Turbulence in Market Demand on Supply Chain Networks

Cannella, Salvatore; Domínguez, Roberto; Framiñán, José M.

doi:10.2507/ijsimm15(3)5.346

Cited by 8 publications

(3 citation statements)

References 16 publications

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“…An isolated impulse in the variability of customer's orders yields the same bullwhip in serial or divergent SCNs [23]. A similar case study is investigated by Buchmeister et al [24] with a difference that used change in demand was reducing order quantity for only 5 %.…”

Section: Single Change In Demand -Case Studymentioning

confidence: 92%

Simulation of Bullwhip Effect in a Supply Chain for Lean Learning Factory Purposes

Gjeldum¹,

Crnjac²,

Bilić³

2016

Int. j. simul. model.

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A mission of the Learning Factory (LF) is a development of practice-based engineering curriculum through the simulation of a real factory by specialized equipment. In this paper, supply chain network simulator is developed, as a replacement for the traditional board Beer Game. The board Beer Game could be misused by participants in order to avoid significant bullwhip effect, which occurrence is the Beer Game main aim. The disadvantage of the board Beer Game, that participants have insight in inventory levels and placed orders and thus strategically act upon their knowledge or even activate a sort of decentralized information sharing policy, is avoided. Further considerations prove the mandatory occurrence of bullwhip effect without decentralized information sharing policy on three case studies. Optimal mathematical model for placing orders within a supply chain was defined according to multi-criteria optimization process using spreadsheet simulation. As even optimal model results with the bullwhip effect, newly developed system will always lead to the tremendous bullwhip effect during the learning process.

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Section: Single Change In Demand -Case Studymentioning

confidence: 92%

Simulation of Bullwhip Effect in a Supply Chain for Lean Learning Factory Purposes

Gjeldum¹,

Crnjac²,

Bilić³

2016

Int. j. simul. model.

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“…Organizations with complex-large and decentralized SCs are dynamic structures exchanging asymmetric information and lack coordination [52], adopting inefficiencies that lead to waste of resources, supply discrepancies, financial losses, and suboptimal performance [53,54]. Synchronization strategies use information sharing (IS) as the main component to align the SC at strategic, tactical, and operational levels, vertically and horizontally, to mitigate the BWE [55,56]. The collaboration schemas have two main scenarios; cooperation, where organizations have mutual commitment, goals, and dedicated resources [57]; coordination which joins the decision-making process and IS, to enhance cross-organizational performance [58].…”

Section: Information Shared and Synchronizationmentioning

confidence: 99%

Assessing the Bullwhip effect in supply chain: trends, gaps, and overlaps

Tamayo,

Orjuela-Castro,

Herrera

2023

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Due to operational and behavioral factors, the Bullwhip effect (BWE) arises with variations in the logistics flow, yielding uncertainty and disturbances along the supply chain (SC). Previous studies have discussed isolated approaches, underestimating the influence of behavioral aspects over operational ones, and multi-factor analysis, which helps to measure and diminish the BWE. This study systematically assesses the bodies of knowledge to identify new trends, emphasizing gaps and overlaps to underly behavioral-operational links and multi-factor scenarios through a unified frame of reference built during the paper review. The results from this research spot new BWE trends like COVID and closed-loop supply chain (CLSC) driven by disruption and return flows; the influence of behavioral causes over pricing, returning flows, production capacity, and batching; the combination of multi-factor topics like pricing, production capacity, synchronization, and order batching. This overview contributes to understanding new trends and connections in the issues, highlighting logistic challenges and opportunities to explore future studies with a broader scope. It also elucidates the BWE causes and how to handle them, which could assist in comprehending its effects and advantages on the technical elements of logistics.

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“…How to avoid the model space breakdown during the modelling of complex systems is a focal point in future research. Meanwhile, the steel production logistics system can be viewed as a complex network, and the topology and optimization of the network can be studied with theories related to the complex network [13][14][15][16][17][18][19][20][21]. So far, little research has been done on modelling of the steel production logistics system on the basis of complex system theories and methods.…”

Section: Introductionmentioning

confidence: 99%

Simulation of Steel Production Logistics System Based on Multi-Agents

Zhao¹,

Wang²,

Xu³

2017

Int. j. simul. model.

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To deal with the complex structure and difficulty in precise expression of the interaction between entities in the steel production logistics system, this paper uses complex network theory and multiagent system engineering to simulate the complex steel production logistics system, and thereby calculate related parameters, gather statistics, and optimize the steel production logistics system. According to the analysis, the processing of logistics is low in efficiency because 19 pieces of equipment are involved from the beginning of the logistics subject processing to the final formation of steel, while only a few processes are required for about half of the auxiliary material or auxiliary process. The system logistics is not compact because most of the equipment used in steel production has only a single function and a limited service area, whereas a higher degree distribution indicates a higher importance in a piece of equipment in the network. This is a must to guarantee the normal operation of the equipment with a higher degree distribution. The simulation results are basically the same with the actual production results, and the error is within the acceptable range, which proves that the simulation system is correct and effective.

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Turbulence in Market Demand on Supply Chain Networks

Cited by 8 publications

References 16 publications

Simulation of Bullwhip Effect in a Supply Chain for Lean Learning Factory Purposes

Simulation of Bullwhip Effect in a Supply Chain for Lean Learning Factory Purposes

Assessing the Bullwhip effect in supply chain: trends, gaps, and overlaps

Simulation of Steel Production Logistics System Based on Multi-Agents

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