A Capacity and Variance model to investigate yard layouts.

Transcription

1 A Capacity and Variance model to investigate yard layouts. J.F.J. Pruyn MSc, Delft University of Technology, R.G. Hekkenberg MSc, Delft University of Technology, J.A.C. Ebbelaar MSc, Damen Shipyards Group, Summary Early stage development of shipyards is a complex and fuzzy process; often lay-out still needs to be determined and the products that the company anticipates to build at the yard are still unclear. The models presented in this paper aim to support the decision making process at this stage; A first model under discussion provides a relatively crude way of assessing the effects various product mixes have on the effectiveness of a yard lay-out. Similarly, it assesses the effects that changes in the product mix have on the effectiveness of a certain lay-out. Based on results from this model, a second, more detailed model is used to verify the rough results and analyze the effect of changes in production order and the level of fluctuation in manpower, material flows and store requirements, thereby providing deeper insight in the consequences of the choices made in terms of yard lay-out and product mix. Introduction This paper is based on a joint research project by Delft University of Technology (DUT) and Damen Shipyards Group (Damen). DUT is the only university within the Netherlands with a research department specialised in ship production. Damen owns a large group of yards specialised in the sales and production of e.g. tugs and small high speed crafts. The research is focused on the development of a new outfitting shipyard for Damen in Vietnam (Damen Vinashin Shipyard, DVS). The new yard itself is a joint venture between Damen and Vinashin, the state owned Vietnamese Shipbuilding Industry Corporation. The focus of the yard under review is the outfitting of bare hulls of tugs and small high speed crafts. Some examples are presented in figure 1a-1c Figure 1a-1c: A Stan Tender 1905, an ASD Tug 2411 and a Fast Crew Supply Vessel 3507 (From left to right). In the close vicinity of the yards several other yards are already present, but at the current location for the yard, no infrastructure exists. The yard is a total green field scenario. Despite the fact that DUT is regularly involved in optimisation and innovation of existing shipyards, see a.o. Van Rijssen et al. [1], Kaarsemaker and Nienhuis [2] and Van Alphen [3.], this situation presents a unique challenge as the shipyard needs to be designed from scratch. This opportunity does not bind the designer/developer with any restrictions resulting from pre-existing buildings and processes. On the other hand, it also creates many extra difficulties for the same reasons. The number of possible solutions is infinite. The only known inputs are the 1

2 adopted area as shown in the figure below. This is further complicated by the fact that the yard will not come to full production until 2014, making market predictions and expectations highly unreliable. As product mix estimates are based on this, they are even more unreliable. Figure 2: Area available for Damen Vinashin Shipyard, including a wet basin In order to come to a solution efficiently, 2 models are developed. First, a high level approach is used to estimate the average production capacity and after that a more detailed model is set up to get a proper estimation of the variance of these averages. The generic approach used within both of these models will be described in this paper. While the case study referred to in this paper is the Damen-case, the fundamentals of the approach are such that wherever ships are mentioned, they can be replaced by blocks or sections and halls can be replaced by production sites without changing the underlying principles of the model, allowing a generic first approach to any shipyard production process, taking into consideration blocks of different size and production duration with ease. Goal The goal of the research is to check the interaction between the choice of yard facilities and a selected mix of vessels to be produced in one year. Aspects under consideration consist of the number and size of the outfitting halls needed, the manhours and their variance for various disciplines (Construction, Mechanical, Painting, Carpentry and Electrics), the production efficiency, transport distances and store dimensions. A detailed dynamic model such as in use at the Flensburger Schiffbau Gesellshaft shipyard as described by Steinhauer, [4] is difficult to use and overly complex for global assessment purposes. The level of detailed information needed, combined with the work of specifying, collecting and validating all this does not weigh up against the increased detail in our case. 2

