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PurposeProduction planning and resource allocation are ongoing issues that organisations face on a day-to-day basis. The purpose of this paper is to address these issues by developing a dynamic performance measurement system (DPMS) to effectively re-deploy manufacturing resources, thus enhancing the decision-making process in optimising performance output. The study also explores the development of dynamic capabilities through exploitation of the organisational tacit knowledge.Design/methodology/approachThe study was conducted using six-stage action research for developing DPMS with real-time control of independent variables on the production lines to study the impact. The DPMS was developed using a hybrid approach of discrete event simulation and system dynamics by using the historical as well as live data from the action case organisation.FindingsThrough the development of DPMS and by combining the explicit and tacit knowledge, this study demonstrated an understanding of using cause and effect analysis in manufacturing systems to predict performance. Such a DPMS creates agility in decision making and significantly enhances the decision-making process under uncertainty. The research also explored how the resources can be developed and maintained into dynamic capabilities to sustain competitive advantage.Research limitations/implicationsThe present study provides a starting-point for further research in other manufacturing organisations to generalise findings.Originality/valueThe originality of the DPMS model comes from the approach used to build the cause and effect analysis by exploiting the tacit knowledge and making it dynamic by adding modelling capabilities. Originality also comes from the hybrid approach used in developing the DPMS.

Purpose: A low-carbon economy is the pressing need of the hour. Despite several efforts taken by the government and large corporations, there is still research to be conducted exploring the role of top management commitment in translating external pressures into responses that help to build low-carbon emissions in supply chains. Design/methodology/approach: The authors have grounded their framework in institutional theory, agency theory and contingency theory. On the basis of existing literature, four hypotheses were drawn. To test these hypotheses, a questionnaire was developed and pre-tested. Finally, statistical analyses were performed to test the research hypotheses using 176 samples gathered using a pre-tested questionnaire following Dillman's (2007) total design test method. Findings: The results suggest that coercive pressures and mimetic pressures under the mediating effect of top management commitment have a significant influence on organizational response to low-carbon emissions. The authors further note that supply base complexity has moderating effects on the link between top management commitment and organizational response towards low-carbon emissions. Originality/value: This study offers valuable insights to those managers and environmental consultants who view supply base complexity as a limitation. However, the results indicate that supply base complexity may help to enhance the effectiveness of the top management commitment on organizational response towards low-carbon emissions.

PurposeThe purpose of this paper is to examine when and how organizations create agility, adaptability, and alignment as distinct supply chain properties to gain sustainable competitive advantage.Design/methodology/approachThe current study utilizes the resource-based view (RBV) under the moderating effect of top management commitment (TMC). To test the research hypotheses, the authors gathered 351 usable responses using a pre-tested questionnaire.FindingsThe statistical analyses suggest that information sharing and supply chain connectivity resources influence supply chain visibility capability, which, under the moderating effect of TMC, enhance supply chain agility, adaptability, and alignment (SCAAA).Originality/valueThe contribution lies in: providing a holistic study of the antecedents of agility, adaptability, and alignment; investigating the moderating role of TMC on SCAAA; following the RBV and addressing calls for investigating the role of resources in supply chain management, and for empirical studies with implications for supply chain design.