Spare parts classification and demand forecasting for stock control: Investigating the gap between research and practice

This paper investigates the gap between research and practice in spare parts management, with specific reference to durable goods addressed to private or professional customers. The paper provides a critical literature review of theoretical contributions about spare parts classification and demand forecasting for stock control. The discussion of ten case studies, then, allows to analyze the reasons for this gap, by addressing the limitations of models developed in literature, the role of contextual factors and the maturity in companies' spare parts management practices. Four main directions for research are proposed in order to bridge the gap, namely: to develop integrated approaches to spare parts management; to define contingency-based managerial guidelines, to favor the knowledge accumulation process in companies, and to supplement theoretical models with practical relevance.     

需求预测综合模型及其与库存决策的集成研究

准确的预测有助于企业做出有效的决策,包括生产计划、定价和促销决策等,以减少库存、提高客户满意度和企业竞争力． 以快速消费品为研究对象,分析其需求影响因素,建立了时间序列分析与多元回归整合的需求预测综合模型; 将此预测模型引入到库存决策中,构建了基于库存成本最小的需求预测与库存决策集成模型,并借助变邻域搜索算法获得模型的参数值; 最后,选用实际数据,验证了所构建的需求预测综合模型、物流需求预测与库存决策集成模型及其求解方法的有效性．     

A hybrid inventory management system responding to regular demand and surge demand

This paper proposes a hybrid policy for a stochastic inventory system facing regular demand and surge demand. The combination of two different demand patterns can be observed in many areas, such as healthcare inventory and humanitarian supply chain management. The surge demand has a lower arrival rate but higher demand volume per arrival. The solution approach proposed in this paper incorporates the level crossing method and mixed integer programming technique to optimize the hybrid inventory policy with both regular orders and emergency orders. The level crossing method is applied to obtain the equilibrium distributions of inventory levels under a given policy. The model is further transformed into a mixed integer program to identify an optimal hybrid policy. A sensitivity analysis is conducted to investigate the impact of parameters on the optimal inventory policy and minimum cost. Numerical results clearly show the benefit of using the proposed hybrid inventory model. The model and solution approach could help healthcare providers or humanitarian logistics providers in managing their emergency supplies in responding to surge demands.     

Efficiency decomposition in dealers from the perspectives of demand forecasting,sales force,and inventory control: a case study

Efficiency decomposition is being able to know the most effective way to improve a company’s organisational efficiency. This study proposes a network performance evaluation model with non-controllable variables to evaluate dealers with an emphasis on their internal processes, including demand forecasting, sales force and inventory control. The proposed model decomposes the organisational efficiency into a weighted average of internal process efficiencies, where the weights associated with the internal processes are data-driven. This is applied to an empirical case consisting of 27 automobile parts dealers in Taiwan. Based on the analysis results, dealers know the relative contribution of each internal process to the organisational efficiency, and inefficient dealers are assigned targets for enhancing the organisational efficiency as well as the process efficiencies.     

Improving spare parts inventory control at a repair shop

We study spare parts inventory control for an aircraft component repair shop. Inspection of a defective component reveals which spare parts are needed to repair it, and in what quantity. Spare part shortages delay repairs, while aircraft operators demand short component repair times. Current spare parts inventory optimization methods cannot guarantee the performance on the component level, which is desired by the operators. To address this shortfall, our model incorporates operator requirements as time-window fill rate requirements for the repair turnaround times for each component type. In alignment with typical repair shop policies, spare parts are allocated on a first come first served basis to repairs, and their inventory is controlled using (s, S) policies. Our solution approach applies column generation in an integer programming formulation. A novel method is developed to solve the related pricing problem. Paired with efficient rounding procedures, the approach solves real-life instances of the problem, consisting of thousands of spare parts and components, in minutes.A case study at a repair shop reveals how data may be obtained in order to implement the approach as an automated method for decision support. We show that the implementation ensures that inventory decisions are aligned with performance targets.     

