scholarly journals Analysis of Variance Amplification and Service Level in a Supply Chain with Correlated Demand

2020 ◽  
Vol 12 (16) ◽  
pp. 6470 ◽  
Author(s):  
Ahmed Shaban ◽  
Mohamed A. Shalaby ◽  
Giulio Di Gravio ◽  
Riccardo Patriarca
Keyword(s):  
Supply Chain ◽  
Performance Measures ◽  
Bullwhip Effect ◽  
Service Level ◽  
Operating Conditions ◽  
Supply Chain Performance ◽  
Chain Model ◽  
Ordering Policy ◽  
Smoothing Parameters ◽  
The Impact

The bullwhip effect reflects the variance amplification of demand as they are moving upstream in a supply chain, and leading to the distortion of demand information that hinders supply chain performance sustainability. Extensive research has been undertaken to model, measure, and analyze the bullwhip effect while assuming stationary independent and identically distributed (i.i.d) demand, employing the classical order-up-to (OUT) policy and allowing return orders. On the contrary, correlated demand where a period’s demand is related to previous periods’ demands is evident in several real-life situations, such as demand patterns that exhibit trends or seasonality. This paper assumes correlated demand and aims to investigate the order variance ratio (OVR), net stock amplification ratio (NSA), and average fill rate/service level (AFR). Moreover, the impact of correlated demand on the supply chain performance under various operational parameters, such as lead-time, forecasting parameter, and ordering policy parameters, is analyzed. A simulation modeling approach is adopted to analyze the response of a single-echelon supply chain model that restricts return orders and faces a first order autoregressive demand process AR(1). A generalized order-up-to policy that allows order smoothing through the proper tuning of its smoothing parameters is applied. The characterization results confirm that the correlated demand affects the three performance measures and interacts with the operating conditions. The results also indicate that the generalized OUT inventory policy should be adopted with the correlated demand, as its smoothing parameters can be adapted to utilize the demand characteristics such that OVR and NSA can be reduced without affecting the service level (AFR), implying sustainable supply chain operations. Furthermore, the results of a factorial design have confirmed that the ordering policy parameters and their interactions have the largest impact on the three performance measures. Based on the above characterization, the paper provides management with means to sustain good performance of a supply chain whenever a correlated demand pattern is realized through selecting the control parameters that decrease the bullwhip effect.

Information Sharing in Supply Chain Management with Demand Uncertainty

2011 ◽  
pp. 44-62 ◽  
Author(s):  
Zhensen Huang ◽  
Aryya Gangopadhyay
Keyword(s):  
Supply Chain ◽  
Information Sharing ◽  
Demand Uncertainty ◽  
Bullwhip Effect ◽  
Supply Chain Performance ◽  
Chain Model ◽  
Simulation Approach ◽  
Supply Chain Model ◽  
Trading Partners ◽  
The Impact

Information sharing is a major strategy to counteract the amplification of demand fluctuation going up the supply chain, known as the bullwhip effect. However, sharing information through interorganizational channels can raise concerns for business management from both technical and commercial perspectives. The existing literature focuses on examining the value of information sharing in specific problem environments with somewhat simplified supply chain models. The present study takes a simulation approach in investigating the impact of information sharing among trading partners on supply chain performance in a comprehensive supply chain model that consists of multiple stages of trading partners and multiple players at each stage.

scholarly journals Exploring Different Order Decision Behaviors with Bullwhip Effect and Service Level Measures in Supply Chain System

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Zhuoqun Li ◽  
Guangle Yan
Keyword(s):  
Supply Chain ◽  
Bullwhip Effect ◽  
Service Level ◽  
Decision Makers ◽  
Supply Chain Performance ◽  
Supply Chain System ◽  
Study Results ◽  
The Individual ◽  
Two Parameters ◽  
The Impact

Since decision makers’ bounded rationality would impact supply chain performance, it is necessary to explore how the individual decisions work in the supply chain. This paper investigates bullwhip effect variation and service level tendency while the decisions are made by different decision makers. Based on the existing study results, the paper establishes a system dynamics model of supply chain conforming to modern supply chain characters. In the model, two adjustment parameters are adopted to describe individual differences in decision makers. The simulation result demonstrates that the behavioral adjustment with different extent results in different supply chain performance. The impact of two parameters is very different. The decision makers should try to avoid the overadjustment to the scarcity of supply from their upstream member.

