Developing lean and responsive supply chains : a robust model for alternative risk mitigation strategies in supply chain designs

This paper investigates how organization should design their supply chains (SCs) and use risk mitigation strategies to meet different performance objectives. To do this, we develop two mixed integer nonlinear (MINL) lean and responsive models for a four-tier SC to understand these four strategies: i) holding back-up emergency stocks at the DCs, ii) holding back-up emergency stock for transshipment to all DCs at a strategic DC (for risk pooling in the SC), iii) reserving excess capacity in the facilities, and iv) using other facilities in the SC’s network to back-up the primary facilities. A new method for designing the network is developed which works based on the definition of path to cover all possible disturbances. To solve the two proposed MINL models, a linear regression approximation is suggested to linearize the models; this technique works based on a piecewise linear transformation. The efficiency of the solution technique is tested for two prevalent distribution functions. We then explore how these models operate using empirical data from an automotive SC. This enables us to develop a more comprehensive risk mitigation framework than previous studies and show how it can be used to determine the optimal SC design and risk mitigation strategies given the uncertainties faced by practitioners and the performance objectives they wish to meet

Review of Quantitative Methods for Supply Chain Resilience Analysis

Supply chain resilience (SCR) manifests when the network is capable to withstand, adapt, and recover from disruptions to meet customer demand and ensure performance. This paper conceptualizes and comprehensively presents a systematic review of the recent literature on quantitative modeling the SCR while distinctively pertaining it to the original concept of resilience capacity. Decision-makers and researchers can benefit from our survey since it introduces a structured analysis and recommendations as to which quantitative methods can be used at different levels of capacity resilience. Finally, the gaps and limitations of existing SCR literature are identified and future research opportunities are suggested

Agribusiness supply chain risk management: A review of quantitative decision models

Supply chain risk management is a large and growing field of research. However, within this field, mathematical models for agricultural products have received relatively little attention. This is somewhat surprising as risk management is even more important for agricultural supply chains due to challenges associated with seasonality, supply spikes, long supply lead-times, and perishability. This paper carries out a thorough review of the relatively limited literature on quantitative risk management models for agricultural supply chains. Specifically, we identify robustness and resilience as two key techniques for managing risk. Since these terms are not used consistently in the literature, we propose clear definitions and metrics for these terms; we then use these definitions to classify the agricultural supply chain risk management literature. Implications are given for both practice and future research on agricultural supply chain risk management

Sustainable supply chain management towards disruption and organizational ambidexterity:A data driven analysis

Balancing sustainability and disruption of supply chains requires organizational ambidexterity. Sustainable supply chains prioritize efficiency and economies of scale and may not have sufficient redundancy to withstand disruptive events. There is a developing body of literature that attempts to reconcile these two aspects. This study gives a data-driven literature review of sustainable supply chain management trends toward ambidexterity and disruption. The critical review reveals temporal trends and geographic distribution of literature. A hybrid of data-driven analysis approach based on content and bibliometric analyses, fuzzy Delphi method, entropy weight method, and fuzzy decision-making trial and evaluation laboratory is used on 273 keywords and 22 indicators obtained based on the experts’ evaluation. The most important indicators are identified as supply chain agility, supply chain coordination, supply chain finance, supply chain flexibility, supply chain resilience, and sustainability. The regions show different tendencies compared with others. Asia and Oceania, Latin America and the Caribbean, and Africa are the regions needs improvement, while Europe and North America show distinct apprehensions on supply chain network design. The main contribution of this review is the identification of the knowledge frontier, which then leads to a discussion of prospects for future studies and practical industry implementation

Risk Decision for Dual-Channel Supply Chain of Agricultural Products Under Disturbance

