Dynamic supply chain scheduling

Based on a combination of fundamental results of modern optimal program control theory and operations research, an original approach to supply chain scheduling is developed in order to answer the challenges of dynamics, uncertainty, and adaptivity. Both supply chain schedule generation and execution control are represented as an optimal program control problem in combination with mathematical programming and interpreted as a dynamic process of operations control within an adaptive framework. Hence, the problems and models of planning, scheduling, and adaptation can be consistently integrated on a unified mathematical axiomatic of modern control theory. In addition, operations control and flow control models are integrated and applicable for both discrete and continuous processes. The application of optimal control for supply chain scheduling becomes possible by formulating the scheduling model as a linear non-stationary finite-dimensional controlled differential system with the convex area of admissible control and a reconfigurable structure. For this model class, theorems of optimal control existence can be used regarding supply chain scheduling. The essential structural property of this model are the linear right parts of differential equations. This allows applying methods of discrete optimization for optimal control calculation. The calculation procedure is based on applying Pontryagin’s maximum principle and the resulting essential reduction of problem dimensionality that is under solution at each instant of time. The gained insights contribute to supply chain scheduling theory, providing advanced insights into dynamics of the whole supply chains (and not any dyadic relations in them) and transition from a partial “one-way” schedule optimization to the feedback loop-based dynamic and adaptive supply chain planning and scheduling.     

A survey on control theory applications to operational systems,supply chain management,and Industry 4.0

Modern production and logistics systems, supply chains, and Industry 4.0 networks are challenged by increased uncertainty and risks, multiple feedback cycles, and dynamics. Control theory is an interesting research avenue which contributes to further insights concerning the management of the given challenges in operations and supply chain management. In this paper, the applicability of control theory to engineering and management problems in supply chain operations is investigated. Our analysis bridges the fundamentals of control and systems theory to supply chain and operations management. This study extends our previous survey in the Annual Reviews in Control (Ivanov et al. 2012) by including new literature published in 2012–2018, identifying two new directions of control theory applications (i.e., ripple effect analysis in the supply chains and scheduling in Industry 4.0) and analysis towards the digital technology use in control theoretic models. It describes important issues and perspectives that delineate dynamics in supply chains, operations, and Industry 4.0 networks and identifies and systemizes different streams in the application of control theory to operations and supply chain management and engineering in the period from 1960–2018. It updates the existing applications and classifications, performs a critical analysis, and discusses further research avenues. Further development of interdisciplinary approaches to supply chain optimization is argued. An extended cooperation between control engineers and supply chain experts may have the potential to introduce more realism to dynamic planning and models, and improve performance in production and logistics systems, supply chains, and Industry 4.0 networks. Finally, we analyze the trends towards the intellectualization of control and its development towards supply chain control analytics.     

A structural equation model for analyzing the impact of ERP on SCM

Enterprise resource planning (ERP) and supply chain management (SCM) represent important information technology investment options for operation or IT managers, and have been acclaimed in the practitioner and academic literature for their potential to improve business performance. The purpose of this article is to provide further insights into the adoption of ERP systems and the impacts on firm competence in SCM. We propose a model featuring ERP benefits to firm competences in supply chain management. We also hypothesize that three constructs of ERP benefits positively impact firm competences in SCM. To clarify the relationships among these constructs, structural equation model (SEM) is conducted to examine the model fit and nine hypotheses. The SEM results clearly demonstrate that there exist close interrelations among the benefits of implementing ERP systems and firm competences in SCM. The data from Taiwanese IT firms was collected through interviewing of experts and surveys. The results provide empirical evidence that the beneficial impacts of ERP on the supply chain do lead to better overall SCM competence. That evidence confirms that operational benefits, business process and management benefits, and strategic IT planning benefits of ERP in turn enhance firm competences of SCM in operational process integration, customer and relationship integration, and planning and control process integration.     

