Purpose The purpose of this paper is to examine (1) how the recovery speed using promotional investment and (2) distributed production using additive manufacturing (AM) improve the resilience of the supply chain to manage any disruptions in the diffusion of green products. Design/methodology/approach The environmental performance, service level performance and economic performance are the measures of interest. These measures are studied through the integration of inventory and production planning (I&PP) of the reverse logistics system and consumer behavior using Bass (1969) model of diffusion of innovation under the paradigm of Industry 4.0 architecture. The Taguchi experimental design framework was used for the simulation analysis. Findings The adoption patterns based on the Bass model in conjunction with recovery speed and production on AM during the disruption period suggest that there exist tradeoff decisions between various combinations of information-sharing and I&PP policies. Practical implications The extensive sensitivity analyses provide real-time support for managerial decisions. Besides the potentials of Industry 4.0 capabilities, the present research suggests paying close attention to the recovery speed in conjunction with the inventory management system. Social implications The integration of consumers' behavior (Bass model) to digital technologies is an additional contribution of the present research toward sustainability issues from the social perspective. Originality/value Previous research studies have discussed resilience to manage the ripple effect. However, none of them have addressed the changing scope of resilience to manage the ripple effect caused by the disruption in the diffusion of green products in a reverse logistics setup.

Under the paradigm of Industry 4.0, the present research attempts to model the reverse logistics and examine how product diffusion dynamics in the market affect the economic and environmental performances of an inventory and production planning (I&PP) system. We use the classic Bass (1969) model of diffusion of innovation to capture the returns of a single-generation of a product under the proposed architecture of systematical deployment of information-sharing strategies and I&PP policies under the notions of Industry 4.0 components. The key feature of Industry 4.0 characterized by virtualization of factory operations is captured using the simulation model. For the analysis, using the Taguchi experimental design framework, we present valuable managerial insights. Our findings suggest the relevant adoption patterns based on the combination of information-sharing and I&PP policies for the tradeoff between environmental and economic performance. An extensive sensitivity analysis shows the robustness of the model. Further, the managerial decisions on the environmental and economic performance measures reveal that in spite of the presence of Industry 4.0 technology capabilities, a close attention should be paid to operational parameters and their related costs when socially influenced green product adoption with the parameters such as size of end-user market and collection investment are governing the returns of the product to the reverse logistics system. Accordingly, the model exhibits a real-time decision support tool for the sustainable reverse logistics system in Industry 4.0 environment at large.

One of the major issues a designer of Big Data Architecture has to trade with is incorporating real-time predictive analytics capability using offline synergistic approaches like simulation, fuzzy analytic network process, and Technique for Order Preference. Further, under this setting, which involves re-engineering of operational units, the present study proposes a simple, yet practical heuristic to quickly handle the unstructured relational key-performance-indicators (KPIs) data of a supply chain that are obtained from the results of the simulation. Within the big data framework, the proposed model can be used as a decision support tool by the companies to evaluate their KPIs in a real-time dynamic system.

This research studies inventory and production planning in a closed-loop system while considering both manufacturing and remanufacturing. We studied five inventory and production planning models under the continuous and periodic review systems using a discrete event simulation. Under the above review policies, different demand and return rates, as well as manufacturing and remanufacturing lead times, are considered. The total recoverable and serviceable inventory costs and production order variance are considered as the main performance indicators. From the total inventory cost viewpoint, our findings reveal the trade-off between stochastic demand, stochastic lead times, and review periods. It was found that the periodic review system outperforms the continuous review system for higher values of the review period and return to demand rate ratio. Furthermore, remanufacturing demonstrates an appreciable contribution to low order variance in periodic review systems for high values of return to demand ratio and lead times.

Due to short product life cycle, it is expedient to reconfiguration an existing supply chain from time to time. Companies need to impose the standards on operational units for finding the best or the near best alternative configuration. Thus, it becomes imperative to effectively adapt various enablers in a supply chain by understanding the dynamics between them that help to reconfigure a supply chain for high levels of performance. This paper presents an integration of agent-based simulation and decision tree learning as the data mining techniques to determine adaptive decisions of operational units of a mobile phone supply chain. Agent-based simulation output is subjected to data mining analysis to understand system behaviour in terms of interactions and the factors influencing the performance. An entropy-based formulation is proposed as the basis for comparing different operational units in the supply chain. The insights obtained are then encapsulated as operational rules and guidelines supporting better decision-making.

