基于通用件的ATO系统对家电行业的影响分析

面对消费者个性化的需求以及家电产品更新换代的加快，家电行业MTS的供应链模式逐渐体现其局限性：产品库存积压、旧产品零部件难以配型、消费者个性化需求不能满足等。基于通用件的ATO系统通过风险混同，减少了通用件的安全库存，缩小了成本，更大满足了消费者需求。本文分别建立了单期与多期最小成本模型，比较有通用件ATO系统与没有通用件MTS系统成本。证明有通用件ATO模型可以减少成本，同时减少通用件库存。。在一定服务水平前提下，当通用件的价格比其替代零部件的价格增加百分比不超过16％时，无论是单期模型还是多期模型（缺货不可补），采用通用件成本更低。而对于多期模型（缺货可补时），在一定服务水平前提下，当通用件的价格比其替代零部件的价格增加酉分比不超过0．5％时，采用通用件成本更低。依据家电行业的现状，只需对零部件进行合理的模块化设计以及与零售企业建立信息交换系统，就能很好的移植ATO系统，但各家电企业还需根据自身情况，斟酌利弊，权衡ATO系统带来的成本的节省与移植系统带来的额外成本的影响。     

大型冶金矿山企业工序标准成本责任分析体系的构建与实施

<正>一、工序标准成本责任分析体系构建的背景在实行工序标准成本管理过程中发现,由于各厂矿成本管理水平参差不齐,没有统一的分析模式,导致工序成本分析只是流于表面的简单分析,没有与生产、设备等专业分析进行有效地结合,不能为公司全方位的成本管控提供有力的支撑。为此,急需建立一套完整的工序标准成本责任分析体系,优化和统一工序成本管理模式,从生产、设备、科技、人员管理等方面全方位进行分析,进一步明确各个专业部门和各个管理层级所承担的成本管理责任,发挥其在成本管理乃至整个企业管理中的重要作用,推进工序标准成本管理不断深化。     

基于提前期的通用件库存模型研究

通用件正作为企业在市场上提供更高的产品多样化,而在企业内部保持零部件低多样化的优化技术而被广泛研究和应用。本文在考虑提前期影响的前提下,建立无通用件和有通用件的库存模型,并以风神汽车有限公司有关数据进行案例研究分析,通过数据分析揭示通用件应用的一些管理启示,并修正了一些理论界的原有观点。     

不确定环境下的Supply-hub协同补货决策

个性化需求与零部件创新使得产品需求和补货提前期不确定,对供应链补货决策和运行成本产生重要影响。将提前期不确定因素引入Supply-hub协同补货研究中,探讨提前期随机和需求不确定情况下,考虑零部件配套性的三供应商单制造商生产两定制产品的Supply-hub协同补货决策问题;提出了三种补货策略,以供应链运行成本最小化为目标,建立不同策略下的供应链补货模型并求解最优补货批量和供应链最小运行成本;发现三种补货策略均存在唯一最优补货批量,基于Supply-hub的两种协同补货策略和基于分散决策的供应商独立补货策略各有优势,但基于Supply-hub的批量及时间协同的补货策略恒优于基于Supply-hub的集中补货策略。最后,通过MATLAB进行算例分析验证结论,发现基于Supply-hub的批量及时间协同的补货策略能有效降低需求不确定性带来的成本增加风险;通用件的提前期波动对于供应链期望运行成本的影响要高于定制件提前期波动的影响,因此在进行供应链补货策略选择时更加关注通用件提前期。     

基于工序费用标准的成本控制方法探索

针对大型设备制造企业的单件、小批、多品种、大成套生产类型和生产组织特点,对比分析了传统成本控制方法在成本控制工作中的局限性,阐述了工序费用标准方法的特点及在这一类企业成本控制工作中的具体应用.     

