SUPPLY CHAIN MANAGEMENT BASED ON HIERARCHICAL DECISION SUPPORT MODEL
Background. Information is one of the key elements for effective supply chain (SC) management. Availability of timely information ensures the continuous movement of commodity flows, minimizes the company's losses associated with the lack of goods or excess stocks, allows you to plan production and financial flows, take into account changes in consumer interests. The systems of information exchange between the participants of the SC are used. Predicting future sales is necessary to control the flow of goods, so firms do consumer demand forecasting.
Objective. Since the explicit information has different usefulness for forecasting and decision-making for different SC participants, the problem arises of assessing the priority of various types of information in the SC management system in order to obtain a more accurate forecast of consumer demand. The aim of the work is to develop an algorithm for calculating the values of the relative importance or priority of various types of information in the SC based on a hierarchical model of criteria using the opinions of the SC participants.
Methods. The task of systemic assessment of the priority of information needs in the SC management system is formulated as a task of multi-criteria decision support. There are several types of information that can be used to predict consumer demand, these are alternative solutions. A hierarchical model of SC has been built, which includes criteria for the cost of obtaining information from other participants of the SC, accuracy and reliability of the source of information, degree of its use and other criteria affecting the efficiency of using information for forecasting demand.
Results. An algorithm for analyzing the SC management system based on a decision support hierarchical model is proposed. The functional capabilities of the developed decision support system for solving multiple criteria decision support problems are shown, when the input information are evaluations of the SC subjects.Conclusions. The developed algorithm is used for a wider class of decision support problems, in particular when part of the expert assessments is not sufficiently consistent. In this case, the most inconsistent assessments are searched without the participation of an expert, the set of expert evaluations is adjusted by various methods depending on the properties of the corresponding pairwise comparison matrices. The proposed algorithm includes several methods for calculating the local weights of the elements of decision support hierarchical model and various methods of aggregating these weights, which increase the reliability of the obtained results.
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