A multi-structural framework for adaptive supply chain planning and operations control with structure dynamics considerations

A trend in up-to-date developments in supply chain management (SCM) is to make supply chains more agile, flexible, and responsive. In supply chains, different structures (functional, organizational, informational, financial, etc.) are (re)formed. These structures interrelate with each other and change in dynamics. The paper introduces a new conceptual framework for multi-structural planning and operations of adaptive supply chains with structure dynamics considerations. We elaborate a vision of adaptive supply chain management (A-SCM), a new dynamic model and tools for the planning and control of adaptive supply chains. SCM is addressed from perspectives of execution dynamics under uncertainty. Supply chains are modelled in terms of dynamic multi-structural macro-states, based on simultaneous consideration of the management as a function of both states and structures. The research approach is theoretically based on the combined application of control theory, operations research, and agent-based modelling. The findings suggest constructive ways to implement multi-structural supply chain management and to transit from a “one-way” partial optimization to the feedback-based, closed-loop adaptive supply chain optimization and execution management for value chain adaptability, stability and crisis-resistance. The proposed methodology enhances managerial insight into advanced supply chain management.     

Better management of blood supply-chain with GIS-based analytics

This paper presents a novel application of operations research, data mining and geographic information-systems-based analytics to support decision making in blood supply chain management. This, blood reserve availability assessment, tracking, and management system (BRAMS), research project has been funded by the Office of the Secretary of Defense. (This DoD funded SBIR project is performed by the researchers at Knowledge Based Systems, Inc. (KBSI).) The rapidly increasing demand, criticality of the product, strict storage and handling requirements, and the vastness of the theater of operations, make blood supply-chain management a complex, yet vital problem for the department of defense. In order to address this problem a variety of contemporary analytic techniques are used to analyze inventory and consumption patterns, evaluate supply chain status, monitor performance metrics at different levels of granularity, and detect potential problems and opportunities for improvement. The current implementation of the system is being actively used by 130 mangers at different levels in the supply chain including facilities at Osan Air Force Base in South Korea and Incirlik Air Force Base in Turkey.     

Optimising a value-based performance indicator in mid-term sales and operations planning

Economic Value Added (EVA^®) and corresponding value driver trees are prevalent frameworks of value-based management to measure and analyse shareholder value creation. However, they are explanatory models from an operations research perspective and do not provide decision support for performance optimisation. In this paper, we develop a comprehensive value-based decision framework for mid-term sales and operations planning (S&OP) in the supply chain implementing EVA as the objective function. The pivotal element of our framework is a decision-oriented extension of EVA-based value driver trees bridging the gap to the decision variables of S&OP as the operational performance levers. We utilise a numerical example to highlight the significant improvement potential due to the value-based optimisation approach. Working capital management emerges as the major mid- to short-term value driver in the supply chain.     

Demand uncertainty in construction supply chains: a discrete event simulation study

The delivery of construction projects is typically an assembly operation involving a high number of subassemblies and materials brought on site by the supply chain. However, although supply chain management in construction has attracted significant attention, paradoxically little focus has been placed on construction supply networks and operations. This paper places emphasis on supply chain operations by looking at the logistics function of construction material suppliers. Specifically, the paper examines the impact of demand uncertainty on supply chain performance in order to assess the capacity of material distribution companies to provide a timely and cost-efficient service to the construction industry. The study adopts a discrete event simulation approach to assess the impact of demand fluctuations on two crucial logistics performance measures; lead time and cost efficiency. The results show that lead times are particularly sensitive to fluctuations under conditions of low demand. The findings also reveal that, although transport is a significant cost element for lower demand levels, higher inventory costs result in a negative exponential relationship between increasing demand and cost efficiency.     

RFID对供应链提前期压缩的影响及协调研究

提前期压缩是基于时间竞争的供应链管理的核心,是供应链竞争优势的有力来源。RFID技术通过提高供应链中信息共享、加速物流操作,进而缩短提前期。在市场需求预测精度随提前期变化的假设下,研究由生产商和零售商组成的两阶段供应链中,生产商承担压缩成本采用RFID技术压缩提前期对供应链及其成员收益的影响。确定了可使供应链收益提高的标签成本约束条件,并提出了相应的收益协调机制。研究结果表明:供应链成员的收益随服务水平和标签成本变化而变化,通过引入收益协调机制,可以实现供应链成员收益的Pareto改进。最后通过数值算例对结论进行了验证。     

Design of an IT-driven decision support system for vehicle routing and scheduling