Production planning and inventory control in a company manufacturing PC parts: A case study

In this paper, we present a case study on the production planning and inventory system in a company manufacturing personal computer (PC) parts. In the case study, the targets are specified for developing a system for production planning and inventory control. The current state of the company is analysed for clarifying the points necessary to achieve the targets. Also, a system based on the analysis is proposed and its effects are estimated.     

The lognormal approximation to the lead time demand in inventory control

This paper is intended to show that the lognormal distribution can be used to approximate the lead time demand in inventory control problems. The computations involved with this approximation are shown to be simpler compared to the gamma distribution approximation. Some pertinent properties of the lognormal distribution are summarized. Numerical examples are given in two cases of distributed demands and lead times and in each case the lognormal approximation results are compared with the exact and/or other approximations.     

Sound branch cash management for less: A low-cost forecasting algorithm under uncertain demand

This paper deals with cash management for bank branches, under the assumption that branches have a role to play in the improvement of global bank institution performance. In the current scenario of unprecedented pressure amongst banks to keep costs under control, our contribution is the design of a sound and low-cost algorithm to optimize branch cash holdings using software implementation in SageMath. It is accompanied by data processing based on 60,000 real banking records. This is the first academic paper to run such an extensive database at branch level.We find that our algorithm by and large performs well when forecasting the cash amounts that the branch might require from the central hub to satisfy all branch necessities, avoiding the generation of either surplus or shortage of cash. It is also extremely easy to implement in daily branching practice, leading to an overall reduction in operating costs. In addition, our algorithm may be easily adjusted as required and be tailor-made to the special requirements of each banking institution.     

A method of spare parts inventory planning

A mathematical and simulation technique is discussed to obtain integer (s1, S) solutions for a group of part numbers at spares stocking locations. The technique utilizes cost function based on stochastic integer (s, S) inventory model formulation. The results are successfully implemented to plan several million dollars of spares inventory and to plan multi-echelon initial spares provisioning in the field.     

航空公司收入管理价格与舱位控制的统一分析

收入管理对于改善民航企业的经济效益、增强民航企业的竞争能力具有重大意义. 文章 运用随机过程理论和最大凹向包络原理,探讨航空公司客运收入管理研究中动态价格与舱位 控制的统一分析模型,即在任意订票时刻,决定航班的哪些舱位该开放,以什么价格开放,从而 实现单个航班的收入最大化. 文章指出,航空公司可通过三阶段方法来获取最优的动态价格与 舱位控制策略,即确定最优价格集、开放舱位数及最优价格. 最后给出了实例分析.     

行为库存管理研究综述及前景展望

库存管理是运作管理学科的研究重点之一.由于其在经济活动中具有重要的地位,库存管理在行为运作管理这一新兴学科中再次成为学术界关注的焦点.本文首先简述了行为运作管理的行为理论基础以及行为经济学的发展,根据行为运作管理的研究目标构建了行为库存管理的整体研究框架,进而在这一框架下对现有的行为库存管理相关研究进行系统综述,并进一...     

面向批量订货和多需求类的生产与库存配给

针对一个面向两个需求类的生产企业,根据客户每次订货是否可分批交货,提出了当客户订货可分割和不可分割时供应商的最优生产和库存配给策略.分析表明,供应商的最优生产控制策略可用一个取决于系统状态的基准库存水平表示,最优的库存配给策略则用一个多层的取决于状态的配给水平向量表示.随后,该结论被推广至包含任意多个需求类的生产系统.数值分析验证了文中最优策略的有效性.     

Materials receiving capacity and inventory management

This paper analyses the optimal level of materials receiving capacity for a manufacturer that receives deliveries from many suppliers. Inventory levels and inventory carrying costs depend on the frequency of deliveries and thus, on the materials receiving capacity. An analytic model that captures the tradeoff between inventory costs and materials receiving costs is presented and discussed. The receiving cost is modeled as increasing in discrete jumps of varying sizes whenever materials receiving resources are added. Practical issues in implementing the model are highlighted and methods to reduce the marginal materials receiving cost are discussed. The paper also discusses connections to the JIT approach for production environments where materials receiving is heavily automated.     

