Performance of Supply Chain in an Uncertain Environment Using Fuzzy Logic (original) (raw)

SUPPLY CHAIN MODELING WITH UNCERTAINTY IN DEMAND AND SUPPLY USING FUZZY LOGIC

IAEME, 2019

In this paper, we have analyzed a serial operational level Supply Chain Performance like fill rate, Build –To - Stock etc., under an uncertainty environment for the optimality using a stochastic customer order. It evaluates minimum cost in supply chain, simultaneously optimize quantitative decision variables and illustrates the significance of capacity variable. Numerical examples are presented to illustrate the benefit of the proposed strategies and the effects of changes on the cost and parameters are studied

Supply chain modelling using fuzzy sets

International Journal of Production Economics, 1999

This paper considers a production supply chain (SC) with all facilities in a serial connection. The SC includes inventories and production facilities between them. It is assumed that the SC operates in an uncertain environment. Uncertainty is associated with: (1) customer demand, (2) supply deliveries along the SC and (3) external or market supply. Uncertainties are described by vague and imprecise phrases that are interpreted and represented by fuzzy sets. The SC fuzzy model described in this paper is developed to determine the order quantities for each inventory in the SC in the presence of uncertainties, that give an acceptable service level of the SC at reasonable total cost. Two control concepts of the SC are treated: (1) decentralised control of each inventory and (2) partial coordination in the inventories control. A special purpose simulator has been developed for examining the dynamics and performance of all the parts of the SC and the SC as a whole. Various simulation tests have been carried out to assess particularly the effects of uncertain external supply on the SC service level. Different approaches to improve SC performance in an uncertain environment have been simulated and analysed.

Handling uncertainty in supply chain management

Real-life problems don't usually have complete certainty in its variables. Classical mathematical representation isn't always appropriate to deal with real-life problems. Supply chain (SC) is one of these real-life problems with uncertain decision making variables. The paper aims to employ fuzzy and rough sets in the representation of uncertainty which is always valid in SC problems. Rough sets are used to give a better optimization basing on better accurate mathematical models. SC linear programming model tries to minimize the total transportation costs between elements of Supply chain management (SCM). It is assumed that the SC works in an uncertain environment. Uncertainty may occur in customer demand, supply deliveries in the SC and market supply. Fuzzy sets and rough sets are the most adequate for this application in respect of their suitability for representing uncertain information. Finally, comparison between fuzzy set and rough set optimization results under uncertainty is presented. Rough set is expected to be convenient for decision maker for giving a wide optimized performance range.

A fuzzy logic approach to supply chain performance management

The aim of this paper is to propose a supply chain performance model based on fuzzy logic to predict performance based on causal relationships between metrics of the Supply Council Operations Reference model (SCOR) model. The main contribution and originality of this proposal relates to the application of Fuzzy logic to predict performance based on performance metrics levels 1 and 2 of the SCOR model. Fuzzy logic is a technique suitable for dealing with uncertainty and subjectivity, which becomes an interesting auxiliary approach to manage performance of supply chains. A descriptive quantitative approach was adopted as research method, based on the prediction model. Statistical analysis of the prediction model results confirmed the relevance of the causal relationships embedded in the model. The findings reinforce the proposition that the adoption of a prediction model based on fuzzylogic and on metrics of the SCOR model seems to be a feasible technique to help managers in the decision making process of managing performance of supply chains.

Inventory Management Under Uncertainty Condition with Fuzzy Logic: A Literature Review

Jurnal sistem teknik industri, 2023

Fuzzy set theory, or fuzzy logic, has been in use in inventory systems since the 1980s, with vaguely defined, ill-defined, or imprecise values, or decisions based on individual subjective beliefs. Provides a framework for describing the parameters that can be passed. There are many uncertainties in inventory management that can arise due to many things, such as: Order Changes, Random Supplier Capabilities, or Unexpected Events. Fuzzy logic as a method of inventory control that provides a framework by considering parameters that are vague or poorly defined, or whose values are imprecise and determined based on individual subjective beliefs make it available. The purpose of this white paper is to review previous studies using fuzzy logic in inventory management to see the uncertainty variables used. The method used is a thematic analysis of articles on the application of fuzzy logic in various industries. The results of the review show that variables like fuzzy logic make it easy to obtain results from uncertainty variables that can be applied to industrial activities to enable manufacturing activities to be carried out effectively and efficiently increase.

