Fuzzy Approach to Decision Support System Design for Inventory Control and Management (original) (raw)

Fuzzy model for inventory control under uncertainty

Central European Researchers Journal, Vol.1 Issue 2, 2015

Modern enterprise faces the huge stream of rapidly changing information. Thereby competitive advantage is fast response of changing external environment by tactical decision making. Decision making problems concern a lot of present science. Many decisions are making under uncertainty or risk. It becomes necessary from faithful deterministic representation goes to sphere of associative fuzzy thinking. These steps give you guidelines for developing decision support system with neuro-fuzzy.

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.

Inventory System Design by Fuzzy Logic Control: A Case Study

Advanced Materials Research, 2013

Existing inventory lot-sizing models assume certain demand and sufficient supply, which are not practical for industry. Dynamic inventory models can serve uncertain demand, but supply is assumed to be available. However, in the real world situation, supply is not always offered. So, the method that can deal with both uncertain demand and supply should be developed. Fuzzy logic control is now being the effective methodology in many applications under uncertainty. Therefore, a fuzzy logic approach for solving the problem of inventory control under uncertainty was proposed for a case study factory. In the proposed Fuzzy Inventory System (FIS), both demand and availability of supply are described by linguistic terms. Then, the developed fuzzy rules are used to extract the fuzzy order quantity and the fuzzy reorder point continuously. The order quantity and reorder point are both adjusted according to the FIS system. In this research, the suitable ranges for the inputs of the FIS model a...

Adaptive Inventory Control Based on Fuzzy Neural Network under Uncertain Environment

Complexity, 2020

In order to achieve the actual inventory effectively tracking the target inventory under uncertain environment, this paper investigates an adaptive inventory controller for the production-inventory system. First, an uncertain production-inventory model is constructed, and then, the uncertainty of the production-inventory model is approximated by a fuzzy neural network. Secondly, in terms of the design of adaptive control law, the adaptive inventory controller is developed. Under the adaptive inventory controller, the actual inventory can track the target inventory in real time and the production-inventory system can be robustly stable in uncertain environment. Finally, the results of three simulation experiments show that the proposed adaptive inventory controller can realize both the fast tracking speed and the high tracking accuracy.

Fuzzy knowledge-based approach to treating uncertainty in inventory control

Computer Integrated Manufacturing Systems, 1994

Invenory control in complex manufacturing environments encounters various sources of uncertainity and imprecision. This paper presents one fuzzy knowledge-based approach to solving the problem of order quantity determination, in the presence of uncertain demand, lead time and actual inventory level. Uncertain data are represented by fuzzy numbers, and vaguely defined relations between them are modeled by fuzzy if-then rules. The proposed representation and inference mechanism are verified using a large numbers of examples. The results of three representative cases are summarized. Finally a comparison between the developed fuzzy knowledge-based and traditional, probabilistic approaches is discussed.

A Decision Support Tool (DST) for Inventory Management

2019

Loss of customer goodwill is one of the greatest losses a business organization can incur. One reason for such a loss is stock outage. In an attempt to solve this problem, an overstock could result. Overstock comes with an increase in the holding and carrying cost. It is an attempt to solve these twin problems that an economic order quantity (EOQ) model was developed. Information on fifteen items comprised of 10 non-seasonal and 5 seasonal items was collected from a supermarket in Ikot Ekpene town, Nigeria. The information includes the quantity of daily sales, the unit price, the lead time and the number of times an item is ordered in a month. Based on this information, a simple moving average and y-trend method of forecasting were used to forecast the sales quantity for the following month for the non-seasonal and seasonal items. The forecast value was used to compute the EOQ for each of the items. Different scenarios were created to simulate the fuzzy logic EOQ after which the res...

Inventory control as an identification problem based on fuzzy logic

Cybernetics and Systems Analysis, 2006

The approach to inventory control problems solution, which uses the available information about current demand values for the given brand of stock and it quantity-on-hand in store, is proposed. The approach is based on the method of nonlinear dependencies identification by fuzzy knowledge bases. It was shown that fuzzy model tuning by training data sample allows to approximate model control to the experienced expert decisions. The advantage of the approach proposed consists in the fact that it does not require the statement and solution of the complex problems of mathematical programming. INTRODUCTION Minimization of the inventory storage cost in enterprises and trade firms stocks including raw materials, stuffs, supplies, spare parts and products, is the most important problem of management. It is accepted that the theory of inventory control relates to operations research [1]. The models of this theory [2, 3] are built according to the classical scheme of mathematical programming: goal function is storage cost; controllable variables are time moments needed to order (or distribute) corresponding quantity of the needed stocks.

An inventory control model using fuzzy logic

International Journal of Production Economics, 2001

A model based on fuzzy logic is proposed for inventory control. The periodic review model of inventory control with variable order quantity is considered. The model takes into account the dynamics of production–inventory system in a control theoretic approach. The control module combines fuzzy logic with proportional-integral-derivative (PID) control algorithm. It simulates the decision support system to maintain the inventory of the finished product at the desired level in spite of variations in demand. The effectiveness of the proposed control model is illustrated using the actual data of a typical packaging organization operating in the Sultanate of Oman.

A framework for decision support system in inventory management area

laccei.org

The Enterprise Resource Planning's (ERP´s) are computer systems that help companies to standardize operations integrating business information. For inventory management these packages have some models for control and management purposes that require the definition of several parameters, which in many cases are set arbitrarily by the inventory managers ignoring the impact that these have over the inventories, costs and service levels. In order to help inventory managers to define, in a more technical way the parameters of inventory control policies, this paper presents a framework for decision support system for inventory management area. The model's outline includes the underlying inputs, a general description of the morel and the expected outputs, which allow companies to define more technically all the information that an inventory control model requires to improve the effectiveness of the system.