八钢烧结降低烧结矿电耗的实践

烧结工序在钢铁冶炼过程是仅次于高炉的用能大户,占钢铁生产用能10%~20%,八钢烧结用电量占铁前系统总用电量的50%,降低烧结工序电耗对降低铁水成本具有重要的意义,特别是钢铁市场低迷钢材价格下跌,降低制造成本更显得非常重要。2015年下半年开始八钢烧结产能只有高峰时期产能的40%,各种制造成本不断上升。八钢烧结分厂通过技术改造和在管理上下功夫,在产能降低的情况下保证电耗不上升。     

服务多类需求串行供应链的最优控制策略

研究由一系列生产环节采用串行方式组成多级供应链的最优控制策略.在此供应链中,原材料经过各级生产环节顺序加工形成最终产品.各级生产环节的加工时间服从随机分布.对最终产品存在多类随机需求.在每个时刻,管理者需要决定:1)是否该启动某个生产环节的生产;2)当有需求到达时,是否该满足此需求.管理者期望系统运行的总期望折扣成本最小.构造了该系统的马尔可夫决策模型并深入研究了其最优控制策略及其动态协同特性.在生产策略方面,证明系统的最优生产策略就是对各级生产环节采用动态的基本库存策略.该策略的动态协同特性主要体现在各级生产环节的最优基本库存水平受其他生产环节的库存水平影响.在产品分配方面,证明系统的最优分配策略是动态配给策略.该策略的动态协同特性主要体现在每类随机需求最优配给水平受各级生产环节的库存水平影响.     

基于工序异质性的关键链项目缓冲监控

通过关键链项目缓冲监控可以防止缓冲在项目执行阶段被浪费,并保证项目的工期,有效的监控方法有利于提升项目的整体绩效。为了克服统一缓冲监控方法的不足,在保证项目工期的基础上,本文考虑成本因素对项目的影响,提出了一种基于工序成本和工期敏感度的差别动态缓冲监控模型。该模型考虑工序异质性将工序划分为成本敏感型工序和工期敏感型工序,根据工序的综合感知效用进行缓冲监控分配,并结合敏感度对不同类型的工序设置不同的监控基准点和纠偏措施。仿真结果表明,差别监控相对于统一的监控模式有利于实现项目工期和成本的双优化,验证了本文方法的有效性。     

基于二层规划的供应链多阶响应周期决策模型

优化和缩短供应链多阶响应周期是供应链快速响应市场需求、缩短产品上市周期的有效途径,也是供应链管理总体目标之一.全文首先阐明了供应链多阶响应周期的概念,以核心企业作为协调中心,建立了供应链整体计划决策机制,并运用二层规划方法对供应链多阶响应周期进行了建模研究.最后用实际调研的数据作为一个算例对模型进行了数值演算和验证.结果表明,该模型能够协调管理供应链整体利益和节点企业局部利益、合理分配生产时间与物流时间,为供应链优化和缩短多阶响应周期提供了一个有效的决策工具.     

非对称信息条件下供应链的生产策略

本文研究信息不对称条件下供应链中生产策略问题。考虑了生产库存的成本和利润函数,将生产者作为委托人,库存者作为代理人。进一步,在非对称信息条件下,将生产者最低利润的个人理性约束转化为二次型函数,以库存者利润对于库存存储成本参数的一阶条件作为状态方程,运用极大值原理,推导了非对称信息条件下生产策略。文中还分析了不同信息环境下的生产策略,并作了仿真试验计算。     

Assessing Service Level Targets in Production and Inventory Planning*

In production and stock planning, the relationship between customer service, defined as the ability to meet demand for finished goods from in-stock inventory, and expected profits or expected costs can be represented by a simple reliability curve. The shape of this curve depends upon the parameters of the demand process, specifically the expected level of demand, standard deviation and correlation structure, as well as upon the capacities and initial state of the production and inventory system. A model is presented which explicitly determines this trade-off curve for a firm. The model is intended both as an operational model to aid managers in setting revenue and service targets which are compatible with the capacities and resources of the firm, and as a tool for exploring relationships between the parameters of the demand process and the constraints of the physical production and inventory system. The results illustrate that the level of risk depends strongly on the variability of the demand process, the cost structure, the capacities and initial state of the system and, to a lesser extent, the correlation in demand between succeeding periods. Results suggest that establishing service level targets consistent with the firm's strategic orientation must be done in consideration of both the characteristics of the demand process and the capacities of the production and inventory system. The model provides a tool for estimating the premium above unit cost which must be paid to provide a designated service level.     

