Outsourcing practices in automotive supply networks: an exploratory study of full service vehicle suppliers

Most automotive companies outsource the design, manufacturing and assembly of some complete cars to independent suppliers or full service vehicle companies (FSVs). FSVs represent a relatively unexplored form of outsourcing which combines joint efforts in developing a modular product with the externalisation of specific niche manufacturing processes. On the basis of comparative cases and of qualitative information collected within three FSVs and three OEMs, this study aims to investigate the associated advantages and risks for FSVs, commonalities/differences from the practices of outsourcing to component/module specialists that can be found in other industries and why and for what kind of products FSVs can be appropriate in automotive supply networks. The study contributes to the debate on outsourcing in the automotive industry by providing an original interpretation on why FSVs have recently seen a shakeout with some growing whilst others have faltered.     

Latent structure modeling underlying theophylline tablet formulations using a Bayesian network based on a self-organizing map clustering

The “quality by design” concept in pharmaceutical formulation development requires the establishment of a science-based rationale and design space. In this article, we integrate thin-plate spline (TPS) interpolation, Kohonen’s self-organizing map (SOM) and a Bayesian network (BN) to visualize the latent structure underlying causal factors and pharmaceutical responses. As a model pharmaceutical product, theophylline tablets were prepared using a standard formulation. We measured the tensile strength and disintegration time as response variables and the compressibility, cohesion and dispersibility of the pretableting blend as latent variables. We predicted these variables quantitatively using nonlinear TPS, generated a large amount of data on pretableting blends and tablets and clustered these data into several clusters using a SOM. Our results show that we are able to predict the experimental values of the latent and response variables with a high degree of accuracy and are able to classify the tablet data into several distinct clusters. In addition, to visualize the latent structure between the causal and latent factors and the response variables, we applied a BN method to the SOM clustering results. We found that despite having inserted latent variables between the causal factors and response variables, their relation is equivalent to the results for the SOM clustering, and thus we are able to explain the underlying latent structure. Consequently, this technique provides a better understanding of the relationships between causal factors and pharmaceutical responses in theophylline tablet formulation.     

Modelling joint outsourcing and offshoring decisions

Offshoring and outsourcing have emerged as two most essential and important strategies for the manufacturing firms globally. This has happened due to the relentless forces of competition and globalisation, which has forced modern manufacturing firms to disaggregate themselves and reach for foreign investments, technological inputs through collaboration and cooperation among markets and partners. Outsourcing and offshoring simultaneously has potential to help the firm to meet three strategic goals i.e. ‘efficiency’ through cost minimisation; ‘exploration’ through an access to the right and technologically capable firm vendors/ Suppliers/ players; and ‘exploitation’ from development of global markets. Both outsourcing and offshoring have grown to a great extent either by the number of companies involved or by the number of multiple nations are involved. Hence, addressing both offshoring and outsourcing strategies together can provides value to the supply chain in economical and efficient way. Therefore, this paper is an attempt to model joint outsourcing and offshoring decision integrated with fuzzy-MCDM approaches using fuzzy data to optimise total cost through right selection of suppliers to meet demand of global markets, thus, achieving efficiency, exploration and exploitation. The model addresses qualitative and quantitative criteria in joint decisions and is also illustrated to show the cost effectiveness.     

Using artificial neural networks for mapping of scienceand technology: A multi-self-organizing-maps approach

We argue in favour of artificial neural networks for exploratory data analysis, clustering andmapping. We propose the Kohonen self-organizing map (SOM) for clustering and mappingaccording to a multi-maps extension. It is consequently called Multi-SOM. Firstly the KohonenSOM algorithm is presented. Then the following improvements are detailed: the way of namingthe clusters, the map division into logical areas, and the map generalization mechanism. Themulti-map display founded on the inter-maps communication mechanism is exposed, and thenotion of the viewpoint is introduced. The interest of Multi-SOM is presented for visualization,exploration or browsing, and moreover for scientific and technical information analysis. A casestudy in patent analysis on transgenic plants illustrates the use of the Multi-SOM. We also showthat the inter-map communication mechanism provides support for watching the plants on whichpatented genetic technology works. It is the first map. The other four related maps provideinformation about the plant parts that are concerned, the target pathology, the transgenictechniques used for making these plants resistant, and finally the firms involved in geneticengineering and patenting. A method of analysis is also proposed in the use of this computerbasedmulti-maps environment. Finally, we discuss some critical remarks about the proposedapproach at its current state. And we conclude about the advantages that it provides for aknowledge-oriented watching analysis on science and technology. In relation with this remark weintroduce in conclusion the notion of knowledge indicators.     

Should competing original equipment manufacturers outsource procurement activities under asymmetric cost information?

