Framework for modelling and simulating the supply process monitoring to detect and predict disruptive events

Disruptive events that take place during supply process execution produce negative effects that propagate throughout a supply chain. Event management systems for supply chains have emerged to provide functionality for monitoring schedules, managing disruption, and repairing schedules affected by a disruptive event. A Web service that provides a schedule monitoring functionality for supply chain event management was developed. This paper provides a framework to allow enterprises that hire this service to develop simulation models of monitoring processes and evaluate their ability to detect and anticipate disruptive events. The framework, based on discrete event simulation, is implemented in a library that can be used for developing and testing monitoring processes by means of a friendly interface. A marine freight transport process was used as a case study to show how a supply process and its environment can be modelled and simulated by using the library. Simulation results show the ability of this approach to anticipate disruptive events and identify critical stages of a supply process in order to prevent disruptive events.     

System for supply network management

Strategies and algorithms for operational planning and control are quite important to successful operations of a supply network. Implementation of a strategy requires substantial information system support, but few detailed designs of such systems have appeared in the literature. We designed an information system for a centralized management concept that could handle any type of strategy or algorithm. We developed algorithms for two different strategies deemed important by researchers and practitioners, push and pull, and implemented them in system modules. This information system performed well in simulations of the management of operations of an example six-stage supply network. This system provides an example for operational development as well as a platform for laboratory experiments.     

Simulation-based optimization of process control policies for inventory management in supply chains

A simulation-based optimization framework involving simultaneous perturbation stochastic approximation (SPSA) is presented as a means for optimally specifying parameters of internal model control (IMC) and model predictive control (MPC)-based decision policies for inventory management in supply chains under conditions involving supply and demand uncertainty. The effective use of the SPSA technique serves to enhance the performance and functionality of this class of decision algorithms and is illustrated with case studies involving the simultaneous optimization of controller tuning parameters and safety stock levels for supply chain networks inspired from semiconductor manufacturing. The results of the case studies demonstrate that safety stock levels can be significantly reduced and financial benefits achieved while maintaining satisfactory operating performance in the supply chain.     

A flexible agent system for change adaptation in supply chains

The importance of supply chain management is increasing with globalization and the advancement of electronic business. Supply chains are dynamic due to frequent changes in suppliers, customers, and products. However, information systems for supply chains are not flexible enough to adapt to the changes, requiring high cost and time for re-implementation and re-customization. This paper suggests a flexible agent system for supply chains that can adapt to the changes in transactions introduced by new products or new trading partners. The flexible agent system consists of an interpretable conversation policy model, a procedure for exchanging new conversation policies, and a mechanism for actual transactions with newly acquired conversation policies. By using the flexible agent system, formation of supply chains becomes more robust to the dynamic changes. To show how the approach can be realized, a PC supply chain application is illustrated with a prototype agent platform.     

Multi-tag and multi-owner RFID ownership transfer in supply chains

In any supply chain, there is a high likelihood for individual objects to change ownership at least once in their lifetime. As RFID tags enter the supply chain, these RFID-tagged objects should ideally be able to seamlessly accommodate ownership transfer issues while also accomplishing their primary intended purpose. Physical ownership transfer does not translate to strict ownership transfer in the presence of RFID tags given the wireless nature of communication with these tags. Moreover, whereas existing protocols implicitly assume a single tag that is owned by a single entity, it is not uncommon to encounter scenarios where tag ownership is shared among multiple entities. A dual of this is the case of an object with multiple tags. We consider ownership transfer scenarios for shared ownership transfer and single object with multiple RFID tags. In the multiple-tagged object case, we consider the possibility where objects gain and lose tags over time. We also present a protocol for simultaneous transfer of ownership of multiple tags between owners. Since ownership transfer without a trusted third party (TTP) is difficult to achieve, we propose a shared ownership sharing protocol and evaluate its properties.     

