Decision Support System for Inventory Management in Pharmacy Using Fuzzy Analytic Hierarchy Process and Sequential Pattern Analysis Approach (original) (raw)
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Fuzzy Tsukamoto based Decision Support Model for Purchase Decision in Pharmacy Company
International Journal of Recent Technology and Engineering (IJRTE), 2019
The difficulty in determining a number of item purchased is one of essential activities in inventory management. This study scientifically proposes a decision support model to decide how much number of next item purchased by a pharmacy company. The main objective of the developed model is to control a minimum stock at a certain time and condition and support in making the decision on how many items should be purchased at next time. Decision support model considers two independent parameters; lead time and stock. Tsukamoto’s fuzzy system is functioned in this study to avoid blurring parameter values from someone making a decision. Each criterion is divided into three membership functions; with nine fuzzy-rules used. The model also supports changing parameters if parameter values are changed. Based on the results of model test done, the optimized number of item purchased at the Pharmacy Company is able to be proposed practically.
International Journal of Industrial and Systems Engineering, 2013
A systematic approach to the inventory control and classification may have a significant influence on company competitiveness. In practice, all inventories cannot be controlled with equal attention. To efficiently control the inventory items and to determine the suitable ordering policies for them, multicriteria inventory classification is used. The objective of this research is to develop a multi-criteria inventory classification model through integration of fuzzy analytic hierarchy process (FAHP) and artificial neural network approach. FAHP is used to determine the relative weights of the attributes or criteria using Chang's extent analysis and to classify inventories into different categories. Various structures of multi-layer feed-forward back-propagation neural networks have been analysed and the optimal one with the minimum mean absolute percentage of error between the measured and the predicted values have been selected. To accredit the proposed model, it is implemented for 351 raw materials of switchgear section of Energypac Engineering Limited, a large power engineering company of Bangladesh.
International Journal of Industrial and Systems Engineering, 2015
Inventory items are usually grouped into two categories. The first category includes items which constitute the major portion of inventory value but have little diversity. However, the second category contains a great diversity of items which are less valuable than the items in the first category in general. Thus, using a method of inventory control for all of these items does not seem reasonable. In this paper by ABC classification, 77 items are categorised into three groups at first, in which group A includes 11 items, group B includes 16 items, and group C includes 50 items. Then, we considered criticality, scarcity and putrescence of items by combining ABC method and fuzzy classification to categorise aforementioned 77 items into 14 'very important ', 20 'important', and 43 'unimportant' items. Considering that those criteria are not equally important in fuzzy classification of items, in this research fuzzy analytic hierarchy process (FAHP) technique was used to determine the importance and weights of criteria. Since probable dearth of items can impose some problems to manufacturing companies, making a precise cluster of items considering qualitative criteria can help these companies in order to plan and design an effective inventory control systems.
11.Fuzzy Logic Analysis Based on Inventory Considering Demand and Stock Quantity on Hand
The approach is based on fuzzy logic analysis as it does not require the statement and solutions of complex problems of mathematical equations. Here consideration is inventory control problem solutions, for which demand values for the stock and it quantity-on-hand in store is proposed. Expert decisions are considered for developing the fuzzy models, and the approach is based on method of nonlinear dependencies identifications by fuzzy knowledge. The linguistic variables are considered for the membership functions. Simple IF-Then rules are used with expert advices.
Fuzzy Logic Analysis Based on Inventory Considering Demand and Stock Quantity on Hand
The approach is based on fuzzy logic analysis as it does not require the statement and solutions of complex problems of mathematical equations. Here consideration is inventory control problem solutions, for which demand values for the stock and it quantity-on-hand in store is proposed. Expert decisions are considered for developing the fuzzy models, and the approach is based on method of nonlinear dependencies identifications by fuzzy knowledge. The linguistic variables are considered for the membership functions. Simple IF-Then rules are used with expert advices.
2014
Inventory cost has become one of the major contributions to enterprise inefficiency.To minimize total cost, an enterprise is urged to manage an effective and efficient inventory system.In this case, an appropriate inventory model is in need. This study aims to propose an optimal inventory model by examining ABC multi-criteria classification approach using FANP (Fuzzy Analytical Network Process) and TOPSIS (Technique of Order Preferences by Similarity to the Ideal Solution) method. This study proposed a continuous review inventory model.
Fuzzy Approach to Decision Support System Design for Inventory Control and Management
Journal of Intelligent Systems, 2017
The ubiquitous nature of inventory and its reliance on a reliable decision support system (DSS) is crucial for ensuring continuous availability of goods. The DSS needs to be designed in a manner that enables it to highlight its present status. Further, the DSS should be able to provide indications about subtle and large-scale variations that are likely to occur in the supply chain within the context of the decision-making framework and inventory management. However, while dealing with the parameters of the system, it is observed that its operations and mechanisms are surrounded by uncertain, imprecise, and vague environments. Fuzzy-based approaches are best suited for such situations; however, these require assistance from learning systems like artificial neural network (ANN) to facilitate automated decision support. When ANN and fuzzy are combined, the fuzzy neural system and the neuro-fuzzy system (NFS) are formulated. The model of the DSS reported here is based on a framework com...
Multiple criteria inventory classification using fuzzy analytic hierarchy process
International Journal of Industrial Engineering
A systematic approach to the inventory control and classification may have a significant influence on company competitiveness. In practice, all inventories cannot be controlled with equal attention. In order to efficiently control the inventory items and to determine the suitable ordering policies for them, multi-criteria inventory classification is used. In this paper, fuzzy analytic hierarchy process for multiple criteria ABC inventory classification has been proposed. Fuzzy Analytic Hierarchy process (Fuzzy AHP) is used to determine the relative weights of the attributes or criteria, and to classify inventories into different categories. To accredit the proposed model, it is implemented for the 351 raw materials of switch gear section of Energypac Engineering Limited (EEL), a large power engineering company of Bangladesh. In this approach, at first, related criteria have been selected (Unit price, last year consumption or annual demand, last use date, supplier, criticality, durability) and the weights of these criteria was determined using Fuzzy AHP. Then a score to each item was assigned for each criterion as triangular fuzzy number and the final normalized weighted score of each item using fuzzy set theory is calculate. Finally, Chang's extent analysis was used for the comparison of fuzzy numbers and the final scores are compared with each other. Then all items were classified into three classes according to their final score.
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...
Modified Fuzzy Analytical Hierarchy Process for Multiple Criteria Inventory Classification
ird.sut.ac.th
A systematic approach to inventory control and classification may have a significant influence on company competitiveness. In order to efficiently control the inventory items and to determine suitable ordering policies for them, a multi-criteria inventory classification is used. In this paper, a Modified Fuzzy Analytic Hierarchy Process (FAHP) is proposed to determine the relative weights of the criteria using Chang's extent analysis and to classify inventories into different categories. To accredit the proposed model, it is implemented for 351 raw materials of the switchgear section of Energypac Engineering Limited (EEL), a large power engineering company in Bangladesh. The results of the study show that 22 items are identified as being in class A or the very important group, 47 items as being in class B or the important group, and the remaining 282 items as being in class C or the relatively unimportant group, and these are used as a basis for the control scheme.