Evolution: Complexity, uncertainty and innovation

Complexity science provides a general mathematical basis for evolutionary thinking. It makes us face the inherent, irreducible nature of uncertainty and the limits to knowledge and prediction. Complex, evolutionary systems work on the basis of on-going, continuous internal processes of exploration, experimentation and innovation at their underlying levels. This is acted upon by the level above, leading to a selection process on the lower levels and a probing of the stability of the level above. This could either be an organizational level above, or the potential market place. Models aimed at predicting system behaviour therefore consist of assumptions of constraints on the micro-level – and because of inertia or conformity may be approximately true for some unspecified time. However, systems without strong mechanisms of repression and conformity will evolve, innovate and change, creating new emergent structures, capabilities and characteristics. Systems with no individual freedom at their lower levels will have predictable behaviour in the short term – but will not survive in the long term. Creative, innovative, evolving systems, on the other hand, will more probably survive over longer times, but will not have predictable characteristics or behaviour. These minimal mechanisms are all that are required to explain (though not predict) the co-evolutionary processes occurring in markets, organizations, and indeed in emergent, evolutionary communities of practice. Some examples will be presented briefly

Barriers in green lean implementation: a combined systematic literature review and interpretive structural modelling approach

Green Lean has recently emerged as an alternative strategy for organizations to pursue both operational and sustainability excellence. The interest on this approach has rapidly risen in both academic and industry circles. However, despite this interest, very limited research has focused on its implementation, and no research has investigated the barriers that hinder the success of such activity. This study investigates the Green Lean implementation barriers and their contextual relationships and effects on the integration and deployment of this approach. A Systematic Literature Review (SLR), Interpretative Structural Modelling and fuzzy Matriced’ Impacts Croise’s Multiplication Appliqée a UN Classement (MICMAC) analyzes were carried out. Fifteen barriers were extracted from the SLR and then validated in consultation with industry and academic experts. The Interpretive Structural Modelling (ISM) method was used to understand the relationship between the fifteen barriers and to develop a hierarchical model of these. The different barriers were classified into ‘linkage’ and ‘dependent’ barriers by using MICMAC analysis. The results suggested that all the identified barriers play an important role, and hence can equally act as a significant hurdle to the implementation of Green Lean projects. This study can help managers and policy makers in better understanding these barriers. Thus, they can be assisted in managing and prioritizing barriers towards the successful implementation of Green Lean initiatives for better financial and environmental performance.N/

Knowledge management in sustainable supply chain management: improving performance through an interpretive structural modelling approach

Sustainable supply chain management is one vital element in achieving competitive advantage in business management and knowledge management is seen to be one key enabler. However, in previous studies the interrelationships between knowledge management and sustainable supply chain management are still under-explored. This study proposes a set of measures and interpretive structural modelling methods to identify the driving and dependence powers in sustainable supply chain management within the context of knowledge management, so as to improve the performance of firms from the textile industry in Vietnam. The research result indicated that learning organisation, information/knowledge sharing, joint knowledge creation, information technology and knowledge storage are amongst the highest driving and dependence powers. These attributes are deemed to be most-effective to enhance the performance of firms. To further enhance the value of this research, theoretical and managerial implications are also discussed in this study

Quantitative modelling approaches for lean manufacturing under uncertainty

[EN] Lean manufacturing (LM) applies different tools that help to eliminate waste as well as the opera-tions that do not add value to the product or processes to increase the value of each performedactivity. Here the main motivation is to study how quantitative modelling approaches can supportLM tools even under system and environment uncertainties. The main contributions of the articleare: (i) providing a systematic literature review of 99 works related to the modelling of uncertaintyin LM environments; (ii) proposing a methodology to classify the reviewed works; (iii) classifyingLM works under uncertainty; and (iv) identify quantitative models and their solution to deal withuncertainty in LM environments by identifying the main variables involved. Hence this article pro-vides a conceptual framework for future LM quantitative modelling under uncertainty as a guide foracademics, researchers and industrial practitioners. The main findings identify that LM under uncer-tainty has been empirically investigated mainly in the US, India and the UK in the automotive andaerospace manufacturing sectors using analytical and simulation models to minimise time and cost.Value stream mapping (VSM) and just in time (JIT) are the most used LM techniques to reduce wastein a context of system uncertainty.The research leading to these results received funding fromthe project 'Industrial Production and Logistics Optimizationin Industry 4.0' (i4OPT) (Ref. PROMETEO/2021/065) granted by the Valencian Regional Government; and grant PDC2022-133957-I00 funded by MCIN/AEI /10.13039/501100011033 and by European Union Next Generation EU/PRTR.Rojas, T.; Mula, J.; Sanchis, R. (2023). Quantitative modelling approaches for lean manufacturing under uncertainty. International Journal of Production Research. 1-27. https://doi.org/10.1080/00207543.2023.229313812

