Enabling sustainable energy futures: factors influencing green supply chain collaboration

Abstract

To explore the relationship between sustainability strategies and future energy needs, supply chains need to reduce their CO2 emissions through developing their green credentials and improving performance. Knowledge management (KM) is an enabler to support collaboration efforts. The SCM and KM areas have largely focused on improving organisational performance. While the latter has yielded successful outcomes in different sectors, there is still a scarcity of research on identifying influential factors highlighting those aspects which may enable green supply chain collaboration (GrSCC), thus leading to sustainable energy futures and carbon-efficient production. This paper examines the role of KM in facilitating GrSCC. Through the identification of key factors extrapolated from the literature, a model for implementing GrSCC using a futures-based perspective is proposed. This paper inductively demonstrates the relationship between identified GrSCC factors through fuzzy cognitive mapping technique. Findings support a futures-based perspective that enhances understanding and refines forward-looking strategies for GrSCC.     

Assemble‐to‐Order Inventory Management via Stochastic Programming: Chained BOMs and the M‐System

We study an inventory management mechanism that uses two stochastic programs (SPs), the customary one‐period assemble‐to‐order (ATO) model and its relaxation, to conceive control policies for dynamic ATO systems. We introduce a class of ATO systems, those that possess what we call a “chained BOM.” We prove that having a chained BOM is a sufficient condition for both SPs to be convex in the first‐stage decision variables. We show by examples the necessity of the condition. For ATO systems with a chained BOM, our result implies that the optimal integer solutions of the SPs can be found efficiently, and thus expedites the calculation of control parameters. The M system is a representative chained BOM system with two components and three products. We show that in this special case, the SPs can be solved as a one‐stage optimization problem. The allocation policy can also be reduced to simple, intuitive instructions, of which there are four distinct sets, one for each of four different parameter regions. We highlight the need for component reservation in one of these four regions. Our numerical studies demonstrate that achieving asymptotic optimality represents a significant advantage of the SP‐based approach over alternative approaches. Our numerical comparisons also show that outside of the asymptotic regime, the SP‐based approach has a commanding lead over the alternative policies. Our findings indicate that the SP‐based approach is a promising inventory management strategy that warrants further development for more general systems and practical implementations.     

The differential impact of COVID‐19 on the work conditions of women and men academics during the lockdown

That the COVID‐19 pandemic has affected the work conditions of large segments of society is in no doubt. A growing body of journalistic accounts raised the possibility that the lockdown caused by the pandemic has affected women and men in different ways, due mostly to the traditionally gendered division of labour in society. We attempt to test this oft‐cited argument by conducting an original survey with nearly 200 academics. Specifically, we explore the extent to which the effect of the lockdown on childcare, housework and home‐office environment varies across women and men. Our results show that a number of factors are associated with the effect of the lockdown on the work conditions of academics at home, including gender, having children, perceived threat from COVID‐19 and satisfaction with the work environment. We also show that having children disproportionately affects women in terms of the amount of housework during the lockdown.     

Stress–strength reliability of a non-identical-component-strengths system based on upper record values from the family of Kumaraswamy generalized distributions

In an attempt to produce more realistic stress–strength models, this article considers the estimation of stress–strength reliability in a multi-component system with non-identical component strengths based on upper record values from the family of Kumaraswamy generalized distributions. The maximum likelihood estimator of the reliability, its asymptotic distribution and asymptotic confidence intervals are constructed. Bayes estimates under symmetric squared error loss function using conjugate prior distributions are computed and corresponding highest probability density credible intervals are also constructed. In Bayesian estimation, Lindley approximation and the Markov Chain Monte Carlo method are employed due to lack of explicit forms. For the first time using records, the uniformly minimum variance unbiased estimator and the closed form of Bayes estimator using conjugate and non-informative priors are derived for a common and known shape parameter of the stress and strength variates distributions. Comparisons of the performance of the estimators are carried out using Monte Carlo simulations, the mean squared error, bias and coverage probabilities. Finally, a demonstration is presented on how the proposed model may be utilized in materials science and engineering with the analysis of high-strength steel fatigue life data.     

Gender as Symbolic Capital and Violence: The Case of Corporate Elites in Turkey

Based on a Bourdieusian approach, drawing on qualitative analyses of 63 life interviews, our study demonstrates that gender is performed as both symbolic capital and violence by corporate elites within the dominant ideologies of patriarchy and family in Turkey. Our analysis reveals that, in the male‐dominated context of Turkey, female elites appear to favour male alliances as a tactical move in order to acquire and maintain status in their organizations, whereas male elites appear to remain blind to the privileges and constraints of their own gendered experience of symbolic capital and violence. Our study also illustrates that gender order is still preserved, despite beliefs to the contrary that equality in education, skills, experience and job performance may liberate women and men from gender‐based outcomes at work.     