Purpose – The modern business community understands the importance of long-term satisfaction of consumer. Enabling the consumer to return products is a significant part of the equation. The purpose of this paper is to analyze the sustainable boundaries in terms of their relationship toward greening a supply chain. Design/methodology/approach – Using interpretive structural modeling the research presents a hierarchy-based model to realize the driving power and dependence of sustainable boundary enablers. Findings – The research shows that there exists a group of enablers having a high driving power and low dependence requiring maximum attention and of strategic importance while another group consists of those variables which have high dependence and are the resultant actions. Practical implications – This classification provides a useful tool to supply chain managers to differentiate between independent and dependent variables and their mutual relationships which would help them to focus while making strategic, tactical or operational decisions as and when required while designing a green supply chain. Originality/value – This research assumes importance in context of greening a supply chain when globally enterprises are getting a lot of pressure from consumers as well as the regulatory measures from the government. Sustainability demands that the resources be used in lean manner through information coordination with all partners in a supply chain. The findings of this study would help delineate those variables that should to be necessarily considered to design a sustainable supply chain.

Managing the green (environmentally sustainable) supply chain is an important step toward broader adoption and development issue for industry. This paper presents an approach to effectively adapt sustainable practices in a supply chain by understanding the dynamics between various enablers. Using interpretive structural modeling, the research presents a hierarchy-based model and the mutual relationships among the enablers of sustainability in a supply chain. The research shows that there exists a group of environmental sustainability boundary enablers having a high driving power and low dependence requiring maximum attention and of strategic importance while another group consists of those enablers which have high dependence and are the resultant actions. This classification provides a useful tool to supply chain managers to differentiate between independent and dependent variables and their mutual relationships which would help them to delineate those key enablers that are imperative for effective implementation of sustainability concepts in the design of a supply chain.

In the contemporary business environment, to adhere to the need of the customers, caused the shift from mass production to mass-customization. This necessitates the supply chain (SC) to be effective flexible. The purpose of this paper is to seek flexibility through adoption of family-based dispatching rules under the influence of inventory system implemented at downstream echelons of an industrial supply chain network. We compared the family-based dispatching rules in existing literature under the purview of inventory system and information sharing within a supply chain network. The dispatching rules are compared for Average Flow Time performance, which is averaged over the three product families. The performance is measured using extensive discrete event simulation process. Given the various inventory related operational factors at downstream echelons, the present paper highlights the importance of strategically adopting appropriate family-based dispatching rule at the manufacturing end. In the environment of mass customization, it becomes imperative to adopt the family-based dispatching rule from the system wide SC perspective. This warrants the application of intra as well as inter-echelon information coordination. The holonic paradigm emerges in this research stream, amidst the holistic approach and the vital systemic approach. The present research shows its novelty in triplet. Firstly, it provides leverage to manager to strategically adopting a dispatching rule from the inventory system perspective. Secondly, the findings provide direction for the attenuation of adverse impact accruing from demand amplification (bullwhip effect) in the form of inventory levels by appropriately adopting family-based dispatching rule. Thirdly, the information environment is conceptualized under the paradigm of Koestler’s holonic theory.

Purpose – Short product life cycle and/or mass customization necessitate reconfiguration of operational enablers of supply chain (SC) from time to time in order to harness high levels of performance. The purpose of this paper is to identify the key operational enablers under stochastic environment on which practitioner should focus while reconfiguring a SC network. Design/methodology/approach – The paper used interpretive structural modeling (ISM) approach that presents a hierarchy-based model and the mutual relationships among the enablers. The contextual relationship needed for developing structural self-interaction matrix (SSIM) among various enablers is realized by conducting experiments through simulation of a hypothetical SC network. Findings – The research identifies various operational enablers having a high driving power towards assumed performance measures. In this regard, these enablers require maximum attention and of strategic importance while reconfiguring SC. Practical implications – ISM provides a useful tool to the SC managers to strategically adopt and focus on the key enablers which have comparatively greater potential in enhancing the SC performance under given operational settings. Originality/value – The present research realizes the importance of SC flexibility under the premise of reconfiguration of the operational units in order to harness high value of SC performance. Given the resulting digraph through ISM, the decision maker can focus the key enablers for effective reconfiguration. The study is one of the first efforts that develop contextual relations among operational enablers for SSIM matrix through integration of discrete event simulation to ISM.

