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This paper aims to develop a bi-objective mixed integer non-linear programing model to optimize the supply chain networks consisting of raw material providers, final product manufacturers and distribution centers. Raw material substitution caused by varying raw material supply amounts, prices and carbon emissions and final product substitution due to different product prices and carbon emissions are considered.

The proposed model aims to achieve total profit maximization and total carbon emission minimization. The objective function of carbon emissions is converted into a maximization problem by changing minimum to maximum. The composite objective function is the weighted sum of the bias value of each objective function. The model is then solved using software Lingo12.

Numerical analysis results show that an increase in the number of alternate raw materials for original raw material helps improve supply chain network performance, and variation in that number causes detectable but not significant changes in downstream final product substitution results.

The major differences between this work and existing research are as follows: first, although previous research considered carbon emissions in supply chain network optimization, it has not considered the substitution effects of products or raw materials. This paper considers the substitution of both raw material and productions. Second, the item substitution considered by previous research is derived from inventory shortage or price difference of original items. However, the substitution considered in the present paper is a response to differences in purchase price, production cost and carbon emissions for items.

Drawing on the resource dependence theory (RDT), this paper investigates ownership composition, export intensity, and industry class as moderating factors to investigate the role of imported raw materials in performance of inward foreign direct investment (IFDI) in Ethiopia.

The hypotheses were tested using secondary data obtained from the 2016 Central Statistical Agency (CSA) on Large- and Medium-Scale Manufacturing and Electricity Industries Survey. The data included basic quantitative information on the country's manufacturing industry. The data items for the 2016 manufacturing and electricity industries surveyed are the numbers of proprietors or establishments involved in various sectors. The report did not record small firms that employed fewer than 10 people and did not use power-driven machinery. Two-Stage least squares (2SLS) regression analysis was performed to test the proposed hypotheses.

The results of this study indicate that three moderators (ownership composition, export intensity, and industry classification) interact with the hypothetical relationships between imported raw materials and performance. These findings enrich the knowledge of IFDI firms' operations in Ethiopia and in other least-developed countries (LDCs). The findings could provide information for IFDI firms that are looking to invest in LDCs.

Like all social science research, this study has some limitations. First, the research was conducted with the data found in the Report on Large- and Medium-Scale Manufacturing and Electricity Industries Survey In 2016. This was the first year of the second five-year Growth and Transformation Plan (GTP II), a national development plan for the 2016–2020 period. Continual research on IFDI in Ethiopia in the following years will be needed to get a full picture of the effects of the determinants on IFDIs.

To IFDI investors, the result of this thesis demonstrates several alternatives to overcoming hurdles in manufacturing operation. The results find that J.V. firms make better use of imported raw materials than W.O. subsidiaries in order to achieve better performance. Concerning the choice between focusing on export or domestic markets, the study suggests that domestic market—oriented companies require less imported raw materials to achieve better performance. Concerning the comparative advantage on different industries, this study found the performance of firms in Industry 12 depended on imported raw materials. These findings highlight the challenges and opportunities for potential foreign investors. Ownership composition, market factors, and industry factors should be well considered in making investment decisions.

This is one of few studies on IFDI in Ethiopia, the most populous LDC. Ownership composition, export intensity, and industry class are used as moderating variables to investigate the difference between imported raw materials and the level of expatriate deployment to IFDI performance. For IFDI investors, the results of this study demonstrate several alternatives to overcoming hurdles in manufacturing operation.

In the process industries, it is essential to have a well-articulated manufacturing strategy within companies. However, to facilitate manufacturing strategy development, it is important to start with a good characterisation of the material transformation system and company production capabilities. The paper aims to discuss these issues.

A grounded theory approach, with inspiration from configuration modelling, attempted to characterize the material transformation system as a set of variables. The variable development was based on a literature review and the knowledge base of five industry experts. Two exploratory mini-case studies were carried out, primarily to illustrate the use of the model, but additionally to test its industrial usability.

A set of 31 variables was developed, and related measures and scales were tentatively defined. Two mini-cases supported the usability of the model. The model, focussing on company generic process capabilities, is a conceptual taxonomy and the study’s theoretical contribution.

The lucidity of the definitions and scales for the variables are open to further refinement, and the limited discussions of variable relationships in this study are addressed in an agenda for further research.

The model can be deployed as a facilitative instrument in the analysis of company material transformation systems and may serve as a platform in further discussions on companies’ strategy development.

The model is a new instrument for analysing company generic process capabilities and an effort to build new theory rather than to test an existing one.

In manufacturing industries, the levels of inventories at all stages (i.e. raw material, work-in-process and finished goods inventories) indicate the firm's competitive positioning, strategies, internal processes and relationships with suppliers and downstream customers. The authors identify patterns of manufacturing industries based on levels of raw material and finished goods inventories to classify inbound and outbound supply chain strategies.

The authors review literature on supply chain inventory strategy and perform cluster analysis to analyze patterns of manufacturing industries based on manufacturing industry data collected from US Census of Bureau. Following Porter's Five Forces Model, the authors perform in-depth case studies of four representative industries to analyze factors driving supply chain strategies, including industry intensity of rivalry, threat of new entrants, threat of substitutes, bargaining power of suppliers, and bargaining power of buyers.

This study identifies three streams of research on supply chain strategy: Fisher's model and its variations, lean and agile paradigms, and push/pull systems. It finds that whether an industry shows low or high raw materials or finished goods inventories depending on its products, processes, and the dynamics of all forces described in the Five Forces Model.

