scholarly journals An Empirical Study of Supply Chain Frangibility and Risk Management in Indian Automobile Industry

2021 ◽  
Author(s):  
Arora Ankit ◽  
Rajagopal Rajesh
Keyword(s):  
Risk Factors ◽  
Supply Chain ◽  
Automobile Industry ◽  
Lead Time ◽  
Risk Mitigation ◽  
Transportation Network ◽  
Demand Forecasting ◽  
Bullwhip Effect ◽  
Effect Analysis ◽  
Supply Chain Efficiency

Abstract The automobile sector in India is one the key segment of Indian economy as it contributes to 4% of India’s GDP and 5% of India’s Industrial production. The supply chain of any firm is generally dependent on six driving factors out of which three are functional (information, inventory, and facilities) and 3 are logistic (sourcing, pricing, and transportation). The risk causing factors in supply chains consists of various levels of sub-factors under them. Say for instance, under supply risk, the sub-factors can be poor logistics at supplier end, poor material quality etc., under demand risk, the sub-factors can be inaccurate demand forecasting, fluctuating demand, bullwhip effect, and under logistics risk, the sub-factors can be poor transportation network, shorter lead time, stock outs. Through this study, we observe to find the effect of these factors in the supply chain. We use Failure Mode and Effect Analysis (FMEA) technique to prioritize the various types of risk into zones namely high, medium and low risk factors. Also, we use the Best Worst Method (BWM), a multi-criteria decision-making technique to find out the overall weightings of different risk factors. The combination of these methods can help an organization to prioritize various risk factors and proposing a proper risk mitigation strategy leading to increase in overall supply chain efficiency and responsiveness.

Evaluation of risks in foodgrains supply chain using failure mode effect analysis and fuzzy VIKOR

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Rishabh Rathore ◽  
J. J. Thakkar ◽  
J. K. Jha
Keyword(s):  
Risk Factors ◽  
Decision Making ◽  
Supply Chain ◽  
Failure Mode ◽  
Risk Mitigation ◽  
Supply Chain System ◽  
Effect Analysis ◽  
Content Type ◽  
Fuzzy Vikor ◽  
Multiple Attribute

PurposeThis paper investigates the risks involved in the Indian foodgrain supply chain (FSC) and proposes risk mitigation taxonomy to enable decision making.Design/methodology/approachThis paper used failure mode and effect analysis (FMEA) for risk estimation. In the traditional FMEA, risk priority number (RPN) is evaluated by multiplying the probability of occurrence, severity and detection. Because of some drawbacks of the traditional FMEA, instead of calculating RPN, this paper prioritizes the FSC risk factors using fuzzy VIKOR. VIKOR is a multiple attribute decision-making technique which aims to rank FSC risk factors with respect to criteria.FindingsThe findings indicate that “technological risk” has a higher impact on the FSC, followed by natural disaster, communication failure, non-availability of procurement centers, malfunctioning in PDS and inadequate storage facility. Sensitivity analysis is performed to check the robustness of the results.Practical implicationsThe outcomes of the study can help in deriving detailed risk mitigation strategy and risk mitigation taxonomy for the improved resilience of FSC.Originality/valueSpecifically, this research investigates the risks for foodgrains supply chain system for a developing country such as India, an area which has received limited attention in the present literature.

scholarly journals Modeling Supply Chain Sustainability-Related Risks and Vulnerability: Insights from the Textile Sector of Pakistan

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Muhammad Hashim ◽  
Muhammad Nazam ◽  
Muhammad Zia-ur-Rehman ◽  
Muhammad Abrar ◽  
Sajjad Ahmad Baig ◽  
...  
Keyword(s):  
Risk Factors ◽  
Supply Chain ◽  
Large Scale ◽  
Risk Mitigation ◽  
Sustainable Manufacturing ◽  
Manufacturing Industries ◽  
Sustainable Supply Chain ◽  
Effect Analysis ◽  
Pareto Analysis ◽  
Textile Supply Chain