3 Approach Data Handling Capacity Model Variance Model Transport Model Figure 3: Project subdivision The project is divided into three phases, see the figure above. First a capacity model is developed to estimate the various capacities needed on a yearly basis. The second step is a variance model to make an estimate of the deviations from the average that can be expected. The third step consist of an analysis of the transport flows over the designed lay-out, but this is left out of the paper due to limited modelling effort in this phase. Next to that, an important aspect discussed in this paper is the collection of data and its conversion to representative data for the new yard in Vietnam. Although data handling is needed for any model or case, in this particular case it is a particular challenge and therefore included in this paper. Capacity model; Data Handling The quality of any analysis is highly dependent on the quality of the data used, so care needs to be taken when acquiring this data; Damen Shipyards Group owns a substantial number of shipyards in a large number of different countries, and the group cooperates with numerous others all over the world. The vessels in the intended product mixes for the new yard currently each have only a limited number of yards at which they are produced. This brings with it the problem of production depth and differences in productivity when attempting to obtain reliable estimates of production parameters. Production depth expresses the amount of work done by the personnel of the shipyard; one location might use just bare steel as a starting point and both build and outfit the vessel, while others receive a bare hull without outfitting or a hull already including most of the piping. This does not only affect the man-hours directly, but also influences the ratios between the man-hours spent on various disciplines. It is a well known fact that the productivity differs greatly between nations and even shipyards within the same nation, so to arrive at production data for the new yard, all of these factors need to be adequately compensated for. Data Structure A first step to allow a proper comparison of the data to be used for DVS is to create a consistent data structure to be used. This basis, a common, neutral, structure is proposed for both the activity types and locations on board the ships, into which all data from various sources can be combined. This resulted in the following two lists: Locations: - Engine Room - Steering Gear Room - Accommodation - Wheelhouse 3

4 - Outside Activity types: - Constructional - Mechanical - Electrical - Outfitting - Painting Data Conversion The second step is the conversion of data, which starts with assigning all available man-hours (and at a later stage other elements, such as materials and items used) to this matrix of 25 (5x5) elements. Below, two figures (4a&b) are presented to give an impression of the division of man-hours for various products, both per discipline and location, based on raw data from a yard (production depth and productivity is therefore equal for all vessels). The displayed similarity in relative workloads supports the assumption that the relative fractions of work can be assumed constant across similar vessel types at the level of detail used. It provides confidence that the common structure of work as proposed earlier is indeed acceptable. This assumption is used later on in the described approach manhours Constructional Mechanical Electrical Outfitting Painting manhours Engineroom Steering gear room Accommodation Wheelhouse outside Figure 4a-b: Workload fractions per discipline and location After setting this framework, every man-hour planning (which may contain hundreds of elements) is converted to a planning solely representing the 25 elements described before, since this allows comparison of the various plannings. However, it was soon discovered that not only productivity varies with the region of production, but so does the planned duration of the activities. The missing activities from various plannings are estimated based on the comparability of the fractions as demonstrated above. Whenever available, ship types built at two different yards are used. When this is not possible, highly similar ships from different yards are used. Most used yards are located in Southeast Asia, Eastern or Western Europe. Typically a total throughput time factor of 1.5 and a man-hour factor of 2.2 between South East Asia and Western Europe are established as acceptable conversion factors. With only a need for data on total man-hours per discipline, total items installed (taken from item lists of built vessels) and an overall duration of work on the vessel, the data for the capacity model can be generated from the information discussed above. 4

