Simulation-based support tool for operator performance in batch manufacturing

In industry, the workload and utilization of shop floor operators is often misunderstood. In this paper, we will present several real case studies, using Discrete Event Simulation (DES) models, which allow us to better understand operators in a batch manufacturing environment. The first study investigates labour in a machining plant consisting of multiple identical CNC machines that batch produce parts. The second study investigates labour in an eight station, gravity die casting rotary table. The results from these studies have shown that there can be potential improvements made by the production planners in the current labour configuration. In the first case study, a matrix is produced that estimates what the operator's utilization levels will be for various configurations. From this, the preferred operator to machine ratio over a range of cycle times is presented. In the second study, the results have shown that by reducing the casting cycle time, the operator would be overloaded. A discrete event simulation of these two cases highlighted areas that were misunderstood by plant management, and provided them with a useful decision support tool for production planning.

Unifying manufacturing simulation models using HLA

The increasing use of simulation in manufacturing has seen an increase in simulation models created using many simulation package. This use of different simulators can create simulation islands in a manufacturing factory, making it difficult to get a true simulated overview of the factory. At present, there are only a few cases where manufacturing simulations have been linked to enable multiple simulation models to run as one. This research expands upon these cases. For this paper the topic of discussion is the research in connecting different 'Commercial Off The Shelf' simulators together to allow flow of all information through the connected models using high level architecture.

A decision support tool for resource allocation in batch manufacturing

A decision support tool for production planning is discussed in this paper to perform the job of machine grouping and labour allocation within a machining line. The production plans within the industrial partner have been historically inefficient because the relationship between the cycle times, the machine group size, and the operator's utilisation hasn't been properly understood. Starting with a simulation model, a rule-base has been generated to predict the operator's utilisation for a range of production settings. The resource allocation problem is then solved by breaking the problem into a series of smaller sized tasks. The objective is to minimise the number of operators and the difference between the maximum and minimum cycle times of machines within each group. The results from this decision support tool is presented for the particular case study.

Productivity growth and efficiency change in Malaysian manufacturing: recent evidence from disaggregated data

This paper estimates productivity growth in Malaysian manufacturing over the period 1983-1999. Malmquist productivity Indices (MPIs) have been computed using non parametric Data Envelopment Analysis (DEA) type linear programming, which show productivity growth sourced from efficiency change and growth in technology. Unlike previous studies, this study identifies the sources of productivity growth in Malaysian manufacturing industries at the five digit breakdown of Malaysian Standard Industrial Classification (MSIC) thereby revealing more industry specific efficiency and technical growth patterns. Results indicated that a high majority of the industries operated with low levels of technical efficiency with little or no improvement over time. Growth estimates revealed that two third of the industries (76 out of total 114 categories) experienced average annual productivity improvement ranging from 0.1% to 7.8%. Average annual technical progress was recorded by 95 industry categories while technical efficiency improvement was achieved by 53 industries. Overall yearly average indicated relatively low productivity growth from the mid 1990’s onwards caused by either efficiency decline or technical regress. Summary results for industries showed that some of the high rates of productivity growth have been recorded in glass and glass products (7.3%), Petroleum and coal (7.2%), industrial chemicals (4.9%) contributed from both efficiency improvement and technical progress ranging from 0.8% to 5.4% and from 1.7% to 4.1%, respectively. These results are expected to have some implications for ongoing and future strategic policy reform in Malaysian manufacturing generating a more sustainable growth for specific industry categories.

Input usage, output mix and industry deregulation: an analysis of the Australian dairy manufacturing industry

In this paper we estimate a Translog output distance function for a balanced panel of state level data for the Australian dairy processing sector. We estimate a fixed effects specification employing Bayesian methods, with and without the imposition of monotonicity and curvature restrictions. Our results indicate that Tasmania and Victoria are the most technically efficient states with New South Wales being the least efficient. The imposition of theoretical restrictions marginally affects the results especially with respect to estimates of technical change and industry deregulation. Importantly, our bias estimates show changes in both input use and output mix that result from deregulation. Specifically, we find that deregulation has positively biased the production of butter, cheese and powders.