A statistical tolerancing approach for design of synchronized supply chains

Supply chain (SC) can be visualized as a means to fulfilling the customer's requirement with the objective to maximize the overall value generated. With the rapid change of world economy, firms need to deploy alternative methodologies to improve the responsiveness of SC. In this regard, one important issue is the synchronization and co-ordination of supply chain network (SCN). In order to ensure the dispatch of finished product to the customer, with in customers specified delivery window, with a fierce high probability, the delivery performance of any SCN is to be minimized by forcing the work in the system. These requirements can be achieved by SC coordination, where timing of the actions performed by each of the business entities must be perfectly synchronized in SCN.This research is first of its kind where a probabilistic model is conceptualized to allocate the tolerances on lead times of internal business processes of a SCN. Various linear and nonlinear constraints have been considered with an end goal of minimizing the total cost of given network. In the proposed approach, synchronization among the members of SC has been achieved in the similar fashion as that of tolerance allocation on mechanical assemblies.The effectiveness of the proposed model is illustrated with a case study. The probabilistic model for ensuring synchronization shows much promise for improving the entire performance of network in terms of greater profit, speed, flexibility, quality, and product development etc., in a long run manufacturing enterprise.     

Modeling enablers of supply chain risk mitigation in electronic supply chains: A Grey–DEMATEL approach

Supply chains are becoming perpetually complex and potentially vulnerable due to increased globalization and vertical integration. Supply chain managers are conscious that the success of any supply chain lies in its proficiency to manage risks efficiently and effectively. Supply chain risk management (SCRM) signifies proactive practices of managing vulnerability of supply chains. Various firms espouse strategic level measures for risk mitigation and implement practices for enhancing supply chain resilience. In order to enhance supply chain resilience, the enablers of supply chain risk mitigation need to be acknowledged and implemented into practice. This research emphasizes on ascertaining the major enablers of supply chain risk mitigation with emblematic focus on electronic supply chains. A blend of Grey theory and DEMATEL approaches has been employed in this research to find out cause/effect relationships among the enablers of supply chain risk mitigation. This exercise helps to find which can be the driver to initiate the effects of other driven enablers. Sensitivity analysis was also conducted of the results to ensure reliability of solutions. The results show that that the enablers of supply chain risk mitigation are intertwined and one enabler can be the cause/effect of one or more enablers of supply chain risk mitigation. Dynamic assortment planning is found to be the decisive causal enabler, as it initiates the effects of many other enablers of supply chain risk mitigation, followed by accurate demand forecasting and flexible supply contracts. Cause–effect relationships plotted facilitate managers to ascertain primary causal enablers that need imperative attention in dealing with vulnerability issues of supply chain. Managers can take proactive steps to address and implement primary causal enables of risk mitigation into practice for reducing total risk impacts of the supply chain.     

A multi-agent system model for supply chains with lateral preventive transshipments: Application in a multi-national automotive supply chain

In this study, a multi-agent system model is developed to observe the effects of ordering parameters on a supply chain with lateral preventive transshipments. The proposed model involves ordering and premium freight bidding processes of the agents. The model is implemented to a multi-national supply chain considering both supply and demand side uncertainties. Effects of safety stock and supplier flexibility levels on performance are examined by simulation from both agent and system-wide perspective. The results reveal the viability of the proposed model.     

VMI for single-vendor multi-retailer supply chains under stochastic demand

This paper discusses how a vendor and multiple retailers interact in a vendor managed inventory (VMI) system under stochastic demand. It is assumed that the vendor replenishes all the retailers at the same time. The vendor replenishment cycle is taken to be an integer multiple of the retailer replenishment cycle. In case of a shortage at the vendor, the available stock is allocated to the retailers on the basis of equal stock out probability. Approximate expressions for minimizing the expected total cost for the VMI system have been developed. Various levers affecting the performance of the system have been analyzed. The validity of the approximate model has been tested through simulation.     