SUPPLY SYSTEM DYNAMICS: DISTRIBUTED CONTROL IN SUPPLY CHAINS AND NETWORKS

The purpose of this article is to make a contribution to a more effective management for supply chains and networks, which we are subsuming under the title supply systems. We conceive of supply systems management as the design, control, and development of logistics along the value chain or in value networks. In this article, we concentrate on control, i.e., the regulation and steering of supply systems from production to customer and vice versa, with the help of system dynamics modeling and simulation. Traditionally, supply chain management has been heavily supported by discrete event simulation and optimization models on very detailed levels. Established tools, e.g., Manufacturing Resources Planning, Enterprise Resources Planning, and Production Planning Systems, have emphasized highly specialized functions, such as the planning for optimal capacity use and achievement of delivery goals, via the sequencing, scheduling, and dispatching of individual orders, or the global allocation of resources but without the possibility to evaluate different outcomes of the allocation process. In the quest for efficiency and effectiveness, new approaches to control, which lead beyond these functions of disposition, are needed. We present a model that combines two systemic methodologies that operate on higher levels of aggregation and complement each other: system dynamics to model and simulate the supply chain and cybernetic control to apply control-theoretical concepts, namely proportional, derivative, and integral control, in a combined mode as well as according to a recursive logic of distributed control. This way, substantial improvements in both efficiency and effectiveness can be achieved, and ultimately organizational viability can be enhanced.     

An agent-based approach for e-manufacturing and supply chain integration

A major problem facing manufacturing organisations is how to provide efficient and cost-effective responses to the unpredictable changes taking place in a global market. This problem is made difficult by the complexity of supply chain networks coupled with the complexity of individual manufacturing systems within supply chains. Current systems such as manufacturing execution systems (MES), supply chain management (SCM) systems and enterprise resource planning (ERP) systems do not provide adequate facilities for addressing this problem. This paper presents an approach that would enable manufacturing organisations to dynamically and cost-effectively integrate, optimise, configure, simulate, restructure and control not only their own manufacturing systems but also their supply networks, in a co-ordinated manner to cope with the dynamic changes occurring in a global market. This is realised by a synergy of two emerging manufacturing concepts: Agent-based agile manufacturing systems and e-manufacturing. The concept is to represent a complex manufacturing system and its supply network with an agent-based modelling and simulation architecture and to dynamically generate alternative scenarios with respect to planning, scheduling, configuration and restructure of both the manufacturing system and its supply network based on the coordinated interactions amongst agents.     

Supply chain management information systems capabilities. An exploratory study of electronics manufacturers

Supply Chain Management Information Systems (SCM IS) play an increasingly critical role in the ability of firms to reduce costs and increase the responsiveness of their supply chain. This paper develops an empirically supported model of the organizational capabilities enabled by SCM IS. The model integrates and enriches theoretical and empirical studies of competitive strategy, supply chain management, and interorganizational information systems. Evidence from an exploratory case study of three large firms in the electronics manufacturing industry is examined to build a better-supported theory of SCM IS capabilities. The findings suggest the organizational capabilities enabled by SCM IS can be conceptualized as the level of support provided for: operational efficiency; operational flexibility; internal planning and analysis; and external planning and analysis. The theoretical model furthers an understanding of SCM IS capabilities and is sufficiently developed to permit operationalization for future studies evaluating the effectiveness of SCM IS.     

Dynamic modeling and control of supply chain systems: A review

Supply chains are complicated dynamical systems triggered by customer demands. Proper selection of equipment, machinery, buildings and transportation fleets is a key component for the success of such systems. However, efficiency of supply chains mostly depends on management decisions, which are often based on intuition and experience. Due to the increasing complexity of supply chain systems (which is the result of changes in customer preferences, the globalization of the economy and the stringy competition among companies), these decisions are often far from optimum. Another factor that causes difficulties in decision making is that different stages in supply chains are often supervised by different groups of people with different managing philosophies. From the early 1950s it became evident that a rigorous framework for analyzing the dynamics of supply chains and taking proper decisions could improve substantially the performance of the systems. Due to the resemblance of supply chains to engineering dynamical systems, control theory has provided a solid background for building such a framework. During the last half century many mathematical tools emerging from the control literature have been applied to the supply chain management problem. These tools vary from classical transfer function analysis to highly sophisticated control methodologies, such as model predictive control (MPC) and neuro-dynamic programming. The aim of this paper is to provide a review of this effort. The reader will find representative references of many alternative control philosophies and identify the advantages, weaknesses and complexities of each one. The bottom line of this review is that a joint co-operation between control experts and supply chain managers has the potential to introduce more realism to the dynamical models and develop improved supply chain management policies.     

