INVENTORY DECISION IN VUCA WORLD USING ECONOMIC LOGIC QUANTITY
Lidia Vesa 1, Marcel Ioan Boloș 2, Claudia Diana Sabău-Popa 2
Doctoral School of Economic Sciences, Faculty of Economic Sciences, University of Oradea, Romania,
University of Oradea, Faculty of Economic Sciences, Oradea, Romania
Abstract: If ever the concept “VUCA” (Volatility, Uncertainty, Complexity, and Ambiguity) seemed appropriate to use, it is now. National and global companies experience the highest level of instability due to the Covid-19 pandemic, which is the classic example of a highly volatile, uncertain, complex, and ambiguous world. In this world, decision-makers have to face more challenges appealing to the VUCA Prime leadership approach: vision against volatility, understanding against uncertainty, clarity against complexity, and agility against ambiguity. Some of the ways through which managers can overcome the VUCA characteristics include: providing a shared vision as a criterion for all decisions to be made, identifying the reason for the decision problems and sharing the idea with the followers, going through the entire decision process, following steps in proper order, and developing quick solutions. In an inventory decision taken in a VUCA context, the above ways are possible if using fuzzy inventory methods dealing with volatility, uncertainty, complexity, and ambiguity. This paper aims to adapt a traditional inventory method, Economic Production Quantity (EPQ), to the challenges of the VUCA world, through the fuzzy logic system (FLS). To achieve the best solution for the decision problem in the shortest time possible, the managers can employ a conversion by using the computing platform MATLAB. There are some advantages of this conversion for these two methods, EPQ and FLS. Firstly, the transformation of EPQ in ELQ (Economic Logic Quantity) allows managers to formulate the decision problem, even if they cannot identify and measure precisely the EPQ parameters. Secondly, using FLS to solve ELQ provides the possibility to simulate more alternatives and generate the solution in the shortest amount of time. Thirdly, it allows the decision-makers to evaluate the impact of the solution provided by each simulation on the company’s performance. Using these methods has the following primary limit: the problem formulation step depends on the managers’ understanding ability and managing a large volume of information. Therefore, there may be a risk of obtaining a relevant solution for a decision problem if the decision-makers do not understand the cause of the problem or do not know how to organize and manage a large volume of information. This limit could be overcome by using AHP (Analytic Hierarchy Process), but this is the topic of further research.
Keywords: fuzzy logic system, economic production quantity, demand, cost, fuzzy inference, fuzzification, defuzzification.