scholarly journals Exploring the Bullwhip Effect and Inventory Stability in a Seasonal Supply Chain

10.5772/56833 ◽  
2013 ◽  
Vol 5 ◽  
pp. 23 ◽  
Author(s):  
Francesco Costantino ◽  
Giulio Di Gravio ◽  
Ahmed Shaban ◽  
Massimo Tronci
Keyword(s):  
Supply Chain ◽  
Information Sharing ◽  
Performance Measures ◽  
Moving Average ◽  
Bullwhip Effect ◽  
Fill Rate ◽  
Supply Chain Dynamics ◽  
Effect Ratio ◽  
The Impact ◽  
Demand Seasonality

The bullwhip effect is defined as the distortion of demand information as one moves upstream in the supply chain, causing severe inefficiencies in the whole supply chain. Although extensive research has been conducted to study the causes of the bullwhip effect and seek mitigation solutions with respect to several demand processes, less attention has been devoted to the impact of seasonal demand in multi-echelon supply chains. This paper considers a simulation approach to study the effect of demand seasonality on the bullwhip effect and inventory stability in a four-echelon supply chain that adopts a base stock ordering policy with a moving average method. The results show that high seasonality levels reduce the bullwhip effect ratio, inventory variance ratio, and average fill rate to a great extent; especially when the demand noise is low. In contrast, all the performance measures become less sensitive to the seasonality level when the noise is high. This performance indicates that using the ratios to measure seasonal supply chain dynamics is misleading, and that it is better to directly use the variance (without dividing by the demand variance) as the estimates for the bullwhip effect and inventory performance. The results also show that the supply chain performances are highly sensitive to forecasting and safety stock parameters, regardless of the seasonality level. Furthermore, the impact of information sharing quantification shows that all the performance measures are improved regardless of demand seasonality. With information sharing, the bullwhip effect and inventory variance ratios are consistent with average fill rate results.

scholarly journals Study of the Bullwhip Effect in a Multistage Supply Chain with Callback Structure considering Two Retailers

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Junhai Ma ◽  
Liqing Zhu ◽  
Ye Yuan ◽  
Shunqi Hou
Keyword(s):  
Supply Chain ◽  
Moving Average ◽  
Bullwhip Effect ◽  
Lead Times ◽  
Chain Model ◽  
Supply Chain System ◽  
Forecasting Method ◽  
Supply Chain Model ◽  
The Impact ◽  
Demand Processes

With the purpose of researching the bullwhip effect when there is a callback center in the supply chain system, this paper establishes a new supply chain model with callback structure, which has a material supplier, a manufacture, and two retailers. The manufacture and retailers all employ AR(1) demand processes and use order-up-to inventory policy when they make order decisions. Moving average forecasting method is used to measure the bullwhip effect of each retailer and manufacture. We investigate the impact of lead-times of retailers and manufacture, forecasting precision, callback index, and marketing share on the bullwhip effect of both retailers and manufacture. Then we use the method of numerical simulation to indicate the different parameters in this supply chain. Furthermore, this paper puts forward some suggestions to help the enterprises to control the bullwhip effect in the supply chain with callback structure.

scholarly journals Decoupling paradigm of push-pull theory of oscillation in the FMCG industry

2016 ◽  
Vol 47 (2) ◽  
pp. 53-66 ◽  
Author(s):  
T.P. Mbhele
Keyword(s):  
Supply Chain ◽  
Bullwhip Effect ◽  
Modern Technology ◽  
Supply Chain Performance ◽  
Chain Model ◽  
Supply Chain Network ◽  
Innovative Strategies ◽  
Supply Chain Model ◽  
Demand Information ◽  
Order Variability

