范文一：SF物流网络优化方案

物

流

优

化

方

案

目 录

引 言 ......................................................................................... 错误！未定义书签。

第1章 **公司大东南区网络的优化 ....................................... 错误！未定义书签。

1.1大东南配送区物流一体化建设 ....................................... 错误！未定义书签。

1.1.1东区物流一体化的必要性及其概念........................ 错误！未定义书签。

1.1.2东区物流业发展的现状与存在的问题.................... 错误！未定义书签。

1.1.3东区物流一体化的实现途径.................................... 错误！未定义书签。

1.1.4 对策建议................................................................... 错误！未定义书签。

1.1.5东区物流一体化结论................................................ 错误！未定义书签。

1.2南区物流业的发展 ........................................................... 错误！未定义书签。

1.2.1南区物流业发展的现状及问题................................ 错误！未定义书签。

1.2.2南区物流业发展建议................................................ 错误！未定义书签。

1.3 物流网络的概述 . ............................................................ 错误！未定义书签。

1.3.1 物流网络的类型....................................................... 错误！未定义书签。

1.3.2 物流网络布局与规划............................................... 错误！未定义书签。

1.4 企业物流网络的布局 . .................................................... 错误！未定义书签。

1.4.1 企业物流节点布局................................................... 错误！未定义书签。

1.4.2 一元节点布局........................................................... 错误！未定义书签。

1.4.3多元节点布局............................................................ 错误！未定义书签。

第2章 华东干线网络优化分析 ............................................... 错误！未定义书签。

2.1华东的经济、交通和物流背景分析 ............................... 错误！未定义书签。

2.1.1经济............................................................................ 错误！未定义书签。

2.1.2 交通........................................................................... 错误！未定义书签。

2.1.3物流............................................................................ 错误！未定义书签。

2.2华东干线网络现状 ........................................................... 错误！未定义书签。

2．3 SWOT 分析和战略决策 ................................................... 错误！未定义书签。

2.3.1优势............................................................................ 错误！未定义书签。

2.3.2劣势............................................................................ 错误！未定义书签。

2.3.3机会............................................................................ 错误！未定义书签。

2.3.4威胁............................................................................ 错误！未定义书签。

2．4市场的战略决策 ............................................................. 错误！未定义书签。

2.4.1国际物流的发展趋势................................................ 错误！未定义书签。

2.4.2战略决策.................................................................... 错误！未定义书签。

2.5 SFR 三角干线网络的整合的可行性分析 ....................... 错误！未定义书签。

2.5.1从物流市场空间角度来分析.................................... 错误！未定义书签。

2.5.2从行业发展的角度来分析........................................ 错误！未定义书签。

2.5.3从公司发展角度来分析............................................ 错误！未定义书签。 2.6 长三角干线网络整合方案设计 ................................... 错误！未定义书签。

2.6.1 RDC选址.................................................................... 错误！未定义书签。

2.6.2三角运量最优路径选择............................................ 错误！未定义书签。

2．7小结 ................................................................................. 错误！未定义书签。

第3章 华南干线的调整 ......................................................... 错误！未定义书签。

3.1干线的现状及原因分析 ................................................... 错误！未定义书签。

3.1.1 **公司粤闽干线现状............................................... 错误！未定义书签。

3.1.1原因分析.................................................................... 错误！未定义书签。

3.2干线调整的环境与时代背景 ........................................... 错误！未定义书签。

3.2.1政治环境.................................................................... 错误！未定义书签。

3.2.2经济环境.................................................................... 错误！未定义书签。

3.2.3技术环境.................................................................... 错误！未定义书签。

3.3干线调整的方向和目标 ................................................... 错误！未定义书签。

3.4干线调整的方案设计 ....................................................... 错误！未定义书签。

3.4.1两步走战略................................................................ 错误！未定义书签。

3.5调整方案的成本、SWOT 及原因分析 .............................. 错误！未定义书签。

3.5.1调整方案的成本测算................................................ 错误！未定义书签。

3.5.2粤闽干线调整———SWOT 分析............................... 错误！未定义书签。

3.5.3 论据........................................................................... 错误！未定义书签。

3.7调整方案的可行性 ........................................................... 错误！未定义书签。

3.7.1可行性........................................................................ 错误！未定义书签。 结束语 ......................................................................................... 错误！未定义书签。

范文二：物流网络优化

Collaborative Logistics: Developing a Framework to Evaluate Socio-Technical Issues in Logistic-Based Networks

Joseph Lyons Jill Ritter Krystal Thomas Air Force Research Laboratory joseph.lyons@wpafb.af.mil jill.ritter@wpafb.af.mil krystal.thomas@wpafb.af.mil Laura Militello University of Dayton Research Institute laura.militello@udri.udayton.edu Patrick Vincent Northrop Grumman Corp. patrick.vincent@ngc.com

on mission type and location. Further, individuals may move in and out of the team on a regular basis as personnel are rotated through assignments. In addition, the military is facing novel demands in terms of peacekeeping, humanitarian, and disaster relief operations which require instant and effective collaboration between the local, state, and federal governments; military; and civilian organizations. This collaboration may also traverse cultural and geographic boundaries, which adds another degree of complexity to logistics teams. Military logistics must be adaptable to evolving situations and have the capability to rapidly respond to global demands. Such adaptability resonates with the concept of distributed adaptive logistics. Distributed adaptive logistics, also referred to as sense and respond logistics or knowledge enabled logistics, involves enabling flexible, selfsynchronizing logistics networks capable of expanding and retracting upon demand signals [1]. By incorporating a flexible logistics network, the military can be more resilient to the high demands of rapidly evolving mission requirements. However, the current logistics infrastructure is assembled around traditional supply chains developed to meet the needs of cold war defense. The need for a quicker, more robust logistics response to meet contemporary military needs is increasingly evident [1]. As military operations evolve to meet the unique demands of modern-day warfare, so must the systems, organizations, policies, and processes that support these operations.

ABSTRACT

The concepts of collaborative logistics, logisticsbased networks, focused logistics, and distributed adaptive logistics describe a futuristic approach to logistics planning encompassing automated, adaptive technologies, and proactive human collaboration. Such an approach creates novel challenges for collaboration within socio-technical systems, which encompass the collaborative technologies, the people and machines involved in the act of collaboration, and social context of collaboration. These challenges need to be addressed by theoretical models incorporating multidisciplinary approaches to the study of collaboration. The present paper introduces a framework for collaboration which may inform the design and implementation of collaborative technologies and systems from a socio-technical perspective.

