This paper proposes a slow-moving management method for a system using of intermittent demand per unit time and lead time demand of items in service enterprise inventory models. Our method uses zero-inflated truncated normal statistical distribution, which makes it possible to model intermittent demand per unit time using mixed statistical distribution. We conducted numerical experiments based on an algorithm used to forecast intermittent demand over fixed lead time to show that our proposed distributions improved the performance of the continuous review inventory model with shortages. We evaluated multi-criteria elements (total cost, fill-rate, shortage of quantity per cycle, and the adequacy of the statistical distribution of the lead time demand) for decision analysis using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). We confirmed that our method improved the performance of the inventory model in comparison to other commonly used approaches such as simple exponential smoothing and Croston’s method. We found an interesting association between the intermittency of demand per unit of time, the square root of this same parameter and reorder point decisions, that could be explained using classical multiple linear regression model. We confirmed that the parameter of variability of the zero-inflated truncated normal statistical distribution used to model intermittent demand was positively related to the decision of reorder points. Our study examined a decision analysis using illustrative example. Our suggested approach is original, valuable, and, in the case of slow-moving item management for service companies, allows for the verification of decision-making using multiple criteria.

中文翻译：

---

管理滞销商品：需求建模的零膨胀截断正态方法


利用服务企业库存模型中的单位时间间歇性需求和提前期物品需求模型，提出了一种系统的慢速移动管理方法。我们的方法使用零膨胀截断正态统计分布，这使得可以使用混合统计分布对每单位时间的间歇性需求进行建模。我们基于用于预测固定提前期内间歇性需求的算法进行了数值实验，以表明我们提出的分布提高了连续审查库存模型的性能。我们使用与理想解决方案相似的优先顺序技术 (TOPSIS) 评估了多标准要素（总成本、填充率、每个周期的数量短缺以及交货时间需求的统计分布的充分性），以进行决策分析）。我们确认，与其他常用方法（例如简单指数平滑法和克罗斯顿方法）相比，我们的方法提高了库存模型的性能。我们发现每单位时间的需求间歇性、同一参数的平方根和再订购点决策之间存在有趣的关联，这可以使用经典的多元线性回归模型来解释。我们证实，用于模拟间歇性需求的零膨胀截断正态统计分布的变异性参数与再订购点的决策呈正相关。我们的研究使用说明性示例检验了决策分析。我们建议的方法是原创的、有价值的，并且在服务公司的缓慢移动项目管理的情况下，允许使用多个标准来验证决策。