The amplification of demand order variability germinates from distorted demand information upstream while sometimes reacting to demand-driven inventory positioning influenced by the custodians of downstream information. This studyuses factor analysis to tentatively develop a supply chain model to enhance the competence of supply chain performance in terms of responsiveness, connectivity and agility. The results of the analysis indicate that the magnitude of control on the bullwhip effect and access to economic information on demand orders in the supply chain network are associated with the modelling of the push-pull theory of oscillation on three mirror dimensions of supply chain interrelationships (inventory positioning, information sharing and electronically-enabled supply chain systems). The findings provide the perspective on managing amplification in consumer demand order variability upstream in the supply chain network while enhancing the overall efficiency of supply chain performance. This article provides insight into the use of innovative strategies and modern technology to enhance supply chain visibility through integrated systems networks.

Configuration of supply chain integration and delivery performance

2016 ◽  
Vol 11 (1) ◽  
pp. 43-74 ◽  
Author(s):  
Premaratne Samaranayake ◽  
Tritos Laosirihongthong
Keyword(s):  
Supply Chain ◽  
Performance Evaluation ◽  
Performance Measures ◽  
Supply Chain Performance ◽  
Chain Model ◽  
Content Type ◽  
Delivery Performance ◽  
Supply Chain Model ◽  
Evaluation And Monitoring ◽  
Integrated Supply Chain

Purpose – The purpose of this paper is to develop a conceptual framework of integrated supply chain model that can be used to measure, evaluate and monitor operational performance under dynamic and uncertain conditions. Design/methodology/approach – The research methodology consists of two stages: configuration of a conceptual framework of integrated supply chain model linked with performance measures and illustration of the integrated supply chain model and delivery performance using a case of dairy industry. The integrated supply chain model is based on a unitary structuring technique and forms the basis for measuring and evaluating supply chain performance. Delivery performance with variation of demand (forecast and actual) is monitored using a fuzzy-based decision support system, based on three inputs: capacity utilization (influenced by production disruption), raw materials shortage and quality of dairy products. Findings – Integration of supply chain components (materials, resources, operations, activities, suppliers, etc.) of key processes using unitary structuring approach enables information integration in real time for performance evaluation and monitoring in complex supply chain situations. In addition, real-time performance monitoring is recognized as being of great importance for supply chain management in responding to uncertainties inherent in the operational environment. Research limitations/implications – Implementation of an integrated model requires maintenance of supply chain components with all necessary data and information in a system environment such as enterprise resource planning. Practical implications – The integrated model provides decision-makers with an overall view of supply chain components and direct links that need to be maintained for supply chain performance evaluation and monitoring. Wider adaptation and diffusion of the proposed model require further validation of the model and feasibility of implementation, using real-time data and information on selected performance measures. Originality/value – Integration of supply chain components across supply chain processes directly linked with performance measures is a novel approach for effective supply chain performance evaluation and monitoring in complex supply chains under dynamic and uncertain conditions.

Understanding big data-driven supply chain and performance measures for customer satisfaction

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ramadas Thekkoote
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Customer Satisfaction ◽  
Performance Measures ◽  
Data Driven ◽  
Equation Modeling ◽  
Supply Chain Performance ◽  
Content Type ◽  
Different Dimensions ◽  
The Impact

PurposeSupply chain analytics with big data capability are now growing to the next frontier in transforming the supply chain. However, very few studies have identified its different dimensions and overall effects on supply chain performance measures and customer satisfaction. The aim of this paper to design the data-driven supply chain model to evaluate the impact on supply chain performance and customer satisfaction.Design/methodology/approachThis research uses the resource-based view, emerging literature on big data, supply chain performance measures and customer satisfaction theory to develop the big data-driven supply chain (BDDSC) model. The model tested using questionnaire data collected from supply chain managers and supply chain analysts. To prove the research model, the study uses the structural equation modeling technique.FindingsThe results of the study identify the supply chain performance measures (integration, innovation, flexibility, efficiency, quality and market performance) and customer satisfaction (cost, flexibility, quality and delivery) positively associated with the BDDSC model.Originality/valueThis paper fills the significant gap in the BDDSC on the different dimensions of supply chain performance measures and their impacts on customer satisfaction.