Keywords: collaborative logistics, socio-technical systems, logistics networks, distributed adaptive logistics, human-centric collaboration

1. LOGISTICS CONTEXT

Contemporary military logistics operations are executed in dynamic global theaters of operation characterized by geographic dispersion, distributed and disparate information, and decentralized decision-making [1]. Logistics teams are generally formed ad-hoc as team membership varies depending

2. COLLABORATIVE TOOLS

0-9785699-0-3/06/$20.00?2006 IEEE

208

A key component of distributed adaptive logistics is collaboration. Collaboration can be defined as two or more individuals working towards a common goal by sharing and exchanging information [2]. Though collaboration may occur between humans and machines, or even between two machines, the focus of the present research is on human-human collaboration. There are a multitude of collaborative tools, systems, and applications that have been created to facilitate collaboration. Yet many of these collaborative tools will fail [3]. In spite of recent experiences highlighting the critical breakdowns in technology development, many new forms of technology continue to fail to reach the market, due in part, to inadequate evaluation during definition of user requirements, product development, and implementation [4]. One of the key problems with the development of collaborative tools is the lack of metrics available to evaluate these systems [3]. Improved metrics/methodologies to evaluate and assess collaboration may significantly impact the collaboration process. Without standard metrics for assessing collaboration, software developers are left to use their own judgment in determining the value and utility of their products. Perhaps even more fundamental than standard metrics, research indicates that software developers and users (and their management) have different opinions about what is critical [3]. Therefore, a first step in improving collaborative tools is to include users in the design process. However, technological issues are often not the only problem. A second step is to define critical metrics for assessing collaboration from a sociotechnical perspective. Currently, collaborative tools are designed to achieve technological reliability and innovation goals. In the future, we must also consider socio-technical factors. Cultural, organizational, and social factors are often overlooked during program design [3]. The true measure of success for a collaborative tool is whether it facilitates collaboration, an inherently human activity. So rather than focus solely on technological measures of success, we should examine human-centric issues influencing collaboration. The proposed framework addresses these human-centric issues by studying collaboration at various levels within a sociotechnical system.

Despite a plethora of attention given towards the IT aspects of collaborative systems, little attention has been given to the human, or team aspects of collaborative system design or implementation within organizations [5]. The literature on distributed/virtual teams addresses many of the human and team-centric issues that may impact collaborative logistics. Virtual teams are characterized by: 1) two or more individuals who, 2) work towards a common goal, 3) are geographically dispersed, and 4) use technology (email, phone, video teleconference, instant messaging, etc.) to coordinate their activities [6]. Modern work teams are formed to address the increasing complexity of contemporary work because they can perform complex tasks better than individuals may have otherwise performed. Generally, the implementation of face-to-face (FTF) work teams has been shown to be an effective way for organizations to ensure that complex tasks are performed adequately [7]. However, virtual teams must deal with a host of problems not found in FTF teams. In a comprehensive report exploring differences between FTF and virtual teams, the Rand Corporation identified several barriers to collaboration that epitomize computer-mediated teams [8]. The Rand report listed many barriers to effective collaboration; but the present paper will focus only on factors that are relevant to collaborative logistics.

3.1. Information Loss

Nonverbal cues such as facial expressions, voice tone, eye contact, and body gestures represent valuable pieces of information during group communications. These socio-emotional cues can convey shared understanding, a lack of understanding, agreement, or conflict between team members. Distributed logistics teams will lack these cues due to their geographic dispersion. Similar to distributed logistics teams, research shows that computer-mediated teams suffer from misunderstandings due, in part, to the loss of paraverbal cues [9]. Virtual teams may perform similarly to FTF teams over time, but they tend to interact less and take longer to complete tasks partially because of the information loss that is characteristic of virtual teams [9].

3. HUMAN COLLABORATION

CENTRIC

209

3.2. Reduced Trust

A critical component of an effective team is trust. Trust is essential for the success of collaborating teams, particularly geographically dispersed teams because trust orients individuals towards collective interests rather than self interests [10] and it facilitates cooperation within teams [11]. Emotional cues play an integral role in the development and maintenance of trust [12]. However, the nonverbal cues often needed to convey emotional information are impaired during computer-mediated communication (CMC) [11]. This creates challenges for collaborative logistics as distributed logistics teams typically rely on technology to communicate and they are often assembled ad-hoc. Virtual/distributed teams may eventually develop trust over time, albeit taking longer to develop than FTF teams, but this trust has been found to be more fragile than the trust developed in FTF teams [13]. Communication can be an essential catalyst for the development of trust [11]. Therefore, interfaces that infuse more of the paraverbal cues relevant to emotions and those that convey a sense of trustworthiness by facilitating communication may develop and maintain trust among distributed logistics teams.

teams, as distributed teams are more likely to form local coalitions (a situation where co-located team members develop adversarial attitudes against the distributed team members) [15]. Local coalitions may lead co-located members of distributed teams towards groupthink (a lack of critical analysis of alternative decisions) [16] and thus lower their decision quality relative to FTF teams. Effective decision making is critical for distributed logistics teams since these teams are often required to make time-critical decisions, decisions in dynamically changing wartime environments and in many cases situations relevant to national defense.

3.5. Suppressed Leadership Emergence

Indices of status and power are more difficult to detect in distributed teams. Thus, leaders are less likely to emerge within these distributed teams [8]. Leadership skills are well-trained and executed in military settings as they are essential for the successful deployment of military operations. However, in other settings leadership roles are not so clearly defined, and decision making is much more likely to occur via consensus rather than command. This raises particular challenges in disaster relief efforts where the military (i.e., logistics planners) must coordinate activities with civilian agencies. In these situations, no clear leader may be evident, and different organizations may be accustomed to different leadership and decision making styles. The much needed leadership in this type of setting may take longer to emerge and require negotiation and collaboration skills somewhat different from those traditionally trained and used in military settings. Given the challenges that distributed logistics teams are likely going to experience, what can we do to improve their chances for successful collaboration? The literature clearly recommends that virtual teams first have a FTF meeting prior to engaging in distributed work [6]. However, this certainly is not always feasible given that the logistics domain is often characterized by geographically-dispersed, cross-cultural, ad-hoc teams. One approach might be to consider the types of tasks that characterize the logistics domain. Certain tasks may lend themselves better to a virtual team context. For example, generative tasks [17] like brainstorming may be enhanced by a virtual setting [18], however other tasks such as problem solving and negotiating may be impaired in a virtual context because these tasks are

3.3. Reduced Cohesion

In addition to trust, another group process variable that is critical to team success is cohesion (the members’ attraction towards and liking of the team). Highly cohesive teams tend to perform better than teams that are not cohesive [14]. Due to their physical separation, virtual teams develop lower cohesiveness than FTF teams [15]. Cohesion is a necessary element for distributed logistics teams in order to promote effective team performance and high morale during any military planning or execution operation.