Abstract Sustainability-related risk and vulnerability management have attained significant attention from academia and industry. Manufacturing industries in developing countries such as Pakistan are under severe economic pressure and striving to boost sustainable supply chain practices for achieving business excellence. In this context, the objectives of the present research are to examine the critical supply chain risks associated with sustainable development goals, namely social, economic, and environmental factors. The failure mode and effect analysis (FMEA) technique is employed for categorizing the risk factors and Pareto analysis for highlighting the more crucial and risky factors. For this purpose, a large-scale survey was carried out in the textile industries of Pakistan to develop a risk mitigation model for sustainability-related risks and vulnerability in a textile supply chain (TSC). It captures the input expressions of experts for risk factors, namely severity (s), occurrence (o), and detection (d) for calculating the risk priority numbers (RPNs) of identified alternatives. The results depict that endogenous environmental risks categorize as the most significant for the textile manufacturing industries, and the interfaces between the various risks associated with sustainability-related are also found very high. This study would be a toolkit for the industrial managers and policy-makers for creating sustainable manufacturing culture on organizational premises.

Bullwhip Effect Analysis in Two-Level Supply Chain Distribution Network Using Different Demand Forecasting Technology

2016 ◽  
Vol 33 (03) ◽  
pp. 1650016 ◽  
Author(s):  
Xi Gang Yuan ◽  
Nan Zhu
Keyword(s):  
Supply Chain ◽  
Moving Average ◽  
Minimum Mean Square Error ◽  
Distribution Network ◽  
Demand Forecasting ◽  
Bullwhip Effect ◽  
Effect Analysis ◽  
First Order ◽  
Basic Work ◽  
The Impact

Following the basic work conducted by Lee et al. [(1997a), The bullwhip effect in supply chains. Sloan Management Review, 38(3), 93–102; (1997b), Information distribution in a supply chain: The bullwhip effect. Management Science, 43(4), 546–558] and using two first-order autoregressive AR(1) models, respectively, this paper provides three quantitative models of the bullwhip effect of the two-level supply chain distribution network consisting of a single manufacturer and two retailers. The paper assumes that two retailers adopt the order point method, uses three kinds of demand forecasting technology, i.e., moving average, exponential smoothing and minimum mean square error methods, respectively, provides three corresponding models for analyzing the impact of bullwhip effect of two-level supply chain distribution network. At the same time, this paper compares and analyzes the results of the three models through simulation.

Buffet Effect Simulation Model on Demand Chain Management

2010 ◽  
Vol 44-47 ◽  
pp. 688-692
Author(s):  
Xiao Yan Wang ◽  
Jian Sun
Keyword(s):  
Supply Chain ◽  
Side Information ◽  
Bullwhip Effect ◽  
Chain Management ◽  
Demand Chain ◽  
Supply Chain Efficiency ◽  
Demand Fluctuations ◽  
Partner Collaboration ◽  
Transmission Ability ◽  
Demand Chain Management

Bullwhip effect means the magnification of demand fluctuations, which is evident in a supply chain when demand increases and decreases, while the concept of Demand Chain Management means to make the planning on the basis of the demand side information so as to solve the problem of inconsistent upstream and downstream information by means of partner collaboration in the supply chain. Demand chain emphasizes the customer demand as its core value so as to achieve the best balance between the supply chain efficiency and customer satisfaction. Compared with the supply chain, the demand chain advises the enterprise to strengthen the information transmission ability to promote the performance. Under the demand chain management, the extent of bullwhip effect are weakened, and the fluctuation range against demand chain management is lower than against traditional supply chain.

Bullwhip Effect Analysis in a Supply Chain

2012 ◽  
pp. 646-665
Author(s):  
Mehdi Najafi ◽  
Reza Zanjirani Farahani
Keyword(s):  
Supply Chain ◽  
Statistical Method ◽  
Moving Average ◽  
Bullwhip Effect ◽  
Effect Analysis ◽  
Forecasting Methods ◽  
Effect Measure ◽  
Forecasting Method ◽  
Efficiency And Effectiveness ◽  
Main Factors

