Lauri Lättilä

Hybrid simulation models - When, Why, How?

Agent-Based Modeling and Simulation (ABMS) and System Dynamics (SD) are two popular simulation paradigms. Despite their common goal, these simulation methods are rarely combined and there has been a very low amount of joint research in these fields. However, it seems to be advantageous to combine them to create more accurate hybrid models. In this research, the possible ways to combine these methods are studied. The authors have found five different situations where it will be useful to combine these methods. All of them have already been used in earlier studies, so modelers should use them as possible interfaces to combine the methodologies. By using hybrid simulation models it is possible to create more accurate and reliable Expert Systems (ES).

Agent based decision support in the supply chain context

Purpose – The purpose of this paper is to investigate the benefits and the barriers of agent based decision support (ABDS) systems in the supply chain context.Design/methodology/approach – Two ABDS systems have been developed and evaluated. The first system concerns a manufacturing supply chain while the second concerns a service supply chain. The systems are based on actual case companies.Findings – This research shows that the benefits of ABDS systems in the supply chain context include the possibility to increase versatility of system architecture, to improve supply chain visibility, to conduct experiments and what‐if analyses, to improve the understanding of the real system, and the possibility to improve communication within and between organizations in the supply chain. The barriers of ABDS systems in the supply chain context include the difficulty to access data from partners in the supply chain, the difficulty to access data on a higher level of granularity, and the difficulty to retrieve data fro...

Forecasting long-term demand of largest Finnish sea ports

Investments in large scale logistical infrastructure are significant and they require a long time and as such good forecasts play an important part. In this research, we combine advanced forecasting methods with system dynamics to study the development of Finnish sea ports. We use regression and ARIMA models in conjunction with a system dynamic Monte Carlo simulation model to estimate the development of Finnish sea ports. Based on long-term data, industrial production was found to be the driving factor behind sea port demand development in Finland. GDP is generally regarded as the explanatory variable, but at least in Finland, industrial production drives the growth. Russian oil exports also explain transit traffic through Finland to Russia, and four scenarios were generated to estimate the development of Finnish sea ports.

Throughput accounting and stochastic system behaviour: importance of low throughput products

Nearly for two decades, throughput accounting has argued against practices of management accounting. It has built mainly its alternative theory on simple and static example of choosing right product mix to maximize profitability of a manufacturing unit. In this research work, we simulate original throughput accounting product mix problem and use stochastic variation in the production process phases and in the end product demand. Results show that variation creates situation, where system profits increase significantly; this is mostly caused by the reason that we have enough WIP in the process, constraint resource is having occasionally lower cycle-times and un-favoured low throughput product is being produced due to the reason that there does not exists that high demand high yield one. Based on these findings, we propose that throughput accounting theory should be modified – variability in some situations and with low throughput products is the main factor for higher profitability.

Agent Based Decision Support in Manufacturing Supply Chain

Supply Chain Management (SCM) is becoming increasingly complex and an intensified competition in the end-markets has started to create a situation where co-operation requirements between companies in a Supply Chain (SC) are increasing. The old mechanistic operations management solutions are becoming obsolete and advanced decision support is increasingly needed to realize efficient and effective management of complex SCs. The objective of this research is to contribute to the understanding of how Agent Based Modeling (ABM) can advance decision making and to discuss why ABM should be regarded as method to realize Information Fusion (IF). In this research work an agent based model of SCM has been implemented in a simulation platform to provide an approach for evaluation of decision and management alternatives. Research shows that this kind of decision support system is based on IF, since it collects and fuses information from different sources into a situation image that provides effective support for human decision making

Multimodal transportation risk in Gulf of Finland Region

Globalisation has resulted in larger volumes of foreign trade, especially in maritime transportation. No studies exist on how the maritime volumes could be handled, if the operational environment changes unexpectedly. The objective of this paper is to identify possible risks in transportation routes that use seaports in the Gulf of Finland and to evaluate the functionality of the transportation system under selected risk scenarios. Four system dynamics simulation experiments reveal that the impact of a spillage depends on concentration of resources and cargo types carried and highlight the importance of international cooperation.

Framework for demand chain and supply chain coordination

Organisations must coordinate the value creation (demand chain) and the value delivery (supply chain) processes in order to survive in todays competitive market environment. The need to coordinate ...

Analyzing Supply Chain Vulnerability Through Simulation

Supply chain vulnerabilities have attracted little research attention despite being recognized as an important issue. To provide further insights into this issue, this chapter uses a simulation approach for analyzing supply chain vulnerability. Specifically, simulations are used to gain additional insights into multiechelon supply chains and the impact of supply disruptions. This study employs an integrated literature review of supply chain vulnerability and risk management and a discrete-event-simulation. The presented framework and simulation models provide important information about the feasibility of using a simulation for analyzing supply chain vulnerability. The results of this study suggest that supply chain vulnerability depends on both the complexity of the supply chain as well as the disruption risks inherent in it. To gain a more holistic view of an entire supply chain system, it is imperative to use proper tools to analyze vulnerabilities. Simulations can be used to model both the system complexity and the different levels of operation holistically and gain insights into managing supply chain vulnerability. By analyzing supply chain vulnerability, managers can ground their decisions in a more holistic understanding of the issue.

Simulation modelling challenge of transportation logistics systems

Simulation has been used in operations management discipline for decades, but applications in transportation logistics have been rather scarce. Situation is the same in rapid modelling sub-branch. This is mostly due to the reason of needs in high computing power, sophistication of artificial intelligence, and integration of other data sources to the simulation models. However, with ever increasing global trade and derived transportation needs, economically and ecologically sustainable solutions are sought for. We illustrate challenges in this branch with two agent-based simulation models and with one system dynamics model. These are all based on real life observations and data. Complexity and time of model building is likely to increase with this stated problem area, but rapid modelling part is enabled with different scenarios, optimisation and Monte Carlo features during simulation run, but also with feature to put models online to internet and enable significant user interaction.