Model Based on Bayesian Networks for Monitoring Events in a Supply Chain (original) (raw)

Supply chain diagnostics with dynamic Bayesian networks

Computers & Industrial Engineering, 2005

This paper proposes a dynamic Bayesian network to represent the cause-and-effect relationships in an industrial supply chain. Based on the Quick Scan, a systematic data analysis and synthesis methodology developed by Naim, Childerhouse, Disney, and Towill (2002). [A supply chain diagnostic methodlogy: Determing the vector of change. Computers and Industrial Engineering, 43, 135-157], a dynamic Bayesian network is employed as a more descriptive mechanism to model the causal relationships in the supply chain. Dynamic Bayesian networks can be utilized as a knowledge base of the reasoning systems where the diagnostic tasks are conducted. We finally solve this reasoning problem with stochastic simulation.

A Multi-Agent Decision Support System for Dynamic Supply Chain Organization

In this work, a multi-agent system (MAS) for supply chain dynamic configuration is proposed. The brain of each agent is composed of a Bayesian Decision Network (BDN); this choice allows the agent for taking the best decisions estimating benefits and potential risks of different strategies, analyzing and managing uncertain information about the collaborating companies. Each agent collects information about customer's orders and current market prices, and analyzes previous experiences of collaborations with trading partners. The agent therefore performs a probabilistic inferential reasoning to filter information modeled in its knowledge base in order to achieve the best performance in the supply chain organization.

Compound Web Service for Supply Processes Monitoring to Anticipate Disruptive Event

IFIP Advances in Information and Communication Technology, 2010

The execution of supply process orders in a supply chain is conditioned by different types of disruptive events that must be detected and solved in real time. In this work we present a compound web service that performs the monitoring and notification functions of a supply chain event management system. This web service is designed based on a reference model that we have proposed to improve the event management activity through a deeper analysis of the occurrence and causality of events, leading to anticipate an exception during the execution of a supply process order. The web service composition is defined based on business processes. The ability to proactively detect, analyze and notify disruptive events is given through of a Bayesian network with decision nodes.

University of Iowa An extended Bayesian network approach for analyzing supply chain disruptions

Supply chain management (SCM) is the oversight of materials, information, and finances as they move in a process from supplier to manufacturer to wholesaler to retailer to consumer. Supply chain management involves coordinating and integrating these flows both within and among companies as efficiently as possible. The supply chain consists of interconnected components that can be complex and dynamic in nature. Therefore, an interruption in one subnetwork of the system may have an adverse effect on another subnetworks, which will result in a supply chain disruption.

Managing Supply Chain Events to Build Sense-and-Respond Capability

2006

As supply chains become more dynamic, there is a need for a sense-and-respond capability to react to events in a real-time manner. In this paper, we propose Petri nets extended with time and color (for case data) as a formalism for doing so. Hence, we describe seven basic patterns that are used to capture modeling concepts that arise commonly in supply chains. These basic patterns may be used by themselves and also be combined to create new patterns. Next, we show how to use the patterns as building blocks to model a ...

An autonomous multi-agent approach to supply chain event management

2008

Event management is a problem in the supply chain context that requires a solution with the goal of mitigate the event effect during the execution plan. We present an autonomous agent-based approach to support a system for this problem. Our proposal introduces two novel aspects: we conceive the system as a collaborative inter-organizational information system and we aim to provide autonomous mechanisms for the system to perform proactive control actions. We develop an example illustrating the principal concept and how this decomposition and collaborative negotiations allow finding a solution to an exception.

A multi-agent based framework for supply chain risk management

Journal of Purchasing and Supply Management, 2010

The high level of complexity of supply chains and the inherent risks that exist in both the demand and supply of resources-especially in economic downturns-are recognized as major limiting factors in achieving high levels of supply chain performance. The use of modern information technology (IT) decision support systems is fast becoming an indispensable tool for designing and managing complex supply chain systems today. This paper develops a framework for the design of a multi-agent based decision support system for the management disruptions and mitigation of risks in manufacturing supply chains.

Application of Bayesian Networks to Analyze in Analyzing Incidents and Decision-Making

2005

Incident management requires a full understanding of the characteristics of incidents to accurately estimate incident durations and to help make more efficient decisions, reducing the impact of non-recurring congestion. The goal of this paper is to have an articulate description of incident clearance patterns and to represent these findings with formalisms based on Bayesian Networks (BNs). BNs can be an innovative tool for incident management practice and can be used to create dynamic estimation trees that are extracted in the presence of an incident, enabling operators to create case-specific incident management strategies. We introduce the use of BNs to the transportation field to better understand the prevailing circumstances of incidents. This can only be accomplished by considering the stochastic variation of the data and bi-directional induction in decision-making. After a comprehensive review of the application of BNs to our problem, the dependency relations among all variables in a BN that can be used for quantitative and qualitative analysis are also presented.

Data-Mining-Enhanced Agents in Dynamic Supply-Chain-Management Environments

IEEE Expert / IEEE Intelligent Systems, 2009

In modern supply chains, stakeholders with varying degrees of autonomy and intelligence compete against each other in a constant effort to establish beneficiary contracts and maximize their own revenue. In such competitive environments, entities-software agents being a typical programming paradigm-interact in a dynamic and versatile manner, so each action can cause ripple reactions and affect the overall result. In this article, the authors argue that the utilization of data mining primitives could prove beneficial in order to analyze the supply-chain model and identify pivotal factors. They elaborate on the benefits of data mining analysis on a well-established agent supply-chain management network, both at a macro and micro level. They also analyze the results and discuss specific design choices in the context of agent performance improvement.