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The purpose of this paper is to develop a conceptual framework for extending an understanding of resilience in complex adaptive system (CAS) such as supply chains using the adaptive cycle framework. The adaptive cycle framework may help explain change and the long term dynamics and resilience in supply chain networks. Adaptive cycles assume that dynamic systems such as supply chain networks go through stages of growth, development, collapse and reorientation. Adaptive cycles suggest that the resilience of a complex adaptive system such as supply chains are not fixed but expand and contract over time and resilience requires such systems to navigate each of the cycles’ four stages successfully.

This research uses the adaptive cycle framework to explain supply chain resilience (SCRES). It explores the phases of the adaptive cycle, its pathologies and key properties and links these to competences and behaviors that are important for system and SCRES. The study develops a conceptual framework linking adaptive cycles to SCRES. The goal is to extend dynamic theories of SCRES by borrowing from the adaptive cycle framework. We review the literature on the adaptive cycle framework, its properties and link these to SCRES.

The key insight is that the adaptive cycle concept can broaden our understanding of SCRES beyond focal scales, including cross-scale resilience. As a framework, the adaptive cycle can explain the mechanisms that support or prevent resilience in supply chains. Adaptive cycles may also give us new insights into the sort of competences required to avoid stagnation, promote system renewal as resilience expands and contracts over time.

The adaptive cycle may move our discussion of resilience beyond engineering and ecological resilience to include evolutionary resilience. While the first two presently dominates our theorizing on SCRES, evolutionary resilience may be more insightful than both are. Adaptive cycles capture the idea of change, adaptation and transformation and allow us to explore cross-scale resilience.

Knowing how to prepare for and overcoming key pathologies associated with each stage of the adaptive cycle can broaden our repertoire of strategies for managing SCRES across time. Human agency is important for preventing systems from crossing critical thresholds into imminent collapse. More importantly, disruptions may present an opportunity for innovation and renewal for building more resilience supply chains.

This research is one of the few studies that have applied the adaptive cycle concept to SCRES and extends our understanding of the dynamic structure of SCRES

This paper aims to empirically analyze how adaptive collaboration in supply chain management impacts demand forecast accuracy in short life-cycle products, depending on collaboration intensity, product life-cycle stage, retailer type and product category.

The authors assembled a data set of forecasts and sales of 169 still-camera models, made by the same manufacturer and sold by three different retailers in France over five years. Collaboration intensity, coded by collaborative planning forecasting and replenishment level, was used to analyze the main effects and specific interaction effects of all variables using ANOVA and ordered feature evaluation analysis (OFEA).

The findings lend empirical support to the long-standing assumption that supply chain collaboration intensity increases demand forecast accuracy and that product maturation also increases forecast accuracy even in short life-cycle products. Furthermore, the findings show that it is particularly the lack of collaboration that causes negative effects on forecast accuracy, while positive interaction effects are only found for life cycle stage and product category.

Investment in adaptive supply chain collaboration is shown to increase demand forecast accuracy. However, the choice of collaboration intensity should account for life cycle stage, retailer type and product category.

This paper provides empirical support for the adaptive collaboration concept, exploring not only the actual benefits but also the way it is achieved in the context of innovative products with short life cycles. The authors used a real-world data set and pushed its statistical analysis to a new level of detail using OFEA.

This paper aims to explain the role of supply chain capital (SCC) in developing transformative supply chain resilience (SCRes) to cope with environmental dynamism. Through a panarchy theory lens, this paper holistically examines how supply chains and their resilience are impacted by the multilevel structure in which they are embedded.

Contextualised explanation-building case studies are used to examine the transformative SCRes of 10 companies. Data were collected via interviews, documents, archival records and observations.

Studying transformative SCRes leads to generating insights into the application of SCC for managing environmental dynamisms at the organisational and supply chain levels. Furthermore, the linkages between different levels of the panarchy and their impact on the change in SCC to cope with the dynamisms are identified and explained.

