过程参与式审计是防范物资采购风险的有效方法

一、企业物资采购过程中的风险类型 (一)采购计划风险 物资采购部门计划管理不科学,不能合理编制采购计划,使采购数量、目标、时间、运输计划、使用计划、质量计划与目标偏离.     

基于煤化工项目大宗材料框架协议采购管理研究

本文以框架协议采购为理论基础,深入分析石油、石化及煤化工项目传统材料采购管理对于大宗材料产生供应商数量多,变化大,技术能力参差不齐;采买人员忙于"一单一询"、"一单一谈"、"一单一签";价格、质量难以控制,监管难度大;材料库存物资得不到有效控制和利用等问题,并以基于国内大型煤化工项目大宗材料框架协议采购模式创新实例进行研究。     

集团企业大宗物资集中采购探讨

随着经济全球化趋势日趋加剧,以及我国社会主义市场经济体制的建立,对集团企业采购体制的改革提出了较高的要求,基于此,集团企业就应该不断完善大宗物资集中采购制度,加强集团企业大宗物资集中采购管理,发挥规模优势,规范采购行为,并以此降低企业运营成本、提高工作效率,促进集团企业的健康发展。但是在实践探索中,集团企业大宗物资集中采购制度仍然存在诸多不完善之处,影响到了大宗物资集中采购效率,基于此,分析问题,找到根源,探求解决之道,就成为集团企业大宗物资集中采购可持续发展的必由之路。     

浅论采购计划在石油物资招标中的地位及应用

石油企业物资计划招标采购不仅能降本增效,还可以提前谋划、分部实施,合理配置资源,进而有效规避风险,实现阳光采购,取得最佳的经济效益。因此,采购计划在石油企业物资采购中就显得尤为重要。本文以石油企业为例,从物资采购计划、目标、作用、编制内容、主要方式及影响因素等阐述了采购计划在石油企业物资招标中的重要地位及作用,为石油企业物资采油招标管理提供了借鉴及有效的管理支撑。     

企业物资需求计划与采购统计分析

物资采购是企业生产环节的起点。物资采购是企业购买所需要的材料、燃料和各种经营管理耗材的活动。采购过程当中需要根据企业生产需要完成进行物资需求计划的制定,以便确定企业生产经营所需要的全部消耗品的总量;在采购之前需要进行统计分析。统计分析的对象包括各种所需物资的价格、数量和质量要求等信息,为实现购买决策奠定信息基础。物资需求计划和采购信息分析共同构成企业购买行为的决策过程。     

县级供电企业物资采购管理的优化策略

县级供电企业持续深入开展物资集约化管理,取得了很好的成效。物资采购管理是物资管理过程中的重要环节,但在实际工作中存在供应商选择范围狭窄、推荐供应商不规范、需求计划偏差大、物资询价或谈判的评价方法过于简单等问题,影响了物资采购管理的最终成效,本文从以上几个方面进行分析并提出一些优化策略。     

谈核电EPC总承包模式下核级大宗材料的供需

核级大宗材料具有品种多、规格杂、数量大、紧急增补频繁、小批量采购困难且成本高等特点。在核电工程建设中，核级大宗材料的供需矛盾往往体现为三大方面：一为所到非所需；二为所需未采购；三为所需未交付。为解决以上问题，本文探讨了如何充分发挥EPC总承包优势，通过“三步控制法”及核级大宗材料多项目间调用来解决核级大宗材料供需矛盾的问题，分析了“三步控制法”和多项目调用的可行性及具体实施方法。     

部队异地军事任务中物资招标采购浅探

当前部队异地军事任务日益频繁,环境艰苦,任务繁重,保障难度大,物资采购部门高效、良性地对所需生活物资实施招标采购与保障是解决这一现实问题的良策。     

基于看跌期权契约的应急物资采购储备模型

应急物资具有峰值需求量大,需求不确定性强,缺货成本高等特点,这使得政府必须在事前进行一定数量的物资储备。然而,由于应急物资需求的发生概率较低,针对食品药品等一类具有保质期限的物资,一旦在保质期限内需求未发生,则会造成大量的浪费与损失,加重政府的财政负担。虽然传统供应链中的回购契约可使政府将剩余物资回售给供应方,能够在一定程度上降低政府成本,但是供应方并没有因承担多余风险而获取到额外收益。基于此,本文设计了一种基于看跌期权契约的应急物资采购储备模型,用于解决保质期风险而引发的损失问题,并分析了实现政企供应链协调的机制,探讨了双方实现共赢协调的具体条件。此外,本文进一步表明与回购契约相比,基于看跌期权契约所建立的采购储备模式能够在降低政府成本的同时,合理地补偿供应方因承担多余风险而造成的损失,更好地保障了供应方的利益,达到了政府和供应方双赢的目的。     

