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The purpose of this paper is to develop a better understanding of how the supply chain structure (i.e. degree of vertical integration) of a focal organization shapes the breadth of its portfolio of technologies.

In total, three case studies were conducted involving key players in the Australian mass grocery retail sector. Each had a distinct supply chain structure (i.e. totally vertically disintegrated, partially vertically integrated and totally vertically integrated). Each supply chain case study included manufacturers or suppliers, transport and logistics service providers, wholesalers/distributors, as well as the mass grocery retail organizations. Interviews with key personnel from these organizations and other relevant information informed the findings and conclusions.

The information technologies employed by the three focal case organizations and their extended trading partners varied in terms of level, type, complexity and sophistication. The authors highlight how the choice of supply chain technologies is affected by supply chain structure (extent of vertical integration). The authors found that disintegrated supply chain structures have a broader portfolio of technologies, whereas integrated supply chains have a narrow portfolio.

This study is confined to three organizations in the Australian mass grocery retail sector, so any extensions should be made with caution.

The framework presented in this study can guide organizations in assessing the appropriateness of their supply chain portfolios of technologies with the structure of their supply chains. For standard setting bodies, the findings of this study suggest that technologies need to be tailored to the requirements of the supply chains, with the level of vertical integration being one easy way to segment the supply chain types.

The study adapts and extends the “arcs of integration” framework. The propositions enhance the understanding of how supply chain structure, in the form of degree of vertical integration influences an organization’s supply chain portfolio of technologies.

Best-value supply chains characterized by agility, adaptability, and alignment, have become a crucial strategic means for firms to create and sustain competitive advantage in today’s turbulent environment. The purpose of this paper is to investigate linkage between best-value supply chains and firms’ competitive performance.

In Study 1, survey data from 76 firms is used to test the impact of the three qualities of best-value supply chains on firms’ competitive performance. In Study 2, to test if a firm’s competitive advantage can be sustained through building best-value supply chains, a long-run performance analysis is conducted, which is based on a stock portfolio of firms identified from the American Marketing Association’s annual list of “Supply Chain Top 25.”

The results of Study 1 indicate that the three qualities of best-value supply chains are positively related to firms’ competitive performance. The results of Study 2 show that firms having best-value supply chains generate significant and positive abnormal returns for shareholders over time.

This is a multiple-method research, providing two-level empirical evidence to the investigation of theoretical linkage between best-value supply chains and firms’ competitive performance.

Tackling the challenges of water scarcity requires comprehensive management according to financial, environmental and social issues. This paper aims to develop a planning approach for systematic decision-making and pay attention to uncertainties in water demand management and supply investment.

This study presents a multiobjective optimization model to manage water resources based on the balance of supply and demand. The objectives of the model include economic, social and environmental (sustainable development) factors. The model achieves an optimal urban water portfolio by using a scenario tree.

The mathematical goal programming (GP) in a multiobjective optimization model is applied and solved by the branch and bound method. The results indicate the selected supply augmentation and demand management options in each stage for 20 years according to the dry, normal and wetness year scenarios.

This model is based on a real-world case and has been implemented in the city of Karaj. It can be applied for water management of other cities concerning sustainable development as well.

This paper innovates by considering the sustainable development criteria that are defined using three objective functions, including economic, social and environmental factors. The balance of supply and demand concerning uncertainty has not been investigated in any urban water portfolios. The standardized precipitation evapotranspiration index (SPEI) is incorporated to generate different scenarios. To the best of the authors’ knowledge, this approach is used for the first time.

This paper aims to minimize the mean-risk cost of sustainable and resilient supplier selection, order allocation and production scheduling (SS,OA&PS) problem under uncertainty of disruptions. The authors use conditional value at risk (CVaR) as a risk measure in optimizing the combined objective function of the total expected value and CVaR cost. A sustainable supply chain can create significant competitive advantages for companies through social justice, human rights and environmental progress. To control disruptions, the authors applied (proactive and reactive) resilient strategies. In this study, the authors combine resilience and social responsibility issues that lead to synergy in supply chain activities.

The present paper proposes a risk-averse two-stage mixed-integer stochastic programming model for sustainable and resilient SS,OA&PS problem under supply disruptions. In this decision-making process, determining the primary supplier portfolio according to the minimum sustainable-resilient score establishes the first-stage decisions. The recourse or second-stage decisions are: determining the amount of order allocation and scheduling of parts by each supplier, determining the reactive risk management strategies, determining the amount of order allocation and scheduling by each of reaction strategies and determining the number of products and scheduling of products on the planning time horizon. Uncertain parameters of this study are the start time of disruption, remaining capacity rate of suppliers and lead times associated with each reactive strategy.