This paper contributes to the new and timely paradigm of transformative SCRes by studying this phenomenon in a holistic manner (rather than a traditional reductionistic view). Through a panarchy lens, the need to examine and analyse different hierarchy levels simultaneously to interpret SCRes responses to environmental dynamism is highlighted.

Valuable insights are provided to practitioners in developing an understanding of structural and relational SCC and their management in the development of transformative SCRes.

This paper is one of the first empirical studies using a multilevel social-ecological-based panarchy framework in the supply chain management context. Applying this novel approach is highly relevant and reveals several new research opportunities.

Intense competition forces companies to become involved in supply chain collaboration with their upstream and downstream partners. The key to ensuring that the participating members are progressing on the right track of creating the best‐in‐class practice is to conduct benchmarking. Benchmarking stimulates collective learning for performance improvement that brings benefits to all participating members. However, previous research has focused mainly on supply chain benchmarking at the intra‐company ‐‐ rather than the inter‐company ‐‐ level. Inter‐company benchmarking requires a new perspective for understanding collaborative learning amongst the participating members that encourages them to improve supply chain performance as a whole. This research aims to develop a benchmarking scheme for supply chain collaboration that links collaborative performance metrics and collaborative enablers. The proposed benchmarking scheme can be used to examine the current status of supply chain collaboration among the participating members, identify performance gaps and systematize improvement initiatives.

This study aims to indicate the advantages of using cloud computing services, including the ways in which firms use cloud-based services to achieve accessibility, better communication, flexibility and effective provision of services. However, little evidence has been obtained related to the effectiveness of applying cloud computing services to supply management chains.

In this study, a sample of 223 top 1,000 manufacturing firms in Taiwan that had implemented cloud-based supply chain management systems (CSCMs) was surveyed to determine what kind of internal resources in these companies were allocated to this implementation effort, how collaborative relationships were established in the existing supply chain to help make the transitions successful, how well their systems are working now that they have been implemented and whether these new systems have improved cycle time performance and the overall performance of their organizations. The study also examines the interrelationships among these variables.

The results reveal that, from the perspective of the managers who were surveyed, an effective allocation of internal organizational resources does have a positive, strong effect on external CSCM conditions. They also showed that when the relationship between internal and external resources is well constructed, the result is that CSCM improves supply chain management cycle time performance, which, in turn, leads to positive organizational performance.

The study reports some useful insights from the managers of CSCM systems related to how the execution of CSCM solutions can improve cycle time and organizational performance by enhancing internal organizational management and joint collaboration among supply chain partners. The findings from this survey will be useful to managers who are considering creating cloud-based supply management systems in the future.

This conceptual paper aims to examine the notion that supply chain integration is an extension and application of vertical integration theory.

The paper initially defines a foundation in the supply chain and vertical integration literature, with particular attention to the seminal works of Harrigan in vertical integration and Hayes and Wheelwright in product life cycles. The paper then offers an assessment of the state of the supply chain integration literature. Subsequently, the stage of product/process life cycle and environmental variables such as complexity and munificence are examined in detail and are the basis for the theoretical model and propositions.

This study argues that the stage of life cycle variables is associated with the various dimensions of supply chain integration, and that environmental complexity and munificence have significant moderating effects on the relationships. The paper posits that, for efficiency and success, a strategic fit must exist between environmental, strategic and operations variables, and that specific dimensions of integrative effort are appropriate for given situations. That fit would attenuate bullwhip inefficiencies, either of inventories and other mechanical decisions, or of the less tangible human and structural interaction.

As such, this paper represents a cross‐functional and interdisciplinary approach to operations and strategic management theory by identifying and facilitating appropriate operations decisions pertaining to the contingencies of the supply chain.

Drawing on the resource‐based view, we posit that the learning climate is an intangible, strategic resource that influences important outcomes. Data from 141 supply chain units within a multinational corporation reveal that four constructs (team‐, systems‐, learning‐, and memory orientations) function as first‐order indicators of the higher‐order phenomenon of the learning climate. In turn, learning is inversely related to supply chain cycle time. The results are robust across the 1994 and 1999 data, suggesting that learning offers a persistent tool for managing outcomes.