3.4. Local Coalitions

Decision making is one the most common reasons for assembling a group. Groups are often perceived to make higher quality decisions compared to individuals because they can critically evaluate various decision options better than individuals could alone. These benefits are less evident in distributed

210

more dependent on non-verbal cues and shared understanding between individuals. Needless to say, the types of tasks most relevant for the logistics domain involve problem solving and negotiation, both of which would likely be hindered by a virtual context. Clearly, tools tailored to the challenges of distributed collaboration across agencies have the potential to facilitate smooth team performance in difficult situations. Though not a panacea, technology may assist distributed logistics teams to better collaborate. Developing effective technology to meet these challenges is not trivial. If we are to be successful in creating collaborative tools that minimize information loss and the formation of adversarial local coalitions, as well as facilitate trust, cohesion, and leadership, we must both design to and evaluate against criteria developed from a socio-technical perspective. The entire socio-technical system must be considered in studies of collaborative logistics. This necessitates a framework that encompasses collaboration at disparate levels.

descriptive observation where researchers embed themselves into a work domain to observe work in

Phase I Phase II Phase III

Domain Survey

Directed Assessment

Analysis

Collaboration Collaboration Domain Domain

Recommendations

Phase IV

Figure 1. Collaboration Framework situ. Past research has identified five goals of ethnographic methods, to: 1) describe how individuals manage context variability, 2) describe the social organization of work, 3) describe how workers have developed “work arounds” and other tools to reduce their mental workload, 4) describe the influence of historical factors on current working conditions and practices, and 5) describe the techniques that workers create to get their work done [19]. Ethnography can provide researchers with a detail-rich description or naturalistic account of a work domain. In conjunction with ethnographic methods, descriptive questionnaires and interviews can be used to gain a better understanding of the work domain from an individual perspective. Questionnaires will allow individuals to remain anonymous, thus encouraging veracity among respondents. However, in many cases, responses to questionnaires require some type of follow-up related to clarification of responses, additional inquiries, etc. Hence, interviews with respondents can complement questionnaires by providing researchers opportunities to follow up on specific topics. Social network modeling can also be used in conjunction with ethnographic methods to model communication patterns between team members or to inform researchers about work flow within an organization or team. The primary outcome resulting from the Phase I effort is a detailed description characterizing the nature of collaboration break downs or collaborative successes in the team context. This can be very

4. COLLABORATION FRAMEWORK

The framework discussed below (see Figure 1) represents a multidisciplinary amalgamation of methods that should allow researchers to examine factors that influence collaboration within sociotechnical systems. Such an approach should address the impact of collaborative technologies and systems at the individual, team, and organizational levels. The present framework integrates methods from psychology, sociology, and anthropology into a comprehensive framework to assess and evaluate collaboration from a human-centric perspective. There are four phases outlined in the current framework: domain survey, directed assessment, analysis, and recommendation.

4.1. Phase I: Domain Survey

The objective of Phase I is to apply descriptive methods to the observation of a team collaboration domain with the goal of gaining a better understanding of the barriers to or facilitators of effective collaboration. There are several methods that could be deployed during Phase I: ethnography, descriptive questionnaires, interviews, and social network modeling. Ethnography represents a form of

211

informative in terms of identifying facilitators and barriers to collaboration. At this point, researchers could offer suggestions to organizations as to how they might go about improving collaboration within this domain. For example, researchers may discover that the collaborative technology used by the team is insufficient. Furthermore, organizational factors (such as a lack of top management support) may be found to inhibit team collaboration. The impetus for examining the socio-technical system draws from the notion that successful team collaboration relies on numerous factors (e.g., team characteristics, organizational factors, technology issues, etc.).

performance must remain inchoate without further specification of a domain of interest. The results of Phase II will comprise a set of quantitative data based on the collaboration metrics of interest for the present domain.

4.3. Phase III: Analysis

The objective of Phase III is to analyze the qualitative data from Phase I and quantitative data collected in Phase II to diagnose the severity of collaboration breakdowns or the extent of collaboration facilitators present in the domain. Statistical tools (e.g., SPSS) in conjunction with other tools (e.g., Social Network Analysis) can be deployed to make comparisons between different technologies, team dimensions, or organizational factors in terms of how they influence collaboration. The primary outcome of Phase III involves statistical and descriptive reports documenting elements of the collaborative domain and or differences in collaboration effectiveness for specific dimensions within the socio-technical system.

4.2. Phase II: Directed Assessment

The objective of Phase II is to pull from an existing set of methods, tools, and techniques to quantitatively measure the barriers or facilitators identified in the Phase I effort as well as variables identified in past research and elaborated on above. There are three primary methods that can be applied in Phase II: guided observations, questionnaire-based assessments, and performance data. The observations in this phase should be directed towards some phenomenon of interest and coded to provide quantitative data. For example, if leadership suppression was identified as a barrier to successful team collaboration in Phase I, then behavioral indicators of leadership emergence, such as whether or not a team member took charge, could be used to measure if and when team members engage in this leadership behavior. To further understand the influence of collaborative systems on trust we can look at communication patterns and behaviors. Research has found that high levels of personal (socially-oriented) communications are related to the development of trust in computer-mediated teams [20]. Essentially, behavioral indicators could be used to assess information loss, trust, cohesion, local coalitions, and leadership emergence. Table 1 provides a sample list of behavioral indicators. These directed observations can be buttressed by questionnaire-based measures of information loss, trust, cohesion, local coalitions, and leadership emergence. Additional metrics for collaborating teams have been proposed by researchers [21] and are under consideration. Ultimately, teams will be collaborating for a purpose. Therefore, performance data is needed to validate our collaboration metrics. This performance data will vary with the context or domain under examination, therefore specific

4.4. Phase IV: Recommendation

The objective of Phase IV is to summarize the information resulting from the Phase I-III efforts, and to offer recommendations that better inform the requirements, design, and/or implementation of collaborative systems from a socio-technical perspective. The specific components of the recommendations will be guided by the requirements of the user. For example, if the goals of the user involve system design, then the recommendations will focus on this aspect of the socio-technical system. Ideally, researchers will be able to identify recommendations to be implemented at various levels within the socio-technical system (i.e., organizational, team, individual, or technology). This multivariate approach grants greater flexibility to researchers and should facilitate a more extensive impact on collaborative systems than focusing on just one facet of the socio-technical system would have otherwise accomplished.

5. SUMMARY

Developing a comprehensive framework for use between multiple disciplines is challenging given the disparate argots and perspectives between different

212

fields. However, a unified framework can be a catalyst for the development of universal theories of Table 1. Sample List of Behavioral Indicators for the Barriers to Effective Team Collaboration Dimension Example Indicators Information Loss Seeking additional information, admissions of confusion Trust Verbally questioning a member’s actions, rechecking others’ work Cohesion Interpersonal conflict, expressions of attachment within the team, physical distance and contact Local Coalitions Excessive agreement within local members coupled with conflict between distributed members Leadership Emergence Member taking charge, dominant actions, delegation collaboration in socio-technical systems. Modern-day logistics requirements call for a novel approach to investigating collaboration. The proposed framework can be used to better understand collaborative logistics networks and thus better inform the design and implementation of distributed adaptive logistics systems. Approved for public release; distribution is unlimited. HEA-06-048