In this paper, several numerical examples along with different sensitivity analyses (on risk parameters, minimum sustainable-resilience score of suppliers and shortage costs) were presented to evaluate the applicability of the proposed model. The results showed that the two-stage risk-averse stochastic mixed-integer programming model for designing the SS,OA&PS problem by considering economic and social aspects and resilience strategies is an effective and flexible tool and leads to optimal decisions with the least cost. In addition, the managerial insights obtained from this study are extracted and stated in Section 4.6.

This work proposes a risk-averse stochastic programming approach for a new multi-product sustainable and resilient SS,OA&PS problem. The planning horizon includes three periods before the disruption, during the disruption period and the recovery period. Other contributions of this work are: selecting the main supply portfolio based on the minimum score of sustainable-resilient criteria of suppliers, allocating and scheduling suppliers orders before and after disruptions, considering the balance constraint in receiving parts and using proactive and reactive risk management strategies simultaneously. Also, the scheduling of reactive strategies in different investment modes is applied to this problem.

The purpose of this paper is to establish an effective risk assessment approach based on the conditional value-at-risk (CVaR) in the agricultural supply chain.

This study analyzes and assesses the risks of breeding, processing, transportation and warehousing in the agricultural supply chain. The ordered weighted averaging operator is used to sort risk control factors according to their importance and determine the main risk indicators of an enterprise. The CVaR model is utilized to establish the risk loss function, and an improved genetic algorithm is employed to identify the optimal risk control portfolios in the case of the smallest risk loss.

Based on the approach, the optimal combination of risk control to minimize risk losses is determined. Results show that the proportion of capital investment in risk control differs at three confidence levels, and a large amount of money needs to be invested in the production process at the source. Thus, any attempt to control the risks inherent in the agricultural supply chain must begin with the production process at the source.

Supply chain risk management has become increasingly important and significant to the operation and production of enterprises in recent years. The proposed method to assess the risk in the agricultural supply chain can benefit managers in making smart decisions to control total risk.

Uncertainties caused by political risks can drastically affect global supply chains. However, the supply chain management literature has thus far developed rather limited knowledge on firms' perception of and reactions to increased political risks. This study has two main purposes: to explore the relationship between extant risk exposure and perceived firm-specific political risk and to understand the impact of firm-specific political risk on firms' vertical integration and diversification strategies.

The authors developed a unique dataset for testing our hypotheses. Specifically, the authors sampled manufacturers (SIC20-39) listed in the United States from 2002 to 2019. The authors collected financial and diversification data from Compustat, vertical integration data from the Frésard-Hoberg-Phillips Vertical Relatedness Data Library and political risk data from the Economic Policy Uncertainty database. This data collection process yielded 1,287 firms (8,329 observations) with available data for analysis.

A two-way fixed-effect regression analysis of panel data revealed that firms tend to be more sensitive to political risk when faced with income stream uncertainty or strategic risk. By contrast, exposure to stock returns uncertainty does not significantly influence firms' sensitivity toward political risk. Moreover, firm-specific political risk is positively associated with vertical integration and product diversification. However, firm-specific political risk does not result in higher levels of geographical diversification.

This study joins the literature that systematically explores the antecedents and implications of firm-specific political risk, thus broadening the scope of supply chain risk management.

The purpose of this paper is to investigate the performance of the arbitrage pricing theory (APT) in the Istanbul Stock Exchange (ISE) on a monthly basis, for the period January 2001 to September 2005.

This study examines six pre‐specified macroeconomic variables which are: the term structure of interest rate, unanticipated inflation, risk premium, exchange rate and money supply. All these are the same as those used by Chen, Roll and Roll for the US market. In this study, the authors develop one more variable namely unemployment rate, which has a relation with the stock return.

Using the OLS technique, the authors observed that there are some differences among the market portfolios. Before starting to comment on the result of OLS, the serial correlation problem was discussed by using Durbin‐Watson statistics. In this study, the critical values were ranged from between 1.33 and 1.81 (T=57, K=6). Our test results confirmed that in ten out of the 13 there were no serial correlations. Our results show that there are big differences among market portfolios against macroeconomic variables through the variation of R². In the remaining portfolios; there was no evidence to suggest.