Manufacturing influence on the delamination fracture behavior of the T800H/3900-2 carbon fiber reinforced polymer composites

'Torayca' T800H/3900-2 is the first material qualified on Boeing Material Specification (BMS 8-276) which utilizes the thermoplastic-particulate interlayer toughening technology. Two manufacturing processes, the autoclave process and the fast heating rated Quickstep™ process, were employed to cure this material. The Quickstep process is a unique composite production technology which utilizes the fast heat transfer rate of fluid to heat and cure polymer composite components. The manufacturing influence on the mode I delamination fracture toughness of laminates was investigated by performing double cantilever beam tests. The composite specimens fabricated by two processes exhibited dissimilar delamination resistance curves (R-curves) under mode I loading. The initial value of fracture toughness GIC-INIT was 564 J/m2 for the autoclave specimens and 527 J/m2 for the Quickstep specimens. However, the average propagation fracture toughness GIC-PROP was 783 J/m2 for the Quickstep specimens, which was 2.6 times of that for the autoclave specimens. The mechanism of fracture occurred during delamination was studied under scanning electron microscope (SEM). Three types of fracture were observed: the interlayer fracture, the interface fracture, and the intralaminar fracture. These three types of fracture played different roles in affecting the delamination resistance curves during the crack growth. More fiber bridging was found in the process of delamination for the Quickstep specimens. Better fiber/matrix adhesion was found in the Quickstep specimens by conducting indentation-debond tests.

The impact of cell formation on layout designs in cellular manufacturing

This paper provides a procedure to address all three phases of the design for cellular manufacturing namely parts/machines grouping, intra-cell and inter-cell layout designs concurrently. It provides a platform to investigate the impact of the cell formation method on intracell and inter-cell layout designs and vice versa by generating multiple efficient layout designs for different cell partitioning strategies. This approach enables the decision maker to have wider choices with regard to the different number of cells and to assess various criteria such as travelling cost, duplication of machines, space requirement against each alternative. The performance of the model is demonstrated by applying it to an example selected from literature.

Advanced manufacturing technology adoption - the German experience

The decision process that organisations utilise when evaluating technology investment opportunities is a complex and even political process; however, the correct decision can provide the organisation with considerable operational and competitive benefits. The research presented in this paper presents the findings of a postal survey of the benefits provided by technology investments to large German manufacturers. It was found that only where middle management generated the idea for the advanced manufacturing technology (AMT) investment was success in that investment significantly more likely. Respondents who established a project team to plan the technology proposal, regardless of the department which generated the ideas for technology investment, were not significantly associated with a greater likelihood for success.

The respondents typically took between 3 and 12 months before making the final decision to invest, irrespective of the department generating the idea for the AMT, and a further 6 months to implement the AMT. Respondents who utilised a discounted cashflow analysis took significantly longer to make the final decision to invest. The greatest number of manufacturing outcomes of significantly higher importance was identified for respondents where Engineering, IT or R&D generated the AMT ideas. It was also determined that the respondents most frequently considered AMT investments in computer hardware or software and technical training for process workers to be necessary at the time of considering the investment. Middle management were found to be significantly more concerned than managers on other levels about opposition of workers to the AMT, while the process workers were significantly more concerned about interruptions to the process during installation.

Computational simulation of chassis design and manufacturing

Australian Universities including Deakin University are entering the Formula One SAE car competition this year which was initialised in USA many years ago and has attracted many universities to participate all over the world. There are many categories to be competed for novelty of the design. To achieve optimised design of the car, computational techniques have widely been used in all aspects of the design and manufacturing of the car for the team at Deakin University. In this work, the design and manufacturing of the chassis was computationally simulated to optimise the structure for stress and natural frequencies. The chassis is designed using solid modelling code IDEAS with the geometry exported to ABAQUS, a FEM software, for optimisation.