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This paper applies fuzzy grey cognitive maps (FGCM) to support multicriteria group decision making (GDM) on supply chain performance (SCP) considering the role of organizational culture as a moderating factor.

This paper follows the quantitative axiomatic prescriptive model-based research. It introduces a MGDM model that relies on the SCOR® model performance attributes and Hofstede’s cultural dimensions. The proposal is underpinned by the soft computing technique of FGCM, aimed at addressing the inherent subjectivity associated with evaluating the culture-performance relationship within supply chains.

The FGCM-based model proposes a management matrix tool for supporting SPC management. It results in a graphical representation that deconstructs SCP and organizational culture into key elements and provides directives for action plans that align improvement efforts. An illustrative application is presented to guide and promote the model’s application in different configurations of supply chains.

This model offers valuable insights into addressing the impact of organizational culture on decision-making related to SCP. Additionally, it facilitates scenario simulation. The management matrix visually illustrates how each performance attribute is influenced by each cultural dimension on a quantitative scale. It also ranks these attributes based on the overall level of influence they receive from culture.

The study provides a unique outlook on the use of FGCMs to support the SCP decisional process by detailing and accounting for the influence of organizational culture. This is done through the development of a novel matrix that allows for visual management and benchmarking.

Small organizations within profit maximization supply chains form industrial clusters to share resources. They mainly deliver products and services in a reactive manner, where the cluster is only facilitating. However, the cluster management can lead business development and assign work packages intelligently to appropriate cluster members by respecting collaboration and innovation. This upgrade of the cluster management requires a systematic approach. Therefore, the intelligent Cluster Assignment Tool concept is developed and an illustrative example is given. The paper aims to discuss these issues.

Interviews and workshops were used to isolate the hierarchy of the assignment model, supported by a literature research. Fuzzy analytic hierarchy process method was applied to determine weights, consolidating data delivered by members of a Turkish aviation and defence cluster. The approach was discussed at the IEEE 2016 ICE Conference in Norway with experts to assess regional restrictions.

Cluster members are actively looking for possibilities to enhance innovation potential that they are ready to participate in systematic approaches. Cluster organizations can differ by leading their members actively, when respecting all isolated dimensions of assignment. This can lead towards a cooperation base enhancing the potential for innovation and new product development (NPD).

The illustrative example indicates a good fit to clusters without a dominant anchor firm. The generic framework was deliberated on cross-sectional perspective as satisfactory to be applied to different sectors in developing economies.

This paper helps clusters and small organizations to upgrade for innovation. Furthermore, it delivers a strategic tool supporting organizational transformation, preventing the price trap as well.

This approach is focussing on multi-criteria assignment across cluster members, upgrading it to a united organization. It delivers a strategic framework for cluster management, building a collaboration environment leading to innovation and NPD.

The purpose of this paper is to propose a supply chain risk management (SCRM) maturity model combined with a fuzzy TOPSIS classification method to evaluate and sort an organization into a pre-defined maturity level.

An axiomatic and prescriptive research method guided this study. Therefore, it proposes a prescriptive approach of maturity classification based on a theoretical SCRM maturity model combined with a multi-criteria decision technique.

The results of a pilot application indicated a consistent classification and the value of the model for diagnosing flaws and pointing directions for improving operational and disruption risk management. Its comprehensiveness allows applying it to supply chains of several industry sectors.

The proposed model does not include all possible risks and could be revised in further developments. Also, adjustment of the maturity profiles of the multi-criteria decision-making (MCDM) model requires a learning process from practical applications.

The adoption of the risk management maturity grid by practitioners may bring the benefit of a more objective and comprehensive evaluation of risk management processes in the supply chain context.

An immediate social implication derives from the improvement actions that may result from the diagnosis of risk management vulnerabilities identified in the pilot application. In general, the proposed model has the potential to reduce risks, improve results and contribute to economic sustainability.

The maturity grid and decision model integrate overall aspects of risk management, bringing together managerial concepts to deal with a variety of supply chain operational risks. The combined multi-criteria classification procedure to sort the maturity level of an organization is also a novelty.

The purpose of this paper is to propose an approach to evaluate product performance of returned products, using four key performance attributes as the basis for improving sustainability through product recovery.

A fuzzy logic approach is developed to account a trade-off scenario for a manufactured product with recovery options. This approach is demonstrated using a numerical example and is validated using a case study in the automotive parts and components industry.

Product utilisation value (PUV) is found to be a useful index that manufacturers can use to assess product recovery options, as it brings together a number of conflicting parameters into a rationalised value for decision making. In addition, PUV provides a rationalised approach for comparing and selecting the most appropriate recovery configuration option.

The authors only utilise four key performance measures to derive PUV. Further research is needed to modify and incorporate other measures that are important to decision makers to improve sustainability in manufacturing supply chains.

The proposed approach may motivate decision makers to consider sustainable recovery options by comparing PUVs of products for primary and secondary markets. The case study demonstrated the conflict and complexity organisations face in a global supply chain of a competitive industry.

The authors propose an approach to optimise trade-off considerations of selected performance attributes through PUV. This PUV as a benchmark can help improve recovery of the returned products and reduce landfill.

