions of the data can then be formed at any point without data-ops latency or interruption. This will create suitable security and data portability for the foreseeable future.

 

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Following the design, development of the data foundation and its testing. The aspects of how partners can build upon the data in a natural, fast, and low cost, yet modular way. We present a best practice which is Interface Automation. Here you would provide two major components as a simplified basis to start. An SDK and UI guide which is accompanied by APIs which are key entry for data access. Securely using two standards of JSON and XML formats provides legacy and modern partners to quickly bring their own visions to a reality, it will extend and allow for future proofing the data streams. Interfaces are defined in two ways, a channel to GET and POST (Send) data which is purely programmatic and is System-to-System. Secondly, we have our Human or User Interfaces which began to suggest a cost basis for participants. These in a natural expectation for large corporations, who have large development staff who may wish to design their own systems. The SDK would fast track efforts while maintaining the data demands set forth above. This SDK can be adapted for major ERP, CRM, PLM systems where a more natural user/data flow would create a more opportune means for Manufacturers to participate.

Interface Automation:

 

  • Freedom to create own
  • APIs support open standards
  • Mobility

Support Systems

  • Registration Aids (Selection and Design Criteria)
  • Design economics, Impact analysis
  • Design modeling
  • Process-aware
  • Change management
  • Entity reporting designer

Data Governance

  • Permission controls (Granular)
  • Data chain of custody
  • Portability