This paper presents a decision analysis model for the dual-channel supply chain of agricultural products under the disturbance of emergency. Mean variance analysis tool and utility function risk tool are used to describe risk indicators in supply chain. In this study, retailer plays a leading role in agricultural supply chain. By means of the Kuhn-Tucker condition of the retailer’s maximum utility, the optimal price and optimal demand are obtained. The study also shows that risk averse retailer has higher wholesale price, lower retail price and greater supply as well as the demand for the pursuit of greater utility; Supplier has a certain robustness to the sudden event disturbance, when the disturbance is large, the quantity of initial supply quantity will be adjusted. The relationship between the demand change rate of the two channels and the market share of the channel is found. Finally, some numerical examples are presented to illustrate the results. The study provides a possible way of thinking in emergency decision analysis

Dynamic temporary blood facility location-allocation during and post-disaster periods

The key objective of this study is to develop a tool (hybridization or integration of different techniques) for locating the temporary blood banks during and post-disaster conditions that could serve the hospitals with minimum response time. We have used temporary blood centers, which must be located in such a way that it is able to serve the demand of hospitals in nearby region within a shorter duration. We are locating the temporary blood centres for which we are minimizing the maximum distance with hospitals. We have used Tabu search heuristic method to calculate the optimal number of temporary blood centres considering cost components. In addition, we employ Bayesian belief network to prioritize the factors for locating the temporary blood facilities. Workability of our model and methodology is illustrated using a case study including blood centres and hospitals surrounding Jamshedpur city. Our results shows that at-least 6 temporary blood facilities are required to satisfy the demand of blood during and post-disaster periods in Jamshedpur. The results also show that that past disaster conditions, response time and convenience for access are the most important factors for locating the temporary blood facilities during and post-disaster periods

Fuzzy Robust Optimization in Closed-Loop Supply Chain Network Model for Hazardous Products (Lead-Acid Battery)

Purpose: This paper models a closed-loop supply chain network problem for hazardous products in the face of demand uncertainty and variable costs. The designed model includes a set of suppliers, production centers, distribution, recycling, disposal, collection and end customers in which strategic and tactical decisions are made simultaneously. Among the decisions made in this paper is the location of production, distribution and collection centers and determining the optimal amount of product flow between the levels of the supply chain network. Methodology: In this paper, the Epsilon constraint method is used to solve a multi-objective model in GMAS software. This article also uses uniform data to solve the problem. Findings: The results of solving the model with fuzzy robust optimization method show that with increasing the uncertainty rate and also reducing the transfer time of hazardous products, the total network costs as well as the amount of greenhouse gas emissions have increased. Also, the study of Pareto front to optimize the total design costs and the amount of greenhouse gas emissions shows that by reducing the amount of greenhouse gas emissions in the network, the costs related to location and routing increase. Originality/Value: In this paper a fuzzy robust optimization is used in closed-loop supply chain network model for hazardous products (Lead-Acid Battery)

What we know and do not know about organizational resilience

[EN] We present a literature review about organizational resilience, with the goal of identifying how organizational resilience is conceptualized and assessed. The two research questions that drive the review are: (1) how is organizational resilience conceptualized? and (2) how is organizational resilience assessed? We answer the first question by analysing organizational resilience definitions and the attributes or characteristics that contribute to develop resilient organizations. We answer the second question by reviewing articles that focus on tools or methods to measure organizational resilience. Although there are three different ways to define organizational resilience, we found common ideas in the definitions. We also found that organizational resilience is considered a property, ability or capability that can be improved over time. However, we did not find consensus about the elements that contribute to improving the level of organizational resilience and how to assess it. Based on the results of the review, we propose a conceptualization of organizational resilience that integrates the three views found in the literature. We also propose a four-level Maturity Model for Organizational Resilience – MMOR. Using this model, the organization can be in one of the following levels based on its ability and capacity to handle disruptive events: fragile, robust, resilient or antifragile.This research is partially supported by University of Valladolid, Banco Santander and NSERCRuiz-Martin, C.; López-Paredes, A.; Wainer, G. (2018). What we know and do not know about organizational resilience. International Journal of Production Management and Engineering. 6(1):11-28. doi:10.4995/ijpme.2018.7898SWORD11286