A typology of the situations of cooperation in supply chains

Pushed by globalization and its consequent increased competition, supply chain managers have understood the importance of information sharing, joint decision-making and cooperation across supply chains. Therefore, how to synchronize local activities through global processes and how to establish a collaborative supply chain relationship are actual difficulties that supply chain members have to address. In this context, this paper suggests a model of the situations of cooperation in supply chains for coping with real industrial situations, based on the analysis of the limitations of previous models. It is shown how the suggested model may allow to identify dysfunctions in the cooperation process, especially when both large and small companies are involved, and can also be used to describe and monitor the possible evolution of the cooperation process. Finally, the model may help to specify the way information should be efficiently processed all along a supply chain, depending on the situation of cooperation.     

Price and leadtime competition,and coordination for make-to-order supply chains

This paper develops a game theoretic model of a one-manufacturer and one-retailer supply chain facing an outside integrated chain (manufacturer) to study the price and leadtime competition and investigate coordination of the supply chain, where the make-to-order production mode is employed and consumers are sensitive to retail price and leadtime. We find that decentralization of the supply chain increases its leadtime while decreases the rival’s leadtime; and the decentralization increases the retail prices. The existence of the outside competitor raises the leadtime. A higher reservation price or brand differentiation increases the retail prices but decreases the leadtimes; a higher transportation cost or lower leadtime sensitivity increases the retail prices and the leadtimes. The coordination of the supply chain facing integrated rival harms the integrated rival. We design contracts to coordinate the supply chain under leadtime-decision-first scenario and wholesale-price-decision-first scenario, respectively. Further, we find that the sequence of decisions affects the validity of the all-unit quantity discount scheme in coordinating the supply chain.     

Agent-based simulation of competitive and collaborative mechanisms for mobile service chains

A new paradigm for a mobile service chain’s competitive and collaborative mechanism is proposed in this study. The main idea of the proposed approach is based on a multi-agent system with optimal profit of the pull, push, and collaborative models among the portal access service provider (PASP), the product service provider (PSP), and the mobile service provider (MSP). To address the running mechanism for the multi-agent system, an integrated system framework is proposed based on the agent evolution algorithm (AEA), which could resolve all these modes. To examine the feasibility of the framework, a prototype system based on Java-Repast is implemented. The simulation experiments show that this system can help decision makers take the appropriate strategies with higher profits. By analyzing the expectations and variances (or risks) of each player’s profit, the interaction between and among entities in the chain is well understood. It is found that in the situation where a collaborative mechanism is applied, the performance of players is better as compared to the other two situations where a competitive mechanism is implemented. If some constraints are applied, the risk will be kept at a low level.     

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.     

Reducing the negative effects of sales promotions in supply chains using genetic algorithms

This paper considers the detrimental effect of promotions on the supply chain (SC), one of the main causes of the bullwhip effect. A genetic algorithm (GA) is proposed to reduce these negative effects. In order to validate the GA, it is used to determine the optimal ordering policy in an online version of the MIT beer distribution game. Subsequently, the GA is applied in a number of experiments involving deterministic and random demand and lead times combined with sales promotions. It is shown how GAs can be used to dampen the impact of the bullwhip effect and can be used to assist supply managers in predicting reorder quantities along the supply chain.     

Agent-based negotiation and decision making for dynamic supply chain formation

Modern businesses are facing the challenge of effectively coordinating their supply chains from upstream to downstream services. It is a complex problem to search, schedule, and coordinate a set of services from a large number of service resources under various constraints and uncertainties. Existing approaches to this problem have relied on complete information regarding service requirements and resources, without adequately addressing the dynamics and uncertainties of the environments. The real-world situations are complicated as a result of ambiguity in the requirements of the services, the uncertainty of solutions from service providers, and the interdependencies among the services to be composed. This paper investigates the complexity of supply chain formation and proposes an agent-mediated coordination approach. Each agent works as a broker for each service type, dedicated to selecting solutions for each service as well as interacting with other agents in refining the decision making to achieve compatibility among the solutions. The coordination among agents concerns decision making at strategic, tactical, and operational level. At the strategic level, agents communicate and negotiate for supply chain formation; at the tactical level, argumentation is used by agents to communicate and understand the preferences and constraints of each other; at the operational level, different strategies are used for selecting the preferences. Based on this approach, a prototype has been implemented with simulated experiments highlighting the effectiveness of the approach.     