This study is not able to include supplier selection, production strategies, warehousing and distribution, and even product design into the analysis of supply chain strategy due to data limitation. This study classifies industries based on average inventory levels of raw materials and finished goods, while inventory levels and supply chain strategies for specific firms may vary significantly within each industry.

This study contributes to the supply chain management literature by providing a parsimonious framework of mapping inbound and outbound supply chain inventory strategies, and the results based on the analyses of all US manufacturing industries provide a baseline picture for supply chain management professionals with manufacturing firms.

The classical definition of PDM focuses on the broad spectrum of distribution activity, from the inbound raw materials; to the finished product flow; and to the end user. The definition is often stated as “all of the activities involved in the flow of goods from the manufacturer to the consumer which include inventory control, transportation, warehousing, order processing, materials management, and purchasing”. But despite the broad view described in the definition, little attention is given to the raw materials flow and to the entire area of Materials Management. Physical Distribution managers tend to disregard the inbound flow and regard it as the responsibility of some other management group in the company, specifically purchasing and/or production. The need for a well co‐ordinated and efficient distribution system demands that the PD manager pay more attention to the inbound material flow. The outcome of the decisions that a PD manager makes depends to a great degree on how well materials management and PDM are co‐ordinated in a firm:

A robust description of the material transformation system is fundamental for understanding its capabilities and thus for communicating, prioritising and changing the system. Deploying a previously developed configuration model the purpose of this paper is to test the industrial usability of the model as an instrument to gain a better understanding of the material transformation system through externalising the generic production capabilities of the system.

In a multiple case study approach and using a prior conceptual configuration model of the material transformation system in the process industries as a research instrument, company-generic production capabilities were investigated in three companies representing the mineral, food and steel industries.

The empirical results supported the utility of the model as an instrument in providing a coherent set of elements that define operations and thus serve as a platform to model company-generic production capabilities and serve as input to strategizing though implicating needed change to the material transformation system. The theoretical contribution was mainly the empirical validation of the previously developed conceptual model as a tool in knowledge formation of the capabilities of the system and to outline the concept of “production capabilities configuration”.

Three sectors of the process industries were studied but it is recommended that the results should be replicated in complementary case studies or a survey of larger samples from the process industries. Those studies should not only be limited to increase the empirical knowledge base, but possibly to identify additional new variables, further refine the set of variables in the present model and investigate their relationships.

It is argued that the model can already be used as a tool to support both horizontal and vertical communication on production capabilities, thus facilitating, e.g. manufacturing strategy development.

The validated conceptual model supported by the empirical evidence is new knowledge to be used in the analysis of company-generic production capabilities in the process industries.

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.

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.

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.

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.

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.

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.

The textile industry holds immense significance in the economy of any nation, particularly in the production of synthetic yarn and fabrics. Consequently, the pursuit of acquiring high-quality products at a reduced cost has become a significant concern for countries. The primary objective of this research is to leverage data mining and data intelligence techniques to enhance and refine the production process of texturized yarn by developing an intelligent operating guide that enables the adjustment of production process parameters in the texturized yarn manufacturing process, based on the specifications of raw materials.

This research undertook a systematic literature review to explore the various factors that influence yarn quality. Data mining techniques, including deep learning, K-nearest neighbor (KNN), decision tree, Naïve Bayes, support vector machine and VOTE, were employed to identify the most crucial factors. Subsequently, an executive and dynamic guide was developed utilizing data intelligence tools such as Power BI (Business Intelligence). The proposed model was then applied to the production process of a textile company in Iran 2020 to 2021.

The results of this research highlight that the production process parameters exert a more significant influence on texturized yarn quality than the characteristics of raw materials. The executive production guide was designed by selecting the optimal combination of production process parameters, namely draw ratio, D/Y and primary temperature, with the incorporation of limiting indexes derived from the raw material characteristics to predict tenacity and elongation.

This paper contributes by introducing a novel method for creating a dynamic guide. An intelligent and dynamic guide for tenacity and elongation in texturized yarn production was proposed, boasting an approximate accuracy rate of 80%. This developed guide is dynamic and seamlessly integrated with the production database. It undergoes regular updates every three months, incorporating the selected features of the process and raw materials, their respective thresholds, and the predicted levels of elongation and tenacity.

The purpose of this paper is to analyze raw materials, labor, capital, demand, related industries, strategies and policies influencing international competitiveness of Chinese textile and clothing industry.

The analysis is conducted using “Diamond Model”, in which raw materials, labor, capital, demand, related industries, strategies and policies are included as explanatory variables, and the impacts of international competitiveness on market share (MS), trade competitiveness(TC) and revealed comparative advantage(RCA) are examined based on the estimated coefficients of these variables.

These factors have different effects on TC, MS and RCA. While their effects on TC and MS are similar in sign even though their degree of significance differs, their effects on RCA are opposite to TC and MS except for capital. Raw materials and capital have negative effects on TC and MS, while the other factors have positive ones. Raw materials have positive effects on RCA, but all other factors have negative ones.

The results from this study imply that it is necessary to increase investment in fixed assets of Chinese textile and clothing industry, speed up the pace of upgrading equipment, improve the level of industrialization, while strengthening the supply of textile raw materials, and lowering raw material prices, thereby reducing the cost of textile and clothing enterprises.

To the best of the authors’ knowledge, this is the first empirical research made using econometric model about the impact of the main factors of trade competitiveness in Chinese textile and clothing industry based on the “Diamond Model”.