Managing risks in the Indonesian seaweed supply chain

Seaweed supply chains in Indonesia, especially carrageenan and agar products, are subject to risks arising both inside the participating companies and in their external networks. Uncertainties in yield, quality, price, and infrastructure in one part of the supply chain can affect the whole chain. To ensure a sustainable seaweed industry, an appropriate supply chain risk management (SCRM) is needed. There are four critical steps in SCRM: identifying seaweed supply chains, identifying and categorizing risks, assessing risks, and mitigating risks. To identify seaweed supply chains, we conducted field research, in-depth interviews, and literature studies. The field survey was conducted in the Provinces of South Sulawesi, West Java, East Java, Banten, and West Nusa Tenggara. The seaweed supply chains were modeled by the software Umberto to get a better understanding of material and energy flows between the key members. To identify and categorize the risks, we started with the risks mentioned in the existing literature works, and then applied the Delphi method to analyze the potential risk sources, their causes, and their impacts. To assess risks, we used a semiquantitative approach through the face-to-face interviews to generate a risk map showing the likelihood, and impact of adverse events. Afterward, the risk intensity was categorized based on the value of the responses and classified into five categories: negligible, marginal, critical, most critical, and catastrophic risks. The mitigation strategies considered sustainability (environment, economy, and social) and risks criteria. Multi-criteria decision analysis was used to evaluate these strategies.     

Multi-objective mixed integer programming and an application in a pharmaceutical supply chain

Multi-objective integer linear and/or mixed integer linear programming (MOILP/MOMILP) are very useful for many areas of application as any model that incorporates discrete phenomena requires the consideration of integer variables. However, the research on the methods for the general multi-objective integer/mixed integer model has been scant when compared to multi-objective linear programming with continuous variables. In this paper, an MOMILP is proposed, which integrates various conflicting objectives. We give importance to the imprecise nature of some of the critical factors used in the modelling that can influence the effectiveness of the model. The uncertainty and the hesitation arising from estimating such imprecise parameters are represented by intuitionistic fuzzy numbers. The MOMILP model with intuitionistic fuzzy parameters is first converted into a crisp MOMILP model, using appropriate defuzzification strategies. Thereafter, the MOMILP is transformed into a single objective problem to yield a compromise solution with an acceptable degree of satisfaction, using suitable scalarisation techniques such as the gamma-connective technique and the minimum bounded sum operator technique. The proposed solution method is applied to several test problems and a multi-objective pharmaceutical supply chain management model with self generated random data.     

On the robust decision-making in a supply chain under disruption risks

This paper considers a robust decision-making problem associated with supplies of parts and deliveries of finished products in a customer driven supply chain under disruption risks. The robustness refers to an equitably efficient performance of a supply chain in average-case as well as in the worst-case, which reflects the decision-makers common requirement to maintain an equally good performance of a supply chain under different conditions. Given a set of customer orders for products, the decision-maker needs to select suppliers of parts required to complete the orders, allocate the demand for parts among the selected suppliers and schedule the orders over the planning horizon, to equitably optimise average and worst-case performance of the supply chain. The supplies are subject to independent random local and regional disruptions. The obtained combinatorial stochastic optimisation problem is formulated as a mixed-integer program with conditional value-at-risk as a risk measure. The ordered weighted averaging aggregation of the expected value and the conditional value-at-risk of the selected optimality criterion is applied to obtain a robust solution. The risk-neutral, risk-averse and robust solutions that optimise, respectively average, worst-case and equitable average and worst-case performance of a supply chain are determined and compared for cost and customer service level objective functions. Numerical examples and computational results, in particular comparison with the mean-risk approach, are presented and some managerial insights are reported.     

Disruption risks in supply chain management: a literature review based on bibliometric analysis

Disruption risks in supply chain management have a great negative influence on the performance of supply chain members. Therefore, the field of supply chain disruption (SCD) has received increasing attention on mitigating the risks and improving the supply chain performance. This paper presents a comprehensive bibliometric overview and visualisation of the field of SCD based on 1,310 publications derived from the core collection of the Web of Science. The influential authors, organisations, and SCD keywords are discussed in detail based on some visualisation tools. Then, the leading publications and main clusters of SCD are identified to find out the key research topics based on citation analysis and reference co-citation analysis. The paper will be a helpful resource for researchers and practitioners who are interested in the field of SCD to capture the current research hotspots and potential research directions.     