WSN和RFID在供应链管理语义监控中的应用

针对我国现有供应链管理(SCM)监控的不足,采用本体技术实现了供应链管理中的信息语义交互和共享,为供应链中信息的及时、无歧义理解提供了便利.该设计利用蛙跳算法解决了供应链优化调度和协同服务机制的难题,同时,融合无线传感器网络(WSN)和射频识别(RFID)技术,提出了基于传感器网络和RFID的语义监控平台.平台的提出对现代供应链管理的发展具有重要的作用.     

Inventory lot-sizing with supplier selection using hybrid intelligent algorithm

In supply chain management (SCM), multi-product and multi-period models are usually used to select the suppliers. In the real world of SCM, however, there are normally several echelons which need to be integrated into inventory management. This paper presents a hybrid intelligent algorithm, based on the push SCM, which uses a fuzzy neural network and a genetic algorithm to forecast the rate of demand, determine the material planning and select the optimal supplier. We test the proposed algorithm in a case study conducted in Iran.     

Supervisory control of a multi-echelon supply chain: A modular Petri net approach for inter-organizational control

Petri nets (PNs) are frequently utilized to model system dynamics due to their ability to handle concurrencies and sequential dependence. In this paper, a portion of the supply chain operations reference (SCOR) model has been extracted and modeled using PNs for the purpose of exerting supervisory control upon a multi-echelon supply chain (SC). The activities of source, make and deliver, inherent in the SCOR model form the basis of the representation of the PN model for each echelon considered in the SC model. Two control nets are utilized: one above the base model of each echelon to exert local constraints and an enterprise level supply chain manager (SCM). The local constraints are at the tactical and operational levels while the SCM enforces additional constraints consisting of long term planning goals at the strategic level. Place invariants are used to create the supervisors. Performance measures of the total SC are formulated to determine the effectiveness of any partnership. An efficient method for finding the current state of the system is developed which is used to determine the performance measures of each echelon. This paper presents a modular approach to the overall structure and PN modeling for a SC system. It is intended to extend the use of supervisory control from a shop-floor level to an inter-organizational facility and enterprise level.     

CMieux: Adaptive strategies for competitive supply chain trading

Supply chains are a central element of today’s global economy. Existing management practices consist primarily of static interactions between established partners. Global competition, shorter product life cycles and the emergence of Internet-mediated business solutions create an incentive for exploring more dynamic supply chain practices. The supply chain trading agent competition (TAC SCM) was designed to explore approaches to dynamic supply chain trading between automated software agents. TAC SCM pits trading agents developed by teams from around the world against one another. Each agent is responsible for running the procurement, planning and bidding operations of a PC assembly company, while competing with others for both customer orders and supplies under varying market conditions. This paper presents Carnegie Mellon University’s 2005 TAC SCM entry, the CMieux supply chain trading agent. CMieux implements a novel approach for coordinating supply chain bidding, procurement and planning, with an emphasis on the ability to rapidly adapt to changing market conditions. We present empirical results based on 200 games involving agents entered by 25 different teams during what can be seen as the most competitive phase of the 2005 tournament. Not only did CMieux perform among the top five agents, it significantly outperformed these agents in procurement while matching their bidding performance. We also simulated 40 games against the best publicly available agent binaries. Our results show CMieux has significantly better average overall performance than any of these agents.     