以精细化管理为抓手降低企业物资采购成本

电力企业是技术和资金的密集型企业,影响电力物资采购成本的因素有很多,包括物资的数量和质量、物资的运输费用、物资价格的异动、采购工作人员的素质和其他隐含的成本等。加强电力物资采购成本的控制,对提升电力企业管理水平、保证企业效益具有重要意义。本文我们将基于精细化管理理论的基本含义,以及电力企业物资采购成本控制的重要性,对以精细化管理为抓手降低企业物资采购成本问题展开深入的分析及探究。     

A joint optimal pricing and order quantity model under parameter uncertainty and its practical implementation

We consider a robust optimization model of determining a joint optimal bundle of price and order quantity for a retailer in a two-stage supply chain under uncertainty of parameters in demand and purchase cost functions. Demand is modeled as a decreasing power function of product price, and unit purchase cost is modeled as a decreasing power function of order quantity and demand. While the general form of the power functions are given, it is assumed that parameters defining the two power functions involve a certain degree of uncertainty and their possible values can be characterized by ellipsoids. We show that the robust optimization problem can be transformed into an equivalent convex optimization which can be solved efficiently and effectively using interior-point methods. In addition, we propose a practical implementation of the model, where the stochastic characteristics of parameters are obtained from regression analysis on past sales and production data, and ellipsoidal representations of the parameter uncertainties are obtained based on a combined use of genetic algorithm and Monte Carlo simulation. An illustrative example is provided to demonstrate the model and its implementation.     

A joint optimal pricing and order quantity model under parameter uncertainty and its practical implementation

We consider a robust optimization model of determining a joint optimal bundle of price and order quantity for a retailer in a two-stage supply chain under uncertainty of parameters in demand and purchase cost functions. Demand is modeled as a decreasing power function of product price, and unit purchase cost is modeled as a decreasing power function of order quantity and demand. While the general form of the power functions are given, it is assumed that parameters defining the two power functions involve a certain degree of uncertainty and their possible values can be characterized by ellipsoids. We show that the robust optimization problem can be transformed into an equivalent convex optimization which can be solved efficiently and effectively using interior-point methods. In addition, we propose a practical implementation of the model, where the stochastic characteristics of parameters are obtained from regression analysis on past sales and production data, and ellipsoidal representations of the parameter uncertainties are obtained based on a combined use of genetic algorithm and Monte Carlo simulation. An illustrative example is provided to demonstrate the model and its implementation.     

生产能力储备模式下应急物资储备与采购定价模型

为更好地促进政企双方开展应急物资生产能力储备合作,政府和协议企业需对物资的采购定价及生产能力的储备数量进行合理决策。基于此,文章从供应链的角度出发,利用供应链契约中的数量柔性契约建立了生产能力储备模式下的应急物资储备与采购定价模型,并进一步考虑了协议企业存在储备约束的情况。在利用逆序归纳法求解出合作下企业的生产能力储备量及相应的政府采购定价决策后,文章进一步分析了储备周期内灾害事件的发生概率与企业最大储备量约束对双方决策及各自成本收益的影响,为政府的采购定价及企业的储备策略提供了科学依据。此外,文章还给出了约束双方决策时的参数条件,因而也为政府在目标企业的选择方面提供了参考。     

考虑通行约束和运力限制的灾后应急物资联合调度优化研究

考虑到灾后路网受损难以运输应急物资，本文研究了应急响应中车辆-直升机联合调度的路径优化问题。针对受灾地区的实时路况，通往灾区的救援工具受到数量以及装载量的约束，本文将受灾点等待救援的平均时间最短以及应急网络总费用最低设为目标，构建运力受限条件下带通行约束的救援物资联合运输多目标优化模型，然后根据随机邻域搜索变异和分级交叉的思想构建出一种带精英策略的非支配排序混合进化算法（NSHEA-II）得到模型的解，并利用算例分析对该算法进行可行性检验。结果发现，本文构建的NSHEA-II算法相对NSGA-II算法能够得到较好的结果且波动性较小，这为决策者制定救援物资的配送方案提供有效的技术支撑。     

