Agent-based demand forecast in multi-echelon supply chain

Liang, Wen‐Yau; Huang, Chun‐Che

doi:10.1016/j.dss.2005.01.009

Cited by 118 publications

(36 citation statements)

References 25 publications

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“…An agent is characterised by its ability to exploit significant amounts of domain knowledge, overcome erroneous input, use symbols and abstraction, learn from the decision environment, operate in real time and communicate with others in natural language (Newell 1989). Exploiting such characteristics, an agent-based system has often been employed to handle various SC issues including shop floor control (Van Dyke Parunak 1998, Shen et al 2000, Usher 2003, Wang and Shen 2003; logistics planning (Satapathy et al 1998); air traffic control (Iordanova 2003); aggregate demand planning and forecasting (Yu et al 2002, Liang andHuang 2006); joint production planning (Lima et al 2006); new product development (Liang and Huang 2002); order monitoring (Chen and Wei 2007); business-to-business negotiation Saleh 2000, Lenar andSobecki 2007); bidding evaluation (Kang and Han 2002); outsourcing relationship management (Logan 2000); customer relationship management (CRM) (Baxter et al 2003); SC relationship management (Ghiassi and Spera 2003); SC performance assessment (Gjerdrum et al 2001); SC coordination (Swaminathan 1998, Fox et al 2000, Nissen 2001, Sadeh et al 2001, Ono et al 2003, Chan and Chan 2004, Lou et al 2004, Xue et al 2005; SC collaboration under uncertainty (Kwon et al 2007); information exchange among SC partners (Garcia-Flores et al 2000, Turowski 2002); information tracking across the SC (Zimmermann et al 2001); material handling (Ito and Mousavi Jahan Abadi 2002); retail merchandise purchasing (Park and Park 2003); e-logistics (Santos et al 2003); strategic e-procurement (Cheung et al 2004...…”

Section: Agent-based Systemsmentioning

confidence: 99%

Artificial intelligence in supply chain management: theory and applications

Min

2009

International Journal of Logistics Research and Applications

327

123

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Artificial intelligence (AI) was introduced to develop and create "thinking machines" that are capable of mimicking, learning, and replacing human intelligence. Since the late 1970s, AI has shown great promise in improving human decision-making processes and the subsequent productivity in various business endeavors due to its ability to recognise business patterns, learn business phenomena, seek information, and analyse data intelligently. Despite its widespread acceptance as a decision-aid tool, AI has seen limited application in supply chain management (SCM). To fully exploit the potential benefits of AI for SCM, this paper explores various sub-fields of AI that are most suitable for solving practical problems relevant to SCM. In so doing, this paper reviews the past record of success in AI applications to SCM and identifies the most fruitful areas of SCM in which to apply AI.

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Section: Agent-based Systemsmentioning

confidence: 99%

Artificial intelligence in supply chain management: theory and applications

Min

2009

International Journal of Logistics Research and Applications

327

123

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“…The paper proposes a variant of the Beer Game, which they called "Quebec Wood Supply Game". Liang and Huang (2006) developed, based on a multiagent architecture, a model which allowed predicting the order quantity in a supply chain with several nodes, where each one of them could use a different system of inventory. De la Fuente and Lozano (2007) presented an application of Distributed Intelligence to reduce the Bullwhip Effect in a supply chain, based on a genetic algorithm.…”

Section: Multiagent Systems In the Supply Chain Managementmentioning

confidence: 99%

Multiagent Methodology to Reduce the Bullwhip Effect in a Supply Chain

Ponte

Pino

García

2014

Transactions on Computational Collective Intelligence XVII

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Abstract. There are several circumstances which, in recent decades, have granted the supply chain management a strategic role in the search for competitive advantage. One of the goals is, undoubtedly, the reduction of Bullwhip Effect, which is generated by the amplification of the variability of orders along the chain, from the customer to the factory. This paper applies multiagent methodology for reducing Bullwhip Effect. To do this, it considers the supply chain as a global multiagent system, formed in turn by four multiagent subsystems. Each one of them represents one of the four levels of the traditional linear supply chain (Shop Retailer, Retailer, Wholesaler and Factory), and it coordinates various intelligent agents with different objectives. Thus, each level has its own capacity of decision and it seeks to optimize the supply chain management. The problem is analyzed both from a non collaborative approach, where each level seeks the optimal forecasting methodology independently of the rest, and from a collaborative approach, where each level negotiates with the rest looking for the best solution for the whole supply chain.

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“…In addition, ineffective communication affects material flows and creates long lead times. One of the great disadvantages of separated decision making in supply chain which is a result of also rational human behavior is bullwhip effect (Liang et al, 2005).…”

Section: Agent-based Supply Chain Managementmentioning

confidence: 99%