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Smart Manufacturing Systems Design and Analysis
INTRODUCTION
The Smart Manufacturing Systems Design and Analysis (SMSDA) program has the objective of delivering measurement science, standards, and tools needed to design and analyze smart manufacturing systems. Accomplishment of this objective would enable manufacturers to improve productivity, agility, sustainability, and quality of their products. The SMSDA program consists of six major projects, staffed by 19 NIST personnel, 2 contractors, and 10 guest researchers. These projects are in the following areas: (1) Agri-Food Manufacturing System and Supply Chain Integration; (2) Data Analytics for Smart Manufacturing Systems; (3) New Technology Adoption and Industry Operations Analysis for Smart Manufacturing; (4) Modeling Methodology for Smart Manufacturing Systems; (5) Operations-Driven Performance Measurement for Smart Manufacturing Systems; and (6) Service-Oriented Architectures for Smart Manufacturing Systems.
The SMSDA budget is approximately $7 million per year, and the SMSDA is envisioned to be a 5-year program. With the exception of the Agri-Food Manufacturing System and Supply Chain Integration project, which has been under way for approximately one year, all SMSDA projects have been ongoing for approximately three and one half years. During the assessment, four of the six SMSDA projects were reviewed. The two projects that were not reviewed in depth were the Agri-Food Manufacturing System and Supply Chain Integration project and the New Technology Adoption and Industry Operations Analysis for Smart Manufacturing project.
SMSDA personnel stated that their work was in two different categories. The end objective of both of these categories was to produce standards; however, in the first category, best practices were known or could be determined. Therefore, the job was to encode the best practice. This definition of standards as encoding best practices was very useful during the review of the projects. The Service-Oriented Architectures for Smart Manufacturing Systems project is discussed below and is an example of this first category of work. The second category of work is one where the best practice is not known. In this case the job of the researchers is to produce the structure, databases, prototypes, or techniques that will form the basis of a best practice. Three of the projects reviewed are in this second category. The Operations-Driven Performance Measurement for Smart Manufacturing project will be utilized as an example of this type of work and is discussed below.
ASSESSMENT OF TECHNICAL PROGRAMS
Accomplishments
During the review it became evident that all the activities of the reviewed projects where aligned with, and supportive of, the stated project objectives. To begin, the Service-Oriented Architectures for Smart Manufacturing project remediates the problem of moving syntax information (verbal or written
orders) across an organization with different enterprise platforms. For example, an engineering change from the parent company’s engineering organization needs to be passed to the scheduling organization (material organization), which in turn will send the change to manufacturing floor and vendors. Vendors may, in turn, send it to subtier vendors. Inside the parent company the engineering change is also sent to the customer support organization, which in turn needs to order spare parts and put warrantee reserves in place. The change is then sent to the finance department, where the engineering change is billed to customers. As engineering changes pass through each of the organizations, the attributes of the engineering change affecting that organization need to be entered into the organization’s computer system by hand. It is not unusual for engineering change attribute data to be input by hand into 20 to 30 different computer programs and several different computer languages. The cost of people entering the data is very large, the reentry of data by hand into different operating systems over and over slows the process, and increases the possibility of errors. The Service-Oriented Architectures for Smart Manufacturing Systems project remediates the inefficiency of this problem by developing a smart manufacturing solution. The solution consists of developing a large database of standard syntax terms (e.g., engineering change and purchase order) together with the attributes of each syntax term. The database allows individual users to add attributes if their application requires them. The database of integration message standards allows translation into the commonly used language for systems integration. Three translations are in development, and, as requirements for new languages emerge, new translators can be developed. The team has transferred the first version of syntax-independent database and computer language translators to the not-for-profit Open Applications Group Integration Specification (OAGIS) organization for maintenance and dissemination to the public.
This project is a major success, and it is likely to have a profound impact on smart manufacturing. Providing improvements in company speed, cost, quality, and supply chain integration. This project is best in class and could be a benchmark for other NIST projects. Regardless of what a particular company’s goal is—speed, efficiency, cost, flexibility, competitiveness, and so on—the great simplification of standard syntax database and translation systems that the SMSDA program has developed and made available to the public domain will be of great value. This database will continue to grow, and will benefit companies who are deploying Enterprise Resource Planning (ERP) systems, integrating supply chains, and integrating new companies or products into their portfolio. Similarly, this standard is also beneficial to large factories producing and consuming large amounts of data and information, which have the requisite staff to help manage all of this data, as well as small and medium enterprises (SMEs), which usually do not have sufficiently talented staff to deal with the data explosion.
