Purpose Investing in Industry 4.0 is an important consideration for manufacturing firms who strive to remain competitive in this global economy, but the uncertainty and complexity of where to focus technology investments is a problem facing many manufacturers. The purpose of this paper is to highlight a region of manufacturing firms in the Midwest USA to investigate the role of firm size, access to funds and industry type on decision to invest in and deploy various Industry 4.0 technologies. Design/methodology/approach A survey was developed, piloted, and deployed to manufacturing companies located in the Midwest USA, specifically, Indiana, USA. A total of 138 manufacturing firms completed the full survey. The survey participants were requested to rank order the various technology categories with respect to previous historical spending, workforce capabilities and anticipated return on investment. The survey was supplemented with publically available data. Due to the use of rank-order data to identify Industry 4.0 priorities, a non-parametric analysis was completed using the Kruskall Wallis test. Findings The findings suggest that manufacturers with less than 20 employees and/or less access to funds (sales less than $10m) prioritize digital factory floor technologies (e.g. technology directly impacting productivity, quality and safety of manufacturing processes). Larger manufacturers with 20 or more employees and/or access to more funds (sales greater than or equal to $10m) prioritize enterprise support operations technologies. Originality/value Research studies and reports tend to lump manufacturing’s perspective of Industry 4.0 into one homogenous group, and rarely acknowledge the limited participation of “smaller” Small and medium-sized enterprises, which account for the far majority of manufacturing firms in the USA. The value of this study is on the “novelty of approach,” in that the data collection and analysis focuses on heterogeneity of manufacturing firms with respect to size, access to funds and industry type. The findings and recommendations are beneficial and relevant to organizations supporting Industry 4.0 efforts through workforce development and economic development initiatives.

Design for Manufacturing (DFM), especially the use of manufacturing knowledge to support design decisions, has received attention in the academic domain. However, industry practice has not been studied enough to provide solutions that are mature for industry. The current state of the art for DFM is often rule-based functionality within Computer-Aided Design (CAD) systems that enforce specific design requirements. That rule-based functionality may or may not dynamically affect geometry definition. And, if rule-based functionality exists in the CAD system, it is typically a customization on a case-by-case basis. Manufacturing knowledge is a phrase with vast meanings, which may include knowledge on the effects of material properties decisions, machine and process capabilities, or understanding the unintended consequences of design decisions on manufacturing. One of the DFM questions to answer is how can manufacturing knowledge, depending on its definition, be used earlier in the product lifecycle to enable a more collaborative development environment? This paper will discuss the results of a workshop on manufacturing knowledge that highlights several research questions needing more study. This paper proposes recommendations for investigating the relationship of manufacturing knowledge with shape, behavior, and context characteristics of product to produce a better understanding of what knowledge is most important. In addition, the proposal includes recommendations for investigating the system-level barriers to reusing manufacturing knowledge and how model-based manufacturing may ease the burden of knowledge sharing. Lastly, the proposal addresses the direction of future research for holistic solutions of using manufacturing knowledge earlier in the product lifecycle.

Technological advances in the last decade have influenced changes in the design and engineering industries on a global scale. Lean and collaborative product development are approaches increasingly adopted by the industry and seen as the core of product lifecycle management. These trends have created the need for new skilled professionals, and universities should adapt their curricula in response. There is an increased need for academia to work with industry in order to meet these challenges. This article reports on the Parametric Technology Corporation Academic Research Symposium held in April 2011. The topics were centred around understanding the essence of product lifecycle management and its impact on design and engineering education. Furthermore, examples of implementing product lifecycle management and collaborative practices in higher education were presented from the United States and France. This article concludes with a discussion of the recommendations made at the symposium for the future development and support of key skills across university curricula.

The manufacturing industry is evolving and starting to use 3D models as the central knowledge artifact for product data and product definition, or what is known as Model-based Definition (MBD). The Model-based Enterprise (MBE) uses MBD as a way to transition away from using traditional paper-based drawings and documentation. As MBD grows in popularity, it is imperative to understand what information is needed in the transition from drawings to models so that models represent all the relevant information needed for processes to continue efficiently. Finding this information can help define what data is common amongst different models in different stages of the lifecycle, which could help establish a Common Information Model. The Common Information Model is a source that contains common information from domain specific elements amongst different aspects of the lifecycle. To help establish this Common Information Model, information about how models are used in industry within different workflows needs to be understood. To retrieve this information, a survey mechanism was administered to industry professionals from various sectors. Based on the results of the survey a Common Information Model could not be established. However, the results gave great insight that will help in further investigation of the Common Information Model.