Budget allocation in coping with supply chain disruption risks

Disruption management, as an important research topic, has attracted scholars’ broad attention in recent years due to the increasing exposure of disruption risks in supply chains. To date, researches in this field often focus on either prevention or mitigation measures and the budget allocation problem is paid relatively little attention. This paper therefore proposes an approach to determine the optimal budget allocation based on prevention measures in combination with mitigation measures. First, considering different disruption situations, the bow-tie is applied to developing the disruption management frameworks that integrate risk prevention and risk mitigation. Second, the corresponding optimization models are formulated to determine the optimal budget allocation plans. In order to validate the proposed approach, we compare the computation results with those obtained from the prevention approach and the mitigation approach. Also, random experiments are conducted to analyse the impacts of randomly generated disruption and response scenarios. Finally, a real-life case is provided to testify the usefulness and merits of our proposed approach. The results show that the proposed approach can help decision-makers reduce more loss caused by the disruption risks.     

Concurrent product and closed-loop supply chain design with an application to refrigerators

Increased concern for the environment has lead to new techniques to design products and supply chains that are both economically and ecologically feasible. A literature study shows that many models exist to support product design and logistics separately. In our research, we develop quantitative modelling to support decision-making concerning both the design structure of a product, i.e. modularity, reparability and recyclability, and the design structure of the logistic network. Environmental impacts are measured by linear-energy and waste functions. Economic costs are modelled as linear functions of volumes with a fixed set-up component for facilities. This model is applied to a closed-loop supply chain design problem for refrigerators using real life R&D data of a Japanese consumer electronics company concerning its European operations. The model is run for different scenarios using different parameter settings such as centralized versus decentralized processing, alternative product designs, varying return quality and quantity, and potential environmental legislation based on producer responsibility.     

Mitigating global supply chain risks through corporate social responsibility

This paper presents a decision model that captures supply-side disruption risks, social risks, and demand-side uncertainty within an integrated global supply chain and corporate social responsibility (CSR) modelling and analysis framework. The global supply chain decision-makers must decide on the level of investment in CSR activities and the choice of trading partners (manufacturer or retailer) given their CSR consciousness and perceived riskiness in order to maximise profit and minimise their overall risk. The model incorporates individual attitudes towards disruption risks among the manufacturers and the retailers, with the demands for the product associated with the retailers being random. The model allows one to investigate the effects of heterogeneous CSR activities in a global supply chain and to compute the resultant equilibrium pattern of product outputs, transactions, product prices, and levels of social responsibility activities. The results show that CSR activities can potentially be used to mitigate global supply chain risk.     

Dynamic supply chain decisions based on networked sensor data: an application in the chilled food retail chain

With large volume of product flows and complex supply chain processes, more data than ever before is being generated and collected in supply chains through various tracking and sensory technologies. The purpose of this study is to show a potential scenario of using a prototype tracking tool that facilitate the utilisation of sensor data, which is often unstructured and enormous in nature, to support supply chain decisions. The research investigates the potential benefits of the chilled food chain management innovation through sensor data driven pricing decisions. Data generated and recorded through the sensor network are used to predict the remaining shelf-life of perishable foods. Numerical analysis is conducted to examine the benefit of proposed approach under various operational situations and product features. The research findings demonstrate a way of modelling pricing and potential of performance improvement in chilled food chains to provide a vision of smooth transfer and implementation of the sensor data driven supply chain management. The research finding would encourage firms in the food industry to explore innovation opportunities from big data and develop proper data driven strategies to improve their competitiveness.     

Resilient and sustainable supply chain design: sustainability analysis under disruption risks

Resilience to disruptions and sustainability are both of paramount importance to supply chains. However, the interactions between the two have not been thoroughly explored in the academic literature. We attempt to contribute to this area by presenting a hybrid methodology for the design of a sustainable supply network that performs resiliently in the face of random disruptions. A stochastic bi-objective optimisation model is developed that utilises a fuzzy c-means clustering method to quantify and assess the sustainability performance of the suppliers. The proposed model determines outsourcing decisions and resilience strategies that minimise the expected total cost and maximise the overall sustainability performance in disruptions. Important managerial insights and practical implications are obtained from the model implementation in a case study of plastic pipe industry.     