5 Capacity model; Modelling The implementation of the capacity model as discussed below consists of a global analysis of the extent to which a certain outfitting hall configuration is able to process a range of different product mixes based on yearly figures. Calculations are all done based on vessel-type specific data sheets containing the information described below. The establishment of the values of aspects 8 and is as discussed before, while for the estimation of the number of containers of items (aspect 9) actual shipping data for a number of vessels was used (NB. All items to be installed at the yard are transported to the yard in dedicated containers). With the size of the vessels and the spacing required around a vessel, dimensions of spots for specific vessels are calculated (rounded up to the nearest multiple of 5m). # Name Description 1 Type The type indication of the vessel 2 Duration The estimated time the vessel will spend in the hall 3 L The length of the vessel in meters 4 B The breadth of the vessel in meters 6 Spacing The average total clearance around a vessel 8 Outfit Items The total number of items to be placed aboard the vessel 9 Containers (TEU) The required number of TEU in which the outfit items are transported 10 Total manhours The total manhours estimated to be used on the vessel 11 Constructional The ratio between Constructional and Electrical manhours 12 Mechanical The ratio between Mechanical and Electrical manhours 13 Electrical The base for the other ratios => 1 14 Outfitting The ratio between Outfitting and Electrical manhours 15 Painting The ratio between Painting and Electrical manhours Table 1: Vessel type data Using these dimensions required in a hall by a ship, it is possible to make a first estimate of the total lane length required for production, which is calculated as follows: LaneLength = n 1 Duration * Length x 52 x By simply dividing the total production duration for all vessels in an annual mix by 52, the average number of vessels under construction in any given week can be determined. This is done to give the user an indication of the absolute minimum total hall length. Next to that, when a restriction of total vessels within a hall is also set, the minimum number of halls can be determined. The main goal of the use of the capacity model is to check how many halls of the same size are required to fit a selected product mix. Similarly, with an inversion of the algorithm, a check can be made of if a combination of halls would fit an assigned product mix. In both cases, this is done on the basis of spot length required for a vessel including clearance (to provide walking space, room for scaffolding etc.). Since the model is a capacity model, not a planning tool, fractions of the production time of a ship are allowed (i.e. analysis is done for a year, including vessels that are not completely finished within the review period). The restriction that a combination of vessels will have to fit in a hall (together) remains and is the basis for the algorithm; all possible combinations to fill a hall with vessels from a product mix are therefore checked, starting with the best fitting largest spot size. As a small example, for a hall of 60 meters the order of combination is presented in table 2 below, using spot lengths between 20 and 40 meters. (1) 5

6 Table 2: Filling up a 60 meter hall, with vessel combinations By combining these options for hall filling with a specific product mix, we arrive at the required number of halls. Lists similar to the one presented for the 60 meter hall are created for each hall size deemed eligible, again using a resolution of 5 meters. In the first part, the product mix is taken as fixed and for each hall length a calculation is made adding up the total durations needed for each of the possible combinations within the hall. Were the combination of halls to be fixed and the product mix taken flexible, the available halls are filled starting with the largest hall. Still, the basic routine remains the same. Capacity Calculation; Example In this example man-hours analysis is not done and fictitious values are used for various elements because of the sensitivity of some yard-specific data used in the project. The input for this case is given in table 3 and in table 4 the main properties for this product mix are calculated. Type # Area required Duration A 3 35 x B 2 30 x C 2 25 x Table 3: Fictitious Input: Vessel data Output on durations and lane length Total Duration 158 Weeks Average # of spots Average Lane Length 93 meter Materials Delivery Delivery Spacing 6 weeks Average Items 3462 Items/Delivery Average TEU TEU/Delivery Table 4: Output of Part 1, excluding man-hours For this example, a hall of 60 meters is used. This text will go step by step through the process of filling the hall, in reality this is done in one calculation, but for clarity this is changed here. 6

