Outsourcing decisions:the case of parallel production

Purpose – The purpose of this paper is to investigate an underexplored aspect of outsourcing involving a mixed strategy in which parallel production is continued in-house at the same time as outsourcing occurs. Design/methodology/approach – The study applied a multiple case study approach and drew on qualitative data collected through in-depth interviews with wood product manufacturing companies. Findings – The paper posits that there should be a variety of mixed strategies between the two governance forms of “make” or “buy.” In order to address how companies should consider the extent to which they outsource, the analysis was structured around two ends of a continuum: in-house dominance or outsourcing dominance. With an in-house-dominant strategy, outsourcing complements an organization's own production to optimize capacity utilization and outsource less cost-efficient production, or is used as a tool to learn how to outsource. With an outsourcing-dominant strategy, in-house production helps maintain complementary competencies and avoids lock-in risk. Research limitations/implications – This paper takes initial steps toward an exploration of different mixed strategies. Additional research is required to understand the costs of different mixed strategies compared with insourcing and outsourcing, and to study parallel production from a supplier viewpoint. Practical implications – This paper suggests that managers should think twice before rushing to a “me too” outsourcing strategy in which in-house capacities are completely closed. It is important to take a dynamic view of outsourcing that maintains a mixed strategy as an option, particularly in situations that involve an underdeveloped supplier market and/or as a way to develop resources over the long term. Originality/value – The concept of combining both “make” and “buy” is not new. However, little if any research has focussed explicitly on exploring the variety of different types of mixed strategies that exist on the continuum between insourcing and outsourcing.

Sustainability and scalability of university spinouts:a business model perspective

Most previous studies of university spinouts (USOs) have focused on what determines their formation from the perspectives of the entrepreneurs or of their parent universities. However, few studies have investigated how these entrepreneurial businesses actually grow and how their business models evolve in the process. This paper examines the evolution of USOs' business models over their different development phases. Using empirical evidence gathered from three comprehensive case studies, we explore how USOs' business models evolve over time, and the implications for the financial sustainability and operational scalability of these ventures. This paper extends existing research on the development of USOs, and highlights three themes for future research.

Multi-threaded parallel numerical modelling of femtosecond pulse propagation in laser machining

Femtosecond laser microfabrication has emerged over the last decade as a 3D flexible technology in photonics. Numerical simulations provide an important insight into spatial and temporal beam and pulse shaping during the course of extremely intricate nonlinear propagation (see e.g. [1,2]). Electromagnetics of such propagation is typically described in the form of the generalized Non-Linear Schrdinger Equation (NLSE) coupled with Drude model for plasma [3]. In this paper we consider a multi-threaded parallel numerical solution for a specific model which describes femtosecond laser pulse propagation in transparent media [4, 5]. However our approach can be extended to similar models. The numerical code is implemented in NVIDIA Graphics Processing Unit (GPU) which provides an effitient hardware platform for multi-threded computing. We compare the performance of the described below parallel code implementated for GPU using CUDA programming interface [3] with a serial CPU version used in our previous papers [4,5]. © 2011 IEEE.

Scalability and process transfer of mesenchymal stromal cell production from monolayer to microcarrier culture using human platelet lysate

Background aims: The selection of medium and associated reagents for human mesenchymal stromal cell (hMSC) culture forms an integral part of manufacturing process development and must be suitable for multiple process scales and expansion technologies. Methods: In this work, we have expanded BM-hMSCs in fetal bovine serum (FBS)- and human platelet lysate (HPL)-containing media in both a monolayer and a suspension-based microcarrier process. Results: The introduction of HPL into the monolayer process increased the BM-hMSC growth rate at the first experimental passage by 0.049 day and 0.127/day for the two BM-hMSC donors compared with the FBS-based monolayer process. This increase in growth rate in HPL-containing medium was associated with an increase in the inter-donor consistency, with an inter-donor range of 0.406 cumulative population doublings after 18 days compared with 2.013 in FBS-containing medium. Identity and quality characteristics of the BM-hMSCs are also comparable between conditions in terms of colony-forming potential, osteogenic potential and expression of key genes during monolayer and post-harvest from microcarrier expansion. BM-hMSCs cultured on microcarriers in HPL-containing medium demonstrated a reduction in the initial lag phase for both BM-hMSC donors and an increased BM-hMSC yield after 6 days of culture to 1.20 ± 0.17 × 105 and 1.02 ± 0.005 × 105 cells/mL compared with 0.79 ± 0.05 × 105 and 0.36 ± 0.04 × 105 cells/mL in FBS-containing medium. Conclusions: This study has demonstrated that HPL, compared with FBS-containing medium, delivers increased growth and comparability across two BM-hMSC donors between monolayer and microcarrier culture, which will have key implications for process transfer during scale-up.

