Process Control Parameters Evaluation Using Discrete Event Simulation for Business Process Optimization

The quest for manufacturing process improvement and higher levels of customer satisfaction mandates that organizations must be equipped with advanced tools and techniques in order to respond towards ever changing internal and external customer demands by maintaining the optimal process performance, lower cost and higher profit levels. A manufacturing process can be defined as a collection of activities designed to produce a specific output for a particular customer or market. To achieve internal and external objectives, significant process parameters must be identified and evaluated to optimize the process performance. This even becomes more important to deal with fierce competition and ever changing customer demands. This paper illustrates an integrated approach using design of experiments techniques and discrete event simulation (Simul8) to understand and optimize the system dynamic based on operational control parameter evaluation and their boundary conditions. Further, the proposed model is validated using a real world manufacturing process case study to optimize the manufacturing process performance. Discrete event simulation tool is used to mimic the real world scenario, which provides a flexible and powerful way to comprehensively understand the manufacturing process variations and allows controlled 'What-If´ analysis based on design of experiments approach. Finally, this paper discusses the potential applications of the proposed methodology in the cable industry in order to optimize the cable manufacturing process by regulating the operational control parameters such as dealing with various product configurations with different equipment settings, different product flows and work in process (WIP) space limitations

Evaluating the impact of design decisions on the financial performance of manufacturing companies

Product design decisions can have a significant impact on the financial and operation performance of manufacturing companies. Therefore good analysis of the financial impact of design decisions is required if the profitability of the business is to be maximised. The product design process can be viewed as a chain of decisions which links decisions about the concept to decisions about the detail. The idea of decision chains can be extended to include the design and operation of the 'downstream' business processes which manufacture and support the product. These chains of decisions are not independent but are interrelated in a complex manner. To deal with the interdependencies requires a modelling approach which represents all the chains of decisions, to a level of detail not normally considered in the analysis of product design. The operational, control and financial elements of a manufacturing business constitute a dynamic system. These elements interact with each other and with external elements (i.e. customers and suppliers). Analysing the chain of decisions for such an environment requires the application of simulation techniques, not just to any one area of interest, but to the whole business i.e. an enterprise simulation. To investigate the capability and viability of enterprise simulation an experimental 'Whole Business Simulation' system has been developed. This system combines specialist simulation elements and standard operational applications software packages, to create a model that incorporates all the key elements of a manufacturing business, including its customers and suppliers. By means of a series of experiments, the performance of this system was compared with a range of existing analysis tools (i.e. DFX, capacity calculation, shop floor simulator, and business planner driven by a shop floor simulator)

Digital design and thermomechanical process simulation for 3D printing with ABS and soyhull fibers reinforced ABS composites.

Recent demonstrations with fused filament fabrication (FFF) 3D printing have shown to produce prototypes as well as production components. Additionally, due to the FFF process platforms being low-cost and readily available there has been a high-demand to produce on-demand parts for various applications in automotive, in-space manufacturing and electronic industries. However, current limitations such as limited availability of advanced composites materials, and guidelines for design-for-manufacturing make the process prone to trial-and-error experiments both at the materials development, product design and manufacturing stage. In this work, new thermomechanical process simulations platform, Digimat-AM has been evaluated to address and demonstrate digital design and manufacturing of FFF process by performing simulation and experiments. With the use of Acrylonitrile butadiene (ABS) material and soyhull fibers reinforced ABS composite (ABS-SFRC) as a basis, an L9 Taguchi design-of-experiment (DOE) was setup by varying key process input parameters for FFF 3D printing such as layer thickness, melt temperature and extrusion multiplier were varied for three levels. A total of 9 DOE simulations and experiments were performed to compare part properties such as dimensions, warpage, and print time were analysed. Additionally, ANOVA analysis was performed to identify the optimum and the worst conditions for printing and correlate them with their effect on the mechanical properties of the printed samples. Furthermore, from the simulation results, a reverse warpage geometry, 3D model was generated that factors for part warpage, shrinkage, or other defects to enable 3D printing parts to design dimensions. Subsequently, using the generated reversed warpage geometry was used to perform 3D printed experiments and analyzed for part dimensions and defects. As a case study, a functional prototype [Two different geometries] was designed and simulated on Digimat-AM and using the above guide, 3D printing was performed to obtain part to specific dimensions. In addition to that, the thermomechanical properties of ABS-SFRC were needed to perform the Digimat simulation of geometries printed with ABS-SFRC. However, the materials property database of ABS-SFRC is very limited and experimental measurements can be expensive and time consuming. This work investigates models that can predict soyhull fibers reinforced polymer material composite properties that are required as input parameters for simulation using the Digimat process design platform for fused filament fabrication. ABS-SFRC filaments were made from 90%ABS 10% soyhull fibers feedstock using pilot scale filament extrusion system. Density, specific heat, thermal conductivity, and Young\u27s modulus were calculated using models. The modeled material properties were used to conduct simulations to understand material-processing-geometry interactions