7 Taking the order given earlier in table 2, a calculation is performed on the product mix, to calculate the time fractions (fraction of a year during which work is done on a vessel of a certain type) for each of the vessels. This is simply done by multiplying the production duration expressed in weeks by the number of vessels and then divide this by 52 to relate it to a yearly production. The results are presented below in two columns added to the input; Length and Requirement Type # Area required Duration Length Requirement A 3 35 x B 2 30 x C 2 25 x Table 5: requirements for the product mix The values should be interpreted as follows; the values larger than 1 for type A and C indicate both will need more than one spot of that length available during the year. Type B on the other hand will not occupy a spot for the whole year. Step 1 The first step of the actual filling of halls is to go over the list of combinations fitting within the hall and check this with spot requirements, until a combination of spots present is found. In our example the first two combinations as discussed in table 2 concern a spot of 40 m which is not present in our product mix. Therefore we can skip these. The next option is a spot 35 m together with a spot of 25 m (#3 in table 2). Considering the spot requirements, we need 1.38 spots of 35 m and 1.08 spots of 25 m in our example. The smallest value of the two dictates the value for the combination (1.08 in this case). This value is deducted from the spot requirements of both spots. Both actions are shown in the tables below. Length Requirement (remaining) Type 60 meter hall A #3 35 m 25 m 1.08 B C Table 6: Results of Step 1 Step 2 When continuing down the list of possible combinations, the next option is one spot of 35 m and one of 20 m, but no 20 m. spots are required with our input. The next option contains only a spot of 35 m (#5). When looking at the remaining spot requirements of table 6, there is still a requirement for spots of 35 m. This value will now be added to the hall overview. Afterwards the spot requirements are adjusted once more. The results are again shown in the table below. Length Requirement (remaining) Type 60 meter hall A #3 35 m 25 m 1.08 B #5 35 m C

8 Table 7: Results of Step 2 Step 3 The next hall filling combination (#6) consists of 2 vessels of 30 m and that is exactly the required spot length we still have left. As two vessels can be build at the same time, the hall requirement is half the spot requirement; 0.29 in our example. The last two hall filling combinations are not present any more, even though the vessel input originally had vessels requiring a spot length of 25 m, none are left at this point in the calculation. The final results are depicted below. Length Requirement (remaining) Type 60 meter hall A #3 35 m 25 m 1.08 B #5 35 m C #6 30 m 30 m 0.29 Table 8: Results of Step 3 The value of 1.67 under the right table indicates the number of halls of 60 meters required for the product mix. Naturally, the fractions of halls can not be allowed in real life, so the yard would need two of these halls to process the mix within one year. When following the same procedure for a hall of 50 meters, the results are as follows Table 9: Results for a 50 meter hall Since analysis of a real life situation is the purpose of this research, results are turned around and reviewed for this combination of halls; their annual utilization is determined to see how well the current product mix would fit in each of the selections. A first check for this is the comparison of the total length of the complete halls with the average lane length utilized over a year. This is an expression for efficient floor usage. The result of this analysis would look like the table below. Size # Efficiency 60 Meter Hall % 50 Meter Hall % Table 10: Floor space efficiency Step 4 Using the process described, one can check the required numbers of halls for a certain product mix. For the Damen-project there is a need to also be able to check if a chosen combination of (non-identical) halls could fit a certain product mix. The procedure starts with the largest hall and continues down the list of available halls filling the available room with vessel combinations and ending as soon as the total would reach the total of halls of that size being available. When using for the same product mix a hall of 60 m and one of 50 m we would get the following output. 8

9 Type Length Requirement (remaining) 60 meter hall 50 meter hall A m 25 m m B m C m 25 m Table 11: Results for a hall combination check The results in this scenario show that a hall of 60 and a hall of 50 meters will be (exactly) sufficient to house the production of the product mix. With a slightly different product mix, it could well be that some spot requirements would not be fulfilled. The importance of these investigations should be clear. Based on the lane length alone, in this case 93 meters, one would expect that 2 halls of 50 meters would be enough. As shown in the first 3 steps of the calculation, that is certainly not the case, yet from the results till than, it cannot be concluded that a combination of 50 and 60 meter would be required to successfully house the production. All steps in the investigations are therefore indispensable for a proper investigation. Variance Calculation; Data Handling In a final step to assess the effects of the combination of a hall configuration with a product mix, it is necessary to consider the effect of the production order on hall occupation levels as well as variations in production levels and the inefficiency resulting with a fixed order in which vessels are built (thus not always allowing optimal fitting of vessels in the halls. The generic data structure with the conversion and mapping of the man-hours as discussed earlier is only the first step. A second step which has to be taken is the standardisation of the plannings for the vessels. Every yard plans work on vessels in its own way, suited to the work performed at the yard and tailored to the working methods and level of outfitting to still be done at the yard. Many times this even reflects the personal input of the project manager. In order to have sufficiently standardised input data, these need to be made comparable. To further increase the challenge, several vessel types planned at DVS yard have never before been constructed and, as a result, no planning data is available. Unified planning The main challenge in this solution is to map all activities mentioned in the plannings to the previously discussed generic data structure. To further solve the incomparability, a time independent planning for each of the vessels is created. Below, an example of such a step is shown, displaying the start time and duration of an activity as a fraction of the total throughput time of the vessel. The values thus created with the available plannings were compared with each other and showed a relatively large similarity for the majority of the work. An average planning is constructed and can be used to fill the gap in planning data of some vessel types. 9

