A fuzzy echelon approach for inventory management in supply chains

Giannoccaro, Ilaria; Pontrandolfo, Pierpaolo; Scozzi, Barbara

doi:10.1016/s0377-2217(02)00441-1

Cited by 178 publications

(78 citation statements)

References 17 publications

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“…Fuzzy approaches in the SC management literature can be found in a range of different ways: to estimate the operative cycle times in the SC [24], to select ideal partners [25], to study fuzzy demands, supplies and/or costs related to the right inventory management policies [26][27][28][29][30]. In spite of these works, there is a dearth of studies applying fuzzy methodologies to traditional SC [31], which becomes more acute in the RL field.…”

Section: Justification Of the Fuzzy Methodology In Rl Contextmentioning

confidence: 99%

A Decision-Making Support System on a Products Recovery Management Framework. A Fuzzy Approach

Fernández

Puente

Fernández

et al. 2008

Concurrent Engineering

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The considerable amount of uncertainty involved in defining the factors that affect reverse logistics (RL) decision-making and the complex interrelationships between those factors make it rather difficult to decide what recovery policy a business should pursue. This article proposes a fuzzy system that helps in such decision-making and thereby mitigates these difficulties. The knowledge related to the decision is incorporated into the system by means of conditional rules, which serve to provide the ideal recovery policy for each particular case. The model proposed is applied to the analysis of a number of examples and proves to be a versatile tool that provides coherent results. These characteristics could be of critical importance especially in the point of entry into the RL pipeline and in the centralized return centres.

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Section: Justification Of the Fuzzy Methodology In Rl Contextmentioning

confidence: 99%

A Decision-Making Support System on a Products Recovery Management Framework. A Fuzzy Approach

Fernández

Puente

Fernández

et al. 2008

Concurrent Engineering

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“…Peidro et al (Peidro et al, 2009) used a fuzzy linear programming model to simulate the behaviours of a SC. Other authors used FL to face the inventory management issue (Petrovic et al, 1998;Petrovic et al, 1999;Giannoccaro et al, 2003;Carlsson & Fullér, 2002). FL also contributed to modelling the vendor selection strategies (Kumar et al, 2004;Amid et al, 2006).…”

Section: Managing Uncertainty In the Scmentioning

confidence: 99%

The role of uncertainty in supply chains under dynamic modeling

Fera¹,

Fruggiero²,

Lambiase³

et al. 2017

10.5267/j.ijiec

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The uncertainty in the supply chains (SCs) for manufacturing and services firms is going to be, over the coming decades, more important for the companies that are called to compete in a new globalized economy. Risky situations for manufacturing are considered in trying to individuate the optimal positioning of the order penetration point (OPP). It aims at defining the best level of information of the client's order going back through the several supply chain (SC) phases, i.e. engineering, procurement, production and distribution. This work aims at defining a system dynamics model to assess competitiveness coming from the positioning of the order in different SC locations. A Taguchi analysis has been implemented to create a decision map for identifying possible strategic decisions under different scenarios and with alternatives for order location in the SC levels. Centralized and decentralized strategies for SC integration are discussed. In the model proposed, the location of OPP is influenced by the demand variation, production time, stock-outs and stock amount. Results of this research are as follows: (i) customer-oriented strategies are preferable under high volatility of demand, (ii) production-focused strategies are suggested when the probability of stock-outs is high, (iii) no specific location is preferable if a centralized control architecture is implemented, (iv) centralization requires cooperation among partners to achieve the SC optimum point, (v) the producer must not prefer the OPP location at the Retailer level when the general strategy is focused on a decentralized approach.

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“…Petrovic (2001) develops a simulation tool, SCSIM, for analyzing SC behaviour and performance in the presence of uncertainty modelled by fuzzy sets. Giannoccaro et al (2003) develop a methodology to define inventory management policies in a SC, which was based on the echelon stock concept (Clark & Scarf, 1960) and the fuzzy set theory was used to model uncertainty associated with both demand and inventory costs. Carlsson and Fuller (2002) propose a fuzzy logic approach to reduce the bullwhip effect.…”

Section: Literature Reviewmentioning

confidence: 99%