Design of experiments for non-manufacturing processes : benefits, challenges and some examples

Design of Experiments (DoE) is a powerful technique for process optimization that has been widely deployed in almost all types of manufacturing processes and is used extensively in product and process design and development. There have not been as many efforts to apply powerful quality improvement techniques such as DoE to improve non-manufacturing processes. Factor levels often involve changing the way people work and so have to be handled carefully. It is even more important to get everyone working as a team. This paper explores the benefits and challenges in the application of DoE in non-manufacturing contexts. The viewpoints regarding the benefits and challenges of DoE in the non-manufacturing arena are gathered from a number of leading academics and practitioners in the field. The paper also makes an attempt to demystify the fact that DoE is not just applicable to manufacturing industries; rather it is equally applicable to non-manufacturing processes within manufacturing companies. The last part of the paper illustrates some case examples showing the power of the technique in non-manufacturing environments

A survey of simulation techniques in commerce and defence

Despite the developments in Modelling and Simulation (M&S) tools and techniques over the past years, there has been a gap in the M&S research and practice in healthcare on developing a toolkit to assist the modellers and simulation practitioners with selecting an appropriate set of techniques. This study is a preliminary step towards this goal. This paper presents some results from a systematic literature survey on applications of M&S in the commerce and defence domains that could inspire some improvements in the healthcare. Interim results show that in the commercial sector Discrete-Event Simulation (DES) has been the most widely used technique with System Dynamics (SD) in second place. However in the defence sector, SD has gained relatively more attention. SD has been found quite useful for qualitative and soft factors analysis. From both the surveys it becomes clear that there is a growing trend towards using hybrid M&S approaches

Computer experiment - a case study for modelling and simulation of manufacturing systems

Deterministic computer simulation of physical experiments is now a common technique in science and engineering. Often, physical experiments are too time consuming, expensive or impossible to conduct. Complex computer models or codes, rather than physical experiments lead to the study of computer experiments, which are used to investigate many scientific phenomena. A computer experiment consists of a number of runs of the computer code with different input choices. The Design and Analysis of Computer Experiments is a rapidly growing technique in statistical experimental design. This paper aims to discuss some practical issues when designing a computer simulation and/or experiments for manufacturing systems. A case study approach is reviewed and presented

Impact of model fidelity in factory layout assessment using immersive discrete event simulation

Discrete Event Simulation (DES) can help speed up the layout design process. It offers further benefits when combined with Virtual Reality (VR). The latest technology, Immersive Virtual Reality (IVR), immerses users in virtual prototypes of their manufacturing plants to-be, potentially helping decision-making. This work seeks to evaluate the impact of visual fidelity, which refers to the degree to which objects in VR conforms to the real world, using an IVR visualisation of the DES model of an actual shop floor. User studies are performed using scenarios populated with low- and high-fidelity models. Study participant carried out four tasks representative of layout decision-making. Limitations of existing IVR technology was found to cause motion sickness. The results indicate with the particular group of naïve modellers used that there is no significant difference in benefits between low and high fidelity, suggesting that low fidelity VR models may be more cost-effective for this group

Validation of production system throughput potential and simulation experiment design