[4] EXTREME CHAOS, The Standish Group International, Inc., 2001. [5] Militello, L.G., J. Ritter, and P. Vincent, “Toward developing and evaluating collaborative technologies,” Proceedings of the Human Interaction with Complex Systems Conference, November, 2005. [6] Hertel, G., S. Geister, and U. Konradt, “Managing virtual teams: A review of the current empirical research,” Human Resource Management Review, Vol. 15, No. 1, 2005, pp. 69-95. [7] Kozlowski, S.W.J., and B.S. Bell, “Work groups and teams in organizations.” In W. Borman and D. Illgen (Eds.), HANDBOOK OF PSYCHOLOGY: INDUSTRIAL AND ORGANIZATIONAL PSYCHOLOGY, Vol. 12, pp. 333-376, John Wiley & Sons Inc.: New York, 2003. [8] Wainfan, L., and P.K. Davis, “Challenges in Virtual Collaboration: Videoconferencing, Audioconferencing, and Computer-Mediated Communications,” (Research Report), RAND Corporation: Santa Monica, CA, 2004. [9] Becker-Beck, U., M. Wintermantel, and A. Borg, “Principles of regulating interaction in teams practicing face-to-face communication versus teams practicing computer-mediated communication,” Small Group Research, Vol. 36, No. 4, 2005, pp. 499-536. [10] Dirks, K.T., “The effects of interpersonal trust on work group performance,” Journal of Applied Psychology, Vol. 84, No. 3, 1999, pp. 445-455. [11] Kasper-Fuehrer, E.C., and N.M. Ashkanasy, “Communicating trustworthiness and building trust in interorganizational virtual organizations,” Journal of Management, Vol. 27, No. 3 2001, pp. 235-254. [12] Wicks, A.C., S.L. Berman, and T.M. Jones, “The structure of optimal trust: Moral and strategic implications,” Academy of Management Review, Vol. 29, No. 1, 1999, pp. 99-116. [13] Jarvenpaa, S.L., and D.E. Leidner, “Communication and trust in global virtual teams,” Organizational Science, Vol. 10, No. 6, 1999, pp. 791-815. [14] Mullen, B., and C. Cooper, “The relation between group cohesiveness and performance: An integration,” Psychological Bulletin, Vol. 115, No. 2, 1994, pp. 210-227. [15] Straus, S.G., “Technology, group process, and group outcomes: Testing the connections in computer-mediated and face-to-face groups,” Human Computer Interaction, Vol. 12, No. 3, 1997, pp. 227-266.

REFERENCES

[1] Cares, J.R., “Distributed adaptive logistics,” Information Age Warfare Quarterly, Vol. 1, No. 1, 2005, pp. 4-12. [2] McQuay, W.K., “Distributed Collaborative Environments for Systems Engineering,” IEEE Aerospace & Electronic Systems Magazine, Vol. 20, No. 8, Part 1, 2005, pp. 7-12. [3] Coleman, D., and J. Young, “Critical factors for adoption of collaborative technologies,” Collaborative Strategies LLC: San Francisco, CA, 2005.

213

[16] Janis, I.L., and L. Mann, DECISION MAKING: A PSYCHOLOGICAL ANALYSIS OF CONFLICT, CHOICE, AND COMMITMENT, Free Press: New York, 1977. [17] McGrath, J.E., GROUPS: INTERACTION AND PERFORMANCE, Prentice-Hall: Englewood Cliffs, NJ, 1984. [18] Dennis, A.R., and J.S. Valacich, “Computer brainstorms: More heads are better than one,” Journal of Applied Psychology, Vol. 78, No. 4, 1993, pp. 531-537. [19] Randall, D., R. Harper, and M. Rouncefield, “Fieldwork and ethnography in design: A state of play from the CSCW perspective,” Proceedings of the EPIC Conference, November, 2005. [20] Moore, D.A., T.R. Kutzberg, L.L. Thompson, and M.W. Morris, “Long and short routes to success in electronically mediated negotiation: Group affiliations and good vibrations,” Organizational Behavior and Human Decision Processes, Vol. 77, No. 1, 1999, pp. 22-43. [21] Noble, evaluation of International Symposium, D.C., 2003. D. and J. Kirzl, “Objective metrics for collaborating teams,” Proceedings of the 8th Command and Control Technologies National Defense University: Washington

214

范文三：物流网络优化设计

物流科技2011年第1期Logistics Sci-Tech No.1, 2011誗产经探索誗

文章编号：1002-3100(2011)01-0136-03

物流网络优化设计

刘

摘斌（神华集团物资贸易有限责任公司，北京100021）要：介绍了物流网络系统的分层框架，并结合物流网络优化的实践，重点对针对战略层和战术层的物流网络战略规划和库存优化分别进行了介绍，从其包含的主要内容、解决的主要问题、设计和优化方法，以及应用工具等方面进行了讨论，最后结合物流网络优化在实践中面临的挑战，提出了建议。

关键词：物流网络；物流战略；库存优化

中图分类号：F724文献标识码：A

2008年以来的全球经济动荡对各个行业的供应链造成了深刻影响。大宗商品的价格和供应异常波动，主要货币汇率风险陡增，金融市场大起大落，地缘政治事件不断和客户渠道全球化持续发展，这使企业的采购、生产和产品分销面临前所未有的压力。与此同时，客户期望不断提高，全球化竞争的不断加剧，产品的生命周期的不断缩短，客户需求及物流成本波动的不断增大，使得如今的企业面临的最大挑战之一就是建立能够实现并且保持卓越绩效的具有活力的供应链。

在这种背景下，很多企业为提高或保持长、中、短期的盈利能力而不得不对现有的供应链进行调整，越来越多的企业还把目光投向了新兴市场。在中国，不少企业开始重新规划自己的业务版图，一些原以出口为主的中国企业也转向了国内市场，很多外资企业也更多将重心转移到中国，希望能获得快速发展。以往不少企业在调整供应链时，把精力放在了建立新的IT 系统或是对现有系统进行升级上，而很少有企业会从供应链网络优化的角度来考虑。这些IT 项目往往需要投入大量的资金，并且通常项目周期会很长，最终达到预期效果的企业却不多。而如今，情况正在改变，越来越多的企业开始认识到供应链网络的重要性，尤其对于生产或销售有形产品的企业，物流网络是支持其供应链运作和管理的前提和基础设施，物流网络设计中所制定的决策不仅对于企业长期赢利能力和竞争地位会产生重要而深远的影响，而且也对供应链绩效形成直接且重大的影响。并且，通常物流网络优化项目的周期远比IT 系统项目短，投资也远比IT 系统低，经过合理优化设计的物流网络还有助于提高实施IT 系统的投资回报。从这个意义上来讲，物流网络设计是企业供应链战略和实际运作的衔接桥梁，既要考虑到企业供应链战略的实现，又要考虑到设计决策对于未来物流运作的约束作用；物流网络的效率很大程度上取决于物流网络设计的合理性，只有设计合理才能使物流系统获得整体的最优。

1物流网络系统的分层结构

供应链物流网络是一个跨时域、跨地域的动态网络，尤其在实践中对那些涉及到多品类、多渠道的物流网络进行优化设计时，其固有的复杂性要求有一个清晰而有效的总体框架作为指导。从系统论的角度来看，物流网络系统可分为战略层、战术层和运作层。战略层主要涉及到物流系统的总体结构设计，各级节点设施的选址等；战术层，指整个系统以及每个节点的设施规划、库存管理；运作层，指具体的运作管理，如车辆调度、路线优化等。可以看出，物流网络结构的分层视角提供了一个从总体上把握其系统结构的框架，每一层的规划任务和内容各不相同，有相互衔接、逐层深化。