Multi-objective simulation optimization using data envelopment analysis and genetic algorithm: Specific application to determining optimal resource levels in surgical services

Simulation is a powerful tool for modeling complex systems with intricate relationships between various entities and resources. Simulation optimization refers to methods that search the design space (i.e., the set of all feasible system configurations) to find a system configuration (also called a design point) that gives the best performance. Since simulation is often time consuming, sampling as few design points from the design space as possible is desired. However, in the case of multiple objectives, traditional simulation optimization methods are ineffective to uncover the efficient frontier. We propose a framework for multi-objective simulation optimization that combines the power of genetic algorithm (GA), which can effectively search very large design spaces, with data envelopment analysis (DEA) used to evaluate the simulation results and guide the search process. In our framework, we use a design point's relative efficiency score from DEA as its fitness value in the selection operation of GA. We apply our algorithm to determine optimal resource levels in surgical services. Our numerical experiments show that our algorithm effectively furthers the frontier and identifies efficient design points.     

Thermal,viscoelastic and mechanical properties' optimization of polyphenylene sulfide via optimal processing parameters using the Taguchi method

Thermal, viscoelastic and mechanical properties of polyphenylene sulfide (PPS) were optimized as a function of extrusion and injection molding parameters. For this purpose, design of experiments approach utilizing Taguchi's L₂₇ (3⁷) orthogonal arrays was used. Effect of the parameters on desired properties was determined using the analysis of variance. Differential scanning calorimeter (DSC) tests were performed for the analysis of thermal properties such as melting temperature (T_m) and melting enthalpy (ΔH_M). Dynamic mechanical analysis (DMA) tests were performed for the analysis of viscoelastic properties such as damping factor (tan?δ) and glass transition temperature (T_g). Tensile tests were performed for the analysis of mechanical properties such as tensile strength and modulus. With optimized process parameters, verification DSC, DMA and tensile tests were performed for thermal, viscoelastic and mechanical properties, respectively. The Taguchi method showed that ‘barrel temperature’ and its level of ‘340°C’ were seen to be the most effective parameter and its level; respectively. It was suggested that PPS can be reinforced for further improvement after optimized thermal, viscoelastic and mechanical properties.     

Assessing End‐Of‐Supply Risk of Spare Parts Using the Proportional Hazard Model

Operators of long field‐life systems like airplanes are faced with hazards in the supply of spare parts. If the original manufacturers or suppliers of parts end their supply, this may have large impacts on operating costs of firms needing these parts. Existing end‐of‐supply evaluation methods are focused mostly on the downstream supply chain, which is of interest mainly to spare part manufacturers. Firms that purchase spare parts have limited information on parts sales, and indicators of end‐of‐supply risk can also be found in the upstream supply chain. This article proposes a methodology for firms purchasing spare parts to manage end‐of‐supply risk by utilizing proportional hazard models in terms of supply chain conditions of the parts. The considered risk indicators fall into four main categories, of which two are related to supply (price and lead time) and two others are related to demand (cycle time and throughput). The methodology is demonstrated using data on about 2,000 spare parts collected from a maintenance repair organization in the aviation industry. Cross‐validation results and out‐of‐sample risk assessments show good performance of the method to identify spare parts with high end‐of‐supply risk. Further validation is provided by survey results obtained from the maintenance repair organization, which show strong agreement between the firm's and the model's identification of high‐risk spare parts.     

Ideology in Action: Symbolic Localization of Kurdistan Workers' Party in Turkey

Revolutionary ideologies such as Marxism and Islamism often aim to transform dominant local structures, leading their proponents to find themselves torn between global ideologies and local politics. A critical question arises: What does happen when a revolutionary movement's ideology drastically contradicts with the movement's local pragmatic purposes? Analyzing Kurdistan Workers' Party (PKK) in Turkey, this article explores the complex process of ideological transformation under the forces of local competition. Drawing on Mikhail Bakhtin's dialogic approach, I introduce the concept of symbolic localization to understand how revolutionary ideologies evolve through pragmatic concerns. Symbolic localization refers to a discursive process of collective reputation work in which social movement activists blend local cultural repertoires and their “we” identity in order to build recognition, legitimacy, and prestige in the eyes of local population. Three major mechanisms of the symbolic localization process are identified: moral authority building, public symbolism, and memory work. Symbolic localization suggests analyzing movement ideology as a discursive process and illuminates how political activists are shaped by relational local engagements.