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Summary
The Manufacturing Economics project, as part of the Advanced Manufacturing Data Infrastructure and Analytics (AMDIA) program, will provide economic insights into the costs, losses, product quality, and/or negative externalities in U.S. manufacturing along with economic insights into opportunities for advancing efficiency and productivity. This will be achieved through targeting two audiences with two distinct elements. This includes providing practitioners and change agents with 1) methodological innovation and development and 2) foundational information and data required for methods application.
The project develops methods, data, tools, analyses, and standards for both manufacturers and change agents. It includes developing and demonstrating an economic framework for economic decision making in advancing manufacturing industry competitiveness; an effort to develop/utilize an agent-based model of supply chains to examine the flow of goods in respect to disruptions; it delivers a recurring publication series characterizing manufacturing to support both industry professionals and researchers in analyzing and enhancing U.S. manufacturing competitiveness; and it includes examining investments in artificial intelligence (AI). The results of this work informs both manufacturers and change agents in identifying high return investments for advancing manufacturing competitiveness and mitigating/preventing supply chain disruptions. It facilitates growth in change agent impact along with aiding manufacturers in identifying investments that result in greater efficiency and productivity advancements.
Description
Objective
To provide measurement science that enables manufacturers and change agents to make economic decisions that are more accurate, timely, and require fewer resources.
Technical Idea
The technical idea focuses on creating standards, methods, data, analyses, and tools to 1) create step increases in the accuracy of manufacturing investment decisions, resulting in selecting/designing higher returns; 2) decrease the cost of investment analyses; and 3) decrease the time required for investment analyses. These efforts include five focus areas:
- Framework: A framework for economic decision making in advancing manufacturing industry competitiveness;
- Standards/Methods: Ongoing investment analysis standards/methods development;
- Annual Review: A recurring publication series characterizing manufacturing;
- AI Economics: Examinations of AI economics; and
- Supply Chains: Analysis of structural supply chain risk.
The first two items affect methodological approaches to investment analyses. Item one largely aims to make step increases in accuracy of economic analyses for change agents in a timely and cost-effective manner. Item two develops standards for investment analysis, which can affect the costs, timeliness, and resources needed for investment analyses for both manufacturers and change agents. In contrast to methods development, the third item provides analyses of the manufacturing industry, resulting in change agents and manufacturers being better informed about the manufacturing economic infrastructure. Item four investigates the economics of a new technology potentially informing manufacturers and change agents of when AI investments are cost effective in manufacturing.
The last item is to use an agent-based model in simulating and examining supply chain disruptions. It is a flexible agent-based model where various aspects of the model can fluctuate, including the number of tiers, establishment per tier, products per tier, flow time, substitutability, and potentially other aspects. Substantial losses have occurred due to supply chain disruption. The results of this effort can inform both manufacturers and changes agents regarding the fundamental risks that underly supply chain thereby allowing them to more cost-effectively mitigate risk.
Research Plan
To advance understanding of the industry, this project develops standards, methods, data, analyses, and tools that fall into five focus areas. The research plan for each area is described below:
Framework: A conceptual framework for economic decision making in advancing manufacturing industry competitiveness was developed in AMS 100-80. Successful implementation relies on various platforms, standards, and classifications. The research plan is to examine potential case study applications of the framework. Additionally, further development of platforms, standards, and classifications will also be necessary.
Standards/Methods: This project pursues understanding the patterns of friction that occur among manufacturing and change agent when conducting investment analyses. Working with and interacting with manufacturers will further understanding of these frictions. Solutions are developed collaboratively.
Annual Review: This effort involves creating a recurring manufacturing series characterizing U.S. innovation and industrial competitiveness in manufacturing. It includes tracking domestic activity and supply chain activity to develop a quantitative depiction of manufacturing in the context of the domestic economy and global industry. Five aspects are explored: growth and size; productivity; economic environment; stakeholder impact; and areas for advancement. The results aim to provide an evidence-based characterization of the industry that can improve economic decision making, leading to high-return investments that increase U.S. manufacturing competitiveness. It also involves identifying potential means for advancing manufacturing industry competitiveness.
AI Economics: This project will work closely with AI efforts at NIST and engage in opportunities for advancing AI economics as they arise. Information and feedback will be provided as requested by AI investigative teams.
Supply Chains: This project develops an agent-based model, which is a type of computational model, to more closely examine supply chain disruption. This model is intended to identify emergent behavior rather than to predict outcomes from individual supply chains. The model is compared to known emergent phenomena to confirm validity. Agent-based models and other analyses have been used to examine and simulate supply chains to gain insight into supply chain management, resiliency, and disruption. The model developed here is a flexible agent-based model where various aspects of the model can fluctuate, including the number of tiers, establishment per tier, products per tier, various flow times at the establishment level, purchase decisions, substitutability, and potentially other aspects. As simulations run, data on losses/shortages can be collected. Moreover, the model allows for examining four aspects of supply chains: 1) supply chain disruption; 2) network level analysis of supply chains; 3) establishment characteristics/behavior within the supply chain context; and 4) short-term downstream losses that result from supply chain disruption. These four items represent gaps in knowledge and in the literature regarding supply chains.