It is important to understand the factors that are significant in supply chain (SC) collaboration decision making and whether supply chain collaborative factors that are considered in the literature are still valid. To date, SC collaboration has not been extensively studied in the literature with supply chain finance (SCF) factors to evaluate SCF performance. Therefore, in this paper, the authors investigate the interrelationships between SCF and supply chain collaborative (SCC) factors for achieving SCF performance. The authors identified the most important factors from the literature on SCF and SCC and with inputs from experts in the textile industry in Pakistan.

The authors employed the Gray-Decision Making Trial and Evaluation Laboratory approach to help examine the cause-and-effect relationship between the factors and identify the influence of each factor on the others.

The findings showed that the most prominent factors of the study are “level of digitalization”, “information sharing”, and “collaborative communication”, and “most effect factors of this study are incentive alignment” and “information quality”. Furthermore, the “Level of digitalization” was identified as the factor with the central role and most significant correlation with other factors.

The major implication of the study is that textile industries should effectively develop their supply chain decisions after analyzing their internal and external factors, which will help in developing strategies that will facilitate better management of SCF relationships. The limitations of the study are that only 15 SCF and supply chain collaborative factors were considered, and time and scope are also limited. This study is only applied in the textile industry, so generalization may be limited.

To date, this study is the only one that has taken into consideration SCC with SCF factors to evaluate supply chain performance. This paper therefore makes this initial attempt and original contribution to this discussion, which can be helpful for those working to enhance supply chain performance, such as practitioners and policymakers.

The aim of this paper is to provide an approach to analyze the performance of TV programs and to identify what can be done to improve them.

The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), the Ng-model, Grey relational analysis (GRA), and principal component analysis (PCA) were applied to evaluate the programs, using audience, share, and duration as the performance criteria.

By comparing TOPSIS to the Ng-model, PCA, and GRA, we verified that SVD and bootstrap SVD TOPSIS provide a good balance between equal-weights TOPSIS and the other models. This is because SVD and bootstrap SVD TOPSIS break down the data to a higher degree, but are less impacted by outliers compared to the long tail models.

To determine which TV programs should be replaced or modified is a complex decision that has not been addressed in the literature. The advantage of using a multi-criteria decision-making (MCDM) approach is that analysts can choose as many criteria as they want to rank TV programs, rather than relying on a single criterion (e.g., audience, share, target rating point).

This work represents the first time that robust MCDM methodology is applied to an audience data set to analyze the performance of TV programs and to identify what can be done to improve them. This study shows the application of a detailed methodology that is useful for the improvement of TV programs and other entertainment industry content.

This research identifies the supply chain performance indices and designs an evaluation framework to assess and compare the Indian petroleum supply chain performance. We presented a case study of three Indian petroleum companies. For this purpose, we identified fifteen performance criteria extracted from previous literature and expert inputs and classified them into four groups.

A fuzzy technique for order preference by similarity to the ideal solution (TOPSIS) method is employed for evaluating the performance of the Indian petroleum supply chain.

The design and evaluation framework suggests that the top three performance measurement criteria, the purity of the products, compliance with environmental laws, and new technology adoption. The result findings also indicate that company C contributes to a maximum satisfaction level of 77%. Simultaneously, companies A and B hold satisfaction levels of 72% and 67%.

The managers should ensure that environmental standards, new technology adoption, and quality are significant concerns in the petroleum supply chain. The managers should follow national and international policies to preserve the environment and ensure safety in operational activities.

This paper makes two contributions in the domain of performance measurement of the petroleum supply chain. First, it identifies the prominent supply chain performance indices. Second, it proposes a model to assess and compare the performance of Indian petroleum companies.

The purpose of this paper was to understand the difficulties related to asset management observed by experts in Brazilian organizations in light of the requirements outlined in the ISO 55001:2014 standard.

A survey was performed with asset management experts. The collected data were analyzed using frequency analysis, hierarchical cluster analysis and fuzzy technique for order preference by similarity to deal solution (TOPSIS).

Based on data analysis, the most critical difficulties observed were related to managing and controlling the impact of changes in the company that affect asset management objectives; to the committing to and supporting the asset management system by the top management of the organization; to manage the processes for dealing with risks and opportunities for the asset management system and plans, and correcting failures in asset performance; and to plan and conduct actions in an integrated manner to identify and minimize adverse impacts associated with the asset management system, and afterwards verifying their effectiveness.

The findings of this study have important theoretical and practical contributions, since they indicate the most critical points observed in asset management in Brazil, which can be used as a source for future research and by professionals to prioritize difficulties in future planning and develop action plans to overcome them. The step-by-step methodological approach presented in this study provides professionals and researchers with a replicable method of identifying potential asset management difficulties in a given specific reality.

This chapter of the book seeks to use famous mathematical functions (statistical distribution functions) in evaluating and analyzing supply chain network data related to supply chain management (SCM) elements in organizations. In other words, the main purpose of this chapter is to find the best-fitted statistical distribution functions for SCM data. Explaining how to best fit the statistical distribution function along with the explanation of all possible aspects of a function for selected components of SCM from this chapter will make a significant attraction for production and services experts who will lead their organization to the path of competitive excellence. The main core of the chapter is the reliability values related to the reliability function calculated by the relevant chart and extracting other information based on other aspects of statistical distribution functions such as probability density, cumulative distribution, and failure function. This chapter of the book will turn readers into professional users of statistical distribution functions in mathematics for analyzing supply chain element data.