Economic Manufacturing Quantity and Its Integrating Implications

Several contradictions are noted among the Economic Order Quantity (EOQ), Just‐In‐Time (JIT), and Optimized Production Technology (OPT) approaches and the economic framework for profit maximization. A fundamental model referred to as the Economic Manufacturing Quantity (EMO) is developed and examined for its integrating implications for the three approaches. An implication for the classic EOQ approach is that the balance between setup and inventory carrying costs is valid when a production facility is operating at or below a certain critical level but not when operating above that level. An implication for the JIT approach is that one must reduce setup cost at non‐bottlenecks and setup time at bottlenecks to reduce inventory. An implication for the OPT approach is that trade‐offs between setup and inventory carrying costs may indeed be ignored while determining process batch sizes, provided each facility in a production system is operating at or above Its critical level. Economic theoretic analysis of the EMO model provides a basis for unification of JIT which advocates stability in operating level as a key to improved productivity and quality, and OPT that advocates maximizing operating level with resultant emphasis on bottlenecks as a key to increased profits. This unifying basis states that a profit‐maximizing production facility or system will operate at the full and stable level as long as market demand remains relatively sensitive to price and operating at the full (maximum) level provides positive unit contribution.     

A coordination mechanism with fair cost allocation for divergent multi-echelon inventory systems

In this paper we study the coordination of inventory control in divergent multi-echelon inventory systems under periodic review and decentralized control. Under decentralized control the installations decide upon replenishment policies that minimize their individual inventory costs. In general these policies do not coincide with the optimal policies of the system under centralized control. Hence, the total cost under decentralized control is larger than under centralized control. We present a simple coordination mechanism that removes this cost inefficiency. The upstream installations increases its base stock level while the downstream installations compensate their supplier for increased costs and provide it with additional side payments. We show that this mechanism coordinates the system; the global optimal policy of the system is the unique Nash equilibrium of the corresponding strategic game. Furthermore, the mechanism results in a fair allocation of the costs; all installations enjoy cost savings.     

DECENTRALIZED MULTIECHELON INVENTORY CONTROL

This paper considers a model for decentralized control of an inventory system consisting of 1 central warehouse and a number of retailers. The cost structure includes holding costs at both echelons and shortage costs proportional to the time until delivery at the retailers. We analyze a procedure for coordinated but still decentralized control of the system. The procedure is based on a simple approximation, in which the stochastic lead times perceived by the retailers are replaced by their correct averages. The approximation enables us to decompose the considered multiechelon inventory problem into a number of single echelon problems, 1 for each installation. The information about how a certain decision at the warehouse affects the retailers is conveyed through the marginal cost increase with respect to a change of the expected lead time. This information about the retailer costs is used as a shortage cost at the warehouse. We show that a coordination procedure based on this information can be used for finding near-optimal reorder points for the system and provide bounds for the approximation errors.     

A new MRP/GT lot sizing heuristic: A simulation study

This paper studies the lot-sizing problem in Material Requirements Planning/Group Technology (MRP/GT) systems. A GT production cell is designed to produce many families of components. A major setup is required when switching from manufacturing one family of components to another family, and a minor setup is needed when switching from manufacturing a component type to another component type within the same family. Inventory holding cost is incurred if inventory level is positive, and inventory shortage cost is incurred if inventory level is negative, that is, backordering. The objective of the proposed lot-sizing problem is to minimize the sum of major and minor setup costs, holding and shortage costs, and regular production cost, and to meet simultaneously the demand requirements. The proposed problem is modelled into a linear integer program, a heuristic method to solve the problem is proposed, and a simulation experiment conducted to evaluate the performance of the proposed heuristic and some existing heuristics. The computational results show that the proposed heuristic is useful to reduce the total cost significantly over a wide variety of simulated environments.     

A Production–Inventory Model for a Push–Pull Manufacturing System with Capacity and Service Level Constraints

We study a hybrid push–pull production system with a two‐stage manufacturing process, which builds and stocks tested components for just‐in‐time configuration of the final product when a specific customer order is received. The first production stage (fabrication) is a push process where parts are replenished, tested, and assembled into components according to product‐level build plans. The component inventory is kept in stock ready for the final assembly of the end products. The second production stage (fulfillment) is a pull‐based assemble‐to‐order process where the final assembly process is initiated when a customer order is received and no finished goods inventory is kept for end products. One important planning issue is to find the right trade‐off between capacity utilization and inventory cost reduction that strives to meet the quarter‐end peak demand. We present a nonlinear optimization model to minimize the total inventory cost subject to the service level constraints and the production capacity constraints. This results in a convex program with linear constraints. An efficient algorithm using decomposition is developed for solving the nonlinear optimization problem. Numerical results are presented to show the performance improvements achieved by the optimized solutions along with managerial insights provided.     

