High quality input data is a necessity for successful Discrete Event Simulation (DES) applications, and there are available methodologies for data collection in DES projects. However, in contrast to standalone projects, using DES as a day-to-day engineering tool requires high quality production data to be constantly available. Unfortunately, there are no detailed guidelines that describes how to achieve this. Therefore, this paper presents such a methodology, based on three concurrent engineering projects within the automotive industry. The methodology explains the necessary roles, responsibilities, meetings, and documents to achieve a continuous quality assurance of production data. It also specifies an approach to input data management for DES using the Generic Data Management Tool (GDM-Tool). The expected effects are increased availability of high quality production data and reduced lead time of input data management, especially valuable in manufacturing companies having advanced automated data collection methods and using DES on a daily basis.

An extension to the application area for discrete event simulation (DES) has been ongoing since the last decade and focused only on economic aspects to include ecologic sustainability. With this new focus, additional input parameters, such as electrical power consumption of machines, are needed. This paper aim at investigating how NC machine power consumption should be represented in simulation models of factories. The study includes data-sets from three different factories. One factory producing truck engine blocks, one producing brake disc parts for cars and one producing forklift components. The total number of data points analysed are more than 2,45,000, where of over 1,11,000 on busy state for 11 NC machines. The low variability between busy cycles indicates that statistical representations are not adding significant variability. Furthermore, results show that non-value-added activities cause a substantial amount of the total energy consumption, which can be reduced by optimising the production flow using dynamic simulations such as DES.

New challenges demand that manufacturing companies adopt sustainable approaches and succeed in this adoption. Energy efficiency plays a key role in achieving sustainability goals, and performance indicators are necessary beyond measurement of data to evaluate energy efficiency. In this landscape, scalable and easy-to-understand metrics providing an energy competitiveness degree of manufacturing resources are currently missing. The study aims to test through simulation applicability and potential offered by a novel Energy Overall Equipment Effectiveness - Energy OEE - indicator for discrete manufacturing firms. A simulation of a discrete manufacturing CNC machine case is used to evaluate the applicability of using Energy OEE assessment for management decision support. As a result, this study paves the way to a better exploitation of data that energy monitoring and sensor technology aim to offer in the future.

Supply chain design and operational decisions may impact the energy needed to keep the products flowing through to the customers. It is a challenge to determine the energy consumption and even more challenging to understand the impact of design and operational decisions on the energy consumption along the supply chain. This paper presents a hierarchical simulation based approach for estimating the energy consumption to keep the products flowing through a supply chain. System dynamics simulation is used at a high abstraction level to understand the major factors that may affect the energy consumption. Discrete event simulation is then used to delve down in detail for evaluating the critical stages in the supply chain. A case study for a closed loop supply chain of forklift brakes is used as an example of application of the approach.

Environmental impact assessments for companies and products are important to increase sales and reduce environmental impact. To support improvements and detailed analyses, researchers have extended the use of simulation of production flows to include sustainability performance indicators. The research cases performed until recently lack standardized methodology and thus have comparability issues and an increase number of common faults. By using a common methodology and gathering best practice, future cases can gain a lot. Especially noted by the authors is that the project startup phase is critical for success. This paper proposes a methodology to support the startup phases of simulation projects with sustainability aspects in production flows. The methodology is developed and applied in an automotive industry study presented in this paper. Using a rigid project startup, such as the proposed methodology, reduces iterations during modeling and data collection and decreases time spent on modeling.

In ten years customers will select products not only based on price and quality but also with strong regard to the product value environmental footprint, including for example the energy consumed. Customers expect transparency in the product realization process, where most products are labeled with their environmental footprint. Vigorous companies see this new product value as an opportunity to be more competitive. In order to effectively label the environmental impact of a product, it is pertinent for companies to request the environmental footprint of each component from their suppliers. Hence, companies along the product lifecycle require a tool, not only to facilitate the computing of the environmental footprint, but also help reduce/balance the environmental impact during the lifecycle of the product. This paper proposes to develop a procedure that companies will use to evaluate, improve and externally advertise their product's environmental footprint to customers.

Product developers are the main target for environmental impact assessment. Every day operation in manufacturing industry considers only site-specific aspects as energy consumption or material usage. Moreover, those aspects are mainly in-place for economic reasons. Discrete event simulation is widely used by industry for problem solving on a factory and logistic level. Including life cycle assessment in simulation models provide detailed assessment for production system. Yet, it requires specialization to create robust environmental models in discrete event simulation. Simplified software supporting production engineers in modelling and data harvesting reduce specialist requirements. This paper present a first version of software developed for the production engineers. The software supports modelling and analyses using discrete event simulation models with life cycle assessment.

Consumers are increasingly becoming conscious of the need to reduce environmental impact. This has motivated the industry to make efforts to improve the sustainability of their products and supply chains. Such efforts require the ability to analyze the sustainability of supply chains and potential improvements. A systematic approach is needed to evaluate the alternatives that may range from those at the supply chain configuration level to those for improving equipment at a production facility. This paper presents a multi-resolution modeling approach that allows analyzing parts of the supply chain at appropriate level of detail. The capability allows studying the supply chain at high level initially and iteratively drilling down to detailed levels in the identified areas of opportunity and evaluating associated improvement alternatives. Multi-resolution modeling directly relates the impact of improvement in one part of the supply chain to overall supply chain performance thus reducing analyst effort and time.