The purpose of this paper is to examine how over-reliance on buyer-supplier relational capital (created through the interconnected supply chain and social network) impacts firm performance in the context of the emerging market, i.e. India.

The study uses the Prowess database (on Indian firms) to identify the firms that rely heavily on relational capital and employs panel data regression analyses to test the effect of relational capital on firm performance (supply chain performance and financial performance).

The results show that over-reliance on relational capital leads to lower supply chain performance (proxied by supply chain cycle) and financial performance (proxied by Tobin's Q). The results also reveal that supply chain performance mediates the relationship between over-reliance on relational capital and financial performance. Together, these results indicate that over-reliance on relational capital created through the interconnected supply chain and social network for supply chain management may negatively affect a firm's competitive advantage, which in turn can significantly impede its financial performance.

In light of the supply chain literature and relevant theories, the study develops an objective understanding of over-reliance relational capital created through the interconnected supply chain and social network, by relying on a large panel dataset of manufacturing firms and hence contributes to the supply chain literature. Also, it presents a novel idea to operationalize the measure for relational capital using the Prowess database.

The chemical industry is struggling to improve its supply chain performance, and improved asset utilization may help get the industry headed in the right direction. Since most chemical firms own or lease their rail fleets, rail utilization can have a substantial impact on overall asset utilization. The paper aims to focus on current managerial processes and situational factors that impact railcar asset performance.

Rail car cycle data are analyzed, focusing on major sources of variation in transit inventory as railcars move from plant to customer and back.

Findings include the importance of establishing and adhering to policies regarding supply chain practices; substantial differences exist between hopper and tank car performance; distance is not a major predictor of total cycle time variance; and vendor‐managed inventory relationships can operate with less customer inventory.

This paper addresses only one component of supply chain performance: railcar cycle time. Further analysis is needed to investigate differences between hopper car and tank car transit times. Additional research should also involve the railroad companies as participants in chemical firms' supply chains.

The paper provides several practical recommendations for chemical company supply chain managers relating to process controls, focusing on large customer accounts, managing transit time and variation of rail cars between plant and factory. The findings and recommendations can be applied across many industries.

This paper focuses on supply chain practices in the chemical industry, which has been slow to adopt supply chain practices. In particular, this paper investigates railcar coordination as one means of enhancing supply chain performance, reducing both inventory and transportation assets.

Environmental impact and changes are becoming essential in textile and yarn industries, where reliable measurement of parameters related to processing harmful substances needs to be examined. Such findings can be cumulated using smart assessment like life cycle analysis. The ecological impact category, supply chain, and climate-changing factors were considered for the necessary assessment.

This paper applies the Life Cycle Assessment technique in the textile and yarn industry to estimate critical environmental potentials. The critical input for the fabric and yarn industry was put in the GaBi software model to estimate various environmental potentials.

Global warming potential, electricity, and raw cotton consumption in the fabric and yarn industry were critical concerns where attention should be focused on minimizing environmental potentials from cradle to gate assessment.

This qualitative study is made via the industry case-wise inputs and outputs, which can vary with demographic conditions. Some machine and human constraints have not been implemented in modelling life cycle model for smart simulation. Smart simulation helps in linking different parameters and simulates their combined effects on the product life cycle.

This modelling approach will help access pollution constituents in different supply chain production processes and optimize them simultaneously.

The raw data used in this analysis are collected from an Indian small scale textile industry. In the textile fabrication industry, earlier assessments were carried out in cotton generation, impact of PET, cradle to grave assessment of textile products and garment processing only. In this research the smart model is drawn to consider each input parameter of yarn and textile fabric to determine the criticality of each input in this assessment. This article mainly talks about life cycle and circular supply assessment applied to first time for both cotton to yarn processing and yarn to fabric industry for necessary estimation of environment potentials.