Innovation as a Nonlinear Process, the Scientometric Perspective, and the Specification of an "Innovation Opportunities Explorer"

The process of innovation follows non-linear patterns across the domains of science, technology, and the economy. Novel bibliometric mapping techniques can be used to investigate and represent distinctive, but complementary perspectives on the innovation process (e.g., "demand" and "supply") as well as the interactions among these perspectives. The perspectives can be represented as "continents" of data related to varying extents over time. For example, the different branches of Medical Subject Headings (MeSH) in the Medline database provide sources of such perspectives (e.g., "Diseases" versus "Drugs and Chemicals"). The multiple-perspective approach enables us to reconstruct facets of the dynamics of innovation, in terms of selection mechanisms shaping localizable trajectories and/or resulting in more globalized regimes. By expanding the data with patents and scholarly publications, we demonstrate the use of this multi-perspective approach in the case of RNA Interference (RNAi). The possibility to develop an "Innovation Opportunities Explorer" is specified.Comment: Technology Analysis and Strategic Management (forthcoming in 2013

Space Station Engineering Design Issues

Space Station Freedom topics addressed include: general design issues; issues related to utilization and operations; issues related to systems requirements and design; and management issues relevant to design

DESIGNING POLICIES FOR LOCAL PRODUCTION SYSTEMS: A METHODOLOGY BASED ON EVIDENCE FROM BRAZIL

Using a previously developed methodology for identifying, classifying and characterizing local production systems (LPS) in Brazil, and evidence produced by a number of case studies, the paper suggests that policies aimed at LPS (or industrial clusters) should be tailored according to a typology of clusters. This typology must take into account the cluster importance for local or regional development, its share in the respective industry, and its characteristics in terms of production structure, trading schemes, institutional infrastructure, governance structures, and social contexts. The paper starts by reviewing, from the point of view of policy-making, the theories that support industrial cluster analyses, namely those that explain clusters as: outcomes of plain agglomeration economies and increasing returns; the result of spatial dynamic processes; knowledge systems emerging from the geography of innovation and agglomeration; governance structures, and as evolving complex systems. Next, the results from an application of the methodology to Brazilian data and information and from a number of case studies are summarized. Finally, the paper suggests policy guidelines with some measures of general application, aimed at problems observed in all LPS, and some specific measures differentiated according to a typology of local production systems that resulted from the application of the methodology.

Designing Policies for Local Production Systems: A Methodology Based on Evidence from Brazil

Using a previously developed methodology for identifying, classifying and characterizing local production systems (LPS) in Brazil, and evidence produced by a number of case studies, the paper suggests that policies aimed at LPS (or industrial clusters) should be tailored according to a typology of clusters. This typology must take into account the cluster importance for local or regional development, its share in the respective industry, and its characteristics in terms of production structure, trading schemes, institutional infrastructure, governance structures, and social contexts. The paper starts by reviewing, from the point of view of policy-making, the theories that support industrial cluster analyses, namely those that explain clusters as: outcomes of plain agglomeration economies and increasing returns; the result of spatial dynamic processes; knowledge systems emerging from the geography of innovation and agglomeration; governance structures, and as evolving complex systems. Next, the results from an application of the methodology to Brazilian data and information and from a number of case studies are summarized. Finally, the paper suggests policy guidelines with some measures of general application, aimed at problems observed in all LPS, and some specific measures differentiated according to a typology of local production systems that resulted from the application of the methodology.Manufacturing Industry, Cluster, Local Production System, Industrial Policy

ILR Research in Progress 2006-07

The production of scholarly research continues to be one of the primary missions of the ILR School. During a typical academic year, ILR faculty members published or had accepted for publication over 25 books, edited volumes, and monographs, 170 articles and chapters in edited volumes, numerous book reviews. In addition, a large number of manuscripts were submitted for publication, presented at professional association meetings, or circulated in working paper form. Our faculty's research continues to find its way into the very best industrial relations, social science and statistics journals.Research_in_Progress_2006_07.pdf: 18 downloads, before Oct. 1, 2020

Inferring Energy Bounds via Static Program Analysis and Evolutionary Modeling of Basic Blocks

The ever increasing number and complexity of energy-bound devices (such as the ones used in Internet of Things applications, smart phones, and mission critical systems) pose an important challenge on techniques to optimize their energy consumption and to verify that they will perform their function within the available energy budget. In this work we address this challenge from the software point of view and propose a novel parametric approach to estimating tight bounds on the energy consumed by program executions that are practical for their application to energy verification and optimization. Our approach divides a program into basic (branchless) blocks and estimates the maximal and minimal energy consumption for each block using an evolutionary algorithm. Then it combines the obtained values according to the program control flow, using static analysis, to infer functions that give both upper and lower bounds on the energy consumption of the whole program and its procedures as functions on input data sizes. We have tested our approach on (C-like) embedded programs running on the XMOS hardware platform. However, our method is general enough to be applied to other microprocessor architectures and programming languages. The bounds obtained by our prototype implementation can be tight while remaining on the safe side of budgets in practice, as shown by our experimental evaluation.Comment: Pre-proceedings paper presented at the 27th International Symposium on Logic-Based Program Synthesis and Transformation (LOPSTR 2017), Namur, Belgium, 10-12 October 2017 (arXiv:1708.07854). Improved version of the one presented at the HIP3ES 2016 workshop (v1): more experimental results (added benchmark to Table 1, added figure for new benchmark, added Table 3), improved Fig. 1, added Fig.

Robust Multi-Objective Sustainable Reverse Supply Chain Planning: An Application in the Steel Industry

In the design of the supply chain, the use of the returned products and their recycling in the production and consumption network is called reverse logistics. The proposed model aims to optimize the flow of materials in the supply chain network (SCN), and determine the amount and location of facilities and the planning of transportation in conditions of demand uncertainty. Thus, maximizing the total profit of operation, minimizing adverse environmental effects, and maximizing customer and supplier service levels have been considered as the main objectives. Accordingly, finding symmetry (balance) among the profit of operation, the environmental effects and customer and supplier service levels is considered in this research. To deal with the uncertainty of the model, scenario-based robust planning is employed alongside a meta-heuristic algorithm (NSGA-II) to solve the model with actual data from a case study of the steel industry in Iran. The results obtained from the model, solving and validating, compared with actual data indicated that the model could optimize the objectives seamlessly and determine the amount and location of the necessary facilities for the steel industry more appropriately.This article belongs to the Special Issue Uncertain Multi-Criteria Optimization Problem

Business Process Redesign in the Perioperative Process: A Case Perspective for Digital Transformation

This case study investigates business process redesign within the perioperative process as a method to achieve digital transformation. Specific perioperative sub-processes are targeted for re-design and digitalization, which yield improvement. Based on a 184-month longitudinal study of a large 1,157 registered-bed academic medical center, the observed effects are viewed through a lens of information technology (IT) impact on core capabilities and core strategy to yield a digital transformation framework that supports patient-centric improvement across perioperative sub-processes. This research identifies existing limitations, potential capabilities, and subsequent contextual understanding to minimize perioperative process complexity, target opportunity for improvement, and ultimately yield improved capabilities. Dynamic technological activities of analysis, evaluation, and synthesis applied to specific perioperative patient-centric data collected within integrated hospital information systems yield the organizational resource for process management and control. Conclusions include theoretical and practical implications as well as study limitations