An assessment of biomass supply chain : a DEA application

Renewable energy generation reduces carbon emissions and responds to the targets for renewable energy sources of most EU countries; it also enhances infrastructure resilience and creates flexibility of the energy matrix. However, the availability of biomass may drastically differ from country to country within the EU. In most cases, the most challenged countries to achieve high targets for sustainability are not those with a sufficiently large supply of biomass. Because of this, it is necessary to design new biomass supply chain networks and improve the existing networks. This paper aims to assess the efficiency of biomass alternative pathways of the supply network from South America to Europe. In this particular work, three scenarios of biomass using two transportation systems were investigated, i.e., transportation of wood logs, pellets and torrefied biomass in the country of origin by truck and train transportation. Efficiency was measured using a data envelopment analysis (DEA) model derived from CCR. The results present the most efficient supply chain alternatives and highlight the feasibility of establishing closer cooperation between Brazil and countries in Europe for green energy generation. This information can assist in the process of planning and decision-making to determine the practicability of the implementation of torrefaction facilities using the most efficient logistical pathway

Managing uncertainty in regional supply chains: The case of Fresh fruit from Lleida province

Supply chain management typically examines a network of companies from production to consumption with the aim to improve performance in terms of cost. During the last decades, supply chain management has evolved to include multi-objective performance measurement goals such as flexibility, reliability, and recently sustainability. In food supply chains sustainability is measured with CO2 emissions and other environmental indicators. However, there are two gaps in our understanding of managing supply chains effectively. Firstly, uncertainty is a key factor that influences the performance of chains. Although, scholars such as van der Vorst and Beulens (2002) have early identified uncertainty as a key parameter in supply chains, there is little empirical evidence on how to model it effectively. This is surprising since supply chains are prone to complexity and uncertainty. Therefore, making well-informed supply chain decisions requires risk analysis, control and mitigation (Heckmann, 2015). According to van der Vorst and Beulens (2002), there are three characteristics of supply chain uncertainty: - Inherent characteristics: variability in demand, supply or process are extremely common in perishable product chains. - Chain characteristics: chain configuration, such consolidation points, may disturb the system. - Exogenous phenomena: they are not controllable by the firm. This category includes weather conditions, governmental regulations, etc. Solutions to such increasing inventory, adding capacity at different locations and having multiple suppliers — undermine efforts to improve supply chain cost efficiency (Sunil, 2014) The second gap in our understanding food supply chains lies on the methods applied where the unit of analysis is usually the firm-level or in fewer studies the dyad between suppliers and retailers. Only but few studies, have examined the region as unit of analysis in food supply chains (Soysal et al. 2014) This study aimed to examine the regional supply chains and assess how uncertainly affects their performance. The method was a case study of Fresh fruit from Lleida province. Data collection included site visits, interviews with key managers and secondary data sources. The study modeled the fruit supply chains from Lleida to EU destinations. It analyses the key risk factors that influence decision making. The study sheds light how regions compete in global supply chains which is significant especially after the Brexit outcome. A number of recommendations and suggestions for further research are also provided. References Heckmann, Iris, Tina Comes, and Stefan Nickel. (2015), A critical review on supply chain risk–Definition, measure and modelling, Omega, Vol 52, No. 119-132. Soysal, M., Bloemhof-Ruwaard, J. and van der Vorst, J. (2014). Modelling food logistics networks with emission considerations: The case of an international beef supply chain. International Journal of Production Economics, Vol. 152, pp.57-70. van der Vorst, J. and Beulens, A. (2002). Identifying sources of uncertainty to generate supply chain redesign strategies. International Journal of Physical Distribution & Logistics Management, Vol. 32, No. 6, pp.409-430

Comparative Analysis of Resilience by Supply Network Structure

This research applies Kim, et al.’s (2015) supply network structure archetypes to case data related to two disruptions in three industries in Brazil. A total of seven supply networks were studied, through in-depth interviews and archival documents. The findings suggest that there may be additional supply network structures that are relevant. Centralization appears to be a function of the size of the focal firm. There was evidence of an evolution of supply network structures with focal firm size

Probabilistic analysis of supply chains resilience based on their characteristics using dynamic Bayesian networks

