In the current global economy, organisations become successful depending on how well they are in a position to utilise and manage their supply chain. Supply chain forms a vital component of flow of materials in any organisation. A majority of the organisations has developed policies aimed at supply chain management, which work differently in different industries.
According to Chang and Makatsoris (2001), supply chain management was coined in the early 90s and it entails the process where manufacturers, suppliers, warehouses, and retailers are integrated with the aim of efficiency and cost reduction. When trying to put up systems to be utilised in organisations, some challenges may be encountered, which may affect the operation of the final product, hence the need for a simulation.
The conventional definition of simulation holds that simulation is a model of the actual system, which can be experimented with the aim of predicting the efficiency of the real system (Banks 1998). Supply chain management can be simulated to establish the likely outcomes as the various parts are reconfigured or interfered with to achieve a particular result. Simulation can currently be achieved using special software that is in a position to generate the actual program, process, or product.
One of the software used for this purpose is the Simul8, which will be utilised in this model. An example of a company that will be utilised in this simulation project is Paisley Pallets (LLc.), which is a family-owned business entity that has produced pallets since the beginning of the year 1965. The company operates in more than 50 countries and it produces pallets that are vital in the logistics industry.
It has continued to be the leader in the market with more than 100,000 people visiting its different outlets in a single year and more than 12,000 people working under the organisation. The company has a clear supply-chain management policy and a simulation of its manufacturing process and supply chain management is possible. This part is a literature review on simulation and supply chain management with a focus on the areas that simulation is applied and some of its advantages and disadvantages.
Simulation and areas of application
As aforementioned, simulation is used to create a model of a part of the real world, with the aim being to use this substitute to experiment or predict changes when alterations are made (Hollocks 1995). Simulation can be applied in a number of fields to predict changes when variations are made to the normal or known operational characteristics of any system.
However, according to Hollocks (1995), the most common field where simulation is applied is in the manufacturing industry. Other sectors where simulation is applied include the finance, health, service, and retail sectors (Hollocks 1995).
Simulation allows the mitigation of crisis in any industry and supply chains are currently being made efficient by simulation. In the present day, global trade is a characteristic of any industry and supply chains of present day organisations run through continents, and thus simulations finds wide application in such areas.
Therefore, supply chain management has evolved to be an important component of any organisational management. The simulation of supply chain is one of the methods that are currently being utilised by organisations to improve on their efficiency and output. As Ebrahimy, AbouRizk, Fernando, and Mohamed (2011) state, there are a number of toolkits that may be utilised in the simulation of a supply chain in organisations, but in their research, they focused on construction simulation tools
Carson (2005, p. 17) describes a simulation model as a ‘descriptive model of a process or system and usually it includes parameters that allow the model to be configurable; that is, to represent a number of somewhat different system or process configurations’. Through the different configurations that are available, one can alter this model to achieve the desired results.
Khorramabady (2006) posits that a good supply chain model should have a detailed model of the project, as well as a detailed upstream supply chain model, with the two constituting the two main important components. Supply chain management is reported to improve over the last few decades and one of the contributing factors is the presence of simulation as a tool. Zee and Vorst (2005) state that simulation has become an important tool in decision making in matters to do with supply chain management.
The construction industry is one of the industries with complex supply chains, and thus in this industry, supply chain management is vital. Demand variability can adversely affect logistics management in the industry and its complexity can be addressed through a stochastic simulation modelling approach (Vadalakis, Tookey & Sommervile 2011).
Other researchers who recognise simulation as being important in supply chain management are Vankateswaran and Son (2004), and their experimental results emphasised on the same. They also state that more research is needed to test the experiments in different conditions of supply chains to prove that the use of simulation is efficient enough in different settings (Vankateswaran & Son 2004).
One of the products of simulation in supply chain management is the scheduling models that are applied in manufacturing, which improve the supply chain performance in the industry (Selvarajah & Zhang 2013). Simulation of interaction between clients and organisations has also led to improvement in the workshop environment, which is attributable to facilitated modelling (Robinson et al. 2014).
Organisations do not exist in a vacuum and thus companies should engage with like-minded partners to improve on their performance in the industry. As such, partnerships have been developed in the supply chain systems especially for the multinational logistics networks. However, for large companies, local simulation paradigms are used in supply chains within the organisation to verify policies (Terzi & Cavalieri 2004).
Agriculture is another important industry where simulation is used in supply chain management. According to Cacho and Power (2014, p. 31), simulation can be used to develop a bio-economic model to estimate a stochastic risk frontier, which ‘is a novel approach to investigate the effects of management on the trade-offs between farm business profit and risk’.
Longo (2011) also states that the use of simulation in supply chain management can be important in reducing crisis especially after the economic crisis that followed the major disasters in history. Long, Lin, and Sun (2011) suggest the use of simulation as a replacement for the traditional analytical model, which is unable to cope with the current supply chains that exist.
According to Yang, Koziel, and Leifsson (2013, p. 859), some of the important trends in simulation and supply chain management include ‘nature-inspired meta-heuristic algorithms, Surrogate-based model and optimisation, Green computing and grid computing’.
Various software tools are used in simulation in supply chain management and some of these tools include the Simul8. This computer package allows users to generate events and objects, which are defined in various characteristics to allow the simulation of the desired results.
The software also comes with a number of plug-ins that maximises on its efficiency and ensures that the user is in a position to create the model as it appears in reality. Updates have also been made available, thus adding a number of advanced features. Gonzales (2013) also presents software that may be used in simulation of multiple models. However, further research is needed in some areas such as in supply chains with more than two stages as it exists in some industries (Albino, Carbonara & Giannoccaro 2007).