This paper presents the development of a decision support system used by an oil downstream company for routing and scheduling purposes. The studied problem refers to a complex delivery process of oil products from a number of distribution centers to all customers. The latest rapid advance of operations research (OR) applications, in the form of advanced planning and scheduling (APS) systems, has shown that OR algorithms can be applied in practice if (a) they are embodied in packaged information technology (IT) solutions, (b) the interface problems to mainstream ERP software applications are solved. In this study the utilisation of advanced IT systems supports effectively the planning and management of distribution operations. The combination of a supply chain management (SCM) application with a geographical information system (GIS) integrated with an enterprise resource planning (ERP) software resulted to this innovative decision support tool. The objectives of this new tool are: optimum use of the distribution network resources, transportation cost reduction and customer service improvement. The paper concludes with the benefits of the new system, emphasising at how new technologies can support transportation processes with the help of operations research algorithms embedded in software applications.     

AI and OR in management of operations: history and trends

The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested.     

Operational knowledge management: identification of knowledge objects,operation methods,and goals and means for the support function

Though much has been written about knowledge management, this field has not been described extensively from an operational management perspective. Consequently, knowledge management seems difficult to implement at the operational levels of the organisations. To solve this problem, the abstract notion of knowledge is translated in operational knowledge objects. These objects are the input and output of two operation methods: (1) transformation or learning; and (2) knowledge logistics. The article describes several activities of these operation methods, and gives a classification of operational goals and means for the operations support function. The author concludes with mentioning challenges for the field of operational knowledge management.     

Managing the supply and demand uncertainty in workforce recruitment: planned or just-in-time acquisition

Managing knowledge workers is highly complex because of the need to balance the costs associated with their training and holding costs against the need to meet market demand as quickly as possible. Unlike previous approaches to this problem in the workforce management literature, this paper develops a stochastic optimization model to examine the impact of not only uncertainty of the demand of knowledge services but also that of the supply of knowledge workers on a recruiting strategy. Hypotheses on optimal recruiting decisions that this paper suggests include: (1) high holding and training costs decrease recruitment of apprentices; (2) high mobility of skilled workers decreases recruitment of apprentices; (3) high elasticity of the supply of skilled workers decreases recruitment of apprentices; (4) high volatility of the demand of knowledge services decreases recruitment of apprentices; and (5) in high seasonal effect on the demand, the decision policy based on decision thresholds proportional to seasonal demands outperforms the decision policy based on a constant decision threshold. Suggested hypotheses are supported by a simulation of the model. Model parameters in the simulation are estimated based on the survey of information security consulting service companies in South Korea.     

Artificial intelligence-based inventory management: a Monte Carlo tree search approach

The coordination of order policies constitutes a great challenge in supply chain inventory management as various stochastic factors increase its complexity. Therefore, analytical approaches to determine a policy that minimises overall inventory costs are only suitable to a limited extent. In contrast, we adopt a heuristic approach, from the domain of artificial intelligence (AI), namely, Monte Carlo tree search (MCTS). To the best of our knowledge, MCTS has neither been applied to supply chain inventory management before nor is it yet widely disseminated in other branches of operations research. We develop an offline model as well as an online model which bases decisions on real-time data. For demonstration purposes, we consider a supply chain structure similar to the classical beer game with four actors and both stochastic demand and lead times. We demonstrate that both the offline and the online MCTS models perform better than other previously adopted AI-based approaches. Furthermore, we provide evidence that a dynamic order policy determined by MCTS eliminates the bullwhip effect.

     

Optimizing the economic lot size of a three-stage supply chain with backordering derived without derivatives

Multi-stage supply chain management integration provides a key to successful international business operations. This is because the integrated approach improves the global system performance and cost efficiency. The integrated production inventory models using differential calculus to solve the multi-variable problems are prevalent in operational research. This paper extends the integrated vendor–buyer inventory problem by Yang and Wee [Yang, P.C., Wee, H.M., 2002. The economic lot size of the integrated vendor–buyer system derived without derivatives. Optimal Control Applications and Methods 23, 163–169] to solve the multi-variable problems in the supply chain, and simplifies the solution procedure using a simple algebraic method. As a result, the solution procedure may be easily understood and applied so as to derive the optimal solution.     

Risk management and coordination in service supply chains: information,logistics and outsourcing

A service supply chain, which is a system formed by a network of suppliers, service designers, service providers and other service partners, aims to transfer available scarce resources into services and deliver them to satisfy customer needs. It is a known fact that service supply chain management is playing a more and more important role in modern economies. However, the unique features of service supply chains also create new challenges that call for proper management of the respective operations. In particular, how to coordinate a service supply chain with risk considerations is a critical issue. In this paper, we concisely examine coordination and risk management challenges in service supply chain systems. We also introduce the technical papers featured in the special issue.     