[EN] The throughput potential of a production system must be designed and validated before implementation.  Design includes creating product flow by setting the takt time consistent with meeting customer demand per time period and the average cycle time at each workstation being less than the takt time.  Creating product flow implies that the average waiting time preceding each workstation is no greater than the takt time.  Kingman’s equation for the average waiting time can be solved for the variation component given the utilization, and the cycle time.  The variation component consists of the variation in the demand and the variation in cycle time.  Given the variation in demand, the maximum allowable variation in cycle time to create flow can be determined.  Throughput potential validation is often performed using discrete event simulation modeling and experimentation.  If the variation in cycle time at every workstation is small enough to create flow, then a deterministic simulation experiment can be used.  An industrial example concerning a tier-1 automotive supplier with two possible production systems designs and various levels of variation in demand assumed is used to demonstrate the effectiveness of throughput validation using deterministic discrete event simulation modeling and experimentation.Standridge, C.; Wynne, M. (2021). Validation of production system throughput potential and simulation experiment design. International Journal of Production Management and Engineering. 9(1):15-23. https://doi.org/10.4995/ijpme.2021.14483OJS152391Atalan, A., Dönmez, C.C. (2020). Optimizing experimental simulation design for the emergency departments. Brazilian Journal of Operations & Production Management, 17(4), e2020854. https://doi.org/10.14488/BJOPM.2020.026Askin, R.G., Standridge, C.R. (1993). Modeling and analysis of manufacturing systems. New York: John Wiley and Sons.Dagkakis, G., Rotondo, A., Heavey, C. (2019). Embedding optimization with deterministic discrete event simulation for assignment of cross-trained operators: an assembly line case study. Computers and Operations Research, 111, 99-115. https://doi.org/10.1016/j.cor.2019.06.008Ferrin, D.M., Miller M.J., Muthler D. (2005). Lean sigma and simulation, so what's the correlation?, in Proceedings of the 2005 Winter Simulation Conference, IEEE, USA. Retrieved July 22, 2020 from: https://informs-sim.org/wsc05papers/249.pdfHopp, W.J., Spearman, M.L. (2011). Factory Physics: Foundations of manufacturing management, 3rd ed. Long Grove, IL: Waveland Press.Jayaraman, A., Gunal, A.K. (1997). Applications of discrete event simulation in the design of automotive powertrain manufacturing systems". In Proceedings of the 1997 Winter Simulation Conference, IEEE, USA. https://doi.org/10.1145/268437.268620Khan, S., Standridge, C.R. (2019). Aggregate simulation modeling with application to setting the CONWIP limit in an HMLV cell. International Journal of Industrial Engineering Computation, 10(2), 149-160. https://doi.org/10.5267/j.ijiec.2018.10.002Kingman, J.F.C. (1961). The single server queue in heavy traffic. Mathematical Proceedings of the Cambridge Philosophical Society, 57(4), 902. https://doi.org/10.1017/S0305004100036094Kleijnen, J.P.C. (2015). Design and analysis of simulation experiments. New York: Springer. https://doi.org/10.1007/978-3-319-18087-8Kleijnen, J.P.C., Standridge, C.R. (1988). Experimental design and regression analysis in simulation: an FMS case study. European Journal of Operations Research, 33, 257-261. https://doi.org/10.1016/0377-2217(88)90168-3Law, A.M. (2014). Simulation modeling and analysis, 5th ed. New York: McGraw-Hill.Little, J.D.C. (1961). A proof for the queuing formula: L = λW. Operations Research, 9(3), 383-387. https://doi.org/10.1287/opre.9.3.383Marvel, J.H., Standridge, C.R. (2009). A simulation enhanced lean design process. Journal of Industrial Engineering and Management, 2(1), 90-113. https://doi.org/10.3926/jiem.2009.v2n1.p90-113Mourtzis, D. (2019) Simulation in the design and operation of manufacturing systems: state of the art and new trends. International Journal of Production Research, 58(7), 1927-1949. https://doi.org/10.1080/00207543.2019.1636321Pinheiro, N.M.G, Cleto, M.G., Zattar, I.C., Muller, S.I.M.G. (2019). Performance evaluation of pulled, pushed and hybrid production through simulation: a case study. Brazilian Journal of Operations & Production Management, 16, 685-697. https://doi.org/10.14488/BJOPM.2019.v16.n4.a13Pritsker, A.A.B. (1989). Why simulation works. In Proceedings of the 1989 Winter Simulation Conference, IEEE, USA. https://doi.org/10.1145/76738.76739Puvanasvaran, P., Teoh, Y.S., Ito, K. (2020). Novel availability and performance ratio for internal transportation and manufacturing processes in job shop company. Journal of Industrial Engineering and Management, 13(1), 1-17. https://doi.org/10.3926/jiem.2755Sanchez, S.M., Sanchez, P.J., Wan, H. (2020). Work smarter, not harder: a tutorial on designing and conducting simulation experiments. In Proceedings of the 2020 Winter Simulation Conference, IEEE, USA. Retrieved December 23, 2020 from https://informs-sim.org/ wsc20papers/135.pdfSchruben, L. (1983). Simulation modeling with event graphs. Communications of the A.C.M., 26(11). https://doi.org/10.1145/182.358460Spearman, M.L., Woodruff, D.L., Hopp, W.J. (1990). CONWIP: A pull alternative to Kanban, International Journal of Production Research, 28(5), 879-894. https://doi.org/10.1080/00207549008942761Standridge, C.R. (2019). Introduction to production: philosophies, flow, and analysis. Allendale Michigan: Grand Valley State University Libraries. Retrieved July 22, 2020 from: https://scholarworks.gvsu.edu/books/22/Tapping, D., Luyster, T., Shuker, T. (2002). Value stream management. Boca Raton: CRC Press. https://doi.org/10.4324/9781482278163Tribastone, M., Vandin, A. (2018). Speeding up stochastic and deterministic simulation by aggregation: an advanced tutorial. . In Proceedings of the 2018 Winter Simulation Conference, IEEE, USA. https://doi.org/10.1109/WSC.2018.8632364Uriarte, A.G., Ng, A.H.C., Moris, M.U. (2020). Bringing together Lean and simulation: a comprehensive review, International Journal of Production Research, 58(1), 87-117. https://doi.org/10.1080/00207543.2019.1643512Zupan, H., Herakovic. N. (2015). Production line balancing with discrete event simulation: a case study", IFAC-PapersOnLine, 48(3), 2305- 2311. https://doi.org/10.1016/j.ifacol.2015.06.43