A number of advantages are associated with the use of simulation in supply chain management. One of the major advantages to the use of simulation in supply chain management is the reduction in operating time and costs (Hollocks 1995). Simulation allows the actual management of the supply chain to be efficient, and one of the ways in which the program achieves this goal is through a faster implementation of changes and reduction of the capital costs (Hollocks 1995).
Simulation has also allowed organisations to reduce the time taken to design a product and avail it to the market. Courtesy of this development, organisations can now ensure that products are always available in the market and at the right time. This move has ensured increased performance and profitability for companies that have adopted these policies. Some of the other benefits of using simulation as opposed to trying things in the real world include the reduction of risks (Vadalakis, Tookey & Sommervile 2011).
Organisations are in a position to simulate the supply chain and make alterations where potential risks arise, which has aided in risk reduction. Through simulation, managers and other parties involved in the supply chain can now have an understanding of how the process works, which leads to increased output and efficiency (Hollocks 1995).
The program is also known to improve communication in organisations, as well as creating better working teams and skills (Hollocks 1995). Carson (2005) also states that simulation allows the identification of problems before a system is built or modified and at the same time allows for studies in dynamic systems.
Whereas simulation in the supply chain has a number of advantages, a number of shortcomings are associated with this program. One of the demerits is that the creation of a simulation is time-consuming (Carson 2005). The time taken to develop the appropriate systems is important for the organisation as a whole and could be utilised in other activities. However, the advantages that come with simulation outweigh this disadvantage.
The data that is also required in the construction of simulations is also costly to obtain and in some instances, it is not available to the organisations (Carson 2005). The making of simulations is important in the decision making for organisations on the appropriate supply chain policies.
For effective decisions to be made, time should be invested. However, in the use of simulation, the time available before the decision making process is insufficient (Carson 2005). These are some of the disadvantage of the use of simulation in supply chain management.
Albino, V, Carbonara, N, & Giannoccaro, I 2007, ‘Supply chain cooperation in industrial districts: A simulation analysis’, European Journal of Operational Research vol. 177 no. 1, pp. 261–280.
Banks, J 1998, Handbook of Simulation: Principles, Methodology, Advances, Applications, and Practice, Wiley, New York.
Cacho, O & Power, B 2014, ‘Identifying risk-efficient strategies using stochastic frontier analysis and simulation: An application to irrigated cropping in Australia’, Agricultural Systems, vol.125 no. 6, pp. 23–32.
Carson, J 2005, Introduction to Modelling and Simulation, <http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.120.8277&rep=rep1&type=pdf>
Chang, Y & Makatsoris, H 2001, ‘Supply Chain Modelling Using Simulation’, Information Journal of Simulation, vol. 2 no. 1, pp. 24-30.
Ebrahimy, Y, AbouRizk, S, Fernando, S & Mohamed, Y 2011, ‘Simulation modelling and sensitivity analysis of a tunnelling construction project’s supply chain’, Engineering, Construction and Architectural Management, vol.18 no.5, pp. 462-480.
Gonzales, F 2013, ‘Real-Time Simulation, and Control of Large Scale Distributed Discrete Event Systems’, Procedia Computer Science, vol. 16 no. 1, pp. 177–186.
Hollocks, B 1995, ‘The Impact of Simulation in Manufacturing Decision Making’, Control Engineering Practice, vol. 3 no. 1, pp. 106-112.
Khorramabady Y 2006, Symphony Supply Chain Simulator: A Toolkit for Modelling Supply Chain Coordination and Information Sharing, University of Alberta, Canada.
Longo, F 2011, ‘Advances of modelling and simulation in supply chain and industry’, Simulation: Transactions of the Society for Modelling and Simulation International, vol.87 no.8, pp. 651–656.
Long, Q, Lin, J & Sun, Z 2011, ‘Modelling and distributed simulation of supply chain with a multi-agent platform’, International Journal of Advanced Manufacturing Technology, vol. 55 no. 1, pp. 1241–1252.
Robinson, S, Worthington, C, Burgess, N & Radnor, Z 2014, ‘Facilitated modelling with discrete-event simulation: Reality or myth’, European Journal of Operational Research, vol. 234 no. 1, pp. 231–240.
Selvarajah, E & Zhang, R, 2013, ‘Supply Chain Scheduling at the Manufacturer to Minimise Inventory Holding and Delivery Costs’, International Journal of Production Economics, vol. 6 no. 1, pp. 117–124.
Terzi, S & Cavalieri, S 2004, ‘Simulation in the supply chain context: a survey’, Computers in Industry, vol. 53 no. 1, pp. 3–16.
Vadalakis, C, Tookey, J & Sommervile, J 2011, ‘Logistics simulation modelling across construction supply chains’, Construction Innovation, vol. 11 no. 2, pp. 212-228.
Vankateswaran, J & Son, Y 2004, ‘Impact of modelling approximations in supply chain analysis – an experimental study’, International Journal of Production Research, vol. 42 no.15, pp. 2971-92
Yang, X, Koziel, S & Leifsson, L 2013, ‘Computational Optimisation, Modelling and Simulation: Recent Trends and Challenges’, Procedia Computer Science, vol.18 no.1, pp. 855 – 860.
Zee, S & Vorst, I 2005 ‘A Modelling Framework for Supply Chain Simulation: Opportunities for Improved Decision’, Decision Sciences, vol. 36 no. 1, pp. 65-95.