In addition to retaining high levels of customer satisfaction, sustainability of businesses is also heavily reliant on the efficiency of their internal and external processes. Continuous performance evaluations using key performance metrics to leverage operations are essential in maintaining a sustainable business while achieving growth objectives for revenue and profitability. Traditionally, companies have considered various financial criteria, quality characteristics, and targeted levels of service as their primary factors for performance evaluation. However, increasing environmental and social awareness and accompanying governmental legislations are now requiring companies to integrate these two aspects into their performance evaluations. With this motivation, this study proposes a Balanced Scorecard (BSC)-based approach combining Decision-Making Trial and Evaluation Laboratory (DEMATEL) and Analytic Network Process (ANP) methodologies for performance evaluation. The grey system theory has been utilized in order to capture the vagueness and the uncertainty in decision making. To demonstrate the functionality of the approach, a case study is conducted on a U.S.-based food franchise. The results of the algorithm and a discussion elaborating on the findings are provided.

The concept of efficiency has always been and will continue to be important for competitive business environments where limited resources exist. Owing to the growing complexity of organizations and economy in general, this trend is expected to continue to remain a high priority for organizations. Continuous performance evaluations that utilize both qualitative and quantitative information play a significant role in sustaining efficient and effective business processes. Therefore, the literature offers a wide range of performance evaluation methodologies to assess the operational efficiency in various industries. Majority of these models, however, focus solely on quantitative criteria, avoiding the interrelations and dependencies between qualitative and quantitative measurements. Furthermore, these methodologies tend to utilize discrete and contemporary information eliminating historical performance data. With these motivations, this paper proposes an integrated approach combining fuzzy decision-making trial and evaluation laboratory (DEMATEL), analytic network process (ANP), and artificial neural network (ANN) methodologies for performance evaluation. In the proposed model, DEMATEL and ANP methodologies are utilized in a group decision-making concept to obtain priorities of the evaluation criteria. Following this, an ANN model is designed and trained with historical performance data collected from the organization and the results of the fuzzy DEMATEL-ANP model. The outcomes include the relational data among the criteria and alternatives used in the model in addition to their relative rankings. A food industry case study is presented to demonstrate the steps of the proposed model.

Growing environmental awareness coupled with stricter governmental regulations has fueled the need for integrating sustainability into supply chain and logistics activities. Accordingly, recent studies in the literature have emphasized the significance of environmentally concerned logistics operations (ECLO). Research in the broad area of ECLO encompasses a wide range of topics including sustainable supply chain, green supply chain, closed-loop supply chain, low-carbon logistics, and waste management. In this paper, a comprehensive content analysis and area review is presented. Over 800 papers published between 1994 and 2017 in peer-reviewed journals, proceedings, and book chapters are utilized. These papers are analyzed in consecutive stages after being reviewed under a structural dimension process that addresses the fields of environmentally concerned logistics operations. Following the state-of-the-art review, a detailed analysis of ECLO research with a special emphasis on fuzzy applications is provided. The findings clearly indicate that the fuzzy multi-criteria decision making technique is a frequently used hybrid method, whereas fuzzy sets theory and other fuzzy hybrid techniques identify a gap in the related literature. This paper provides further critical analysis and other research suggestions in order to clarify these gaps and offer additional research perspectives. This information may provide extensive data that will enable future researchers to fill these gaps within this field.

Driven by a variety of factors, including the advent of digitalization, increasing population and urbanization, and rapid technological advancements, electronic waste (e-waste) has emerged as the fastest growing waste stream globally. Effective management of e-waste is inherently aligned with environmental, social, and governance (ESG) frameworks and is typically examined within this context. Accurate quantification of the current and future accumulation of e-waste is a key step towards ensuring its proper management. Numerous methodologies have been developed to predict e-waste generation, with the grey modeling approach receiving considerable attention due to its ability to yield meaningful results using relatively small datasets. This study aims to introduce a novel forecasting technique for predicting e-waste, particularly when limited historical data are available. The proposed approach, the non-linear grey Bernoulli model with fractional order accumulation NBGMFO(1,1) enhanced by Particle Swarm Optimization, demonstrates superior accuracy compared to alternative forecasting models. Additionally, the Fourier residual modification method is applied to enhance the precision of the forecast. To provide a practical illustration, a case study utilizing waste mobile phone data from Turkey is presented.