The Analysis of design and manufacturing tasks using haptic and immersive VR - Some case studies

The use of virtual reality in interactive design and manufacture has been researched extensively but the practical application of this technology in industry is still very much in its infancy. This is surprising as one would have expected that, after some 30 years of research commercial applications of interactive design or manufacturing planning and analysis would be widespread throughout the product design domain. One of the major but less well known advantages of VR technology is that logging the user gives a great deal of rich data which can be used to automatically generate designs or manufacturing instructions, analyse design and manufacturing tasks, map engineering processes and, tentatively, acquire expert knowledge. The authors feel that the benefits of VR in these areas have not been fully disseminated to the wider industrial community and - with the advent of cheaper PC-based VR solutions - perhaps a wider appreciation of the capabilities of this type of technology may encourage companies to adopt VR solutions for some of their product design processes. With this in mind, this paper will describe in detail applications of haptics in assembly demonstrating how user task logging can lead to the analysis of design and manufacturing tasks at a level of detail not previously possible as well as giving usable engineering outputs. The haptic 3D VR study involves the use of a Phantom and 3D system to analyse and compare this technology against real-world user performance. This work demonstrates that the detailed logging of tasks in a virtual environment gives considerable potential for understanding how virtual tasks can be mapped onto their real world equivalent as well as showing how haptic process plans can be generated in a similar manner to the conduit design and assembly planning HMD VR tool reported in PART A. The paper concludes with a view as to how the authors feel that the use of VR systems in product design and manufacturing should evolve in order to enable the industrial adoption of this technology in the future