（1）战略层

首先明确仓库的层级结构，从企业的成品库到零售店之间到底应该设立几级仓库，两级、三级还是四级，对于每一级仓库，其数量应该设置多少？其次，每一个仓库的位置应该设立在哪里？第三，如何划分每一个仓库的配送区域？

（2）战术层

处于战略层次的选址问题及其所形成的总体物流网络结构确定之后，或者在物流网络结构确定的同时，作为物流战术层解决的问题是：在已有的物流网络体系中，每个层级的设施中的应该维持多少库存？订货周期应该为收稿日期：2010-12-15

作者简介：刘斌（1961-），男，山西临汾人，神华集团物资贸易有限责任公司副总经理，高级经济师，天津大学管理学博士，研究方向：物流与供应链管理。

Logistics Sci-Tech 2011.1

物流网络优化设计

多长？订货批量如何设置？最终形成多层级的库存体系。

（3）运作层

运作层次的物流网络设计主要涉及到对某一区域的配送路线优化。根据销售终端的销售能力、库存水平和资金运转情况，公司的配送策略设定在相对较短的一个时期内，一般以“天”为单位。根据需要，一个配送中心所负责的配送区域可以再细分为几个更小的区域，在此基础之上通过各种模型解决车辆配送路径问题。

物流网络优化设计的目标就是在维持或优化客户服务水平的前提下，尽可能的降低运营成本。通常情况下，物流网络结构和库存策略对于客户服务水平及物流成本的影响起着决定性的作用，虽然已有关于此类问题的大量理论研究，然而由于实际中这两类问题都是数据密集型的，需要专业工具进行建模和分析。在欧美等发达国家的成熟市场，已形成针对物流网络战略层和战术层的网络结构及设施规划和库存优化的广泛应用。随着技术进步，工具软件的进一步完善，以及中国市场的日益成熟，在中国越来越多的企业开始从物流网络优化项目中受益，其中最为典型的就是物流网络战略规划项目和库存优化项目。

2物流网络战略规划

物流网络战略规划优化是用于衡量部分或全部的供应链物流网络的战略研究。研究相关的供应链物流成本，包括库存成本、运营成本（固定成本和变动成本）、运输成本（包括入库运输、转仓运输、出库运输成本）；同时还考虑相关约束要素，包括物流中心的开与关、物流中心吞吐量限制，客户服务水平设置、产品配置策略、运输动线策略、保险约束、物流中心数量等。网络规划研

究最终建立适合企业对应发展阶段的成本与服务水平

最优平衡的供应链网络模型。

物流网络战略规划的成果物输出包括网络多情景

比较分析、仓库地点建议、仓库规模大小、物流成本

估算（运输成本、存储、运营成本）、从工厂到仓库

动线的入库策略、配送中心覆盖客户市场区域策略、

网络动线策略、敏感分析等。

物流网络战略规划是一项复杂的工程，中间涉及

到大量的数据和复杂的建模过程，需要用到作业的软

件工具帮助建模分析。目前市场上这方面的软件也有

很多，但大部分都是以运输优化的功能为主，缺乏一少物流中心数量转运成本配送运输成本多年度物流成本库存成本固定成本最佳优化区域物流总成本劳动力成本

定的整体物流网络战略决策的支持功能。由前Logictools 公司开发的LogicNet 软件，能够给客户在战略和战术上进行供应链网络的优化，是一款战略和战术层面的工具，主要用于决策以时间为基础上的生产和分销策略，同时对供应链上设施的位置和大小进行优化。LogicNet 进行网络优化的优点在于可以很容易地看到各种供应链成本在供应链中如何因为配送中心个数增加而发生变化。

应用软件进行网络规划的一般步骤包括：问题描述与目标确定，数据收集与分析，模型分析，成果陈述四大阶段。由于物流网络建设的投资很大且设立新仓库需要很长时间，所以物流网络规划的所涉及的时间范围是很长的。这是一个以成本分析来帮助决策者制定一个高效决策的过程。模型的输出结果可广泛应用于需要供应链模型的各个阶段，为供应链管理过程提供了主要的输入。同时随着时间推移，模型所采用的假设条件可能发生部分变化，所以需要对模型进行适当调整，最好每年进行一次更新运算，以使得规划的网络模型更符合实际发展的需要。3库存优化

物流网络战略规划在确定物流网络的整体结构的同时，也在战略层面对库存在网络节点上的总体分布进行了优化。但是，由于库存问题本身所固有的复杂性，通常仍会留有较大空间对库存进一步优化。在确定的物流网络结构下，能够对不同类别物资的库存进行更加细致和深入的优化分析，进一步挖掘物流网络中库存优化的潜力。

库存优化是通过科学的分析建模和计划流程改善来识别提高库存效率和服务水平的机会。它可以是对供应链各节点库存的总体分析优化，也可以针对其中某一重要库存节点进行优化。实际上，库存水平与服务水平存在关系曲线，可以根据企业在不同发展阶段对服务水平的战略要求，确定对应的库存水平策略。

库存水平的设置与采购策略有关，采购批量大小和采购周期直接影响库存量的设置；同时，同一种物资在不同层级地点的库存配置，将影响库存的服务水平。因此，可以通过各物资的库存水平分析、采购策略分析、库存配置策略分析和服务水平需求分析等四个方面，开展库存优化工作。

Logistics Sci-Tech 2011.1137

物流网络优化设计

现状

库存成本优化目标库存实际库存变化采购订单

下达点

最小库存最大库存

0246

服务水平810时间库存补货过程示意图

（1）采购策略分析

①采购经济批量分析。采购策略往往以采购单价的优惠幅度来决定采购经济批量的大小，但采购经济批量的大小也影响了库存水平的高低。

②采购前置期分析。采购前置期指从下达采购订单到收到货物的时间。采购前置期越长，需设定的安全库存越高；降低采购前置期，有利于库存水平的降低。

③采购前置期变动性（供应商供货可靠性）分析。当采购前置期变动性大时，库存水平将被提高；必须通过量化分析找出前置期变动性大的物资，从采购策略上解决问题。

（2）库存水平分析

①需求变动性与安全库存水平。安全库存即最小库存，用于抵御物资需求的变动风险。需求的变动越大，安全库存设置越高。因此，通过分析需求变动性，合理设置安全库存，有利于库存水平的总体降低。

②采购前置期变动性与采购订单下达点的设置。采购订单下达点须高于安全库存点，两者的存量差须满足采购周期内库存消耗量；但是，当采购前置期存在波动时，采购订单下达点将被提前，由此造成库存水平被提高。

③最大库存设置。物资需求波动性越大、采购经济批量越大、采购前置期变动性越大，都将促使最大库存设置增大，总体库存水平提高，库存成本增大。

（3）库存配置策略

物资库存包括供应链上不同节点的库存，其库存配置策略将影响服务水平和库存成本。例如，快品采用分散配置库存策略，可以提高服务水平但不显著增加库存；慢品采用集中库存策略，可以大幅度降低库存而不显著影响服务水平。

