In inventory and production decision problems, decision makers are interested to identify the optimal inventory and production level. In a certain decision environment, the optimal inventory level could be determined through traditional inventory methods and the optimal ptoduction level could be determined through linear programming algorithms. In an uncertain decision environment, the traditional methods and algorithms can not provide efficient and relevant solutions for these levels, due to the vague and changing parameters. In this case it is neccesary to develop new methods and models that can deal with vague variables and provide optimal levels. In this paper, the optimal inventory and production levels are determined through a single model that uses fuzzy linear programming. This new model is Fuzzy Optimistic-Reasonable-Pessimistic Inventory Model. It has three scenario: optimistic, reasonable and pessimistic, that are defined through triangular fuzzy numbers. In this way, decision makers can deal with vague parameters. These scenarios help managers to divide the Fuzzy ORP Model into three sub-models, that can be easily solved through traditional Simplex Algorithms. Each sub-model provides a crisp solution for each scenario. The solutions forms the final fuzzy optimal solution. The Fuzzy PRO Inventory Model helps managers to identify three optimal levels and to rank them according to their evaluations. This is useful, also, in predictions, where the decision makers should predict different scenarios for the production process. The limit of this model is the definition of the variables and scenarios. This model consider that all values for all variables and coefficients have the same definition: the inferior limit is related to the optimistic sceanrio, the peak is represents the reasonable limit and the superior limit is related to the pessimistic scenario. In real problem, the decision variables could have different definition than coefficients. The inferior limit of the cost is related to the optimistic scenario, but the superior limit of the production level can be related to the optimistic scenario. There are different representations for the scenarios.
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.
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