Analysis of Decision Support Systems of Industrial Relevance: Application Potential of Fuzzy and Grey Set Theories

The present work articulates few case empirical studies on decision making in industrial context. Development of variety of Decision Support System (DSS) under uncertainty and vague information is attempted herein. The study emphases on five important decision making domains where effective decision making may surely enhance overall performance of the organization. The focused territories of this work are i) robot selection, ii) g-resilient supplier selection, iii) third party logistics (3PL) service provider selection, iv) assessment of supply chain’s g-resilient index and v) risk assessment in e-commerce exercises. Firstly, decision support systems in relation to robot selection are conceptualized through adaptation to fuzzy set theory in integration with TODIM and PROMETHEE approach, Grey set theory is also found useful in this regard; and is combined with TODIM approach to identify the best robot alternative. In this work, an attempt is also made to tackle subjective (qualitative) and objective (quantitative) evaluation information simultaneously, towards effective decision making. Supplier selection is a key strategic concern for the large-scale organizations. In view of this, a novel decision support framework is proposed to address g-resilient (green and resilient) supplier selection issues. Green capability of suppliers’ ensures the pollution free operation; while, resiliency deals with unexpected system disruptions. A comparative analysis of the results is also carried out by applying well-known decision making approaches like Fuzzy- TOPSIS and Fuzzy-VIKOR. In relation to 3PL service provider selection, this dissertation proposes a novel ‘Dominance- Based’ model in combination with grey set theory to deal with 3PL provider selection, considering linguistic preferences of the Decision-Makers (DMs). An empirical case study is articulated to demonstrate application potential of the proposed model. The results, obtained thereof, have been compared to that of grey-TOPSIS approach. Another part of this dissertation is to provide an integrated framework in order to assess gresilient (ecosilient) performance of the supply chain of a case automotive company. The overall g-resilient supply chain performance is determined by computing a unique ecosilient (g-resilient) index. The concepts of Fuzzy Performance Importance Index (FPII) along with Degree of Similarity (DOS) (obtained from fuzzy set theory) are applied to rank different gresilient criteria in accordance to their current status of performance. The study is further extended to analyze, and thereby, to mitigate various risk factors (risk sources) involved in e-commerce exercises. A total forty eight major e-commerce risks are recognized and evaluated in a decision making perspective by utilizing the knowledge acquired from the fuzzy set theory. Risk is evaluated as a product of two risk quantifying parameters viz. (i) Likelihood of occurrence and, (ii) Impact. Aforesaid two risk quantifying parameters are assessed in a subjective manner (linguistic human judgment), rather than exploring probabilistic approach of risk analysis. The ‘crisp risk extent’ corresponding to various risk factors are figured out through the proposed fuzzy risk analysis approach. The risk factor possessing high ‘crisp risk extent’ score is said be more critical for the current problem context (toward e-commerce success). Risks are now categorized into different levels of severity (adverse consequences) (i.e. negligible, minor, marginal, critical and catastrophic). Amongst forty eight risk sources, top five risk sources which are supposed to adversely affect the company’s e-commerce performance are recognized through such categorization. The overall risk extent is determined by aggregating individual risks (under ‘critical’ level of severity) using Fuzzy Inference System (FIS). Interpretive Structural Modeling (ISM) is then used to obtain structural relationship amongst aforementioned five risk sources. An appropriate action requirement plan is also suggested, to control and minimize risks associated with e-commerce exercises

Farm SMEs sustainability assessment based on Bellagio Principles. The case of Messinian Region, Greece

Purpose: Sufficient support of the sustainability of farm products embedded in a region (such as Products of Designated Origin / PDOs) to overcome significant obstacles to access domestic and remote markets. Main research question is how to overcome such inherent difficulties and transform them into challenges and opportunities to the new market environment. Design/methodology /approach: Combination of simplicity with the complicated issue of sustainability for awareness of small farmers SMEs and their collective representatives. Improve the understanding of the Sustainable Supply Chain Management (SSCM), to facilitate sustainability through use of the ‘Bellagio Principles’ for assessing sustainability of local farm products and facilitating further enhancement. Use of certain PDOs farm products of the Messinian region of Greece, such as local Sfela Feta cheese, olive oil, olives and raisins, to assess sustainability and improvement. Formation of a conceptual constructive action R&D framework of broader use in building-up and performing implementation of holistic supply chain strategy. Expected Findings: Providing better understanding of the SSCM. Insights on how SMEs co-operatives can collectively apply holistic strategies concerning local farm PDOs to fulfil competitiveness and sustainability requirements, under variant product and market conditions. Originality / Value : Improving the know-how, focusing on the sustainability of regional, traditional products and its effects upon supply chain performance and market access. Practical implications for regional-based farm SMEs in the design of holistic value creation strategies to produce sustainable competitive advantage. Interactive cause and effect dynamic implications of sustainable development on social, economic and physical environment