（4）服务水平需求分析

服务水平是指物资供应满足需求的时间周期和履约率，但时间周期缩短，履约难度增大，服务水平要求被提高。合理设置服务水平，有利于库存水平的降低。

库存优化工作可以通过上述四个途径全面开展，也可以从四个途径中的局部开展。

库存优化是一项数据驱动型的分析设计工作，需对大量数据进行设置和分析运算，建立库存成本和服务水平之间的关系模型，明确现阶段的库存成本与服务水平关系并确定未来的改善目标。国际物流领域对库存优化均采用专业的数学模型软件工具来分析设计，被世界500强企业大量采用的工具是Toolsgroup 公司的DPM 软件。

●DPM 库存优化建模软件，从物资需求的计划管理出发，建立优化的库存结构和库存水平设置。

●DPM 使用特有的运算法则来平衡最优化库存和特定服务等级之间的关系，它是通过在最优化服务水平和最小化另外一个假设因素之间取一个标准值来实现的，这个假设因素可以是库存的成本，或是仓库的空间或是库存数量等；这样复杂的算法仅凭经验和手工是无法做到的。

●DPM 还可以帮助细分库存物资类型，以帮助客户判断哪些是战略上的需要考虑给予高水平服务的物资，哪些是可以考虑适当降低服务水准的。

4物流网络优化设计在实践中的挑战

尽管物流网络优化设计中物流网络战略规划和库存优化的方法、技术和工具日益成熟和完善，但在现阶段的实践中，仍然面临的着诸多挑战，其中包括基于经验与量化分析的权衡、量化的战略决策支持、动态的战略决策支持以及战略及策略方案的可操作性等。为应对这些的挑战，物流网络优化咨询业务也就慢慢兴起，一些有丰富工程咨询经验的物流咨询公司，可以通过一个业界公认的标准决策工具，建立一个精巧的数学模型，在计算机里置信客户现在的供应链运作状况，并设计多种商业情景或供应链策略，用计算机模型来模拟仿真，按照成本最优或服务最优的原则去求得最佳情景。客户也可以用此模型去模拟未来商业变化对供应链的影响。从长期的意义来讲，网络优化使得咨询公司与客户能够有更密切地合作。

Logistics Sci-Tech 2011.1

范文四：物流网络优化设计

物流网络优化设计

刘斌,神华集团物资贸易有限责任公司, 北京 100021,

摘 要, 介绍了物流网络系统的分层框架, 并结合物流网络优化的实践, 重点对针对战略层和战术层的物流网络战略规划 和库存优化分别进行了介绍, 从其包含的主要内容、 解决的主要问题、 设计和优化方法, 以及应用工具等方面进行了讨论, 最 后结合物流网络优化在实践中面临的挑战, 提出了建议。

关键词, 物流网络, 物流战略, 库存优化

中图分类号, F724 文献标识码, A

2008年以来的全球经济动荡对各个行业的供应 链造成了深刻影响。 大宗商品的价格和供应异常波动, 主要货 币汇率风险陡增, 金融市场大起大落, 地缘政治事件不断和客户渠道全球化持续发展, 这使企业的采购、 生产和 产品分销面临前所未有的压力。 与此同时, 客户期望不断提高, 全球化竞争的不断加剧, 产品的生命周期的不断 缩短, 客户需求及物流成本波动的不断增大, 使得如今的企业面临的最大挑战之一就是建立能够实现并且保持卓 越绩效的具有活力的供应链。

在这种背景下, 很多企业为提高或保持长、 中、 短期的盈利能力而不得不对现有的供应链进行调,整 越来越 多的企业还把目光投向了新兴市场。 在中国, 不少企业开始重新规划自己的业务版图, 一些原以出口为主的中国 企业也转向了国内市场, 很多外资企业也更多将重心转移到中国, 希望能获得快速发展。 以往不少企业在调整供 应链时, 把精力放在了建立新的IT 系统或是对现有系统进行升级上, 而很少有企业会从供应链网络优化的角度来 考虑。 这些 IT 项目往往需要投入大量的资金, 并且通常项目周期会很长, 最终达到预期效果的企业却不多。 而如

今, 情况正在改变, 越来越多的企业开始认识到供应链网络的重要,性 尤其对于生产或销售有形产品的企业, 物流网络是支持其供应链运作和管理的前提和基础设施, 物流网络设计中所制定的决策不仅对于企业长期赢利能力 和竞争地位会产生重要而深远的影响, 而且也对供应链绩效形成直接且重大的影响。 并且, 通常物流网络优化项 目的周期远比 IT 系统项目短, 投资也远比 IT 系统低, 经过合理优化设计的物流网络还有助于提高实施IT 系统的 投资回报。 从这个意义上来讲, 物流网络设计是企业供应链战略和实际运作的衔接桥,梁 既要考虑到企业供应链 战略的实现, 又要考虑到设计决策对于未来物流运作的约束作用, 物流网络的效率很大程度上取决于物流网络设 计的合理性, 只有设计合理才能使物流系统获得整体的最优。

物流网络系统的分层结构 1

供应链物流网络是一个跨时域、 跨地域的动态网络, 尤其在实践中对那些涉及到多品类、 多渠道的物流网络 进行优化设计时, 其固有的复杂性要求有一个清晰而有效的总体框架作为指。导 从系统论的角度来看, 物流网络 系统可分为战略层、 战术层和运作层。 战略层主要涉及到物流系统的总体结构设计, 各级节点设施的选址等, 战 术层, 指整个系统以及每个节点的设施规划、 库存管理, 运作层, 指具体的运作管理, 如车辆调度、 路线优化等。 可

以看出, 物流网络结构的分层视角提供了一个从总体上把握其系统结构的框, 架每一层的规划任务和内容各不 相同, 有相互衔接、 逐层深化。

,1, 战略层

首先明确仓库的层级结构, 从企业的成品库到零售店之间到底应该设立几级仓,库 两级、 三级还是四级, 对 于每一级仓库, 其数量应该设置多少, 其次, 每一个仓库的位置应该设立在哪里, 第三, 如何划分每一个仓库的 配送区域,

,2, 战术层

处于战略层次的选址问题及其所形成的总体物流网络结构确定之,后 或者在物流网络结构确定的同时, 作为 物流战术层解决的问题是, 在已有的物流网络体系中, 每个层级的设施中的应该维持多少库存, 订货周期应该为

收稿日期, 2010-12-15

作者简介, 刘 斌,1961-,, 男, 山西临汾人, 神华集团物资贸易有限责任公司副总经理, 高级经济师, 天津大学管理学博士, 研究方向, 物流与供应链管理。

物流网络优化设计

多长, 订货批量如何设置, 最终形成多层级的库存体系。

,3, 运作层

运作层次的物流网络设计主要涉及到对某一区域的配送路线优。化 根据销售终端的销售能力、 库存水平和资 金运转情况, 公司的配送策略设定在相对较短的一个时期内, 一般以 “天” 为单位。 根据需要, 一个配送中心所