Simulation of supply chain behaviour and performance in an uncertain environment

International Journal of Production Economics, 2001

This paper describes a special purpose simulation tool, SCSIM, developed for analysing supply chain (SC) behaviour and performance in the presence of uncertainty. SCSIM treats a SC which includes a raw material inventory, a number of in-process inventories, an end-product inventory and production facilities between them, linked in a series. Main sources of uncertainty inherent in the serial SC and its environment are identi"ed, including customer demand, external supply of raw material and lead times to the facilities. Uncertainties perceived in these SC data are described by imprecise natural language expressions and they are modelled in SCSIM by fuzzy sets. Two types of models are combined in SCSIM: (1) SC fuzzy analytical models to determine the optimal order-up-to levels for all inventories in a fuzzy environment and (2) a SC simulation model to evaluate SC performance achieved over time by applying the order-up-to levels recommended by the fuzzy models. SCSIM can be used for various SCs analyses to gain a better understanding of SC behaviour and performance in the presence of uncertainty and to enhance decision making on operational SC control parameters. The application of SCSIM in analysing and quantifying the e!ects of changing uncertainty in customer demand is discussed and illustrated by a numerical example.

Seeking Stability of Supply Chain Management Decisions Under Uncertain Criteria

2012

This paper tackles the question of the anticipation of the supply chain partner's decisional behaviour under uncertain criteria. In other words, we propose a model to support sequential decisions under uncertainty where the decision maker has to make hypothesis about the decision criteria. For example, Hurwicz criterion weights extreme optimism and pessimism positions and a classic criticism of this criterion consisting in the difficulty of the weight assessment and the involving decision instability. To achieve this, we present a method based on fuzzy representation of weight vision. Finally, the model allows sequential decision of a Decision Tree to be compute thanks a pignistic probabilities treatment of the fuzzy representation of the decision maker optimism-pessimism index. This approach is illustrated through an industrial case study.

FUZZY LOGIC APPLICATIONS IN SUPPLY CHAIN PERFORMANCE MEASUREMENT

Supply chain, undoubtedly, is an area of wide research and application. It apparently is an incredible network, which comes in to existence with the formation of every organization and, becomes automatically complex as the organization progresses. There are many aspects in supply chain that interest researchers, out of which, development of a supply chain performance measurement method, is having a lot of avenues still left to explore. Fuzzy logic techniques, on the other hand, has wide applications across many fields and, is known to be one of the best means for dealing with uncertainty issues found in data sets. This study is aimed at exploring instances where fuzzy logic technique has been applied to measure supply chain performance. The objective of this study is to capture milestones of fuzzy logic application in supply chain performance measurement, in order to support the interested researchers and practitioners on their further research, and potential real world applications. The findings are anticipated to be of value for academia working on supply chain performance measurement, who are particularly looking at improving the accuracy of results.

Uncertain Supply Chain Management

The purpose of this study is to analyze the effect of viral marketing and purchase intention, the influence between viral marketing and supply chain performance, the influence buzz marketing and purchase intention, the positive buzz marketing and supply chain performance, and the influence between purchase intention and supply chain performance. This study uses quantitative methods and data analysis techniques Structural Equation Modeling Equation Modeling using SmartPLS 3.0 software. The sample selection method used the snowball sampling method. Online questionnaires were sent to respondents as many as 120 Freight Forwarders in DKI Jakarta. Based on data analysis, it was found that there is a positive influence between viral marketing and purchase intention, there is a positive influence between viral marketing and supply chain performance, there is a positive influence between buzz marketing and purchase intention. There is a positive influence between marketing buzz and supply chain performance. There is a positive influence between purchase intention and supply chain performance. The novelty of this research is a model of the relationship between viral and buzz marketing on purchase intention and supply chain performance and the results of this study can be applied in other organizations and in other countries.

Inventory policies in a fuzzy uncertain supply chain environment

European Journal of Operational Research, 2009

Managers have begun to recognize that effectively managing risks in their business operations plays an important role in successfully managing their inventories. Accordingly, we develop a ðQ ; rÞ model based on fuzzy-set representations of various sources of uncertainty in the supply chain. Sources of risk and uncertainty in our model include demand, lead time, supplier yield, and penalty cost. The naturally imprecise nature of these risk factors in managing inventories is represented using triangular fuzzy numbers. In addition, we introduce a human risk attitude factor to quantify the decision maker's attitude toward the risk of stocking out during the replenishment period. The total cost of the inventory system is computed using defuzzification methods built from techniques identified in the literature on fuzzy sets. Finally, we provide numerical examples to compare our fuzzy-set computations with those generated by more traditional models that assume full knowledge of the distributions of the stochastic parameters in the system.