The role of learning on industrial simulation design and analysis

The capability of modeling real-world system operations has turned simulation into an indispensable problemsolving methodology for business system design and analysis. Today, simulation supports decisions ranging from sourcing to operations to finance, starting at the strategic level and proceeding towards tactical and operational levels of decision-making. In such a dynamic setting, the practice of simulation goes beyond being a static problem-solving exercise and requires integration with learning. This article discusses the role of learning in simulation design and analysis motivated by the needs of industrial problems and describes how selected tools of statistical learning can be utilized for this purpose

Factory modelling: data guidance for analysing production, utility and building architecture systems

Work on energy and resource reduction in factories is dependent on the availability of data. Typically, available sources are incomplete or inappropriate for direct use and manipulation is required. Identifying new improvement opportunities through simulation across factory production, utility and building architecture domains requires analysis of model feasibility, particularly in terms of system data composition, input resolution and simulation result fidelity. This paper reviews literature on developing appropriate model data for assessing energy and material flows at factory level. Gaps are found in guidance for analysis and integration of resource-flows across system boundaries. The process for how data was prepared, input and iteratively developed alongside conceptual and simulation models is described. The case of a large-scale UK manufacturer is presented alongside discussions on challenges associated with factory level modelling, and the insights gained from understanding the effect of data clarity on system performance

A Simulation Model for Decision Support in Business Continuity Planning

Enterprises with a global supply network are at risk of lost revenue as a result of disruptive disasters at supplier locations. Various strategies exist for addressing this risk, and a variety of types of research has been done regarding the identification, assessment and response to the risk of disruption in a supply chain network. This thesis establishes a decision model to support Business Continuity Planning at the first-tier supplier level. The decision model incorporates discrete-event simulation of supply chain networks (through Simio software), Monte Carlo simulation, and risk index optimization. After modeling disruption vulnerability in a supply chain network, costs of implementing all combinations of Business Continuity Plans are ranked and then tested in discrete-event simulation for further insight into inventory levels, unmet customer demand, production loss and related costs. A case study demonstrates the implementation of the decision support process and tests a historical set of data from a large manufacturing company. Discrete-event simulation modeling of loss is confirmed to be accurate. The relevance of the model concept is upheld and recommendations for future work are made

Practitioners’ view on command query responsibility segregation

Relational database management systems (RDBMS) have long been a predominant technology in information systems (IS). Today, however, the ever-changing technology landscape seems to be the proving grounds for many alternative approaches. For instance, alternative databases are currently used in many cloud services that affect everyday life. Similarly, a novel way to design applications has come to fruition. It relies on two concepts; command query responsibility segregation (CQRS) and event sourcing. A combination of the concepts is suggested to mitigate some performance and design issues that commonly arise in traditional information systems development (ISD). However, this particular approach hasn’t sparked interest from of academia yet. This inquiry sets out to find opportunities and challenges that arise from adoption of one of the two concepts, namely CQRS. This is done in relative isolation from event sourcing. In total five interviews were conducted with seven participants using open-ended interview questions derived from design patterns research. The results are five themes that provide guidance to IS professionals evaluating adoption. These are alignment between IT-artifacts and business processes, simultaneous development, flexibility from specific database technology, modularization as a means of implementation and risk of introducing complexity. The results indicate that several themes from domain-driven design are influential to the concept. Additionally, results indicate that CQRS may be a precursor to eventually consistent queries and aids fine-tuning of availability, consistency and partition tolerance considerations. It is concluded that CQRS may facilitate improved collaboration and ease distribution of work. Moreover, it is hoped that the results will help to contextualize CQRS and spark additional interest in the field of IS research. The inquiry suggests further inquiries in other areas. These are among others; extract transform load-patterns, operational transforms, probabilistic bounded staleness and occasionally connected systems

Towards a model for managing uncertainty in logistics operations – A simulation modeling perspective

Uncertainty rules supply chains. Unexpected changes constantly occur on all levels; strategically through globalization, introduction of novel technology, mergers and acquisitions, volatile markets, and on an operational level through demand fluctuations, and events such as late arrival of in-bound material, machine equipment breakdown, and quality problems. The problem with uncertainty is increasing as the focus on cost reductions and efficiency in the industry tends to stretch supply chains to become longer and leaner, thus making them more vulnerable to disturbances. The aim of this thesis is to explore strategies for evaluating and managing uncertainties in a logistics context with the objectives; “to propose a method for modeling and analyzing the dynamics of logistics systems with an emphasize on risk management aspects”, and “to explore the impact of dynamic planning and execution in a logistics system”. Three main strategies for handling uncertainties are being discussed; robustness, reliability, and resilience. All three strategies carry an additional cost that must be weighed against the cost and risk of logistical disruptions. As an aid in making this trade-off, a hybrid simulation approach, based on discrete-event simulation and Monte Carlo simulation, is proposed. A combined analytical, and simulation approach is further used to explore the impact of dynamic planning and execution in a solid waste management case. Finally, a draft framework for how uncertainty can be managed in a logistics context is presented along with the key reasons why the proposed simulation approach has proven itself useful in the context of logistics systems

A SIMULATION MODEL FOR MAPPING CARBON DIOXIDE EMISSIONS TO DEVELOP A GREEN LOGISTICS SYSTEM

The aim of this thesis is to develop a simulation tool that helps companies to track and re-evaluate the environmental impacts on their logistics systems. A discrete event based simulation model is used and developed in this thesis and it provides a simple and visible solution to map the carbon emission footprints and calculate carbon emission values throughout an outbound logistics distribution network. The total carbon emission level in a simplified logistics distribution system is primarily determined by the total transport distance and different emission factors which categorized by many other important parameters such as load factor, empty trip rate, batch size, vehicle type and fuel consumption rate and so on. By visualizing carbon emission footprints and understanding how the carbon emission values in different transport paths are accumulated in the whole distribution system, the developed simulation model helps supply chain and logistics planners to investigate their current logistics systems and identify improvement areas in their systems to lead a better and greener logistics design. Website-based simulation software is also purposed as future research recommendation for realistic industry use.fi=Opinnäytetyö kokotekstinä PDF-muodossa.|en=Thesis fulltext in PDF format.|sv=Lärdomsprov tillgängligt som fulltext i PDF-format