The management of risks within the supply chain and external to it has become highly critical component of supply chain management. Inventory management is a vital tool to mitigate these risks. Lead times and review periods are important parameters in inventory management. The organizations focus on these parameters to enhance the system-wide supply chain performance in terms of services to customers. This paper aims at analyzing the efficiency of total supply chain in context of average fill rate performance. We analyze the efficiency of a hypothetical supply chain network structure which is subjected to time delays due to lead time and inventory review period changes. To understand the optimal relative efficiency among different values of average fill rate performance obtained through simulation, we used Data Envelopment Analysis approach (DEA). Taguchi experimental design procedure is used as a vehicle for conducting the simulation experiments and analyzing its outcome. The proposed integration of simulation with DEA framework provides practical implications to the decision maker as well as connotes to the real world situation where different enterprises compete for the frontier supply chain efficiency.

PURPOSE The aim of this study is to highlight the value of the success rate performance of a surgery while planning patient flow within a supply chain of a health care organization/hospital. DESIGN/METHODOLOGY/APPROACH The paper has considered one of the common surgeries, cataract, and the complications that subsequently result from this surgery. The study employs interpretive structural modeling (ISM) approach to draw a roadmap to study various complications causing cataract that subsequently help in planning and coordination of patient flow. FINDINGS The study finds that there is a hierarchy of causes and certain complications, the persistence of which gives a higher success rate performance in cataract surgery as compared to others. PRACTICAL IMPLICATIONS The paper provides leverage to the decision maker while organizing the patient flow depending upon the information of hierarchy of complication of a disease, and accordingly ensures the availability of resources to the patient. ORIGINALITY/VALUE The study is of value in identifying the degree of complications from cataract surgery. Given the degree of complication, the patient logistics can be planned myopically in a health care organization which largely depends upon the degree of success rate. The paper attempts to suggest that the hierarchy obtained through ISM can be implemented in the modules of an enterprise resource planning (ERP) set up.

Given the inherent uncertainties pervading the operational environment within real-world supply chains, it becomes imperative for each partnering echelon to focus on individual information requirements from the viewpoint of global optimization of overall supply chain (SC) performance. With this in perspective, it is expedient to explicitly model the SC network to synchronize activities across the cooperating partners. This research is concerned with the performance behaviour of two different SC network structures given different design and control parameters adopted by the partnering echelons within the assumed SC configurations. Accordingly, the authors developed discrete event simulation models of two hypothetical supply chain structures and exploit the Taguchi experimental design procedure as a vehicle for conducting the simulation experiments and analyzing its outcome. The results highlight the relative effects of the assumed design and controlling factors on system-wide SC performance and identify appropriate combinations of these factors for optimal performance concerned. For the average inventory level performance measure, key results reveal that sharing of demand information between partnering echelons should not automatically be taken for granted as a direction for performance enhancement.

One of the key issues in the current research on supply chain (SC) networks is the need for planning the nature of the inventory policy at each echelon of the SC network structure. Given the inherent uncertainties pervading the operational environment within real-world SC networks, it becomes imperative therefore, for each partnering echelon to focus on its individual inventory review policy from the viewpoint of global optimisation of the overall SC performance. Two key factors contributing to the aforementioned uncertainty are the lead time and their standard deviations, and the extant literature has often advocated the adoption of demand information-sharing between the partnering echelons to mitigate the deleterious impact of these factors on system performance. In this paper, we explicitly focus attention on these factors through their manifestation within two different hypothetical SC networks, and study their impact on the average fill rate performance of the assumed systems with and without demand information-sharing. Towards this end, we develop discrete event simulation models of the hypothetical SC structures and exploit the Taguchi experimental design procedure as a vehicle for conducting the simulation experiments and analysing its outcome. While simulation results highlight the impact of the assumed factors on system-wide performance, the Taguchi paradigm further helps identify appropriate combinations of these factors for optimal fill rate performance. Key results reveal that sharing of demand information between partnering echelons should not automatically be taken for granted as a direction for performance enhancement.