The manufacturing industry is evolving and starting to use 3D models as the central knowledge artifact for product data and product definition, or what is known as Model-based Definition (MBD). The Model-based Enterprise (MBE) uses MBD as a way to transition away from using traditional paper-based drawings and documentation. As MBD grows in popularity, it is imperative to understand what information is needed in the transition from drawings to models so that models represent all the relevant information needed for processes to continue efficiently. Finding this information can help define what data is common amongst different models in different stages of the lifecycle, which could help establish a Common Information Model. The Common Information Model is a source that contains common information from domain specific elements amongst different aspects of the lifecycle. To help establish this Common Information Model, information about how models are used in industry within different workflows needs to be understood. To retrieve this information, a survey mechanism was administered to industry professionals from various sectors. Based on the results of the survey a Common Information Model could not be established. However, the results gave great insight that will help in further investigation of the Common Information Model.

The Model-Based Enterprise (MBE) paradigm is being adopted by manufacturing companies in a variety of industries. Companies benefit from enhanced visualization, documentation, and communication capabilities when 3D annotated product definitions, or Model-Based Definitions (MBD) replace twodimensional drawings throughout an enterprise. It is critical that product information, much of which is defined implicitly in drawings, is not lost in this transition. This presents a challenge to authors and translators of 3D models used through the product lifecycle. They must understand the semantics of the product information typically presented by a drawing then explicitly include this information, in a computer-interpretable form, in the MBD.The research study described in this paper seeks to discover what is the minimum set of required information to carry out all the tasks in a given workflow of a model-based enterprise. A survey was conducted across various industry sectors to identify the foundational elements of this Minimum Information Model (MIM) in selected workflows. This study identified the information used within the specific workflows, the capabilities of 3D CAD models to carry this information, and the implications for doing so. KEYWORDS.Minimum Information Model (MIM) Model-Based Definition (MBD) Model-Based Enterprise (MBE) INTRODUCTIONModel-based definition (MBD) is a digital artifact (representation) of an object or system. It is representative of the physical object or system and all of its attributes, and is used to communicate information within various MBx activities in a model-based enterprise. The model-based definition should be rich in information -shape, behavior, and context -and it travels the information architecture within an enterprise (including its extended supply chain and customers), providing input to the various authors and consumers who need it. However, in today's industrial environment the MBD is often thought of as a replacement for a 2D drawing. A model-based definition's effectiveness in communicating, visualizing and documenting information has led to widespread adoption. [1] Although MBD practices are of significant value, there are still concerns about the transition from drawings to models. One issue is that critical information stored in an MBD can be lost in machine to machine communication due to translation errors. Therefore, there is a need to understand the minimum amount of information required at each phase of a product's lifecycle to ensure critical information is not lost. A second issue is a lack of common understanding regarding the information to be included in an MBD that were historically included in a drawing. This research proposes the identification of the minimum information model (MIM). The minimum information model is the set of information which is required for the completion of tasks within specific phases of the product lifecycle. MOTIVATIONThe manufacturing environment historically used drawings in the engineering design and production process for

passed away on May 1, 2018, 11 days short of being 90 years old.He was an early pioneer in the application of splines to Computer-Aided Geometric Design (CAGD), which provides the mathematical basis for the use of computers to design, engineer and manufacture products and complex systems.My first encounter with Prof. Boehm's work was in early 1981. I was new to CAGD and particularly interested in developing rational B-spline technology for use in engineering applications. Upon discovering his 1980 paper: "Inserting new knots into B-spline curves," my first impression was: what a beautiful work. It was short, just three pages, elegantly simple and clear, but eminently useful. The applications were immediately clear: curve/surface division, modification by means of control point refinement, decomposition into Bezier and other polynomial forms, and rendering sets of curves compatible for the purpose of surface constructions such as lofting, just to name a few. This paper had a profound influence on my work.

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