孟加拉某化肥公司库存管理的人工神经网络方法

M. B. Hasan, Lipi Akhter
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引用次数: 0

摘要

商业组织总是面临需求、供应和库存方面的不确定性。为此,重要的是要制定战略计划,以应对相应的不确定性。为了维持,商业组织必须以这样一种方式规划未来,即库存成本,人工成本将最小化,时间,财务资源和利润的利用将最大化。资源的优化规划也有助于组织避免浪费。一个好的预测技术可以帮助公司的管理者处理不确定性。在本文中,我们将为孟加拉国的一家化肥公司进行这样的规划。为了最小化库存成本,我们将应用一种新的方法,即人工神经网络(ANN),该方法最近被用于预测问题,并通过迭代训练过程分析系统的主要特征。为此,我们将首先利用现有的预测方法对化肥需求进行预测。然后我们将应用ANN来预测公司的化肥需求。我们还将确定经济订单数量(EOQ),以尽量减少总成本,包括公司的库存成本。利用MATLAB编程语言对包括人工神经网络在内的各种预测方法进行了分析。最后,我们将利用这些结果找出具有最优库存成本的化肥公司的正确预测技术。达卡大学学报(自然科学版),69(3):133-142,2022 (6)
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An Artificial Neural Network Method for Managing Inventory of A Fertilizer Company in Bangladesh
Business organizations are always facing uncertainties in demand, supply and inventories. For this, it is important for them to make the strategic plans to cope up with the uncertainty accordingly. To sustain, the business organizations must plan the future in such a way that the inventory cost, labor cost will be minimized and the utilization of time, financial resources and profit will be maximized. The optimum planning of resources also help the organizations to avoid wastage. A good forecasting technique can help the manager of a company to deal with the uncertainties. In this paper, we will work on such a planning fora fertilizer company in Bangladesh. To minimize the inventory cost, we will apply a new approach known as Artificial Neural Network (ANN), which is recently used for the problem of prediction and analyze the main characteristics of a system through an iterative training process. For this, we will first forecast the demand of fertilizer by using existing forecasting methods. We will then apply ANN for forecasting the demand of fertilizer of the company. We will also identify Economic Order Quantity (EOQ) to minimize total cost including inventory costs of the company. We use programming language MATLAB for analyzing different forecasting methods including ANN. Finally, we will use these results to find out the right forecasting technique for the fertilizer company with optimal inventory cost. Dhaka Univ. J. Sci. 69(3): 133-142, 2022 (June)
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