In today’s world, all enterprises in a supply chain are attempting to increase both their and the supply chain’s efficiency and effectiveness. Therefore, identification and consideration of factors that prevent enterprises to attain their expected/desired levels of effectiveness are very important. Since bullwhip effect is one of these main factors, being aware of its reasons help enterprises decrease the severity of bullwhip effect by opting proper decisions. Now that forecasting method is one of the most important factors in increasing or decreasing the bullwhip effect, this chapter considers and compares the effects of various forecasting methods on the bullwhip effect. In fact, in this chapter, the effects of various forecasting methods, such as Moving Average, Exponential Smoothing, and Regression, in terms of their associated bullwhip effect, in a four echelon supply chain- including retailer, wholesaler, manufacturer, and supplier- are considered. Then, the bullwhip effect measure is utilized to compare the ineffectiveness of various forecasting methods. Owing to this, the authors generate two sets of demands in the two cases where the demand is constant (no trend) and has an increasing trend, respectively. Then, the chapter ranks the forecasting methods in these two cases and utilizes a statistical method to ascertain the significance of differences among the effects of various methods.

Quantifying the bullwhip effect in a supply chain with stochastic lead time

2006 ◽  
Vol 173 (2) ◽  
pp. 617-636 ◽  
Author(s):  
Jeon G. Kim ◽  
Dean Chatfield ◽  
Terry P. Harrison ◽  
Jack C. Hayya
Keyword(s):  
Supply Chain ◽  
Lead Time ◽  
Bullwhip Effect ◽  
Stochastic Lead Time

scholarly journals Reverse logistics risk management: identification, clustering and risk mitigation strategies

2020 ◽  
Vol 58 (7) ◽  
pp. 1449-1474 ◽  
Author(s):  
Hamidreza Panjehfouladgaran ◽  
Stanley Frederick W.T. Lim
Keyword(s):  
Risk Factors ◽  
Risk Management ◽  
Supply Chain ◽  
Reverse Logistics ◽  
Risk Mitigation ◽  
Mitigation Strategies ◽  
Supply Chain Risk Management ◽  
Supply Chain Risk ◽  
Content Type ◽  
Risk Mitigation Strategies

PurposeReverse logistics (RL), an inseparable aspect of supply chain management, returns used products to recovery processes with the aim of reducing waste generation. Enterprises, however, seem reluctant to apply RL due to various types of risks which are perceived as posing an economic threat to businesses. This paper draws on a synthesis of supply chain and risk management literature to identify and cluster RL risk factors and to recommend risk mitigation strategies for reducing the negative impact of risks on RL implementation.Design/methodology/approachThe authors identify and cluster risk factors in RL by using risk management theory. Experts in RL and supply chain risk management validated the risk factors via a questionnaire. An unsupervised data mining method, self-organising map, is utilised to cluster RL risk factors into homogeneous categories.FindingsA total of 41 risk factors in the context of RL were identified and clustered into three different groups: strategic, tactical and operational. Risk mitigation strategies are recommended to mitigate the RL risk factors by drawing on supply chain risk management approaches.Originality/valueThis paper studies risks in RL and recommends risk management strategies to control and mitigate risk factors to implement RL successfully.

Analysis and Verification of Bullwhip Effect Based on System Dynamics

2013 ◽  
Vol 340 ◽  
pp. 312-319
Author(s):  
Fu Xin Yang ◽  
Bai Lan Zhang ◽  
Zhi Yuan Su
Keyword(s):  
Supply Chain ◽  
System Dynamics ◽  
Correlation Coefficient ◽  
Lead Time ◽  
Process Model ◽  
Autoregressive Process ◽  
Bullwhip Effect ◽  
Simulation Software ◽  
Simulation Results ◽  
The Impact

To study the bullwhip effect (BWE) in supply chain (SC), this paper built two system dynamics (SD) models strictly referring to the AR(1) (autoregressive process) model constructed by Frank Chen. Using Vensim simulation software, it analyzed the impact of the correlation coefficient of demand, lead time, smoothing time of demand and information to BWE, and then put forward some proposals on how to reduce BWE. By contrasting the simulation results of SD models with the AR(1) models', it verifies the validity of the AR(1) model of Frank Chen from a simulation perspective. It also shows SD model combined with AR(1) model can analyze BWE in SC reliably and powerfully.

Sign in / Sign up

Export Citation Format

Share Document