Previously held under moratorium from 14 December 2016 until 19 January 2022There is an increasing interest in the resilience of supply chains given the growing awareness of their vulnerabilities to natural and man-made hazards. Contemporary academic literature considers, for example, so-called resilience enablers and strategies, such as improving the nature of collaboration and flexibility within the supply chain. Efforts to analyse resilience tend to view the supply chain as a complex system. The present research adopts a distinctive approach to the analysis of supply resilience by building formal models from the perspective of the responsible manager. Dynamic Bayesian Networks (DBNs) are selected as the modelling method since they are capable of representing the temporal evolution of uncertainties affecting supply. They also support probabilistic analysis to estimate the impact of potentially hazardous events through time. In this way, the recovery rate of the supply chain under mitigation action scenarios and an understanding of resilience can be obtained. The research is grounded in multiple case studies of manufacturing and retail supply chains, involving focal companies in the UK, Canada and Malaysia, respectively. Each case involves building models to estimate the resilience of the supply chain given uncertainties about, for example, business continuity, lumpy spare parts demand and operations of critical infrastructure. DBNs have been developed by using relevant data from historical empirical records and subjective judgement. Through the modelling practice, It has been found that some SC characteristics (i.e. level of integration, structure, SC operating system) play a vital role in shaping and quantifying DBNs and reduce their elicitation burden. Similarly, It has been found that the static and dynamic discretization methods of continuous variables affect the DBNs building process. I also studied the effect of level of integration, visibility, structure and SC operating system on the resilience level of SCs through the analysis of DBNs outputs. I found that the influence of the integration intensity on supply chain resilience can be revealed through understanding the dependency level of the focal firm on SC members resources. I have also noticed the relationship between the span of integration and the level of visibility to SC members. This visibility affects the capability of SC managers in the focal firm to identify the SC hazards and their consequences and, therefore, improve the planning for adverse events. I also explained how some decision rules related to SC operating system such as the inventory strategy could influence the intermediate ability of SC to react to adverse events. By interpreting my case data in the light of the existing academic literature, I can formulate some specific propositions.There is an increasing interest in the resilience of supply chains given the growing awareness of their vulnerabilities to natural and man-made hazards. Contemporary academic literature considers, for example, so-called resilience enablers and strategies, such as improving the nature of collaboration and flexibility within the supply chain. Efforts to analyse resilience tend to view the supply chain as a complex system. The present research adopts a distinctive approach to the analysis of supply resilience by building formal models from the perspective of the responsible manager. Dynamic Bayesian Networks (DBNs) are selected as the modelling method since they are capable of representing the temporal evolution of uncertainties affecting supply. They also support probabilistic analysis to estimate the impact of potentially hazardous events through time. In this way, the recovery rate of the supply chain under mitigation action scenarios and an understanding of resilience can be obtained. The research is grounded in multiple case studies of manufacturing and retail supply chains, involving focal companies in the UK, Canada and Malaysia, respectively. Each case involves building models to estimate the resilience of the supply chain given uncertainties about, for example, business continuity, lumpy spare parts demand and operations of critical infrastructure. DBNs have been developed by using relevant data from historical empirical records and subjective judgement. Through the modelling practice, It has been found that some SC characteristics (i.e. level of integration, structure, SC operating system) play a vital role in shaping and quantifying DBNs and reduce their elicitation burden. Similarly, It has been found that the static and dynamic discretization methods of continuous variables affect the DBNs building process. I also studied the effect of level of integration, visibility, structure and SC operating system on the resilience level of SCs through the analysis of DBNs outputs. I found that the influence of the integration intensity on supply chain resilience can be revealed through understanding the dependency level of the focal firm on SC members resources. I have also noticed the relationship between the span of integration and the level of visibility to SC members. This visibility affects the capability of SC managers in the focal firm to identify the SC hazards and their consequences and, therefore, improve the planning for adverse events. I also explained how some decision rules related to SC operating system such as the inventory strategy could influence the intermediate ability of SC to react to adverse events. By interpreting my case data in the light of the existing academic literature, I can formulate some specific propositions

Evolution of Supply Chain Collaboration: Implications for the Role of Knowledge

Increasingly, research across many disciplines has recognized the shortcomings of the traditional “integration prescription” for inter-organizational knowledge management. This research conducts several simulation experiments to study the effects of different rates of product change, different demand environments, and different economies of scale on the level of integration between firms at different levels in the supply chain. The underlying paradigm shifts from a static, steady state view to a dynamic, complex adaptive systems and knowledge-based view of supply chain networks. Several research propositions are presented that use the role of knowledge in the supply chain to provide predictive power for how supply chain collaborations or integration should evolve. Suggestions and implications are suggested for managerial and research purposes

An integrated core competence evaluation framework for portfolio management in the oil industry

Drawing upon resource-based theory, this paper presents a core competence evaluation framework for managing the competence portfolio of an oil company. It introduces a network typology to illustrate how to form different types of strategic alliance relations with partnering firms to manage and grow the competence portfolio. A framework is tested using a case study approach involving face-to-face structured interviews. We identified purchasing, refining and sales and marketing as strong candidates to be the core competencies. However, despite the company's core business of refining oil, the core competencies were identified to be their research and development and performance management (PM) capabilities. We further provide a procedure to determine different kinds of physical, intellectual and cultural resources making a dominant impact on company's competence portfolio. In addition, we provide a comprehensive set of guidelines on how to develop core competence further by forging a partnership alliance choosing an appropriate network topology

The knowledge domain of chain and network science

This editorial paper aims to provide a framework to categorise and evaluate the domain of Chain and Network Science (CNS), and to provide an envelope for the research and management agenda. The authors strongly feel that although considerable progress has been made over the past couple of years in the development of the CNS domain, a number of important and exciting challenges are still waiting to be tackled. This paper provides a definition of the object of study of CNS, its central problem area, the organisation and governance of chain and network co-operation, and the relationships between chain organisation and technology development, market dynamics, and the economy and society at large. It indicates relevant sources of knowledge among the various academic disciplines. It touches upon CNS problem solving by identifying areas for knowledge development and CNS tool construction

Practices for strategic capacity management in Malaysian manufacturing firms

While the notion of manufacturing capabilities is a long-standing notion in research on operations management, its actual implementation and management has been hardly researched. Five case studies in Malaysia offered the opportunity to examine the practice of manufacturing managers with regard to strategic capability management. The data collection and analysis was structured by using the notion of Strategic Capacity Management. Whereas traditionally literature has demonstrated the beneficial impact of an appropriate manufacturing strategy on the business strategy and performance, the study highlights the difficulty of managers to set the strategy, let alone implementing it. This is partly caused by the immense pressure of customers in these dominantly Make-To-Order environments for SMEs. Current concepts for manufacturing capabilities have insufficiently accounted this phenomenon and an outline of a research agenda is presented