Suchergebnisse

This study tried to empirically ascertain the interaction of oil prices (OPs) and macroeconomy by using monthly data from January 1991 to January 2020 in the Indian context. Empirical analysis has been carried out on two separate samples (first from January 1991 to June 2002 and second from July 2002 to January 2020) for accommodating changing macroeconomic context. Further, we also tested the suitability for symmetry/asymmetry in the relationship between OPs and macroeconomic variables. It is found that the oil macroeconomy linkage is well approximated by a linear measure. The structural vector auto-regression framework suggests that a positive oil shock during the early reform period causes a significant drop in output growth. There is an increase in inflation in response to a positive shock in OPs and the central bank pursued an expansionary monetary policy to boost investment and consumption. During the last two decades, the decline in output is less pronounced than the first sub-period, while the response of inflation is sharp and significant and lasts for around six months. During recent times, the inflationary effect of oil shocks is a cause of concern and the central bank needs to respond more actively in the future for minimising its effect. JEL Codes: Q31, Q41, E31, E52, E39

Systematic model-driven decision-making is crucial to design, engineer, and transform manufacturing enterprises (MEs). Choosing and applying the best philosophies and techniques is challenging as most MEs deploy complex and unique configurations of process-resource systems and seek economies of scope and scale in respect of changing and distinctive product flows. This paper presents a novel systematic enhanced integrated modelling framework to facilitate transformation of MEs, which is centred on CIMOSA. Application of the new framework in an automotive industrial case study is also presented. The following new contributions to knowledge are made: (1) an innovative structured framework that can support various decisions in design, optimisation, and control to reconfigure MEs; (2) an enriched and generic process modelling approach with capability to represent both static and dynamic aspects of MEs; and (3) an automotive industrial case application showing benefits in terms of reduced lead time and cost with improved responsiveness of process-resource system with a special focus on PPC. It is anticipated that the new framework is not limited to only automotive industry but has a wider scope of application. Therefore, it would be interesting to extend its testing with different configurations and decision-making levels.

Purpose: The increasing risk of natural disasters is challenging humanitarian actors to create resilient disaster management systems. However, the role of the private sector in disaster management operations (DMOs) is not as prominent as the role played by (inter)governmental agencies. This article aims to investigate the relationship of collaboration and resilience in disaster management supply networks (DMSNs). Design/methodology/approach: Supply network resilience criteria were defined as robustness, flexibility, velocity and visibility based on the literature review. DMSN capabilities were identified characterising each resilience criterion through the development of the Collaboration–Resilience (COLRES) Analysis Framework for DMSNs. This theoretical model was then applied to an empirical case study in the Philippines using semi-structured interviews for data gathering. Findings: A total of 46 cross-sector collaboration activities were identified across four disaster management phases and linked to the resilience criteria. A causal analysis of each collaboration activity and its outcome was conducted to identify relationships between collaboration types and resilience constructs. Based on these results, patterns were identified, and dependencies between collaboration and resilience were defined. Collective DMSN resilience (DMSNRES) enabled by existing cross-sector collaboration activities was evaluated against a future disaster scenario to identify resilience gaps. These gaps were used to recognise new cross-sector collaboration opportunities, thereby illustrating the continuous process of resilience building. Research limitations/implications: This research provides new insights on how private sector is involved within a DMOs through collaboration with the government and other NGOs. It augments existing literature on private sector involvement in DMOs where common perception is that the sector is only involved in short-term response and recovery activities. This study finds that the private sector can be ...

This article attempts to investigate the potential relationship and significance of various determinants of Total Factor Productivity (TFP) in India for the 1980–2016 time period. Specifically, this is achieved in two stages. In the first, the standard growth accounting approach is used to measure the changes in TFP. Then, the main model for establishing the determinants of TFP growth is estimated using the autoregressive distributed lag (ARDL) model. Our results suggest that inflation and financial development have a statistically positive impact on TFP. Foreign direct investment, imports, and capital formation are found to have a positive but insignificant impact on the TFP. On the other hand, exports, government size, and natural calamities have a statistically negative impact on TFP. Therefore, in order to accelerate the TFP, governments and policymakers need to design and implement policies to increase financial access to the private sector, while maintaining price stability; exports of high-value products; and increased economic integration in the global economy to benefit from foreign investment flows, which brings in new technology.JEL Classification: O4, O40, O47

Ensuring long-term performance from key infrastructure is essential to enable it to serve society and to maintain a sustainable economy. The future-proofing of key infrastructure involves addressing two broad issues: (i) resilience to unexpected or uncontrollable events (e.g., extreme weather events); (ii) adaptability to required changes in structure and/or operations of the infrastructure in the future. Increasingly, infrastructure owners, designers, builders, governments and operators are being required to consider possible future challenges as part of the life cycle planning for assets and systems that make up key infrastructure. A preliminary study is reported here that aimed at exploring the following questions related to infrastructure (systems): what does 'future-proofing' of infrastructural assets mean? Why and when should critical infrastructure be future-proofed? How can infrastructure assets (systems) be prepared for uncertain future events? How can future-proofing considerations be incorporated into infrastructure asset management practices? To seek answers to the above questions, two industrial workshops were conducted that brought together leading practitioners in the UK infrastructure and construction sectors, along with government policymakers. This paper captures lessons learnt from the workshops and proposes a framework for linking future-proofing into asset management considerations. Case studies of Dawlish railway and Heathrow airport are also presented. ; The authors would like to acknowledge the Centre for Smart Infrastructure & Construction, EPSRC (Grant EP/K000314/1), Innovate UK and the industrial partners, which collectively funded this project. The authors are thankful to the CSIC industrial partners involved in the futureproofing project. The authors are also thankful to the speakers and delegates from London Underground, Costain, UCL, IBM, Crossrail, John Dora Consulting, Heathrow, Cementation Skanska, CIRIA, Network Rail, Arup, Highways Agency, Atkins, Halcrow/CH2M, Lang O' Rourke, Lend Lease, Infrastructure UK, Committee on Climate Change and CSIC, who attended the CSIC workshop(s) on infrastructure futureproofing. The authors are also thankful to the following companies who responded to our questionnaire on futureproofing strategies for industrial assets and systems: ABB, BAE Systems, Boeing, Caterpillar, EA Technology, Exxon Mobil, Finning, Hitachi, IBM and Rolls-Royce. ; This is the final version of the article. It first appeared from ICE Publishing via http://dx.doi.org/10.1680/jinam.15.00006