负责的配送区域可以再细分为几个更小的区域, 在此基础之上通过各种模型解决车辆配送路径问。题

物流网络优化设计的目标就是在维持或优化客户服务水平的前提, 下尽可能的降低运营成本。 通常情况下, 物

流网络结构和库存策略对于客户服务水平及物流成本的影响起着决定性的作用, 虽然已有关于此类问题的大量 理论研究, 然而由于实际中这两类问题都是数据密集型的, 需要专业工具进行建模和分析。 在欧美等发达国家的 成熟市场, 已形成针对物流网络战略层和战术层的网络结构及设施规划和库存优化的广泛应。 用随着技术进步, 工

具软件的进一步完善, 以及中国市场的日益成熟, 在中国越来越多的企业开始从物流网络优化项目中受,益 其 中最为典型的就是物流网络战略规划项目和库存优化项目。

物流网络战略规划 2

物流网络战略规划优化是用于衡量部分或全部的供应链物流网络的战略研。 究研究相关的供应链物流成本, 包括库存成本、 运营成本 ,固定成本和变动成本,、 运输成本 ,包括入库运输、 转仓运输、 出库运输成本,, 同时 还考虑相关约束要素, 包括物流中心的开与关、 物流中心吞吐量限制, 客户服务水平设置、 产品配置策略、 运输 动线策略、 保险约束、 物流中心数量等。 网络规划研 究最终建立适合企业对应发展阶段的成本与服务水平 最佳优化区域 最优平衡的供应链网络模型。 物流总成本 物流网络战略规划的成果物输出包括网络多情景 比较分析、 仓库地点建议、 仓库规模大小、 物流成本 固定成本 估算 ,运输成本、 存储、 运营成本,、 从工厂到仓库 库存成本 年度物 动线的入库策略、 配送中心覆盖客户市场区域策略、 流成本 网络动线策略、 敏感分析等。 劳动力成本 物流网络战略规划是一项复杂的工程, 中间涉及 到大量的数据和复杂的建模过程, 需要用到作业的软 转运成本 件工具帮助建模分析。 目前市场上这方面的软件也有 配送运输成本很多, 但大部分都是以运输优化的功能为主, 缺乏一 少 物流中心数量 多 定的整体物流网络战略决策的支持功能。 由前 Logictools公司开发 的 LogicNet软 件, 能够给客户在战略和战术上 进行供应链网络的优化, 是一款战略和战术层面的工具, 主要用于决策以时间为基础上的生产和分销策,略 同时 对供应链上设施的位置和大小进行优化。 LogicNet进行网络优化 的优点在于可以很容易地看到各种供应链成本在 供应链中如何因为配送中心个数增加而发生变化。

应用软件进行网络规划的一般步骤包括, 问题描述与目标确定, 数据收集与分析, 模型分析, 成果陈述四大 阶段。 由于物流网络建设的投资很大且设立新仓库需要很长时间, 所以物流网络规划的所涉及的时间范围是很长 的。 这是一个以成本分析来帮助决策者制定一个高效决策的过程。 模型的输出结果可广泛应用于需要供应链模型 的各个阶段, 为供应链管理过程提供了主要的输入。 同时随着时间推移, 模型所采用的假设条件可能发生部分变 化, 所以需要对模型进行适当调整, 最好每年进行一次更新运算, 以使得规划的网络模型更符合实际发展的需。要 3 库存优化

物流网络战略规划在确定物流网络的整体结构的同时, 也在战略层面对库存在网络节点上的总体分布进行了 优化。 但是, 由于库存问题本身所固有的复杂性, 通常仍会留有较大空间对库存进一步优化。 在确定的物流网络 结构下, 能够对不同类别物资的库存进行更加细致和深入的优化分,析 进一步挖掘物流网络中库存优化的潜力。

库存优化是通过科学的分析建模和计划流程改善来识别提高库存效率和服务水平的机。 会它可以是对供应链 各节点库存的总体分析优化, 也可以针对其中某一重要库存节点进行优化。 实际上, 库存水平与服务水平存在关 系曲线, 可以根据企业在不同发展阶段对服务水平的战略要,求 确定对应的库存水平策略。

库存水平的设置与采购策略有关, 采购批量大小和采购周期直接影响库存量的设置, 同时, 同一种物资在不 同层级地点的库存配置, 将影响库存的服务水平。 因此, 可以通过各物资的库存水平分析、 采购策略分析、 库存 配置策略分析和服务水平需求分析等四个方面, 开展库存优化工作。

LogisticsSci -Tech 2011.1 137

物流网络优化设计

最大库存 现状

库存优化目标 库 实际库存变化存 成 采购订单 本下达点 最小库存

时间 0 2 4 6 8 10

服务水平 库存补货过程示意图

,1, 采购策略分析

?采购经济批量分析。 采购策略往往以采购单价的优惠幅度来决定采购经济批量的大,小 但采购经济批量的 大小也影响了库存水平的高低。

?采购前置期分析。 采购前置期指从下达采购订单到收到货物的时。间 采购前置期越长, 需设定的安全库存 越高, 降低采购前置期, 有利于库存水平的降低。

分析。 当采购前置期变动性大时, 库存水平将被提高, 必须通过 ?采购前置期变动性 ,供应商供货可靠性,

量化分析找出前置期变动性大的物资, 从采购策略上解决问题。

,2, 库存水平分析

?需求变动性与安全库存水平。 安全库存即最小库存, 用于抵御物资需求的变动风险。 需求的变动越大, 安 全库存设置越高。 因此, 通过分析需求变动性, 合理设置安全库存, 有利于库存水平的总体降低。

采购订单下达点须高于安全库存点, 两者的存量差须满足采 ?采购前置期变动性与采购订单下达点的设置。

购周期内库存消耗量, 但是, 当采购前置期存在波动时, 采购订单下达点将被提前, 由此造成库存水平被提高。

?最大库存设置。 物资需求波动性越大、 采购经济批量越大、 采购前置期变动性越大, 都将促使最大库存设 置增大, 总体库存水平提高, 库存成本增大。

,3, 库存配置策略

物资库存包括供应链上不同节点的库存, 其库存配置策略将影响服务水平和库存成本。 例如, 快品采用分散 配置库存策略, 可以提高服务水平但不显著增加库存, 慢品采用集中库存策略, 可以大幅度降低库存而不显著影

响服务水平。

,4, 服务水平需求分析

服务水平是指物资供应满足需求的时间周期和履约,率 但时间周期缩短, 履约难度增大, 服务水平要求被提 高。 合理设置服务水平, 有利于库存水平的降低。

库存优化工作可以通过上述四个途径全面开展, 也可以从四个途径中的局部开展。

库存优化是一项数据驱动型的分析设计工作, 需对大量数据进行设置和分析运算, 建立库存成本和服务水平 之间的关系模型, 明确现阶段的库存成本与服务水平关系并确定未来的改善目。标 国际物流领域对库存优化均采 用专业的数学模型软件工具来分析设计, 被世界 500 强企业大量采用的工具是Toolsgroup 公司 的 DPM软 件。

?DPM 库存优化建模软件, 从物资需求的计划管理出发, 建立优化的库存结构和库存水平设置。

?DPM 使用特有的运算法则来平衡最优化库存和特定服务等级之间的关, 系 它是通过在最优化服务水平和最 小化另外一个假设因素之间取一个标准值来实现的, 这个假设因素可以是库存的成本, 或是仓库的空间或是库存 数量等, 这样复杂的算法仅凭经验和手工是无法做到的。