A perspective on operational research prospects for agriculture

This paper discusses the future of operational research (OR) for the agricultural industries in a broad sense, including horticulture and viticulture during a period of increased pressure on natural resources. The authors use their experience in the field along with published literature, to draw insights into new opportunities for OR, and how the OR community might adapt to realise these opportunities best. Trends in demand for food security and biofuels, the quest for sustainability, information technology (IT), and commercial power create new opportunities to support strategic investment and operations management within both primary production and the related supply chains. To realise such potential, the agricultural OR community needs to improve management of stakeholder relations, interdisciplinary synthesis, and the successful application of OR.     

Matching product architecture with supply chain design

Product architecture is typically established in the early stages of the product development (PD) cycle. Depending on the type of architecture selected, product design, manufacturing processes, and ultimately supply chain configuration are all significantly affected. Therefore, it is important to integrate product architecture decisions with manufacturing and supply chain decisions during the early stage of the product development. In this paper, we present a multi-objective optimization framework for matching product architecture strategy to supply chain design. In contrast to the existing operations management literature, we incorporate the compatibility between the supply chain partners into our model to ensure the long term viability of the supply chain. Since much of the supplier related information may be very subjective in nature during the early stages of PD, we use fuzzy logic to compute the compatibility index of a supplier. The optimization model is formulated as a weighted goal programming (GP) model with two objectives: minimization of total supply chain costs, and maximization of total supply chain compatibility index. The GP model is solved by using genetic algorithm. We present case examples for two different products to demonstrate the model’s efficacy, and present several managerial implications that evolved from this study.     

Preservice Teachers’ Conceptual and Procedural Knowledge of Fraction Operations: A Comparative Study of the United States and Taiwan

This study examined (a) the differences in preservice teachers’ procedural knowledge in four areas of fraction operations in Taiwan and the United States, (b) the differences in preservice teachers’ conceptual knowledge in four areas of fraction operations in Taiwan and the United States, and (c) correlation in preservice teachers’ conceptual knowledge and procedural knowledge of fractions in Taiwan and the United States. Participants were preservice teachers (N = 49) in a teacher education program in the United States and comparable Chinese preservice teachers (N = 47). Results indicated that Chinese preservice teachers performed better in procedural knowledge on fraction operations than American preservice teachers. No significant differences were found for conceptual knowledge on fraction division. Further, the correlation in this study showed that for Chinese and American preservice teachers, the relationship between conceptual and procedural knowledge of fraction operations was weak.     

Integrated market selection and production planning: complexity and solution approaches

Emphasis on effective demand management is becoming increasingly recognized as an important factor in operations performance. Operations models that account for supply costs and constraints as well as a supplier’s ability to influence demand characteristics can lead to an improved match between supply and demand. This paper presents a class of optimization models that allow a supplier to select, from a set of potential markets, those markets that provide maximum profit when production/procurement economies of scale exist in the supply process. The resulting optimization problem we study possesses an interesting structure and we show that although the general problem is ${\mathcal{NP}}$ -complete, a number of relevant and practical special cases can be solved in polynomial time. We also provide a computationally very efficient and intuitively attractive heuristic solution procedure that performs extremely well on a large number of test instances.     

需求和汇率风险下的全球供应链汇率风险对冲策略

在下游零售商同时面临市场需求风险和汇率风险的背景下,研究了汇率风险对冲(外汇期货对冲)策略在全球供应链运作及风险管理中的作用。在无/有对冲策略两种情形下分别构建了上游制造商和下游零售商的动态博弈模型,并求解了均衡结果。两种情形下的均衡结果显示,汇率风险对冲策略可以提高供应链系统订货量、增加零售商收益的期望值和确定性等价量、增加供应链系统的总收益。进一步讨论了有对冲策略的情形下,两类外生风险对供应链均衡决策变量和盈利性的影响方式。结果表明,汇率风险对冲策略对汇率风险起到了有效的隔离作用,避免了供应链下游的汇率风险向上游企业传递,并能实现供应链收益与风险的权衡。     

Formalization and Specification of Geometric Knowledge Objects

This paper presents our work on the identification, formalization, structuring, and specification of geometric knowledge objects for the purpose of semantic representation and knowledge management. We classify geometric knowledge according to how it has been accumulated and represented in the geometric literature, formalize geometric knowledge statements by adapting the language of first-order logic, specify knowledge objects with embedded knowledge in a retrievable and extensible data structure, and organize them by modeling the hierarchic structure of relations among them. Some examples of formal specification for geometric knowledge objects are given to illustrate our approach. The underlying idea of the approach has been used successfully for automated geometric reasoning, knowledge base creation, and electronic document generation.