This paper considers a supply chain in which two competing heterogeneous original equipment manufacturers (OEMs) outsource production to a common contract manufacturer (CM) that possesses private cost information. Along with production outsourcing, the OEMs can either purchase components by themselves and only assign the production tasks to the CM (Consignment), or delegate both the procurement and production tasks to the CM (Turnkey). The main question we address is whether the OEMs should adopt Consignment or Turnkey. To answer this question, we first analyse the OEMs’ optimal mechanism design problems under three scenarios: both OEMs adopt Consignment strategy; both OEMs adopt Turnkey strategy; one OEM adopts Consignment and the other one adopts Turnkey. Building upon the OEMs’ equilibrium decisions under each scenario, we then characterise the equilibrium outcome for the procurement game. The results show that the CM’s procurement cost distribution and the market size play critical roles in the equilibrium outcome. Additionally, we find that both the competition intensity and cost heterogeneity between the OEMs affect the equilibrium outcome.     

Modelling of outsourcing decisions in global supply chains. An empirical study on supplier management performance with different outsourcing strategies

Outsourcing decisions in global supply chains are critical to a business’ competitiveness. This study investigates and compares the supplier management in Taiwanese enterprises under different outsourcing strategies. Base on an empirical study through questionnaires, we investigate the supplier management and critical success factors of the Taiwanese enterprises. It is shown that different outsourcing strategies should be deployed by different types of industries, and a good working relationship with suppliers and customers are paramount to success. The important issues related to supplier management include supplier capability to deliver on time, long term contract strategy with suppliers, supplier evaluation, and quality of material from suppliers.     

Contextualisation of the complexity in the selection of developing country outsourcees by developed country outsourcers

Outsourcing research has recognised that selecting the right offshore supplier (outsourcee) in low-cost distant developing countries is complex, but central to outsourcing success. More specifically, the combination of outsourcee contextual internal factors (e.g. capabilities) with outsourced-to country contextual external factors (e.g. political, legal, economic, socio-cultural) as two fundamental and interconnected decisions firms make when outsourcing remains an underexplored research gap. Therefore, through a rigorous three-tier qualitative approach we, firstly, develop a contextual Environmental Separation Index (ESI) decision tool to help outsourcing firms in making more informed decisions when selecting outsourcees and outsourcing locations. Secondly, we operationalise the ESI as intuitive and easy to use decision tool, yet with a provision to deliver a truly context proof outsourcee selection decision. Thirdly, we adopt a complexity theory lens to explain that narrowing the contextual outsourcer–outsourcee gap facilitates a mind-set shift in outsourcing relationships from hierarchies to networks and from controlling to empowering developing country outsourcees. We show from a complexity theory perspective how contextual separation gaps between developed country outsourcers and developing country outsourcees can be an effective way to grasp the evolutionary path of outsourcing relationships.     

产业集群综合一体化物流模式

产业集群作为我国经济的重要基石得到了空前发展,但是其物流支持体系的发展相对缓慢,其中一个重要的原因是产业集群的物流服务企业的功能单一,服务质量跟不上产业集群发展的步伐,无法提供综合一体化的物流服务。本文从我国物流企业的历史以及发展现状出发,提出了产业集群发展综合一体化物流的3种组织模式,并运用威廉姆森的交易成本理论,从资产专用性、不确定性和交易频率这3个交易维度角度对产业集群综合物流组织模式进行了分析。本文的研究对产业集群的综合一体化物流服务网络的构建具有一定参考意义。     

Implementing of a three-phase integrated decision support model for parts machining outsourcing

Parts machining outsourcing (PMO) can be regarded as a strategic weapon to increase product quality and productivity as well as cost cutting in many manufacturing enterprises. However, making an outsourcing decision based on accumulated experience is neither effective nor scientific due to subjective judgement and lack of systematic analysis. Therefore, to systematically help the decision-maker survey and select suppliers to outsource and also to timely achieve the most beneficial portfolio among suppliers in a non-cooperative game structure, this paper proposes an integrated decision support model for PMO using ontology, constraint-based reasoning, fuzzy VIKOR and game theory in three phases, namely supplier prequalification, ultimate selection and order coordination. In the decision-making process, decision factors such as quality, cost and production capability are categorised and put into different phases, respectively, by their characteristics for being measured at a more reasonable method. Finally, a case from a leading printing machinery enterprise is analysed to validate the proposed model. This research is expected to improve the quality and effectiveness of decision-making in PMO.     

