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War stories from the front line of industrializing additive manufacturing
Published in David M. Dietrich, Michael Kenworthy, Elizabeth A. Cudney, Additive Manufacturing Change Management, 2019
David M. Dietrich, Michael Kenworthy, Elizabeth A. Cudney
How can we depart from the ideation session framework? For one, let’s stop formally prescribing an outcome of innovation in ideation sessions. Let’s recognize that innovation can occur more frequently in an informal setting. The authors suggest deploying this strategy in the form of professional goal development at the individual employee level and make sure the employee (new hires and seasoned engineers) attend several AM industry conferences that incubate ideas from a large amount of diverse thought. Also, consider having the employee travel with the sales and marketing teams to interact face to face with customers who use the product on a daily basis. This approach will no doubt lead to more innovative design thinking that may or may not require AM solutions. Remember that AM may not be the solution to new product design. It is a tool in the engineering toolbox, not the entire toolbox. Instead, AM should be regarded as a disruptive technology, not displacement technology.
Potential for growth
Published in Frank Devitt, Martin Ryan, Trevor Vaugh, Arrive, 2021
Frank Devitt, Martin Ryan, Trevor Vaugh
As another example of process sequencing, newly generated solution concepts must be validated with the user and other stakeholders before proceeding to full scale deployment. This does not rule out revisiting an earlier stage on foot of some later stage discovery. After all, continuous refinement through iteration is a distinctive attribute of design thinking. But, it does mandate an overall sequencing of major stages, with early stage outcomes seeding later stages. We are happy to call this a process. See the side box: Toolbox and process.
Variables and Functions
Published in José Miguel, David Báez-López, David Alfredo Báez Villegas, ® Handbook with Applications to Mathematics, Science, Engineering, and Finance, 2019
José Miguel, David Báez-López, David Alfredo Báez Villegas
We can also do a curve fitting using the MATLAB tool called Basic Fitting. This tool is available with the Curve Fitting Toolbox. It can be called from any plot available. For example, if we have sets of data points x, y we just plot them. For example, we plot the data points with an asterisk: >> x = [1 2 3 4 5 6]; >> y = [1 0 4.4 0 5.5 0]; >> plot(x, y, ‘*’)
Lean Production Systems 4.0: systematic literature review and field study on the digital transformation of lean methods and tools
Published in International Journal of Production Research, 2022
Simon Schumacher, Roland Hall, Andreas Bildstein, Thomas Bauernhansl
For consistent use of nomenclature, we will now determine definitions of relevant terminology used throughout this paper. Industrial engineering is a discipline for the design, planning and optimisation of industrial value creation processes based on the use of methods from engineering science (Stowasser 2010; Bokranz and Landau 2012; REFA-Institut e.V. 2016). The term ‘toolbox’ encompasses the collection and description of a set of methods and tools – either physical or digital – for use in industrial engineering practice (VDI 2012). A tool is a standardised, physically or digitally available means that is necessary for the implementation of methods and serves to achieve business objectives (VDI 2012). A use case describes the exemplary application of a technology, method or tool. Use cases are implementation-oriented and provide technical solutions to specific problems. Use cases can be attributed with contextual information such as roles, business practices and relevant key performance indicators (DIN/DKE 2018).
Implementation of Industry 4.0 and lean production in Brazilian manufacturing companies
Published in International Journal of Production Research, 2018
Guilherme Luz Tortorella, Diego Fettermann
For instance, Lee, Bagheri, and Kao (2015) suggest a five-level CPS structure that guides its development and deployment for manufacturing application; they are: (i) smart connection, (ii) data-to-information conversion, (iii) cyber, (iv) cognition and (v) configuration. Anderl (2014) suggests the utilisation of a practical implementation roadmap named ‘Toolbox Industrie 4.0’, which is structured based on six dimensions and five development levels. In turn, the German Government proposed a maturity model for assessing Industry 4.0 with 62 items grouped into nine dimensions; they are: strategy, leadership, customers, products, operations, culture, people, governance and technology (Schumacher, Erol, and Sihn 2016). Complementarily, Chukwuekwe et al. (2016) suggest the existence of key drivers of Industry 4.0 such as cloud computing, 3D printing technology, CPS, Internet of Things (IoT), Internet of Services (IoS) and big data. Particularly within the context of small and medium enterprises (SME), Ganzarain and Errasti (2016) suggest a three-stage maturity model towards Industry 4.0, which are: envision, enable and enact.