?DPM 还可以帮助细分库存物资类型, 以帮助客户判断哪些是战略上的需要考虑给予高水平服务的物,资 哪 些是可以考虑适当降低服务水准的。

4 物流网络优化设计在实践中的挑战

尽管物流网络优化设计中物流网络战略规划和库存优化的方、法 技术和工具日益成熟和完善, 但在现阶段的 实践中, 仍然面临的着诸多挑战, 其中包括基于经验与量化分析的权衡、 量化的战略决策支持、 动态的战略决策

支持以及战略及策略方案的可操作性等。 为应对这些的挑战, 物流网络优化咨询业务也就慢慢兴起, 一些有丰富工程咨询经验的物流咨询公司, 可以通过一个业界公认的标准决策工具, 建立一个精巧的数学模型, 在计算机里 置信客户现在的供应链运作状况, 并设计多种商业情景或供应链策略, 用计算机模型来模拟仿真, 按照成本最优 或服务最优的原则去求得最佳情景。 客户也可以用此模型去模拟未来商业变化对供应链的影。响 从长期的意义来 讲, 网络优化使得咨询公司与客户能够有更密切地合。作

LogisticsSci -Tech 2011.1138

范文五：物流网络优化设计

物流网络优化设计

（物流网络从物流运作形态的角度将物流网络的内涵确立为：建立在物流基础设施网络之上的、以信息网络为支撑、按网络组织模式运作的三大子网有机结合的综合服务网络体系，将物流网络的研究提升到综合物流服务网络体系的大物流层面，将三大子网的网络效应驱动下的资源共享和整合内涵是物流网络的研究方向。物流三大子网络：1）物流组织网络，它是物流网络运行的组织保障；2）物流基础设施网络，它是物流网络高效运作的基本前提和条件；3）物流信息网络，它是物流网络运行的重要技术支撑。）

背景：2008年以来的全球经济动荡对各个行业的供应链造成了深刻影响。大宗商品的价格和供应异常波动，主要货币汇率风险陡增，金融市场大起大落，地缘政治事件不断和客户渠道全球化持续发展，这使企业的采购、生产和产品分销面临前所未有的压力。与此同时，客户期望不断提高，全球化竞争的不断加剧，产品的生命周期的不断缩短，客户需求及物流成本波动的不断增大，使得如今的企业面临的最大挑战之一就是建立能够实现并且保持卓越绩效的具有活力的供应链。 在这种背景下，很多企业为提高或保持长、中、短期的盈利能力而不得不对现有的供应链进行调整，越来越多的企业还把目光投向了新兴市场。

在中国，不少企业开始重新规划自己的业务版图，一些原以出口为主的中国企业也转向了国内市场，很多外资企业也更多将重心转移到中国，希望能获得快速发展。以往不少企业在调整供应链时，把精力放在了建立新的IT 系统或是对现有系统进行升级上，而很少有企业会从供应链网络优化的角度来考虑。这些IT 项目往往需要投入大量的资金，并且通常项目周期会很长，最终达到预期效果的企业却不多。而如今，情况正在改变，越来越多的企业开始认识到供应链网络的重要性，尤其对于生产或销售有形产品的企业，物流网络是支持其供应链运作和管理的前提和基础设施，物流网络设计中所制定的决策不仅对于企业长期赢利能力和竞争地位会产生重要而深远的影响，而且也对供应链绩效形成直接且重大的影响. 并且，通常物流网络优化项目的周期远比IT 系统项目短，投资也远比IT 系统低，经过合理优化设计的物流网络还有助于提高实施IT 系统的投资回报。从这个意义上来讲，物流网络设计是企业供应链战略和实际运作的衔接桥梁，既要考虑到企业供应链战略的实现，又要考虑到设计决策对于未来物流运作的约束作用；物流网络的效率很大程度上取决于物流网络设 计的合理性，只有设计合理才能使物流系统获得整体的最优。

物流网络战略规划优化是用于衡量部分或全部的供应链物流网络的战略研究。研究相关的供应链物流成本， 包括库存成本、运营成本（固定成本和变动成本）、运输成本（包括入库运输、转仓运输、出库运输成本）；同时还考虑相关约束要素，包括物流中心的开与关、物流中心吞吐量限制，客户服务水平设置、产品配置策略, 运输动线策略、保险约束、物流中心数量等。网络规划研究最终建立适合企业对应发展阶段的成本与服务水平最优平衡的供应链网络模型。 物流网络战略规划的成果物输出包括网络多情景比较分析、仓库地点建议、仓库规模大小、物流成本估算（运输成本、存储、运营成本）、从工厂到仓库动线的入库策略、配送中心覆盖客户市场区域策略、网络动线策略、敏感分析等。 物流网络战略规划是一项复杂的工程，中间涉及到大量的数据和复杂的建模过程，需要用到作业的软件工具帮助建模分析。目前市场上这方面的软件也有很多，但大部分都是以运输优化的功能为主，缺乏

一 定的整体物流网络战略决策的支持功能。由前Logictools 公司开发的LogicNet 软件，能够给客户在战略和战术上进行供应链网络的优化，是一款战略和战术层面的工具，主要用于决策以时间为基础上的生产和分销策略，同时对供应链上设施的位置和大小进行优化。LogicNet 进行网络优化的优点在于可以很容易地看到各种供应链成本在供应链中如何因为配送中心个数增加而发生变化。 应用软件进行网络规划的一般步骤包括：问题描述与目标确定，数据收集与分析，模型分析，成果陈述四大阶段。由于物流网络建设的投资很大且设

立新仓库需要很长时间，所以物流网络规划的所涉及的时间范围是很长的。这是一个以成本分析来帮助决策者制定一个高效决策的过程。模型的输出结果可广泛应用于需要供应链模型的各个阶段，为供应链管理过程提供了主要的输入。同时随着时间推移，模型所采用的假设条件可能发生部分变化，所以需要对模型进行适当调整，最好每年进行一次更新运算，以使得规划的网络模型更符合实际发展的需要。

问题：尽管物流网络优化设计中物流网络战略规划和库存优化的方法、技术和工具日益成熟和完善，但在现阶段的 实践中，仍然面临的着诸多挑战，其中包括基于经验与量化分析的权衡、量化的战略决策支持、动态的战略决策支持以及战略及策略方案的可操作性等。为应对这些的挑战，物流网络优化咨询业务也就慢慢兴起，一些有丰富工程咨询经验的物流咨询公司，可以通过一个业界公认的标准决策工具，建立一个精巧的数学模型，在计算机里 置信客户现在的供应链运作状况，并设计多种商业情景或供应链策略，用计算机模型来模拟仿真，按照成本最优或服务最优的原则去求得最佳情景。客户也可以用此模型去模拟未来商业变化对供应链的影响。从长期的意义来讲，网络优化使得咨询公司与客户能够有更密切地合作

转载请注明出处范文大全网 » SF物流网络优化方案