The SMSDA program staff recognizes the impact that information technology has had. The staff has identified the Internet of Things, cloud services, service-based integration, and data analytics as technologies with specific potential for impact on smart manufacturing. It has as one of its primary goals the enablement of SMEs to participate in smart manufacturing, and it has defined agri-food manufacturing as a new section for its program, through its Agri-Food Manufacturing System and Supply Chain Integration project.
The identified technologies are transformative for smart manufacturing, and agri-food manufacturing is an ideal domain for the SMSDA program to explore the need for standards development and measurement science, to aid in developing the required capability to enable SMEs. Agri-food manufacturing is a critical industry involving hundreds of thousands of SMEs in the United States alone. Many of the participating entities are independent and geographically distributed. There is a flood of new low-cost sensors appropriate for monitoring food products from farms to consumers. There is increasing capability to connect these sensors to the Internet, and there are evolving technologies and platforms to effectively use the resulting data captured to improve food manufacturing. However, to date, the standards and measurement capabilities have not nearly kept pace with the technology becoming available.
The Agri-Food Manufacturing System and Supply Chain Integration project could provide a unifying domain for all of the SMSDA program’s current projects as well as a manufacturing domain that everyone can understand for presenting their research accomplishments. They already have two very large
agri-food manufacturing collaborators, Land O’Lakes and General Mills, and need to add multiple SMEs from each segment of the agri-food manufacturing supply chain (i.e., farmers, packers, transporters, wholesalers, distributors, and retailers). They also need to add an international component to their research effort because a large quantity of U.S. food is imported. This project represents an opportunity for the SMSDA program to make a profound impact on a large and very important industry. Therefore, it needs to be pursued vigorously.
Opportunities and Challenges
The SMSDA program, as well as other NIST smart manufacturing programs, seems to be somewhat ad hoc and disconnected—they are involved with a variety of domain experts and different entities that produce standards. This is not a criticism restricted to NIST—even the U.S. National Smart Manufacturing Initiative does not have a clear and comprehensive set of objectives, and more importantly a time-based roadmap. As the definition of smart manufacturing is elusive and ever-changing owing to the fact that new technology is constantly introduced into the workplace, the difficulty NIST faces is understandable.
For clarity a best-in-class example is offered to demonstrate the value of a comprehensive roadmap. For over 50 years the semiconductor industry has been driven by what has become a self-fulfilling prophecy known as Moore’s law. The goal is to double the complexity of computer chips on every device generation (a generation is about 18 months). This simple law has been followed despite numerous enormous, cultural, economic, and technical obstacles. It became the driving force for the industry when it was codified as the Semiconductor Industry Association (SIA) roadmap, which for 20 years has helped define the specific programs within the industry that are needed to remain on the Moore’s law curve. Although the semiconductor industry is ferociously competitive, numerous manufacturers, equipment makers, material suppliers, research institutions, and other support facilities have cooperated to make the SIA roadmap a best-in-class example of how to cooperate on a global scale. This law, or vision, has remained unchanged despite 50 years of progress over many dimensions and domains. It drives technology (feature size, wafer size, speed, capacity, reliability, etc.), economics (cost productivity, wealth generation, etc.), and innovation (smart phones, IPads, household devices, etc.), and it has led to the U.S. dominance in new technology.
Without a roadmap such as this, the quality and value of NIST's approach to managing the smart manufacturing program is difficult to assess, and therefore the value and quality of individual projects are difficult to measure. In particular, the SMSDA program has specific projects aimed at addressing specific topics. However, without a general roadmap with measurable milestones that define smart manufacturing, it is difficult for the program, and for NIST, to determine if it is working in the more critical or most impactful areas to improve smart manufacturing, or even in the most important areas at a given point in time. Regardless of these challenges, smart manufacturing has become a U.S. national priority, and so the question is: How could the efforts and resources focused on smart manufacturing be prioritized to best achieve the initiative’s inferred intent?
