Rapid manufacturing using 3-D printing is a potential solution to some of the most pressing issues for humanitarian logistics. In this paper, findings are reported from a study that involved development of a new type of 3-D printer. In particular, a novel 3-D printer that is designed specifically for reliable rapid manufacturing at the sites of humanitarian crises. First, required capabilities are developed with design elements of a humanitarian 3-D printer, which include, (1) fused filament fabrication, (2) open source self-replicating rapid prototyper design, (3) modular, (4) separate frame, (5) protected electronics, (6) on-board computing, (7) flexible power supply, and (8) climate control mechanisms. The technology is then disclosed with an open source license for the Kijenzi 3-D Printer. A swarm of five Kijenzi 3-D printers are evaluated for rapid part manufacturing for two months at health facilities and other community locations in both rural and urban areas throughout Kisumu County, Kenya. They were successful for their ability to function independently of infrastructure, transportability, ease of use, ability to withstand harsh environments and costs. The results are presented and conclusions are drawn about future work necessary for the Kijenzi 3-D Printer to meet the needs of rapid manufacturing in a humanitarian context. Yet, supply chain logistics for humanitarian responses are some of the most complex that exist. It is challenging to forecast both the demand (due to difficulties in knowing both the timing of a disaster and details of the population affected) and the supply, which is often fueled by donations [4]. A massive mismatch between the supplies delivered and the supplies that are needed is often inevitable both in quantity and kind [2,3,5]. As 60-80% of all aid money is spent on procurement, this mismatch represents not only costly errors but errors that can have negative long-term effects on local markets and economies [5].Rapid manufacturing using 3-D printing is a potential solution to some of these pressing issues in humanitarian logistics. This has become potentially feasible with the open source 3-D printers developed from the self-replicating rapid prototyper (RepRap) project [6][7][8]. The RepRap project has driven down costs of 3-D printing to fit resource-constrained contexts like those found in the developing world or during a crisis [9]. The potential for 3-D printers in humanitarian aid work has been able to capture the interest of practitioners in the field [10], as 3-D printing can have positive effects on nearly every step of the humanitarian supply chain [5].3-D printing can reduce time and money used in the procurement of goods [11,12] by reducing the amount of capital required for manufacturing at a given location, allowing distributed manufacturing [13][14][15] or more localized manufacturing to occur [3,10]. By manufacturing goods locally at the site of a disaster response, the only materials that need to be shipped to the site of a disaster are the raw materials needed...

While the combination of 3D scanning and printing processes holds much promise for the field of new product development, problems with repeatability and accuracy have limited the wider spread adoption of some digital prototyping tools, such as 3D scanners. Studies have explored the errors inherent in higher fidelity scan to print (S2P) processes, yet few have explored the errors in S2P processes that leverage affordable rapid noncontact scanners. Studies have yet to explore the strategies that designers, who are experienced with additive manufacturing, employ to mitigate errors. To address these gaps, a controlled study was conducted using data from 27 scans collected with a prototypical off-the-shelf noncontact optical scanner. The geometric and dimensional integrity of the digital models was found to be significantly out of tolerance at various phases of the S2P process, as compared to the original physical model. Larger errors were found more consistently in the data acquisition phase of the S2P process, but results indicate these errors were not sufficiently filtered out during the remainder of the process. A behavioral study was conducted with 13 experienced designers in digital fabrication to determine strategies for manually cleaning Point Clouds. Actions such as increase or decrease in brush size and select or de-select points were recorded. These actions were analyzed using hidden Markov modeling, which revealed distinct patterns of behavior. Designer strategies were not beneficial and digital models produced by designers were found to be significantly smaller than original physical models.

Prototyping is a crucial part of new product development, and engineers and designers rely on prototyping to bring novel technologies to market. In recent years, tech-based startups like Tesla or Udacity have revolutionized their respective industries. However, many tech-based startups are unable to create a viable product with their available resources and fail before ever making it to market. In this work, we analyze survey responses from 34 startup representatives to investigate the relationship between prototyping practice, startup success, and perceived difficulty of startup tasks. K-means cluster analysis shows three distinct groups, differentiated by (1) their amount of available funding, (2) their use of prototyping best practices, and (3) their reported difficulty in startup tasks. High-performing startups reported having the highest funding, experiencing less difficulty in startup tasks, and using prototyping best practices more frequently than their peers.

Design heuristics created for additive manufacturing (AM) help designers increase manufacturability, reduce waste, and create novel geometries that are generally impossible to create with traditional manufacturing techniques. However, this new manufacturing method has come with its own challenges, specifically for inspection and quality assurance end use parts. Current methods for quality assurance and control are not capable of assessing quality of complex geometries or may be prohibitively expensive for AM produced components. By considering inspection early in the design process, designers can reduce the cost of quality control and prevent expensive redesign associated with non-inspectable components. The Design for Inspectability (DfI) worksheet proposed in the current work serves as a first step towards integrating quality control and quality assurance considerations into early stage design, specifically in the context of AM. The DfI worksheet can be used to assess if a part can be inspected for quality after production and can inform future iterations of design concepts. To determine if the worksheet assists designers in creating a component that can be assessed for quality, a controlled laboratory study was conducted. Findings suggest the worksheet effects design outcomes.

Crowdfunding platforms like Kickstarter provide new product developers with a novel and readily accessible platform to interact with potential customers, seek funding, and gain valuable feedback from target users. Successful startup design processes are often praised due to startups’ agility and ability to adapt to changing markets under extreme resource constraints. The COVID-19 pandemic changed the market in a number of significant ways, and publicly available data on Kickstarter provides valuable insight into the fundamental ways that product developers adapt to volatiles markets created by a global crisis. Leveraging linguistic analysis, topic modelling, k-means clustering, and principal component analysis, startup data was analyzed to identify overarching trends in startup launch campaigns across years. Specifically, this work identifies emerging trends in the Kickstarter market in 2020 to understand how external factors affect launch practices. Subtle yet significant changes in Kickstarter campaigns show that new clusters of campaigns emerged during 2020, not present in prior years, indicating that product developers adapted language to meet the needs of a changing market. Findings motivate a more thorough investigation of the relationships between startup market trends and external events to build a deeper understanding of the fundamental ways developers adapt design processes due to external stimuli.