Statistical analysis of a database of absorption spectra of phytoplankton and pigment concentrations using self-organizing maps

We present a statistical analysis of a large set of absorption spectra of phytoplankton, measured in natural samples collected from ocean water, in conjunction with detailed pigment concentrations. We processed the absorption spectra with a sophisticated neural network method suitable for classifying complex phenomena, the so-called self-organizing maps (SOM) proposed by Kohonen [Kohonen, Self Organizing Maps (Springer-Verlag, 1984)]. The aim was to compress the information embedded in the data set into a reduced number of classes characterizing the data set, which facilitates the analysis. By processing the absorption spectra, we were able to retrieve well-known relationships among pigment concentrations and to display them on maps to facilitate their interpretation. We then showed that the SOM enabled us to extract pertinent information about pigment concentrations normalized to chlorophyll a. We were able to propose new relationships between the fucoxanthin/Tchl-a ratio and the derivative of the absorption spectrum at 510 nm and between the Tchl-b/Tchl-a ratio and the derivative at 640 nm. Finally, we demonstrate the possibility of inverting the absorption spectrum to retrieve the pigment concentrations with better accuracy than a regression analysis using the Tchl-a concentration derived from the absorption at 440 nm. We also discuss the data coding used to build the self-organizing map. This methodology is very general and can be used to analyze a large class of complex data.     

An Empirical Research on the Impacts of organisational decisions’ locus,tasks structure rules,knowledge, and IT function’s value on ERP system success

This research examined the impacts of organisational decisions’ locus, tasks structure, rules and procedures, organisational actors’ information technology (IT) skills/knowledge and IT department’s or function’s value perceptions on enterprise resource planning (ERP) system success. While such antecedent factors matter in the discourse, research on their impacts on ERP success is rare. To increase understanding in the area, we proposed a research model and developed pertinent hypotheses that included the above-mentioned factors. Using a cross-sectional field survey, we collected data from 165 firms in three European countries. Data analysis was performed using the partial least squares (PLS) technique. Statistical support was found for 11 out of the 17 hypotheses formulated. Organisational design constructs, i.e. tasks structure, rules and procedures, in-house IT personnel skills/knowledge have impacts on ERP success, whereas the perceptions of IT function’s value and business employees’ IT skills/knowledge did not. Contributions and practical implications of the research are discussed.     

New classification quality estimators for analysis of documentary information: Application to patent analysis and web mapping

The information analysis process includes a cluster analysis or classification step associated with an expert validation of the results. In this paper, we propose new measures of Recall/Precision for estimating the quality of cluster analysis. These measures derive both from the Galois lattice theory and from the Information Retrieval (IR) domain. As opposed to classical measures of inertia, they present the main advantages to be both independent of the classification method and of the difference between the intrinsic dimension of the data and those of the clusters. We present two experiments on the basis of the MultiSOM model, which is an extension of Kohonen's SOM model, as a cluster analysis method. Our first experiment on patent data shows how our measures can be used to compare viewpoint-oriented classification methods, such as MultiSOM, with global cluster analysis method, such as WebSOM. Our second experiment, which takes part in the EICSTES EEC project, is an original Webometrics experiment that combines content and links classification starting from a large non-homogeneous set of web pages. This experiment highlights the fact that break-even points between our different measures of Recall/Precision can be used to determine an optimal number of clusters for web data classification. The content of the clusters obtained when using different break-even points are compared for determining the quality of the resulting maps. This revised version was published online in June 2006 with corrections to the Cover Date.     

The Nature of Innovation in R&D- and Non-R&D-Intensive Service Firms: Evidence from Firm-Level Latent Class Analysis

Innovation in services has been largely characterised by the predominance of non-technological innovation and low intensity in research and development (R&D) activities. However, most cross-industry analyses still overlook the organisational character of innovation in services. In this article, we use latent class analysis to examine the nature of innovation in 2148 firms from 20 service industries in Spain. On the basis of the analysis of 10 innovation types and 5 kinds of innovation activities, a taxonomy composed of 2 R&D-intensive and 2 non-R&D-intensive clusters is proposed. The findings indicate that organisational innovation counts for three of the four profiles, the new management techniques being the most common organisational innovation in all clusters. Furthermore, micro- and small-sized firms from several subsectors are more likely to be R&D-oriented than medium and large companies. The results underline the coexistence of different innovation patterns within the same industry as well as the predominance of hidden innovators in several industries.     

Two-tier architecture for unconstrained handwritten character recognition

In this paper, we propose an approach that combines the unsupervised and supervised learning techniques for unconstrained handwritten numeral recognition. This approach uses the Kohonen self-organizing neural network for data classification in the first stage and the learning vector quantization (LVQ) model in the second stage to improve classification accuracy. The combined architecture performs better than the Kohonen self-organizing map alone. In the proposed approach, the collection of centroids at different phases of training plays a vital role in the performance of the recognition system. Four experiments have been conducted and experimental results show that the collection of centroids in the middle of the training gives high performance in terms of speed and accuracy. The systems developed also resolve the confusion between handwritten numerals.     