A demand classification scheme for spare part inventory model subject to stochastic demand and lead time

In this study, we aim to develop a demand classification methodology for classifying and controlling inventory spare parts subject to stochastic demand and lead time. Using real data, the developed models were tested and their performances were evaluated and compared. The results show that the Laplace model provided superior performance in terms of service level, fill rate (FR) and inventory cost. Compared with the current system based on normal distribution, the proposed Laplace model yielded significant savings and good results in terms of the service level and the FR. The Laplace and Gamma optimisation models resulted in savings of 82 and 81%, respectively.     

Socio-political forecasting and management information systems

This paper is an attempt to place the forecasting of socio-political variables in the context of actual information needs of industrial companies. Initially discussion is of the effect of socio-political variables upon companies with a review of this significance for decision makers and decision takers. An attempt is then made to categorise existing forecasting techniques in the context of socio-political data whilst regard is paid to the possible effects of socio-political variables on companies. Discussion is then broadened to a consideration of current practice both in the UK and in the USA. Problems implicit in current practice are commented upon and the paper ends with the conclusion that socio-political forecasting is currently significant to many companies and that there is a major potential role for management scientists to play in the development and implementation of appropriate techniques.     

Machine learning for multi-criteria inventory classification applied to intermittent demand

Multi-criteria inventory classification groups inventory items into classes, each of which is managed by a specific re-order policy according to its priority. However, the tasks of inventory classification and control are not carried out jointly if the classification criteria and the classification approach are not robustly established from an inventory-cost perspective. Exhaustive simulations at the single item level of the inventory system would directly solve this issue by searching for the best re-order policy per item, thus achieving the subsequent optimal classification without resorting to any multi-criteria classification method. However, this would be very time-consuming in real settings, where a large number of items need to be managed simultaneously.

In this article, a reduction in simulation effort is achieved by extracting from the population of items a sample on which to perform an exhaustive search of best re-order policies per item; the lowest cost classification of in-sample items is, therefore, achieved. Then, in line with the increasing need for ICT tools in the production management of Industry 4.0 systems, supervised classifiers from the machine learning research field (i.e. support vector machines with a Gaussian kernel and deep neural networks) are trained on these in-sample items to learn to classify the out-of-sample items solely based on the values they show on the features (i.e. classification criteria). The inventory system adopted here is suitable for intermittent demands, but it may also suit non-intermittent demands, thus providing great flexibility. The experimental analysis of two large datasets showed an excellent accuracy, which suggests that machine learning classifiers could be implemented in advanced inventory classification systems.     

Production and inventory control with chaotic demands

This study explores an efficient approach for identifying chaotic phenomena in demands and develops a production lot-sizing method for chaotic demands. Owing to the butterfly effect of chaotic demands, precise prediction of long-term demands is difficult. The experiments conducted in this study reveal that the maximal Lyapunov exponent is very effective in classifying chaotic and non-chaotic demands. A computational procedure of the Lyapunov exponent for production systems has been developed and some real world chaotic demands have been identified using the proposed chaos-probing index. This study proposes a modified Wagner–Whitin method that uses a forward focused perspective to make production lot-sizing decision under chaos demands for a single echelon system. The proposed method has been empirically demonstrated to achieve lower total production costs than three commonly used lot-sizing models, namely: lot-for-lot method, periodic ordering quantity, and Silver-Meal discrete lot-size heuristic under a fixed production horizon, and the conventional Wagner–Whitin algorithm under chaotic demands. Sensitivity analysis is conducted to compare changes in total cost with variations in look-ahead period, initial demand, setup cost and holding costs.