A Smart Manufacturing Leadership Coalition (SMLC) was established some years ago for this purpose. More recently a nonprofit organization, the Clean Energy Smart Manufacturing Innovation Institute (CESMII), has been established to support the coalition. CESMII was established by Congress and is located in Los Angeles. NIST is involved with both of these organizations. The EL or NIST could, in cooperation with these organizations, develop an overarching roadmap with measurable milestones for smart manufacturing for the nation.
Additionally, while all the SMSDA projects had been under way for approximately three and one half years, the project plans did not show intermediate project milestones, and so it was not possible to determine whether the project was making the expected amount of progress. Project planning for multiyear projects need to include intermediate milestones in order for progress and midcourse correction
to be easily understood and executed. The projects need to have a clear definition of deliverables and measurable milestones. Deliverables need to include demonstration and testing at SMEs.
The objectives of each of the reviewed projects focused, in part, on making smart manufacturing easier and more available for SMEs. Most of the company partners shown in the project demonstrations were large to very large companies. It appears to be an implicit assumption that the technology and standards will trickle down from large to small manufacturers, or be mandated by them to their small to medium-size suppliers. A more direct approach involving direct contact with SMEs is desirable.
It was also noted that the SMSDA program does not have a laboratory inside NIST to test the output of its projects. Testing the project output is particularly important where best practices do not exist and the new databases, techniques, and prototypes are being aimed to help SMEs.
The stated objective of the Operations-Driven Performance Measurement for Smart Manufacturing Systems project is to “develop and deploy standards, guidelines and reference data for measuring the performance of manufacturing systems to inform the design and aid in analysis of factory improvements.”1
To this end the project team has chosen to focus, rightly, on four key performance objectives as identified by the SMLC: agility, productivity, sustainability, and quality. This project is in the second category of work undertaken by the SMSDA. That is, no clear best practice exists for this project. The main project effort has been to develop a unit manufacturing process (UMP) depository that will become a resource center to house the collective community knowledge, reference data sources, and performance baselines and trade-offs. This particular project is in the manufacturing engineering domain, and a domain expert needs to be an integral part of the project team.
Once the database is populated, these UMP models could then be utilized for several functions. For example, a group of UMP models could be linked to model an entire production line for a given product. Optimization programs could be deployed to determine the best mix and location of machines.
A demonstration of the utilization of a UMP modeled process was shown during the review. The demonstration consisted of the processing of a small metal part—a heat sink for an avionics board. The process is divided into 11 steps of various milling and drilling operations. Input to the model is product-process info—design specifications, the type and nature of the materials, required electrical energy (which is both speed and process dependent). Along with resource data (operator, machine tool, fixture, software, etc.), the model goes through a set of equations that govern the physics and thermodynamics of the product and process and generates a set of outputs that include the total heat and waste generated. The model is a sophisticated tool—for each of the 11 processing stages, the control variables (e.g., machine speed) are optimized, under constraints (e.g., on total energy consumption).
One stated use case for the UMP is to generate life cycle inventory data. For such a case it is questionable whether the model is developed at the right granularity and with the right proportion. Specifically, it is questionable whether the effort of detailed modeling and optimization of the processing of a tiny part of what could be a complex product (that may involve hundreds of similar parts) is commensurate with the life cycle analysis of the product. Other factors such as choice of raw material and end-of-life recycling provisions can easily wipe out any gains teased out of optimizing the machining speed and British thermal units (BTUs) saved, not to mention what can be achieved via a redesign and overhaul of the entire manufacturing process (as opposed to fine-tuning the tool speed), as embodied in the green manufacturing or remanufacturing movement in recent years.
Overall, in a case like this, where there is a lack of existing standards or guidelines, the SMSDA program needs to broaden its scope to include identifying best practices in a select set of industry sectors. This broadening of scope appears both feasible and practical given that the team is currently collaborating
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1 Simon Frechette, Smart Manufacturing Systems Design and Analysis Program Manager, “Smart Manufacturing Systems Design and Analysis Program, Project Descriptions,” delivered to the panel on March 28, 2017.
with the American Society for Testing and Materials (ASTM), an international standards organization, on this project.
