SIMULATION OF THE ANAEROBIC DIGESTION OF FOOD WASTE

This study has established that the use of a computer model, the Anaerobic Digestion Model 1, is suitable for investigation of the stability and energy balance of the anaerobic digestion of food waste. In simulations, digestion of undiluted food waste was less stable than that of sewage sludge or mixtures of the two, but gave much higher average methane yields per volume of digester. In the best case scenario simulations, food waste resulted in the production of 5.3 Nm3 of methane per day per m3 of digester volume, much higher than that of sewage sludge alone at 1.1 Nm3 of methane per day per m3. There was no substantial difference in the yield per volatile solids added. Food waste, however, did not sustain a stable digestion if its cation content was below a certain level. Mixing food waste and sewage sludge allowed digestion with a lower cation content. The changes in composition of food waste feedstock caused great variation in biogas output and even more so volatile fatty acid concentration, which lowered the digestion stability. Modelling anaerobic digestion allowed simulation of failure scenarios and gave insights into the importance of the cation/anion balance and the magnitude of variability in feedstocks.

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.