Distributed Approaches to Supply Chain Simulation

The field of Supply Chain Management (SCM ) is experiencing rapid strides in the use of Industry 4.0 technologies and the conceptualization of new supply chain configurations for online retail, sustainable and green supply chains, and the Circular Economy. Thus, there is an increasing impetus to use simulation techniques such as discrete-event simulation, agent-based simulation, and hybrid simulation in the context of SCM. In conventional supply chain simulation, the underlying constituents of the system like manufacturing, distribution, retail, and logistics processes are often modelled and executed as a single model. Unlike this conventional approach, a distributed supply chain simulation (DSCS) enables the coordinated execution of simulation models using specialist software. To understand the current state-of-the-art of DSCS, this paper presents a methodological review and categorization of literature in DSCS using a framework-based approach. Through a study of over 130 articles, we report on the motivation for using DSCS, the modelling techniques, the underlying distributed computing technologies and middleware, its advantages and a future agenda, and also limitations and trade-offs that may be associated with this approach. The increasing adoption of technologies like Internet-of-Things and Cloud Computing will ensure the availability of both data and models for distributed decision-making, which is likely to enable data-driven DSCS of the future. This review aims to inform organizational stakeholders, simulation researchers and practitioners, distributed systems developers and software vendors, as to the current state-of-the art of DSCS, and which will inform the development of future DSCS using new applied computing approaches.

CONCEPTUAL FRAMEWORK FOR MATERIAL AND INFORMATION FLOW OPTIMISATION IN NATURAL GAS SUPPLY CHAIN

Natural gas is third most used fossil fuel and energy resource in the world, with significant increase in its consumption over last 20 years. As a consequence, research in optimisation of its supply chain processes are becoming increasingly significant. This paper aims to develop conceptual framework for material and information flow optimisation in natural gas supply chain and suggests its future use. Based on previous researches on mapping natural gas supply chain, bullwhip effect in natural gas supply chain and simulation models in natural gas supply chain, paper proposes new conceptual framework for material and information flow optimisation in natural gas supply chain. Results of implementation of this framework in natural gas supply chain of Republic of Croatia are presented with all suggestions for improvement explained. Keywords: natural gas supply chain, simulation model, bullwhip effect

RESOURCE UTILIZATION ANALYSIS OF HIGHWAY PROJECTS USING ARENA

The construction sector is a significant contributor to the Gross Domestic Product of a developing country. Infrastructure improvement plays a vital role in this wherein highway construction is a dynamic sector requiring proper planning and scheduling multiple resources. Appropriate integration among various associated stakeholders is essential for a project’s success, aided by supply chain management. Resource planning is one of the basic concepts in supply chain management, with material and equipment management being the critical area. The main objective of this study is to develop a conceptual supply chain simulation model using ARENA, to analyze the equipment idling and utilization rate, keeping inter-arrival time for dispatch, the number of equipment, and working hours as constant. This model employs the real-time ‘best fit’ material utilization data as input. Material utilization data collected from 62 construction projects are analyzed to arrive at a ‘best fit’ probability distribution. This study’s conceptual supply chain simulation model helps formulate suitable material and equipment delivery plans to lessen risk in construction projects.

Design And Development Of An Intelligent Agent-Based Supply Chain Simulation System

