Prabjot Kaur

Inventory Control Using Fuzzy-Aided Decision Support System

Virtually every segment of the economy relies on some form of inventory for their operations. The ubiquitous nature of inventory has motivated to carry out this study wherein a decision support system (DSS) could be formulated to assist the decision-makers for effective monitoring of inventory levels and to ensure continuous availability of goods. The DSS needs be designed in a manner which can communicate its information to its user that is comprehensible and useful within the context of the decision situation. However, while dealing with the parameters of the system it is often seen that they are uncertain, imprecise and vague. Fuzzy-based approaches are best suited for such situations but these cannot provide automated decision support unless combined with learning systems like artificial neural network (ANN). When ANN and fuzzy are combined, fuzzy neural system (FNS) and neuro-fuzzy system (NFS) are created. The model of DSS developed in this study is based on a new framework using a system called adaptive neuro-fuzzy inference system. The model established has the advantage of the ANFIS for the DSS for use as part of inventory control.

Intuitionistic Fuzzy-Based Multi-Attribute Decision-Making Approach for Selection of Inventory Policy

Selection of inventory control policies is of great concern in the dynamic business environment as they are the drivers of success towards achieving a competitive advantage in terms of cost, quality and service. Inventory policy selection is affected by a number of criterions some of which may be cost, demand and lead time which are quite conflicting in nature. Therefore, inventory control policy selection can be categorised as a Multi-Criteria Decision-Making technique involved in evaluating a set of alternatives through which the enterprises need to identify optimal inventory policy. This research develops a decision model which is focused towards evaluation, ranking and selection of inventory policies based on these conflicting criteria using intuitionistic fuzzy numbers.

Fuzzy Approach to Decision Support System Design for Inventory Control and Management

Abstract 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 commonly known as adaptive neuro-fuzzy inference system (ANFIS), which is a version of NFS. The configured model has the advantages of both the ANN and fuzzy systems, and has been tested for the design of a DSS for use as part of inventory control. In this work, we report the design of an ANFIS-based DSS configured to work as DSS for inventory management. The system accepts demand as input and generates procurement, ordering, and holding cost to control production and supply. The system deals with a certain profitability rating required to quantify the changes in the input and is combined with the day-to-day inventory records and demand-available cycle. The effectiveness of the system has been checked in terms of number and types of membership used, accuracy generated, and computational efficiency accounted by the computation cycles required.