The role of the customer order decoupling point in production and supply chain management

In order to compete successfully, operations in any type of firm need to be strategically aligned to the market requirements. This concerns both manufacturing and supply chain operations. The customer order decoupling point (CODP) is getting increasing attention as an important input to the design of manufacturing operations as well as supply chains. This paper investigates the impact of the position and role of the CODP on issues of concern for production and supply chain management. The focus is on the design and strategic planning aspects of the supply chain, and the design of manufacturing planning and control systems. The paper proposes a dual design approach for production and supply chain planning systems; one type of system for operations upstream the CODP and another type of system for downstream operations in order to fully support the characteristics and objectives of each respective part of the supply chain.     

Supply chain management ontology from an ontology engineering perspective

Knowledge sharing and reuse are important factors affecting the performance of supply chains. These factors can be amplified in information systems by supply chain management (SCM) ontology. The literature provides various SCM ontologies for a range of industries and tasks. Although many studies make claims of the benefits of SCM ontology, it is unclear to what degree the development of these ontologies is informed by research outcomes from the ontology engineering field. This field has produced a set of specific engineering techniques, which are supposed to help developing quality ontologies. This article reports a study that assesses the adoption of ontology engineering techniques in 16 SCM ontologies. Based on these findings, several implications for research as well as SCM ontology adoption are articulated.     

供应链模型及其优化研究的现状与进展

这是一篇关于供应链模型及其优化分析工作的综述性文章．本文综述了生产运作 管理和供应链管理模型分析．关于供应链的局部情况，评述了生产运作中库存、生产销售、 库存销售、质量控制、财务等问题的模型及其优化应用；关于供应链整体情况，评述了集成 化、供应合同、信息价值、产品管理和国际运作等问题的模型及其优化应用．最后，提出了 供应链模型及其优化分析工作进一步研究的问题．     

Multi-behavior agent model for planning in supply chains: An application to the lumber industry

Recent economic and international threats to western industries have encouraged companies to increase their performance in all ways possible. Many look to deal quickly with disturbances, reduce inventory, and exchange information promptly throughout the supply chain. In other words they want to become more agile. To reach this objective it is critical for planning systems to present planning strategies adapted to the different contexts, to attain better performances. Due to consolidation, the development of integrated supply chains and the use of inter-organizational information systems have increased business interdependencies and in turn the need for increased collaboration to deal with disturbance in a synchronized way. Thus, agility and synchronization in supply chains are critical to maintain overall performance. In order to develop tools to increase the agility of the supply chain and to promote the collaborative management of such disturbances, agent-based technology takes advantage of the ability of agents to make autonomous decisions in a distributed network through the use of advanced collaboration mechanisms. Moreover, because of the highly instable and dynamic environment of today's supply chains, planning agents must handle multiple problem solving approaches. This paper proposes a Multi-behavior planning agent model using different planning strategies when decisions are supported by a distributed planning system. The implementation of this solution is realized through the FOR@C experimental agent-based platform, dedicated to supply chain planning for the lumber industry.     

基于电子商务的企业ERP，CRM与SCM整合

ERP，CRM和SCM都是重要的企业应用软件系统，分别侧重于企业的内部资源管理、客户管理和供应链管理。在电子商务环境下，将这些系统相互连接起来融合为一体，在企业内部实行整合，全方位地构建企业业务支持体系，是现代企业经营追求的目标。文中首先分析了电子商务环境下企业的价值链，然后阐述了系统整合的必要性，最后提出了系统整合的解决方案，使企业赢得长久的竞争优势。     

A system dynamics model for dynamic capacity planning of remanufacturing in closed-loop supply chains

Product recovery operations in reverse supply chains face continually and rapidly changing product demand characterized by an ever increasing number of product offerings with reduced lifecycles due to both technological advancements and environmental concerns. Capacity planning is a strategic issue of increased complexity importance for the profitability of reverse supply chains due to their highly variable return flows. In this work we tackle the development of efficient capacity planning policies for remanufacturing facilities in reverse supply chains, taking into account not only economic but also environmental issues, such as the take-back obligation imposed by legislation and the “green image” effect on customer demand. The behavior of the generic system under study is analyzed through a simulation model based on the principles of the system dynamics methodology. The simulation model provides an experimental tool, which can be used to evaluate alternative long-term capacity planning policies (“what-if” analysis) using total supply chain profit as measure of policy effectiveness. Validation and numerical experimentation further illustrate the applicability of the developed methodology, while providing additional intuitively sound insights.     