Solving the Economic Lot-Scheduling Problem Using the Method of Prime Subperiods

In recent years the reported successes of Japanese production systems, particularly the just-in-time approach to inventory control, has caused managers to focus more of their attention on efficient decision-making procedures for determining production schedules that minimize inventory costs. One such potential area of attention is the economic lot-scheduling problem (ELSP), which occurs in a variety of manufacturing environments where machining operations are prevalent. The economic lot-scheduling problem addresses the determination of lot sizes for N products with constant demand (and cycled through one machine with a given production rate) to minimize setup and inventory costs. The most successful solution approaches to the ELSP have been based on the concept of a basic period that is of sufficient length for the production of all items, even though each item might not be produced during each repetition of the basic period. This paper proposes a heuristic approach to the solution of the ELSP (referred to as the method of prime subperiods), which is an extension of the basic period approaches. The procedure is described and demonstrated via an example and then tested using a set of six example problems previously employed in the literature related to the ELSP. The results indicate as good or superior performance by the proposed method of prime subperiods.     

库存共享和服务水平限制下三级分销网络侧向转运模型与算法

侧向转运是库存共享的一种方式,一方面会减少库存成本,另一方面又会因转运而增加运输成本,针对这一问题考虑不同时间窗W^k的服务水平产生的服务水平的分级,构建三级分销网络中基于库存共享与时间服务水平限制的批量订货模型,分销网络模型中包含一个制造商、一个RDC、m个DC、n个客户。其中客户的需求相互独立且服从泊松分布,DC的订货提前期服从指数分布。首先,建立了基于时间服务水平限制的分销网络系统总成本最小化模型,并将再购点、订购批量等库存参数作为同时作为决策变量,以达到三级备件分销网络的成本优化;其次,根据问题和模型的特点对所建的模型开发了基于采用贪婪增加算法的求解思想求解问题,以便给管理者提供决策参考并通过降低利润的方式增加效益和资源利用率;最后,通过中航材集团推行的“航材共享”项目作为算例分析,将三个分销区域与机场群组成的备件分销系统进行建模分析,验证了本文模型的有效性,成本优化的显著性。研究结果表明,三级分销网络中,RDC采用连续盘点的（Q,R）补货策略、DC采用（Q,R,H）的补货策略时系统总成本与普遍采用的定期盘点策略和连续盘点中的One-for-One订货策略成本相比,DC之间的库存共享策略能有效降低整个系统的成本,例如,在案例研究中发现成本降低约为30%。     

A Bayesian Inventory Model Using Real‐Time Condition Monitoring Information

Lack of coordination between machinery fault diagnosis and inventory management for spare parts can lead to increased inventory costs and disruptions in production activity. We develop a framework for incorporating real‐time condition monitoring information into inventory decisions for spare parts. We consider a manufacturer who periodically replenishes inventory for a machine part that is subject to deterioration. The deterioration is captured via condition monitoring and modeled using a Wiener process. The resulting degradation model is used to derive the life distribution of a functioning part and to estimate the demand distribution for spare parts. This estimation is periodically updated, in a Bayesian manner, as additional information on part deterioration is obtained. We develop an inventory model that incorporates this updated demand distribution and demonstrate that a dynamic base‐stock policy, in which the optimal base‐stock level is a function of some subset of the observed condition monitoring information, is optimal. We propose a myopic critical fractile policy that captures the essence of the optimal policy, but is easier to compute. Computational experiments indicate that this heuristic performs quite well relative to the optimal policy. Adaptive inventory policies such as these can help manufacturers to increase machine availability and reduce inventory costs.     

冷轧生产批量计划与调度问题模型及算法

对冷轧生产系统特点进行分析,将冷轧生产批量计划与调度问题抽象为多阶段、多品种带有中间库的批量计划与调度问题。针对该问题建立了数学模型,通过对库存成本和调整成本惩罚系数的控制可以协调库存水平和调整次数的关系。对所建立的模型,提出了基于二进制粒子群优化与局部搜索的混合求解算法。最后,通过对企业实际生产数据的计算和分析,验证了模型和算法的可行性和有效性。