An intelligent agent-based supply chain simulation model, in which each enterprise/consumer is represented by an agent, is designed. There are six layers in this supply chain simulation model: raw material providers, component manufacturers, product assemblers, product holders, retailers, and final customers. Each entity in the supply chain represented by an agent has five components: interface, task distribution, business processing activities, knowledge management and decision support, and information storage. A detailed agent structure is designed and various functions of an agent including communication among agents are described. Issues in the supply chain integration, information sharing among supply chain partners, demand forecasting, supply chain risk management, and automated communication and negotiation, could be simulated and studied by using the proposed system. Based on the proposed supply chain simulation model, a generic six-layer prototype mobile phone supply chain simulation system is designed, developed and implemented. The system allows a user to setup and adjust a large number of parameters, including (1) simulation period, loan and saving interest rates; (2) customers' behavior and market demand; (3) each retailer's initial cash, loan, market share, inventories, Order Amount Policy and Order Point Strategy; (4) each product holder's initial cash, loan, market share, inventories, Order Amount Policy, Order Point Strategy and inventory strategy; (5) each assembler's and component agent's initial cash, loan, inventories, Order Amount Policy, Order Point Strategy, production strategy, and production capacities; and (6) each material provider's initial cash, loan, inventory, production strategy, and production capacities. Extensive simulation studies are carried out to examine and compare many supply chain management strategies and agent behaviors. This system can be used to test which strategy is most suitable in certain environments, The generic supply chain simulation system developed can be used in a number of ways, including: as an analysis tool for entity in a supply chain from the entity's perspective; as a tool for studying supply chain coordination and integration from the perspective of an entire supply chain, or portion of it; as a tool to design supply chains by answering "what-if" questions.

Design And Development Of An Intelligent Agent-Based Supply Chain Simulation System

An intelligent agent-based supply chain simulation model, in which each enterprise/consumer is represented by an agent, is designed. There are six layers in this supply chain simulation model: raw material providers, component manufacturers, product assemblers, product holders, retailers, and final customers. Each entity in the supply chain represented by an agent has five components: interface, task distribution, business processing activities, knowledge management and decision support, and information storage. A detailed agent structure is designed and various functions of an agent including communication among agents are described. Issues in the supply chain integration, information sharing among supply chain partners, demand forecasting, supply chain risk management, and automated communication and negotiation, could be simulated and studied by using the proposed system. Based on the proposed supply chain simulation model, a generic six-layer prototype mobile phone supply chain simulation system is designed, developed and implemented. The system allows a user to setup and adjust a large number of parameters, including (1) simulation period, loan and saving interest rates; (2) customers' behavior and market demand; (3) each retailer's initial cash, loan, market share, inventories, Order Amount Policy and Order Point Strategy; (4) each product holder's initial cash, loan, market share, inventories, Order Amount Policy, Order Point Strategy and inventory strategy; (5) each assembler's and component agent's initial cash, loan, inventories, Order Amount Policy, Order Point Strategy, production strategy, and production capacities; and (6) each material provider's initial cash, loan, inventory, production strategy, and production capacities. Extensive simulation studies are carried out to examine and compare many supply chain management strategies and agent behaviors. This system can be used to test which strategy is most suitable in certain environments, The generic supply chain simulation system developed can be used in a number of ways, including: as an analysis tool for entity in a supply chain from the entity's perspective; as a tool for studying supply chain coordination and integration from the perspective of an entire supply chain, or portion of it; as a tool to design supply chains by answering "what-if" questions.

Predictive Sales and Operations Planning Based on a Statistical Treatment of Demand to Increase Efficiency: A Supply Chain Simulation Case Study

Forecasting is the basis for planning. Good planning is based on a good prediction of what is going to happen to prepare a company, a department, and their environments for certain future developments and their intermediate states. In this context, resources are allocated to these future states in the most efficient way, given a certain set of resource conditions. Although market volatility demands the high adaptability of companies’ operations, dynamic planning is still not widespread. As a result, the alignment of planning processes with potential scenarios is not given, leading to a lack of solution preparation in the long term, suboptimal decision-making in the medium term, and corrective measures in the short term, with higher costs and a lower service level. Therefore, the aim of this research is to propose a predictive approach that will help managers develop sales and operations planning (S&OP) with higher accuracy and stability. For this purpose, a methodology combining demand scenarios, statistical analysis of the demand, forecasting techniques, random number generation, and system dynamics was developed. The goal of this predictive S&OP is to predict the supply chain system’s behavior to generate plans that prevent potential inefficiencies, thereby avoiding corrective measures. In addition, to assess the methodology, the model is applied in the software Vensim, for an automotive producer´s supply chain, to compare the predictive S&OP model with a classical approach. The results show that the proposed predictive approach can increase a manufacturer’s efficiency by increasing its adaptability through the identification of potential inefficiencies and can also be used to prepare solutions.