The rapid advancement in communication technology, coupled with spiralling customer expectations, are increasingly forcing business enterprises to seek inter-organisational coordination for smooth material and informational flow across the total supply chain justifiably. Therefore, given the inherent uncertainties pervading the operational environment within real-world supply chains, it becomes imperative for each partnering echelon to focus on individual information requirements from a viewpoint of global optimisation of overall SC performance. This information could manifest from 1) downstream echelons; 2) upstream partners; 3) a (hybrid) combination of both 1 and 2 above. From the perspective of a specific SC echelon then, we compare the above operating regimes using the performance measures: average inventory levels, average cycle times, average fill rates and average number of times lots are ordered. In this paper, the coordination among supply chain echelons is viewed from fractal factory theory perspective.

Purpose The purpose of this paper is to analyze the case of a manufacturing firm situated in an industrial city of India, focusing on supply chain management issues of the concerned organization from two operational perspectives: supply side (or the procurement side) and the distribution side of the system. Design/methodology/approach The authors first considered the outsourcing decision‐making problem in a static environment using analytical expression by means of a variable fraction of demand. Next, the authors extended the scope of this problem by considering outsourcing decisions in a dynamic environment, using the sequential decision‐making approach with various operational and inventory factors. Finally, the authors carried out the study of the distribution side of the supply chain of industry using discrete event simulation. Findings It was observed that, in the case study organization, because of the rather unstructured approach in dealing with the outsourcing perspective, the authors suggested the adoption of a more scientific approach in computation of fraction of demand to be outsourced. Further, since the distribution network typically experienced high inventory levels throughout the supply chain, it was decided upfront to optimize this performance measure. Originality/value The primary objectives of this exercise are to: address the operational concerns of a real‐life manufacturing environment; apply the theoretical models in a realistic environment, and compare the results of theory with practice; and provide actionable managerial recommendations.

Purpose As global markets become more customer oriented, rapid response rates are now often among the most important metrics in business. To achieve the required agility, many companies are forced to take decisions of whether to vertically integrate a value chain or to outsource some of its operations. The purpose of this paper is to develop a sequential decision modeling process to enable determination of optimal outsourcing policy decisions with respect to the variables such as warehouse inventory, in‐house manufacturing capacity and the ordering cost to the outsource supplier. Design/methodology/approach In this paper, a discrete dynamic programming‐based modeling framework is developed for analyzing outsourcing policies for supply chain management problems. Specifically, the assumed situation entails a dynamic decision between in‐house production vis‐à‐vis outsourcing, which is contingent upon several factors such as demand during the period under consideration, available inventory, available production capacity of the firm, ordering cost to the outsourced supplier and the fixed capital cost of machine capacity enhancement. Findings The framework enables the determination of a time‐based outsourcing policy, which is a prescription regarding: the optimum quantities to be produced in‐house vs those to be outsourced, and the level of capacity to be set in each period. Originality/value The problem investigates useful managerial decisions that are relevant to a real life dynamic situation within a manufacturing industry when effecting outsourcing decisions.

The rapidly changing global market scenario is increasingly forcing manufacturing firms to pursue outsourcing as an important option. From a research viewpoint, one issue that has remained relatively unexplored is the effect that capacity expansion with availability of overtime option and upstream supplier cost structure has on outsourcing decisions. There is scant research exploring the circumstances in which mixed models (fraction of demand) might be appropriate (Harland et al., 2005). The suggested model explicitly considers pricing and outsourcing simultaneously by operationalising the outsourcing decision through a variable that captures the fraction of demand met by in-house manufacturing through overtime.