The Agri-Food Manufacturing System and Supply Chain Integration project intends to leverage other work being done to improve information flow among participants in the food chain, from growers to consumers. The U.S. agricultural industry is very large and diverse, and smart manufacturing technology can be and needs to be adapted to and deployed in agriculture applications. However, food and agriculture have major sectors with vastly different operations and needs. For example, harvesting operations for major crops are highly automated and dominated by very large corporations, while fresh foods, encompassing hundreds of products from vegetables to tree fruits and nuts to potatoes, are labor intensive. The latter is in dire need of automation, as labor shortages cause a significant portion of crops to rot on trees and in fields. Furthermore, fresh foods have short shelf lives and must be brought to the market quickly by 200,000 U.S. enterprises. The application of the outputs of the Service-Oriented Architectures for Smart Manufacturing Systems project to this project could have a profound effect on all aspects of the information processes food chain; however, they would have to meet the different needs of the different sectors, and need to engage different domain experts. One advantage the agricultural industry has is its strong network of commodity associations that can be used to provide both domain expertise and solution delivery channels.
A big issue that was missing from discussions during the review was the value generated by these programs and projects; there was an underlying assumption that more technology is good. In particular, SMEs will require some proof that smart manufacturing will be able to help their bottom lines. While the SMSDA is the right place to develop tools and standards for gap analysis and valuation of new technologies, it is questionable whether the program will be able to execute the gap analysis and valuation of technologies and standards to obtain low-hanging fruit without a smart manufacturing roadmap with measurable milestones.
PORTFOLIO OF SCIENTIFIC EXPERTISE
Accomplishments
The SMSDA staff are very involved in the standards and technical committees of the professional engineering societies, the academic community and partner companies. Over approximately the last three and one half years the SMSDA program has produced approximately 38 journal papers, created and organized approximately 30 workshops and conference sessions, and received honors, such as the ASTM International President’s Leadership Award and OAGi Outstanding Contributor Awards for its outstanding work. These personnel are also active in the correct standards committees of the U.S. professional societies. The staff were found to be, with a few exceptions that will be discussed below, very capable of executing its assigned tasks.
Opportunities and Challenges
The SMSDA personnel are very well trained and qualified to do the work necessary for the first category of work, which is to produce the basis and structure for a standard and to aid getting it into the public domain where it can be accessed by people and companies wishing to do smart manufacturing. Additionally, the personnel are capable of performing the tasks defined by the project objectives. They appear to work best and are most effective in problem areas where there exists an active standards community. In areas where there is a lack of existing standards or best practices, it is not clear that the SMSDA research is focused on the right problems or solutions to serve the SMEs.
In projects where a best practice does not exist (that is, the second category of work), the SMSDA needs to include a domain expert on the team for areas where NIST personnel may not have technical
expertise. Having a domain expert as an integral part of this category of projects is a necessity. For example, if the project is to produce the structure, databases, prototypes, or techniques that will form the basis of best-practice production control, then a production control domain expert needs to be an integral part of the team working on this project. The SMSDA could acquire the domain expert either by hiring one, or by getting a partner company to provide one. Having a domain expert is required to ensure that developed structures, databases, prototypes, or techniques are truly needed, and will provide the best practice needed by the domain.
The SMSDA also needs to utilize real SMEs as its laboratory to test its output. Making at least two SMEs an integral part of each project team would give the team the means to test its outputs in a real-life environment.
The SMSDA program consists of a number of projects; however, it was unclear from the project presentations shown during the review how the different projects relate to the overarching objectives of the program. Further, it was unclear from the presentations what the main importance, goals, and progress to date are for each project, and how each project contributes to the overall goals of the program. While there are many enablers for smart manufacturing, it was not apparent how the project contributes to these enablers.
ADEQUACY OF FACILITIES, EQUIPMENT, AND HUMAN RESOURCES
Opportunities and Challenges
The SMSDA program does not have a laboratory inside NIST to test the output of their projects. Testing the project output is particularly important where best practices do not exist and the new databases, techniques, and prototypes are being aimed at helping SMEs.
