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The paper seeks to bridge the already familiar benefits of 3D printing (3DP) to the rehabilitation of cultural heritage, still based on the use of complex and expensive handcrafted techniques and scarce materials.

A compilation of different information on frequent anomalies in cultural heritage buildings and commonly used materials is conducted; subsequently, some innovative techniques used in the construction sector (3DP and 3D scanning) are addressed, as well as some case studies related to the rehabilitation of cultural heritage building elements, leading to a reflection on the opportunities and challenges of this application within these types of buildings.

The compilation of information summarised in the paper provided a clear reflection on the great potential of 3DP for cultural heritage rehabilitation, requiring the development of new mixtures (lime mortars, for example) compatible with the existing surface and, eventually, incorporating some residues that may improve interesting properties; the design of different extruders, compatible with the new mixtures developed and the articulation of 3D printers with the available mapping tools (photogrammetry and laser scanning) to reproduce the component as accurately as possible.

This paper sets the path for a new application of 3DP in construction, namely in the field of cultural heritage rehabilitation, by identifying some key opportunities, challenges and for designing the process flow associated with the different technologies involved.

This paper investigate how fit uncertainty impacts product return costs in online retailing and how digital product fitting, a pre-sales fitting practice, can reduce fit uncertainty.

The paper analyzes the current performance of a retailer's e-commerce and return operations by estimating costs generated by product returns, including product handling costs, tied-up capital, inventory holding costs, transportation costs, and order-picking costs. The estimated costs were built on 2,229 return transactions from a Scandinavian fashion footwear retailer. A digital product fitting technology was tested with the retailer’s products and resulted in estimations on how such technology could affect product returns.

The cost of a return is approximately 17% of the prime cost. The major cost elements are product handling costs and transportation costs, which together amount to 72% of the total costs. If well calibrated, the fitting technology can cut fit-related return costs by up to 80%. The findings show how customers reacted to the fitting technology: it was unable to verify fit every time, but it serves as a useful and effective support tool for customers when placing orders.

Virtual fit verification using digital product fitting is key to retailers to reduce fit-related returns. Digital product fitting using three-dimensional scanning is more appropriate for some products, but it is unsuitable for products that are difficult to measure and scan.

The paper contributes an empirical estimate of retail supply chain costs associated with fit uncertainty, as well as theoretical understanding of the role of pre-sales fit verification in avoiding product returns.

The food industry is a well-established and complex industry. New entrants attempting to penetrate it via the commercialization of a new technological innovation could face high uncertainty and constraints. The capability to innovate through collaboration and to identify suitable strategies and innovative business models (BMs) can be particularly important for bringing a technological innovation to this market. However, although the potential for these capabilities has been advocated, we still lack a complete understanding of how new ventures could support the technology commercialization process via the development of BMs. The paper aims to discuss these issues.

To address this gap, this paper builds a conceptual framework that knits together the different bodies of extant literature (i.e. entrepreneurship, strategy and innovation) to analyze the BM innovation processes associated with the exploitation of emerging technologies; determines the suitability of the framework using data from the exploratory case study of IT IS 3D – a firm which has started to exploit 3D printing in the food industry; and improves the initial conceptual framework with the findings that emerged in the case study.

From this analysis it emerged that: companies could use more than one BM at a time; hence, BM innovation processes could co-exist and be run in parallel; the facing of high uncertainty might lead firms to choose a closed and/or a familiar BM, while explorative strategies could be pursued with open BMs; significant changes in strategies during the technology commercialization process are not necessarily reflected in a radical change in the BM; and firms could deliberately adopt interim strategies and BMs as means to identify the more suitable ones to reach the market.

This case study illustrates how firms could innovate the processes of their BM development to face the uncertainties linked with the entry into a mature and highly conservative industry (food).

Terrestrial laser scanning (TLS) point clouds have been widely used in deformation measurement for structures. However, reliability and accuracy of resulting deformation estimation strongly depends on quality of each step of a workflow, which are not fully addressed. This study aims to give insight error of these steps, and results of the study would be guidelines for a practical community to either develop a new workflow or refine an existing one of deformation estimation based on TLS point clouds. Thus, the main contributions of the paper are investigating point cloud registration error affecting resulting deformation estimation, identifying an appropriate segmentation method used to extract data points of a deformed surface, investigating a methodology to determine an un-deformed or a reference surface for estimating deformation, and proposing a methodology to minimize the impact of outlier, noisy data and/or mixed pixels on deformation estimation.

In practice, the quality of data point clouds and of surface extraction strongly impacts on resulting deformation estimation based on laser scanning point clouds, which can cause an incorrect decision on the state of the structure if uncertainty is available. In an effort to have more comprehensive insight into those impacts, this study addresses four issues: data errors due to data registration from multiple scanning stations (Issue 1), methods used to extract point clouds of structure surfaces (Issue 2), selection of the reference surface S_ref to measure deformation (Issue 3), and available outlier and/or mixed pixels (Issue 4). This investigation demonstrates through estimating deformation of the bridge abutment, building and an oil storage tank.

The study shows that both random sample consensus (RANSAC) and region growing–based methods [a cell-based/voxel-based region growing (CRG/VRG)] can be extracted data points of surfaces, but RANSAC is only applicable for a primary primitive surface (e.g. a plane in this study) subjected to a small deformation (case study 2 and 3) and cannot eliminate mixed pixels. On another hand, CRG and VRG impose a suitable method applied for deformed, free-form surfaces. In addition, in practice, a reference surface of a structure is mostly not available. The use of a fitting plane based on a point cloud of a current surface would cause unrealistic and inaccurate deformation because outlier data points and data points of damaged areas affect an accuracy of the fitting plane. This study would recommend the use of a reference surface determined based on a design concept/specification. A smoothing method with a spatial interval can be effectively minimize, negative impact of outlier, noisy data and/or mixed pixels on deformation estimation.

Due to difficulty in logistics, an independent measurement cannot be established to assess the deformation accuracy based on TLS data point cloud in the case studies of this research. However, common laser scanners using the time-of-flight or phase-shift principle provide point clouds with accuracy in the order of 1–6 mm, while the point clouds of triangulation scanners have sub-millimetre accuracy.

This study aims to give insight error of these steps, and the results of the study would be guidelines for a practical community to either develop a new workflow or refine an existing one of deformation estimation based on TLS point clouds.

The results of this study would provide guidelines for a practical community to either develop a new workflow or refine an existing one of deformation estimation based on TLS point clouds. A low-cost method can be applied for deformation analysis of the structure.

Although a large amount of the studies used laser scanning to measure structure deformation in the last two decades, the methods mainly applied were to measure change between two states (or epochs) of the structure surface and focused on quantifying deformation-based TLS point clouds. Those studies proved that a laser scanner could be an alternative unit to acquire spatial information for deformation monitoring. However, there are still challenges in establishing an appropriate procedure to collect a high quality of point clouds and develop methods to interpret the point clouds to obtain reliable and accurate deformation, when uncertainty, including data quality and reference information, is available. Therefore, this study demonstrates the impact of data quality in a term of point cloud registration error, selected methods for extracting point clouds of surfaces, identifying reference information, and available outlier, noisy data and/or mixed pixels on deformation estimation.