Supply chain management context

ABM is a growing body of research with many applications in supply chain operations, such as manufacturing, telecommunications, transportation systems, information management, interactive entertainments, and healthcare (Jennings et al. 1998). The agents are commonly described as companies with decision-making intelligence to manage sourcing, stocking, and shipping (Macal and North 2011).

However, its application is still limited.

The earliest and most popular ABM simulation in SCM is the beer game (North and Macal 2007) although it is more popular to be modelled in system dynamics approach, such as Forrester (1962) and Sterman (2000). The beer game simulates the increase of demand volatility as it moves further up a supply chain, which is known as the bullwhip or whiplash effect. This effect emerges because each company inside the supply chain is a rationally bounded entity and does not coordinate with each other in their decision-making process. The pattern of the increases in demand volatility is considered as the emergent outcome resulting from the interaction of individual firm. The earliest version of the beer game, which was introduced before the computer modelling software was developed, is the first agent-based model in business competition and collaboration.

In addition to the beer game, a vast body of ABM literature in SCM context has been established. However, not all these studies focus on supply chain analysis.

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Most of them are developed under computer science domain, so the research focuses on software development instead of analysing the supply chain problem.

SCM research that employs ABM in analysing the collaboration issue is still relatively limited. Several studies consider supply chain collaboration as firms integration, such as Xue et al. (2005) and Zhang et al. (2006). Xue et al. (2005) employ ABM to address collaboration issue in construction supply chain, but they concentrate on the information flow and negotiation. Zhang et al. (2006) present an ABM as an approach for e-manufacturing to provide flexibility, robustness, and adaptability to the rapid changes. Zhu (2008) also models supply chain collaboration, but it does not consider the collaboration as integration between firms; the study focuses on investigating the impact of information sharing in a single two-echelon supply chain. Chen et al. (2013) conduct a literature review on the use of ABM in supply chain risk management (SCRM). They consider that SCRM as a result of collaboration success in a supply chain. They define the goal of SCRM is establishing a robust supply chain, which is determined by supply chain ability to response changes and supply disruption. Other studies consider supply chain collaboration only in the scope of inventory decision, such as Dimitriou et al.

(2009), Dimitriou (2010), and Robinson et al. (2016). The study examines the effect of bounded rational decisions in a classical inventory model for perishable products (the Newsvendor inventory model) by combining ABM and multiple linear regressions. Nevertheless, all these research are limited to a single supply chain.

Trust between collaborating firms has also modelled and investigated by using ABM, but not many studies can be found in this topic. Only Mohamed et al. (2015) examine this issue in SCM context through an empirical approach in Malaysian industries.

Meanwhile, other works focus on modelling and analysing collaboration issue in the downstream level of supply chain, such as Caridi et al. (2005). They review the literature on ABM applications in managing supply chain processes, particularly in collaborative planning, forecasting, and replenishment (CPFR). In SCM, CPFR involves procedures and guidelines for sharing sales and forecast

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information between buyer and seller. The study finds that compared to traditional CPFR (without the support of intelligent agents), agent-based CPFR can reduce costs, inventory, sales, and shortages.

With regards to all ABM studies in supply chain collaboration, they concentrate mostly on software architecture than investigating the problem. These studies tend to employ ABM as a part of intelligent system in decision making rather than solely use it for simulation. The following are several examples of these studies described in brief. Swaminathan et al. (1998) utilise ABM as a multi-agent approach to develop a supply chain modelling framework. It addresses supply chain configuration, coordination, and contracts issues, which deal with inventory decisions. Julka et al. (2002) propose an ABM framework for developing a decision support system prototype to integrate supply chain processes in a refinery supply chain. However, the goal of the system is optimising a firm’s performance, not the supply chain. Jiao et al. (2006) apply an ABM system to develop a framework of collaborative negotiation in a supply chain. The framework incorporates supply chain network and inventory decisions. Kwon et al. (2007) develop an integrated framework of supply chain collaboration based on ABM and case-based reasoning.

The ABM architecture emphasises on information sharing among supplier, manufacturer, and customer. Cheng (2011) proposes an agent-based supply chain collaboration model that studies production and logistics processes at enterprise-level. The model comprises a single two-stage supply chain, which involves a manufacturer and a supplier. It considers competition to the model, but the competition is only represented by achieving on-time delivery target. Kwon et al.