Multi-threaded parallel simulation of non-local non-linear problems in ultrashort laser pulse propagation in the presence of plasma

We describe a parallel multi-threaded approach for high performance modelling of wide class of phenomena in ultrafast nonlinear optics. Specific implementation has been performed using the highly parallel capabilities of a programmable graphics processor. © 2011 SPIE.

Self-adapting parallel metric-space search engine for variable query loads

This research focuses on automatically adapting a search engine size in response to fluctuations in query workload. Deploying a search engine in an Infrastructure as a Service (IaaS) cloud facilitates allocating or deallocating computer resources to or from the engine. Our solution is to contribute an adaptive search engine that will repeatedly re-evaluate its load and, when appropriate, switch over to a dierent number of active processors. We focus on three aspects and break them out into three sub-problems as follows: Continually determining the Number of Processors (CNP), New Grouping Problem (NGP) and Regrouping Order Problem (ROP). CNP means that (in the light of the changes in the query workload in the search engine) there is a problem of determining the ideal number of processors p active at any given time to use in the search engine and we call this problem CNP. NGP happens when changes in the number of processors are determined and it must also be determined which groups of search data will be distributed across the processors. ROP is how to redistribute this data onto processors while keeping the engine responsive and while also minimising the switchover time and the incurred network load. We propose solutions for these sub-problems. For NGP we propose an algorithm for incrementally adjusting the index to t the varying number of virtual machines. For ROP we present an ecient method for redistributing data among processors while keeping the search engine responsive. Regarding the solution for CNP, we propose an algorithm determining the new size of the search engine by re-evaluating its load. We tested the solution performance using a custom-build prototype search engine deployed in the Amazon EC2 cloud. Our experiments show that when we compare our NGP solution with computing the index from scratch, the incremental algorithm speeds up the index computation 2{10 times while maintaining a similar search performance. The chosen redistribution method is 25% to 50% faster than other methods and reduces the network load around by 30%. For CNP we present a deterministic algorithm that shows a good ability to determine a new size of search engine. When combined, these algorithms give an adapting algorithm that is able to adjust the search engine size with a variable workload.

Parallel Problem Solving from Nature – PPSN XIV:14th International Conference, Edinburgh, UK, September 17-21, 2016, Proceedings

This book constitutes the refereed proceedings of the 14th International Conference on Parallel Problem Solving from Nature, PPSN 2016, held in Edinburgh, UK, in September 2016. The total of 93 revised full papers were carefully reviewed and selected from 224 submissions. The meeting began with four workshops which offered an ideal opportunity to explore specific topics in intelligent transportation Workshop, landscape-aware heuristic search, natural computing in scheduling and timetabling, and advances in multi-modal optimization. PPSN XIV also included sixteen free tutorials to give us all the opportunity to learn about new aspects: gray box optimization in theory; theory of evolutionary computation; graph-based and cartesian genetic programming; theory of parallel evolutionary algorithms; promoting diversity in evolutionary optimization: why and how; evolutionary multi-objective optimization; intelligent systems for smart cities; advances on multi-modal optimization; evolutionary computation in cryptography; evolutionary robotics - a practical guide to experiment with real hardware; evolutionary algorithms and hyper-heuristics; a bridge between optimization over manifolds and evolutionary computation; implementing evolutionary algorithms in the cloud; the attainment function approach to performance evaluation in EMO; runtime analysis of evolutionary algorithms: basic introduction; meta-model assisted (evolutionary) optimization. The papers are organized in topical sections on adaption, self-adaption and parameter tuning; differential evolution and swarm intelligence; dynamic, uncertain and constrained environments; genetic programming; multi-objective, many-objective and multi-level optimization; parallel algorithms and hardware issues; real-word applications and modeling; theory; diversity and landscape analysis.