A Colored Petri Net-based modeling method for supply chain inventory management

SIMULATION ◽  
2021 ◽  
pp. 003754972110387
Author(s):  
Maria Drakaki ◽  
Panagiotis Tzionas
Keyword(s):  
Supply Chain ◽  
Inventory Management ◽  
Petri Net ◽  
Simulation Modeling ◽  
Modeling Method ◽  
Operating Conditions ◽  
Supply Chain Performance ◽  
Colored Petri Net ◽  
Wide Range ◽  
The Impact

Supply chain planning and control approaches need to include a wide range of factors in order to optimize production. Supply chain simulation modeling has been identified as a potential methodology toward increasing the efficiency of current systems to this end. The purpose of this paper is to evaluate the impact of inventory management decisions on supply chain performance using a Colored Petri Net based simulation modeling method. The presented method uses hierarchical timed Colored Petri Nets to model inventory management in a multi-stage serial supply chain, under normal operating conditions, and under the presence of disruptions, for both traditional and information sharing configurations. Disruptions are introduced as canceled orders and canceled deliveries, in a time period. Supply chain performance has been evaluated, in the context of order variance amplification and stockout amplification. Validation of the method is done by comparing results obtained for the bullwhip effect with published literature results, as well as by state space analysis results.

scholarly journals Green Supply Chain Performance Prediction Using a Bayesian Belief Network

2020 ◽  
Vol 12 (3) ◽  
pp. 1101 ◽  
Author(s):  
Md. Rabbi ◽  
Syed Mithun Ali ◽  
Golam Kabir ◽  
Zuhayer Mahtab ◽  
Sanjoy Kumar Paul
Keyword(s):  
Decision Making ◽  
Supply Chain ◽  
Performance Measures ◽  
Performance Indicators ◽  
Bayesian Belief Network ◽  
Sustainable Growth ◽  
Green Supply Chain ◽  
Supply Chain Performance ◽  
Belief Network ◽  
The Impact

Green supply chain management (GSCM) has emerged as an important issue to lessen the impact of supply chain activities on the natural environment, as well as reduce waste and achieve sustainable growth of a company. To understand the effectiveness of GSCM, performance measurement of GSCM is a must. Monitoring and predicting green supply chain performance can result in improved decision-making capability for managers and decision-makers to achieve sustainable competitive advantage. This paper identifies and analyzes various green supply chain performance measures and indicators. A probabilistic model is proposed based on a Bayesian belief network (BBN) for predicting green supply chain performance. Eleven green supply chain performance indicators and two green supply chain performance measures are identified through an extensive literature review. Using a real-world case study of a manufacturing industry, the methodology of this model is illustrated. Sensitivity analysis is also performed to examine the relative sensitivity of green supply chain performance to each of the performance indicators. The outcome of this research is expected to help managers and practitioners of GSCM improve their decision-making capability, which ultimately results in improved overall organizational performance.

scholarly journals Measuring and eliminating the bullwhip in closed loop supply chains using control theory and Internet of Things

2021 ◽  
Author(s):  
Christos I. Papanagnou
Keyword(s):  
Supply Chain ◽  
Internet Of Things ◽  
Supply Chains ◽  
Closed Loop ◽  
Bullwhip Effect ◽  
The Internet ◽  
Chain Model ◽  
Closed Loop Supply Chains ◽  
The Impact ◽  
The Internet Of Things

AbstractClosed-loop supply chains are complex systems as they involve the seamless backward and forward flow of products and information. With the advent of e-commerce and online shopping, there has been a growing interest in product returns and the associated impact on inventory variance and the bullwhip effect. In this paper, a novel four-echelon closed-loop supply chain model is presented, where base-stock replenishment policies are modelled by means of a proportional controller. A stochastic state-space model is implemented, initially to capture the supply chain dynamics while the model is analysed under stationarity conditions with the aid of a covariance matrix. This allows the bullwhip effect to be expressed as a function of replenishment policies and product return rates. Next, an optimisation method is introduced to study the impact of the Internet of Things on inventory variance and the bullwhip effect. The results show that the Internet of Things can reduce costs associated with inventory fluctuations and eliminate the bullwhip effect in closed-loop supply chains.

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