On the risk-averse optimization of service level in a supply chain under disruption risks

The worst-case optimization of service level in the presence of supply chain disruption risks is considered for the two different service levels measures: the expected worst-case demand fulfillment rate and the expected worst-case order fulfillment rate. The optimization problem is formulated as a joint selection of suppliers and stochastic scheduling of customer orders under random disruptions of supplies. The suppliers are located in different geographic regions and the supplies are subject to random local and regional disruptions. The obtained combinatorial stochastic optimization problem is formulated as a mixed integer program with conditional service-at-risk as a worst-case service level measure. The risk-averse solutions that optimize the worst-case performance of a supply chain are compared for the two service level measures. In addition, to demonstrate the impact on the cost in the process of optimizing the worst-case service level, a joint optimization of expected cost and conditional service-at-risk using a weighted-sum approach is considered and illustrated with numerical examples. The findings indicate that the worst-case order fulfillment rate shows a higher service performance than the worst-case demand fulfillment rate. Maximization of the expected worst-case fraction of fulfilled customer orders better mitigates the impact of disruption risks. The supply portfolio is more diversified and the expected worst-case fraction of fulfilled orders is greater for most confidence levels. Finally, the results clearly show that worst-case service level is in opposition to cost.     

Quantifying risks in a supply chain through integration of fuzzy AHP and fuzzy TOPSIS

Risk is inherent in almost every activity of supply chain management. With the ever-increasing push for efficiency, supply chains today are getting more and more risky. Adding to the difficulty of dealing with these risks is the amount of subjectivity and uncertainty involved. This makes analytical examination of the situation very difficult, especially as the amount of information available at a particular time is not sufficient for such an analysis. Thus a supply chain risk index, which captures the level of risk faced by a supply chain in a given situation, is the need of the hour. This study is an effort towards quantifying the risks in a supply chain and then consolidating the values into a comprehensive risk index. An integrated approach, with a fuzzy analytical hierarchy process (AHP) and a fuzzy technique for order preference by similarity to the ideal solution (TOPSIS) as its important elements, has been used for this purpose. Fuzzy values in this study help in capturing the subjectivity of the situation with a final conversion to a crisp value which is much more comprehensible. A case study is used to illustrate the proposed methodology.     

Revisiting local TOC measures in an internal supply chain: a note

While the global theory of constraints (TOC)-based measures of throughput, inventory and operating expense are well known, the literature on local measures of throughput dollar-days and inventory dollar-days (TDDs/IDDs) is sparse and inconclusive. This paper shows how adherence to these measures naturally puts a non-TOC company on a continuous improvement path by assisting in identification of the constraint and implementation of a drum–buffer–rope (DBR)-like system. Thus, by simulating a small company, this note highlights the relationship between TDD/IDD measures and a DBR system, which further promotes TOC principles. Ultimately, we provide future research avenues into the role these measures can play in the development of a holistic incentive system and the implementation of a DBR-like system across the supply chain network.     

Traceability as a means to investigate supply chain sustainability: the real case of a leather shoe supply chain

In recent years, the growing attention to environmental challenges has shown that these issues are becoming of more and more interest to both research and industry. Companies are expected to ensure their products are fully traceable and more sustainable, which requires the involvement of all of the actors in the production network. According to this aim, this study proposes a structured approach that uses the traditional traceability concept as a means to identify the main information needed to assess environmental impacts along the whole supply chain (SC). The proposed approach is composed of four main steps: (i) SC modelling to identify all stakeholders and their inter-relations, (ii) data sharing to collect all relevant data, (iii) data elaboration to calculate performance at different levels of detail and (iv) result interpretation to optimise the SC. The distributed implementation of the approach at different SC steps represents a useful means to practically realise a sustainable SC management. A case study involving a leather shoe SC is used to demonstrate the effectiveness of the approach in identifying criticalities, supporting the selection of the most appropriate suppliers and correctly setting a management strategy towards the optimisation of internal and external traceability and environmental sustainability performances.     

Coordinating supply chain decisions: an optimization model

Coordinating information and material flows are key to effective supply chain management. The complexity of interactions in and the uncertainties surrounding supply chains make such coordination difficult. However, coordination can be realized by optimizing the flows in supply chains with analytical approaches. A mixed integer programming model is presented to support the tactical decisions of ordering, producing and transporting under various conditions of information availability at the loci of decision making. The model is applied to a modified version of MIT's well known Beer Distribution Game. The performance of the modeling approach is contrasted with the results of human decision making under identical conditions and underlines the enormous potential for performance improvement analytical decision support can provide. Several methodological aspects for coping with the difficulties of solving rather large mixed integer models are presented and it is shown that they can contribute significantly in dealing with the inherent computational problems.     

