Exploring geographical dispersion in Thailand-based food supply chain (FSC)

Purpose: The purpose of this paper is to explore the key influential factors and their implications on food supply chain (FSC) location decisions from a Thailand-based manufacturer's view. Design/methodology/approach: In total, 21 case studies were conducted with eight Thailand-based food manufacturers. In each case, key influential factors were observed along with their implications on upstream and downstream FSC location decisions. Data were collected through semi-structured interviews and documentations. Data reduction and data display in tables were used to help data analysis of the case studies. Findings: This exploratory research found that, in the food industry, FSC geographical dispersion pattern could be determined by four factors: perishability, value density, economic-political forces, and technological forces. Technological forces were found as an enabler for FSC geographical dispersion whereas the other three factors could be both barriers and enablers. The implications of these four influential factors drive FSC towards four key patterns of FSC geographical dispersion: local supply chain (SC), supply-proximity SC, market-proximity SC, and international SC. Additionally, the strategy of the firm was found to also be an influential factor in determining FSC geographical dispersion. Research limitations/implications: Despite conducting 21 cases, the findings in this research are based on a relatively small sample, given the large size of the industry. More case evidence from a broader range of food product market and supply items, particularly ones that have significantly different patterns of FSC geographical dispersions would have been insightful. The consideration of additional influential factors such as labour movement between developing countries, currency fluctuations and labour costs, would also enrich the framework as well as improve the quality and validity of the research findings. The different strategies employed by the case companies and their implications on FSC location decisions should also be further investigated along with cases outside Thailand, to provide a more comprehensive view of FSC geographical location decisions. Practical implications: This paper provides insights how FSC is geographically located in both supply-side and demand-side from a manufacturing firm's view. The findings can also provide SC managers and researchers a better understanding of their FSCs. Originality/value: This research bridges the existing gap in the literature, explaining the geographical dispersion of SC particularly in the food industry where the characteristics are very specific, by exploring the internationalization ability of Thailand-based FSC and generalizing the key influential factors - perishability (lead time), value density, economic-political forces, market opportunities, and technological advancements. Four key patterns of FSC internationalization emerged from the case studies. © Emerald Group Publishing Limited.

Growth setbacks in new firms

New firms in emerging industries are subject to complex dynamic processes which defy the attempts at prediction embodied in business conjectures. Discontinuous growth is common, but the issue of interruptions in the early growth of new firms has not been adequately addressed in the mainstream literature. We examine the prevalence of interruptions to growth in a cohort study of the growth trajectories of firms founded in 1990, then look to cases studies of individual firms to investigate underlying causes. We find that substantial growth is rare and continuous growth unusual, and that growth interruptions are the result of both internal and external dynamics. The managers of growing firms face shortages of vital resources and significant problems of resource synchronisation and coordination, many of which can lead to what are, in effect, changes of phase state. Meanwhile, the volatile environment of an emerging industry presents particular problems to young firms which have not yet built up reserves to sustain them through short-term crises. However, problem solving by those that survive provides an important source of learning which can underpin their future development. © 2004 Elsevier Ltd. All rights reserved.

On sparsity and overcompleteness in image models

Computational models of visual cortex, and in particular those based on sparse coding, have enjoyed much recent attention. Despite this currency, the question of how sparse or how over-complete a sparse representation should be, has gone without principled answer. Here, we use Bayesian model-selection methods to address these questions for a sparse-coding model based on a Student-t prior. Having validated our methods on toy data, we find that natural images are indeed best modelled by extremely sparse distributions; although for the Student-t prior, the associated optimal basis size is only modestly over-complete.