The efficient and effective usage of energy and resources is of rising importance in manufacturing companies. This paper argues that manufacturing system simulation is a promising way to realistically cope with those issues and simultaneously consider them with traditional target dimensions. Against this background, the paper analyses whether commercial simulation tools are already capable to address those aspects. It turns out that environmentally related aspects are currently not sufficiently considered as standard functions. Therefore, based on the analysis of on-going research work, different directions for further development are presented and discussed.

The incitements from society for life-cycle assessment (LCA) and credible ecolables are ever-increasing and often important for successful marketing of products. Robust assessment methods are important for comparable, useful and trustworthy LCAs and ecolables. In order to improve the metrics of a product's ecolable, is it important to fully understand its production system. Discrete Event Simulation (DES) models are able to provide more detailed information than traditional LCA approaches. Therefore, methods used to combining LCA in DES have been developed during the last decade. The combined approaches have matured and the experiences grown. This article compares six previous cases and aims to summarize and discuss their experiences to aid future development. The results show where it is specifically important to make good decisions throughout the modeling methodology, for example goal and scope definition, trustworthy input data for sensitive parts, and communicable impact categories.

The incitements from society for life-cycle assessment (LCA) and credible ecolables are ever-increasing and often important for successful marketing of products. Robust assessment methods are important for comparable, useful and trustworthy LCAs and ecolables. In order to improve the metrics of a product’s ecolable, is it important to fully understand its production system. Discrete Event Simulation (DES) models are able to provide more detailed information than traditional LCA approaches. Therefore, methods used to combining LCA in DES have been developed during the last decade. The combined approaches have matured and the experiences grown. This article compares six previous cases and aims to summarize and discuss their experiences to aid future development. The results show where it is specifically important to make good decisions throughout the modeling methodology, for example goal and scope definition, trustworthy input data for sensitive parts, and communicable impact categories.

The environmental footprint of products is an increasingly important measure for companies working to improve their sustainability performance, and the same measure has also become popular for marketing purposes. As a result, the demand for environmental product declarations and, thus, life cycle assessment (LCA) projects grows. To reap the full benefit from LCA studies in production systems analysis, LCA has more frequently been complemented with simulation of production flows (i.e. discrete event simulation) during the latest decade. Several examples of the DES-LCA combination in recent literature report substantial potential and successful implementations. However, a common problem is to establish efficient and credible procedures for collecting, analyzing, and representing the extensive amounts of input data required. The aim of this paper is therefore to provide recommendations for the management of environmental data in sustainability simulations. A review of seven previous DES-LCA projects provides a list of common sustainability parameters and experiences on how they should be collected and represented in simulation models. An important result is that deterministic representations appear to be enough for data not directly linked to production time. This finding makes it possible to replace time-consuming data gathering with collection of secondary data from public databases.

Ecolabled products have shown a competitive advantage to other products. Regulatory changes and market pressure creates an increased need for environmental impact assessments. The dominating method for environmental impact assessments - life cycle assessment (LCA) lacks support to properly analyze the dynamic aspects of business operations and production processes. This Paper proposes to use discrete event simulation to support more extensive and detailed environmental assessments on selected parts of the production process, keeping simplicity for parts of less importance and interest.

Discrete event simulation (DES) provides engineers with a flexible modeling capability for extensive analysis of a production flow and its dynamic behavior.Activity based costing (ABC) modeling can provide additional knowledge about the monetary costs related to the manufacturing processes in DES.In addition, ABC modeling has been proposed as a tool for environmental impact analysis.Thus, previous studies have separately brought ABC into DES and ABC into environmental impact analysis.Bringing all three areas together, an ABC environmental simulation could provide deeper understanding about environmental impacts in the manufacturing processes than a regular Life Cycle Assessment (LCA) analysis.This paper proposes to use ABC modeling in conjunction with DES to perform a more detailed economic and environmental impact cost analysis.It is emphasized that the time to perform both analysis in one simulation is shorter or equal to perform them separately.Moreover, the approach can resolve some LCA problems.

One of the cornerstones in LEAN production is ‘make to order’, which requires small batch sizes and, thus, short Every Part Every Interval (EPEI) times. EPEI-time is defined as the time it takes to produce all product variants, before the first variant in the cycle returns in the schedule. However, many companies are reluctant to reduce their EPEI-times due to the increased number of set-ups. This skepticism is also supported by parts of existing theory, while other research contributions mean that companies often can reduce batch-sizes without affecting productivity. This paper presents a case study which uses discrete event simulation (DES) to evaluate the relation between EPEI-time and productivity. The results show that it is possible to reduce the EPEI-time and still maintain productivity and service levels to customers, without any investments. Increased variation in the production schedule evened out the load among the machines and, hence, the time lost in set-ups was gained in more parallel work.