When risks need attention: adoption of green supply chain initiatives in the pharmaceutical industry

The pharmaceutical industry is very important in delivering life-saving products/services to society. There are many ways for materials/products/services concerned with pharmaceuticals to influence the environment; these include improper disposal of pills/tablets by patients, expired and unused medications, improper release of drugs by pharmacies or household sewage mixed with surplus drugs. In view of this, the present work seeks to integrate green supply chain (GSC) concepts in the pharmaceutical sector in a developing economy Indian context. In so doing, managers need to determine the potential risks in adopting GSC initiatives to achieve sustainability in operational perspectives. In this sense, this work seeks to distinguish the potential risks in adopting GSC initiatives within the pharmaceutical industry. This work uses a literature review and fuzzy Delphi approach in finalising the risks. This research also uses fuzzy Analytical Hierarchy Process (AHP) for prioritisation of the risks under vague and unclear surroundings. According to the findings, cold chain technology and supply risks categories are highly prioritised. This work can assist practising managers and government authorities in effectively developing and managing GSC initiatives in line with sustainable development goals in the context of the pharmaceutical industry. Finally, a sensitivity test is applied to evaluate the stability of ranking of risks.     

A comparison of supply chain vulnerability indices for different categories of firms

Given the high vulnerability of today's supply chains to disruptions, measuring and managing supply chain vulnerability has become critical. In the attempt to support practice in reducing supply chain vulnerability, we (1) discuss and define the concept of supply chain vulnerability and (2) measure and compare supply chain vulnerability for various categories of firms. Normal Accident Theory and High Reliability Theory provide the theoretical foundations for the empirical study, and graph modelling is the method used to construct a measure for supply chain vulnerability. The empirical data largely confirm that there is a negative relationship between supply chain vulnerability and supply chain performance, a positive relationship between structural categories (firm size and production type) and supply chain vulnerability, and a positive relationship between supply chain vulnerability and managerial categories (logistics importance, supply chain risk planning, and supply chain risk management).     

Robust design of a sustainable and resilient bioethanol supply chain under operational and disruption risks

Clean Technologies and Environmental Policy - In this work, we propose a mixed-integer linear programming model to address the design and planning of a multi-feedstock lignocellulosic bioethanol...     

Drivers and consequences of an innovative technology assimilation in the supply chain: cloud computing and supply chain integration

Cloud Computing are innovative technologies that are being applied in the main business functions in the supply chain. This study aims to reveal the determinant factors (drivers and a relevant outcome) of the level of use or assimilation of Cloud Computing in the supply chain. In order to test three hypotheses we conducted an empirical study in 484 companies from sectors in an intermediate position in the supply chain. The data gathering method consisted of a telephone survey using a computerised system (CATI). We used structural equation modelling (SEM) to test the hypotheses. The empirical study reveals that Advanced Manufacturing Technologies pursuing the internal efficiency of the supply chain (Intra-organisational IT) and IT for capabilities in e-business/e-commerce seeking external connection of the supply chain with other companies (Inter-organisational IT) are drivers of Cloud Computing assimilation. Furthermore, supply chain integration is one of the major consequences of Cloud Computing assimilation in the supply chain.     

Sustainable supply chain management in Indian organisations: an empirical investigation

The present work is an attempt to investigate the adoption of sustainable supply chain management (SSCM) practices amongst manufacturing and process based organisations in India and its impact on organisational performance encompassing all three dimensions of sustainability. SSCM practices conceived in the present study include environmental management practices (EMP), socially inclusive practices for employees (SPE), socially inclusive practices for community (SPC), operations practices (OP) and supply chain integration (SCI) which were treated as exogenous variables. Organisational performance considered in this study includes five dimensions, namely environmental performance (EPR), employee-centred social performance (ESP), community-centred social performance (CSP), operations performance (OPR) and competitiveness, which were regarded as endogenous variables. The analysis was carried out with the help of structural equation modelling considering natural logarithm of manpower as a control variable. Few major findings are mentioned. EMP does not have any significant association with OPR, nor does it result in competitiveness. However, when jointly mediated through both EPR and OPR, EMP leads to competitiveness. SPC has significant negative association with competitiveness, when only direct relationship is considered. However, indirect relationship between SPC and competitiveness shows significant positive association when mediated through CSP. The resultant total effect between SPC and competitiveness turns out to be insignificant. Further, OPR fully mediates the relationship between OP and competitiveness. Managerial implications of the findings are discussed.