ISM analysis of CPFR implementation barriers

Collaborative Planning, Forecasting and Replenishment (CPFR) as an interconnection scheme between organizations has been shown to have significant benefits. Since its inception in the 1990s, its uptake has been lower than originally predicted. This paper identifies the major barriers and their interrelationships in CPFR implementations with a focus on high-tech industries. Interpretive Structural Modeling (ISM) is used with a group of CPFR experts from industry/academia and Matrice d’Impacts Croisés Multiplication Appliquée àun Classement (MICMAC) analysis to identify the driving and dependence powers. The paper identified 45 CPFR barriers and classifies them into four categories based on expert opinion, with only 13 of these determined to be significant. The results indicate that in terms of categories, managerial barriers are a significant root cause for both process and cultural barriers and CPFR implementation difficulties. It also indicates that although the importance of information technology to launch collaborative schemes has been addressed by many scholars, technology alone is not the complete solution for successful CPFR implementation. The paper has significant practical implications for organizations as it identifies the main CPFR barriers and their causal relationships. This will help firms in the process of CPFR strategy development particularly for mitigation strategies for dominant barriers

A hierarchiacal framework of barriers to green supply chain management in the construction sector

The research paper presents a hierarchical sustainability framework for evaluating the barriers to the adoption of green supply chain management (GSCM) in the United Arab Emirates (UAE) construction sector. A total of 32 barriers to the adoption of GSCM are identified through extensive literature review and expert interviews with academics and industry professionals. The barriers are grouped on the basis of literature and expert opinion to form 12 criteria. Since the nature of the identified criteria is complex and interdependent; an Interpretive Structural Modeling (ISM) technique is applied to develop a structural model. Driving and dependence power analysis (DDPA) is used to classify and identify the critical barriers. The developed sustainability framework offers a strong and efficient evaluation technique in decision making for policy makers and stakeholders by means of identifying and prioritizing the critical barriers. The barriers identified are also classified as external and internal barriers to the organization and will help policy makers to focus on specific barriers which are important to the adoption of GSCM in the UAE construction sector. The framework has the potential to be applied to other countries across industries

Key antecedents and practices for supply chain management adoption in project contexts

An adequate identification of antecedents is recognized as fundamental in order to set the basis for connecting the inter-organizational networks in a SCM perspective. This work aims to identify key antecedents of SCM in a project-based environment by using Interpretive Structural Modelling (ISM). This is firstly useful in order to highlight the relationships among the antecedents and to deduce priority for their achievement. The findings provide a hierarchical perspective of the 16 identified antecedents. In particular, three macro-classes of prerequisites were defined: cross-organizational cooperation, rules and procedures — accessibility, and super-ordinate goals. Moreover, results from a longitudinal and illustrative case study are also presented in order to compare the out-coming ISM model with evidence from a success case in the Yacht-building context so offering interesting insights about the implementation process. From a managerial perspective, the proposed model offers a conceptual path for SCM adoption, emphasizing most critical issues that have to be considered and organized in this complex and unpredictable setting

A hierarchical framework of barriers to green supply chain management in the construction sector

The research paper presents a hierarchical sustainability framework for evaluating the barriers to the adoption of green supply chain management (GSCM) in the United Arab Emirates (UAE) construction sector. A total of 32 barriers to the adoption of GSCM are identified through extensive literature review and expert interviews with academics and industry professionals. The barriers are grouped on the basis of literature and expert opinion to form 12 criteria. Since the nature of the identified criteria is complex and interdependent; an Interpretive Structural Modeling (ISM) technique is applied to develop a structural model. Driving and dependence power analysis (DDPA) is used to classify and identify the critical barriers. The developed sustainability framework offers a strong and efficient evaluation technique in decision making for policy makers and stakeholders by means of identifying and prioritizing the critical barriers. The barriers identified are also classified as external and internal barriers to the organization and will help policy makers to focus on specific barriers which are important to the adoption of GSCM in the UAE construction sector. The framework has the potential to be applied to other countries across industries