10 Table 12: Example of time independent representation of the planning For missing vessel types, data was interpolated or extrapolated from data on similar vessels. To check the significance of such a simple data structure a test is performed when averaging the plannings. Durations of certain activities are taken as zero when more than 70% of the available plannings do not contain that activity. This is done using all available plannings. The resulting values are considered the basis of the time independent standardised planning. This time independent planning can then be combined with the corrected total durations for each vessel type to create a type specific duration planning. Until now durations and man-hours have been treated separately, the next step is to combine the two. As a result of the various combination, interpolation and extrapolation activities preformed detailed weekly man-hour data in the newly developed format is not available. Two restrictions are leading for the solution to this; first the number of vessels (20+ per year) to be outfitted at the yard justifies the assumption that levelling of personnel over the different projects would be feasible without too much adjusting of the individual vessel planning, so resource levelling at a vessel level is not a requirement for the subdivision of man-hours in the planning. Second, the most important restriction for a vessel planning is the number of people that can work simultaneously in one location. To implement these restrictions the average number of FTE (40 manhours/week) per location are taken as the target values for man-hour subdivision in time and space, fitting the man-hours for each element as well as possible to the planning for each location. An example of the results is presented below (fig 5a-5b), differentiated by discipline and below that by location. Weekly outfitting hours as a fraction of total outfitting hours W ee k # Painting Outfitting Electrical Mechanical Constructional 10

11 Weekly outfitting hours as a fraction of total outfitting hours W e ek # Outside Wheelhouse Accommodation Steering Gear Room Engineroom Figure 5a - 5b: example weekly required man-hours differentiated by system (top) and location (bottom) in fractions of total man-hours. From the above, it can be concluded that the total man-hour subdivision is not as smooth as would be expected in a real planning, but as already discussed before, this is not deemed to be a problem for the purposes of this research. In fact, it is believed that smoothing out workload will present a false sense of accuracy, when the basic planning and man-hours are already aggregated values. Variance Calculation; Modelling In the variance model, all activities as discussed before are placed in space and time; floor space of the outfitting halls is modelled and the various ships in the product mix are fit into them one by one for the duration of their throughput time. The approach used implies that a desired production order for all vessels in the product mix is established by means of a random number generator and the model fits each vessel into a hall as soon as possible (if insufficient space is available in week 1, try week 2, etc., etc. ). This is done without trying to optimize multi-vessel planning. as an example: At a certain time, all halls are full, except for a single spot 30 m long and 15 m wide. The first vessel to be placed according to the production order requires a spot 15 m wide and 30 m long. The first time the vessel fits is 17 weeks later. A vessel further down the production order list does fit within the open space, but requires 18 weeks to be completed. The resulting solution is to place the first vessel at the first possible time, leave a spot open for 17 weeks (unless a different vessel is found that CAN be built in that time and space slot) and find a different spot for the small vessel. This results in sub-optimal use of available space. The consensus between all involved in the project is however that this reflects reality better than full optimization of the floor space usage. As discussed under the data collection topic in this paper, all vessels have been attributed with a week-by-week planning of man-hours per discipline. By linking this to the various vessels under construction at any time, an overview is created of the manpower requirement of the entire yard, which can be used to select a yard setup that naturally minimizes fluctuations in required manpower. The approach of fitting vessels in halls in a certain desired production order makes this production order an important variable; some combinations might fit a certain hall combination better than others, leading to different throughput times for an entire product mix. To compensate for this effect, 20 different production orders are run through the model and results are reviewed on the basis of averages and standard deviations. 11