Production outsourcing: A linear programming model for the Theory-Of-Constraints

This paper presents an analysis of the outsourcing problem. Pertinent variables are identified and the relationships between them are defined. We formulate the outsourcing problem as a Linear-Programming (LP) problem and identify an analytical solution. We proceed with an example examining three decision models: standard cost accounting, standard Theory-Of-Constraints (TOC) and our own solution. The model enables managers to determine which products to manufacture and which to outsource. The solution of the LP formulation enables managers to apply the model by computing an operational ratio, without having to solve a linear programming problem. The final model is simpler to apply and requires the computation of fewer variables than other prevalent models.     

The Kohonen and CP-ANN toolbox: A collection of MATLAB modules for Self Organizing Maps and Counterpropagation Artificial Neural Networks

Kohonen maps (or Self Organizing Maps, SOMs) and Counterpropagation Artificial Neural Networks are two of the most popular Neural Networks proposed in literature and are increasing in uses and applications related to several multivariate chemical issues, since they can handle both supervised and unsupervised problems.This work deals with the presentation of the Kohonen and CP-ANN toolbox, that is a collection of MATLAB modules freely available via the Internet (http://www.disat.unimib.it/chm) for the calculation of the quoted models. A graphical user interface (GUI), which allows an easy model calculation and analysis of results, is also provided. The toolbox features are presented by reproducing the classification of a real multivariate dataset. This work is not an attempt to summarize the general applications of Self Organizing Maps, but to inform chemometricians and practitioners who are not skilled programmers of the existence of a user-friendly Matlab toolbox to develop unsupervised and supervised SOM models.     

A developed autonomous preventive maintenance programme using RCA and FMEA

Total productive maintenance (TPM) was developed in Japan in 1971 and has since been phased into many manufacturing firms to promote productivity and competitiveness. Autonomous preventive maintenance (APM) systems are very special. The fundamental pillar of TPM includes a series of important systematical first-line direct labour activities. The technical cost, human resources and management issues are all considered. Failure modes and effects analysis (FMEA) and root-cause analysis (RCA) are the most popular failure analytical methods, widely adopted over different industries. They are often used to examine the potential problems in the design and manufacturing phase, discovering possible failure causes before product design and manufacturing finalisation. This study integrates the RCA and FMEA techniques to establish an APM system that meets a company’s goal of reducing manufacturing costs and promoting employee and equipment productivity. The major contribution of this study is constructing potential equipment failure modes and their risk priority number through RCA and FMEA integration transformed into a selection of items and their APM maintenance frequencies. A strategy for deploying employee technical capability upgrade through effective training is developed. This study uses the S Company – a key manufacturer of semiconductor material – as a case study to verify the model’s applicability and suitability.     

Diverse Institutional Environments and Product Innovation of Emerging Market Firms

• This study unbundled institutional environment into two distinct aspects: institutional distance (the degree of dissimilarity between the institutional environment of a firm’s home country and an economy into which it expands) and institutional diversity (the variety of all the institutional environments to which a firm is exposed), and related them to product innovation performance of emerging market firms.
• Data on 917 Chinese manufacturing firms in multiple industries over 3 years was analyzed.
• The results show a positive relationship between institutional distance and product innovation success. An inverted U-shaped relationship was found between the institutional diversity of a firm’s foreign markets and its product innovation success.

     

Relational governance and opportunism in logistics outsourcing relationships: empirical evidence from China

Logistics outsourcing is prevalent in today's business world as a strategy to obtain competitive advantages. As in other relational exchanges, it is imperative to understand how to alleviate opportunistic behaviour in logistics outsourcing relationships. Using China's burgeoning logistics industry as a backdrop, and drawing on social exchange and transaction cost theories, this study examines how relational norms and trust mitigate opportunistic behaviour and how environmental uncertainty moderates the effects of norms and trust from logistics users’ perspective. Employing data collected from 119 manufacturing and service firms in China, we empirically test the proposed model and find that trust and norms are effective safeguards in reducing the opportunistic behaviour of logistics service providers, particularly in highly uncertain environments.     

Minimizing the average tardiness: the case of outsource machines

Two important factors that affect outsourcing decisions are the production cost and the potential loss of customer goodwill due to late orders. This article deals with the problem of finding outsourcing strategies or solutions that consider trade-offs between outsourcing cost and average tardiness, an important measure of lost customer goodwill. Assumptions include that outsource costs are a function of machine use and that the planning organization owns no production equipment; instead, the production function is completely outsourced. Furthermore, the planning organization has flexibility in terms of the quantity of parallel production resources to outsource and it controls the assignment of jobs to these parallel production resources. The article presents lower bounds for the problem, which are used for comparisons. Several approaches to generate trade-off solutions are proposed and compared under a variety of experimental factors.