In this paper, the authors face a problem that was no corporate bond in Turkey's market.

This analysis appears to be the first empirical test of APT using the CAPM formula for finding the risk premium point for ISE.

Large-scale projects are the typical delivery model in the engineering and construction industry, with their very own characteristics. Even though well established, only 1 in 1,000 large-scale projects is successful (Flyvbjerg, 2011). A lack of effective supply chain risk management (SCRM) has repeatedly been identified as one of the main causes. While the SCRM body of knowledge seems increasingly well established, a lack of effective methods meeting the specific requirements of large-scale projects can be observed.

This paper presents a structured and prioritized view on the supply chain risk portfolio in this sector: first, the authors identified and categorized the key supply chain risks in the recent literature. Next, the authors surveyed large-scale project managers across multiple industries, mainly coming from the domains of supply chain management and project management. Finally, the authors provide a contextualized risk taxonomy for engineering, procurement and construction (EPC) projects.

The identified risk portfolio deviates from generic projects significantly and shows a very high inherent risk exposure of large-scale projects. In particular, behavioral risks are identified as crucial. Additionally, a bias to considerably underestimate risks at project beginning is found.

The contextualized SCRM taxonomy offers a systematic and structured view on the key supply chain risks in EPC large-scale projects. The identified risks are considerably different in their characteristics compared to generic projects or classical SCRM approaches. The authors thus provide a new perspective on SCRM in this specific setting and complement traditional risk and project risk management techniques.

Optimization of energy planning for growth and sustainable development has become very important in the context of climate change mitigation imperatives in developing countries. Existing models do not capture developing country realities adequately. The purpose of this paper is to conceptualizes a framework for energy strategy optimization of the Indian energy sector, which can be applied in all emerging economies.

Hierarchical multi‐objective policy optimization methodology adopts a policy‐centric approach and groups the energy strategies into multi‐level portfolios based on convergence of objectives appropriate to each level. This arrangement facilitates application of the optimality principle of dynamic programming. Synchronised optimization of strategies with respect to the common objectives at each level results in optimal policy portfolios.

The reductionist policy‐centric approach to complex energy economy modelling, facilitated by the dynamic programming methodology, is most suitable for policy optimization in the context of a developing country. Barriers to project implementation and cost risks are critical features of developing countries which are captured in the framework in the form of a comprehensive risk barrier index. Genetic algorithms are suitable for optimization of the first level objectives, while the efficiency approach, using restricted weight stochastic data envelopment analysis, is appropriate for higher levels of the objective hierarchy.

The methodology has been designed for application to the energy sector planning for India's 12th Five Year Plan for which the objectives of faster growth, better inclusion, energy security and sustainability have been identified. The conceptual framework combines, within the policy domain, the bottom‐up and top‐down processes to form a hybrid modelling approach yielding optimal outcomes, transparent and convincing to the policy makers. The research findings have substantial implications for transition management to a sustainable energy framework.

The methodology is general in nature and can be employed in all sectors of the economy. It is especially suited to policy design in developing countries with the ground realities factored into the model as project barriers. It offers modularity and flexibility in implementation and can accommodate all the key strategies from diverse sectors along with multiple objectives in the policy optimization process. It enables adoption of an evidence‐based and transparent approach to policy making. The research findings have substantial value for transition management to a sustainable energy framework in developing countries.

The purpose of this paper is to develop a transport portfolio framework (TPF) and explore its use as a decision support tool for shippers wanting to improve their transport system in terms of reducing their carbon footprint.

The TPF has been designed on the basis relevant theoretical frameworks in logistics and thereafter tested and further developed by the use of empirical data from a case study. Quantitative methods are used to find patterns in the shipment statistics for import flows obtained from a food retailer and carriers.

The TPF highlights different avenues for decreasing the carbon footprint, by identifying the product flow characteristics that might affect modal split and load factor, and it is believed that these can help shippers’ intent on analysing the largest potential for improvement. This potential is estimated based on how the key variables, modal split and load factor, can be improved.

Shippers can use the TPF as a decision support tool in their efforts to reduce their carbon footprint by: structuring complexity, managing data and finding effective solutions.

Reducing emissions is an increasingly important priority for shippers and the TPF helps them to direct their efforts towards approaches that have a substantial impact.

The TPF provides an opportunity to match different approaches for improving the environmental performance with the potential for reducing carbon footprint in shippers’ transportation networks, by taking into account the complexity of logistics network.