On the receding horizon hierarchical optimal control of manufacturing systems

This paper concerns the development of a hierarchical framework for the integrated planning and scheduling of a class of manufacturing systems. In this framework, dynamic optimization plays an important role in order to define control strategies that, by taking into account the dynamic nature of these systems, minimize customized cost functionals subject to state and control constraints. The proposed architecture is composed of a set of hierarchical levels where a two-way information flow, assuming the form of a state feedback control, is obtained through a receding horizon control scheme. The averaging effect of the receding horizon control scheme enables this deterministic approach to handle random and unexpected events at all levels of the hierarchy. At a given level, production targets to the subsystems immediately below are defined by solving appropriate optimal control problems. Efficient iterative algorithms based on optimality conditions are used to yield control strategies in the form of production rates for the various subsystems. At the lower level, this control strategy is further refined in such a way that all sequences of operations are fully specified. The minimum cost sensitivity information provided in the optimal control formulation supports a mechanism, based on the notion of a critical machine, which plays an important role in the exploitation of the available flexibility. Finally, an important point to note is that our approach is particularly suited to further integration of the production system into a larger supply chain management framework, which is well supported by recent developments in hybrid systems theory.     

Pricing and production decisions in dual-channel supply chains with demand disruptions

This paper develops a two-period pricing and production decision model in a one- manufacturer-one-retailer dual-channel supply chain that experiences a disruption in demand during the planning horizon. While disruption management has long been a key research issue in supply chain management, little attention has been given to disruption management in a dual-channel supply chain once the original production plan has been made. Generally, changes to the original production plan induced by a disruption may impose considerable deviation costs throughout the supply chain system. In this paper, we examine how to adjust the prices and the production plan so that the potential maximal profit is obtained under a disruption scenario. We first study the scenario where the manufacturer and the retailer are vertically integrated with demand disruptions. Then we further assume that the manufacturer bears the deviation costs and obtain the manufacturer’s and the retailer’s individual optimal pricing decision, as well as the manufacturer’s optimal production quantity in a decentralized decision-making setting. We derive conditions under which the maximum profit can be achieved. The results indicate that the optimal production quantity has some robustness under a demand disruption, in both centralized and decentralized dual-channel supply chains. We also find that the optimal pricing decisions are affected by customers’ preference for the direct channel and the market scale change, in both centralized and decentralized dual-channel supply chains.     

Energy supply planning and supply chain optimization under uncertainty

In energy supply planning and supply chain design, the coupling between long-term planning decisions like capital investment and short-term operation decisions like dispatching present a challenge, waiting to be tackled by systems and control engineers. The coupling is further complicated by uncertainties, which may arise from several sources including the market, politics, and technology. This paper addresses the coupling in the context of energy supply planning and supply chain design. We first discuss a simple two-stage stochastic program formulation that addresses optimization of an energy supply chain in the presence of uncertainties. The two-stage formulation can handle problems in which all design decisions are made up front and operating parameters act as ‘recourse’ decisions that can be varied from one time period to next based on realized values of uncertain parameters. The design of a biodiesel production network in the Southeastern region of the United States is used as an illustrative example. The discussion then moves on to a more complex multi-stage, multi-scale stochastic decision problem in which periodic investment/policy decisions are made on a time scale orders of magnitude slower than that of operating decisions. The problem of energy capacity planning is introduced as an example. In the particular problem we examine, annual acquisition of energy generation capacities of various types are coupled with hourly energy production and dispatch decisions. The increasing role of renewable sources like wind and solar necessitates the use of a fine-grained time scale for accurate assessment of their values. Use of storage intended to overcome the limitations of intermittent sources puts further demand on the modeling and optimization. Numerical challenges that arise from the multi-scale nature and uncertainties are reviewed and some possible modeling and numerical solution approaches are discussed.