(2011) propose an agent-based web approach to support supply chain collaboration in e-business. It models a three-stage supply chain that consists of suppliers, manufacturers, and retailers. The framework focuses on inventory decisions and allows flexibility in coping with partnerships changes. Santos et al. (2013) develop a prototype of an agent-based framework for negotiation. The system is intended to support a supply chain collaboration network by improving the interoperability in the single supply chain. Hsieh and Lin (2014) proposes ABM model with

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agent system (a distributed agent-based modelling) to manage collaborative workflows. However, it only focuses on scheduling activities within a firm.

Besides supply chain collaboration, ABM has been widely applied in many SCM issues. To obtain a general view of ABM applications in SCM which is outside the collaboration issue, several of the studies are briefly reviewed. Parunak et al. (1998) compare ABM and equation based modelling for modelling inventory problems. They find the use of ABM is still relatively new compared to equation-based modelling which is more mature in supply chain cases, particularly in inventory decisions. Gjerdrum et al. (2001) combine ABM with optimisation techniques to model a simple supply chain network which focuses on scheduling and inventory control. Kaihara (2003) formulates a supply chain model for resources allocation problem using ABM. Ahn et al. (2003) perform ABM to model adaptation processes in the financial transaction of a supply chain. It considers the dynamic of new products development, customers, and suppliers. D’Amours and Guinet (2003) compile literature on agent-based research in operational research area, which also represents SCM issues. Several research topics are related to ABM application in product development, scheduling, production management system, layout configuration problem, and real-time distributed control system. Akanle and Zhang (2008) introduce a methodology using ABM to optimise supply chain networks configuration of an original equipment manufacturer (OEM). Zarandi et al. (2008) employ ABM to reduce the bullwhip effect by coordinating all entities along the supply chain to minimise the total costs. Fu and Fu (2012) apply ABM to manage collaborative costs in supply chain. Li and Chan (2013) utilise ABM as a tool for studying the dynamic of supply chain in several manufacturing systems. He et al. (2013) examine pricing and inventory policies in a retailer supply chain through a laboratory experiment.

Other studies more focus on simulation software development rather than adopting ABM to analyse the problem. This is because they are conducted under the research area of computer science, not operational research and management science or SCM. For example, Barbuceanu et al. (1997) model a supply chain

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system that focuses on the information architectures; Shen and Norrie (1999) survey the application of agent distributed computing in supporting the mechanism of manufacturing systems; García-Flores et al. (2000) introduce the use of ABM to manage information flow of a manufacturing industries’ supply chain.

For ABM competition models, there are only two ABM studies which model competition by incorporating SCM perspective to the modelling and analysis; they were conducted by Arunachalam and Sadeh (2005) and He et al. (2013).

Arunachalam and Sadeh (2005) simulate competition between manufacturers of electronics industries by using an online participatory simulation approach. To assess the performance, the study compares inventory level, price, market share, and revenue between the competing teams. He et al. (2013) develop an agent-based competition model for multi-product supply chains, and only focuses on competition among retailers. Both these studies examine the competition issue in a particular single supply chain. Although Cheng (2011) claims his study covers competition, the model does not consider other companies in the competition.

Based on the literature that has been reviewed, there is still limited ABM research which incorporates competition and collaboration in SCM. Most previous studies investigate supply chain collaboration and competition in separate studies.

When collaboration issue is addressed, they also do not regard collaboration strategy to the problem. All of these studies only observe a particular single supply chain; none of them views supply chain problems from a market-level perspective.

In short, research that analyses firms’ behaviour in competition and collaboration by using an ABM approach has not yet been carried out in SCM.

Furthermore, compared to DES and SD, the use of ABM in supply chain analysis is still limited to date. This comparison is distinct when no paper has reviewed the applications of ABM in SCM. Where ABM has been applied to the SCM context, it is mostly conducted through computer science research. The works tend to focus on software architecture rather than analysing a problem of the proposed topic.

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A summary of previous research on competition and collaboration in SCM is presented in Table 3.1. The table also outlines the scope of application for each research, which are classified into 12 issues: supply chain planning, bullwhip effect, network/supply chain configuration, scheduling, trust, inventory, product development, logistics, supply chain risks, information sharing, supply chain financial aspect, and product pricing. This categorisation represents the scope of supply chain problems that is popularly discussed in SCM literature. According to the area of applications, it can be seen that all of these research measures collaboration performance based on the performance of supply chain operations.

These measurement approaches could not assess the long-term survivability and performance of the supply chain in the market.