Strategies in supply chain management for the Trading Agent Competition

Negotiating with suppliers and with customers is a key part of supply chain management. However, with recent technological advances, the mechanisms available to carry out such activities have become increasingly sophisticated, and the environment in which these activities take place has become highly dynamic. As a consequence, the overall planning of these complex trades, and the coordination of the various production and scheduling activities, need to be carefully considered by the businesses involved in the supply chain management. In order to guide the overall planning, production, scheduling, and allocation of resources, especially designed strategies are increasingly used by the businesses. In this setting, it is crucial that the intended behaviour, and through that, the desired outcomes, of these strategies be precisely understood. Using an empirical analysis, this paper investigates two fundamental strategies in supply chain management: buy-to-build and build-to-order.     

Interactions of agents in performance based supply chain management

To acquire a competitive advantage in the expanding market, manufacturing companies should be able to manage their supply chain as much effective as possible. Measuring the supply chain performance is one of the main indicators of business success. Supply chain management (SCM) involves managing the flow of materials from suppliers to manufacturing units. This paper proposes a SCM model with performance measurement capabilities. The model is designed in such a way that it well suits to multi agent systems and related implementations. This paper highlights the components of the model especially pointing out the key parameters of performance indicators.     

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.     

An improved demand forecasting method to reduce bullwhip effect in supply chains

Accurate forecasting of demand under uncertain environment is one of the vital tasks for improving supply chain activities because order amplification or bullwhip effect (BWE) and net stock amplification (NSAmp) are directly related to the way the demand is forecasted. Improper demand forecasting results in increase in total supply chain cost including shortage cost and backorder cost. However, these issues can be resolved to some extent through a proper demand forecasting mechanism. In this study, an integrated approach of Discrete wavelet transforms (DWT) analysis and artificial neural network (ANN) denoted as DWT-ANN is proposed for demand forecasting. Initially, the proposed model is tested and validated by conducting a comparative study between Autoregressive Integrated Moving Average (ARIMA) and proposed DWT-ANN model using a data set from open literature. Further, the model is tested with demand data collected from three different manufacturing firms. The analysis indicates that the mean square error (MSE) of DWT-ANN is comparatively less than that of the ARIMA model. A better forecasting model generally results in reduction of BWE. Therefore, BWE and NSAmp values are estimated using a base-stock inventory control policy for both DWT-ANN and ARIMA models. It is observed that these parameters are comparatively less in case of DWT-ANN model.     

Co-op advertising and pricing models in manufacturer–retailer supply chains

Cooperative (co-op) advertising plays a significant role in marketing programs in conventional supply chains and makes up the majority of promotional budgets in many product lines for both manufacturers and retailers. Nevertheless, most studies to date on co-op advertising have only assumed that the market demand is only influenced by the advertising level but not in any way by the retail price. That is why our work is concerned with co-op advertising and pricing strategies in distribution channels consisting of a manufacturer and a retailer. Four different models are discussed which are based on three non-cooperative games (i.e., Nash, Stackelberg retailer and Stackelberg manufacturer) and one cooperative game. We identify optimal co-op advertising and pricing strategies for both firms mostly analytically but we have to resort to numerical simulations in one case. Comparisons are then made about various outcomes, especially the profits, for all cases. This leads to consider more specifically the cooperation case in which profits are the highest for both the retailer and the manufacturer, and how they should share the extra joint profit achieved by moving to cooperation. We solve this bargain problem using the Nash bargaining model.