Scheduling Truck Arrivals at an Air Cargo Terminal

We consider the scheduling of truck arrivals at an air cargo terminal. By coordinating arrivals of cargo delivery trucks with outbound flight departure schedules, some of the shipments can be transferred directly to the departing flights, while others will be stored at the terminal's storage facility and incur extra handling and storage costs. The objective is to obtain a feasible schedule so as to minimize the total cost of operations. We formulate the problem as a time‐indexed integer program and show that, even with limited number of unloading docks at the terminal, the problem is non‐trivial (NP‐hard in the strong sense). Our solution method includes an exact solution procedure to determine an optimal unloading sequence for the shipments carried by each truck, together with a Lagrangian relaxation‐based heuristic for assigning trucks to truck docks and determining truck arrival times. We conducted computational experiments to test the performance of our solution method. Computational results show that our method can generate near‐optimal solutions efficiently. Our simulation results indicate that the scheduling approach proposed in this paper has the potential to generate significant cost savings over a first‐come, first‐served approach currently used at the air cargo terminal that we observed.     

不同决策目标下受资金约束零售商的最优采购策略研究

在提前订购和延时采购两种情景下,分别考虑最大化期望收益和最小化违约概率两种决策目标,对受资金约束零售商的最优采购量和相匹配的融资策略进行分析,结果表明,相比于以最大化期望收益为决策目标,以最小化违约概率为决策目标的零售商在提前订购中将奉行"保守"的采购量和融资策略（仅耗尽自有资金量采购而不融资）,而在延时采购中采取"激进"的采购量和融资策略（与最大化期望收益下一致）;在此基础上,对不同决策目标下受资金约束零售商的最优采购时机问题进行研究发现,在最大化期望收益下,零售商的最优采购时机由产品采购成本、市场容量均值、市场容量方差、自有资金量、银行贷款利率等多个参数共同决定,而在最小化违约概率下,零售商将始终选择延时采购。     

Advance selling with freebies and limited production capacity

We consider a two-period pricing model in which a seller offers freebies along with the product when making advance sales, and production is constrained by capacity. The seller can offer freebies to increase both market base and customer׳s valuation toward the product in advance. The customers strategically determine whether to purchase the product in advance and gain freebies when their valuation on the product is uncertain, or delay their purchase decision until the regular selling period. We characterize the optimal pricing, quality level of the freebie and production quantity decisions that maximize the expected profits of the seller over the two periods.     

不确定需求环境下多级库存系统优化与协调模型研究

论文首先将模糊随机变量扩展为混合随机变量,并用来描述不确定需求.然后对多级单周期库存系统在模糊随机和混合随机需求环境下的优化与协调问题作了研究,提出了通过集成库存管理使库存系统整体收益期望值最大化的优化模型和协调合作伙伴利益的数量折扣契约模型,并根据遗传算法理论和计算机模拟技术设计了求解模型的智能算法.最后通过实例对模型进行了仿真分析.     

Optimal dispatching control of an AGV in a JIT production system

This paper deals with an FMS (Flexible Manufacturing System) in a JIT (Just-In-Time) production system. The FMS consists of m workstations, one dispatching station and a single AGV (Automated Guided Vehicle). Each workstation has an input buffer of limited capacity and its processing times are distributed stochastically. When the processing of a new component starts at the workstation, a withdrawal Kanban attached to it is sent to the dispatching station. The AGV chooses one from workstations whose withdrawal Kanbans are accumulated at the dispatching station, and conveys a component with a withdrawal Kanban from the dispatching station to the workstation. The main purpose of this paper is to find an optimal dispatching policy of the AGV that maximizes the long-run expected average reward per unit time. The problem is formulated as a semi-Markov decision process and an optimal dispatching policy is computed. Numerical experiments are performed to make several comparisons.     

Dynamic pricing in the presence of consumer inertia

Customer behavior modeling has gained increasing attention in the context of dynamic pricing. As an important behavior phenomenon, consumer inertia refers to consumers' inherent tendency of purchase procrastination and may induce consumers to wait even when immediate purchase is optimal from an objective perspective. This paper studies a dynamic pricing problem for a monopolist firm selling perishable goods to consumers who may be influenced by inertia. We formulate this problem using the finite-horizon dynamic programming approach and derive the optimal dynamic pricing policy. We demonstrate that consumer inertia produces negative effects on firms' expected revenues and optimal prices, which are monotonically decreasing in both inertia depth and breadth. Through numerical illustrations, we further show that the marginal effects of inertia depth on optimal prices and expected revenues are decreasing, whereas the marginal effects of inertia breadth are increasing. Finally we propose some suggestions for firms to influence the level of consumer inertia.