12 For a single run, plotting required manpower per discipline vs. time results in a graph as shown below. From the graph, it becomes clear that, as was to be expected, some start-up effects occur in the first weeks, because at that time, the first batch of vessels is initiated simultaneously. What is also clear from the graph is the occurrence of end effects, since not all vessels are completed simultaneously. To negate the influence of these effects, 3 product mixes are run end-to-end and man-hour and material flow data are taken from the data in the mid section of the analysed period single file basic mix manhours/week Figure 6: example man-hours per week week Constructional Mechanical Electrical Outfitting Painting Data on man-hour distribution can roughly be linked to the material flows to each vessel, provided material bills are available for the vessels under review. This results in similar graphs as shown above, on the basis of which peak values in transport and required crane capacity can be established. This is of course done per outfitting hall, not for the entire yard. Since tracking individual items requires an excessive amount of work, it is recommended (and applied) to group items in weight classes, only track the number of items in the models and apply the weight distribution afterwards. Based on these detailed overviews for a large range of product mixes, hall combinations and production orders, a broad overview can be created of throughput times, deviations therein, occupancy levels of hall space, required numbers of personnel per disciplines and fluctuations in man-hours. These can be used to determine the probability that the layout will allow processing of a product mix within an allotted time frame, but can also serve as assessment criteria to find the most suitable configuration e.g. because it gives the most stable workload distribution, provides the solution that is able to deal with the widest range of product mixes in an acceptable time or is most tailored to a specific product mix. Possible use cases of the developed models This section is to re-stress the generic set-up of the presented approach. The models as described in the Damen-case are used in the choice of a shipyard lay-out for outfitting of small vessels. The mesh size of 5 meters for rounding off, the fitting in the hallways, etc. is all tailored towards solving the specific questions to be answered in this project. 12

13 However, with similar ease block production at various production locations of section beds could be represented. Block and time specific data should of course be tailored to that purpose. While this could give the impression that the model is suited only for large elements and items, this is not true. As long as the problem under investigation is a time and space bound problem and a reasonable amount of elements will pass through the system in the typical timeframe of measurement, the model will stand and provide adequate answers. An extreme extension to this could be an investigation into meeting room numbers and sizes, for minimal space use and optimal availability. Typical production times would than be 30 minutes or an hour, with the total time span to investigate a day or week. Still it would also be possible to turn the issue around, instead of finding an optimal lay-out for an anticipated product mix, the models can be used to analyse the effectiveness of existing facilities or to find the most effective/profitable product mix for those facilities. Conclusions The relatively crude way of modelling does however have some limitations: it works well when data about vessels to be built is limited, as is the certainty of vessel type and production order. When advancing into the future, with more data available and clearer outlines of the processes, the added value of such a rough model diminishes. For those cases, the required analyses will focus more on solving logistical bottlenecks, requiring a dynamic simulation model able to review yard processes on an action-by-action basis. These models are also available, as is discussed already in the introduction. The output of the models has been able to support decisions on the design of the new shipyard. Although not treated in this paper, material flows, crane movements, quay and paint hall occupation have also been investigated with these models. Literature 1. Van Rijssen, B.F., Pruyn, J.F.J., Klooster, J,(2008) Simulation based production capacity analysis of a new shipyard, ECPTS 2008 conference 2. Kaarsemaker, J.A.J., Nienhuis, U., (2006) Simulation of a maritime prefabrication process, COMPIT 06 conference 3. Alphen, H. van, 2004, European Shipbuilding Repair & Conversion - the future 4. Steinhauer, D., SAPP- Simulation aided production planning at Flensburger, (2005), COMPIT 05 conference 13