Simulating Production Processes:
Shipbuilding Cost and Scheduling Tradeoffs
 
The US Navy (USN) shipbuilding program consumes a major fraction of the USN annual budget. Decision Dynamics, Inc. (DDI) developed an advanced shipbuilding simulation model, called WorkFlow, to help managers speed ship production and lower construction costs. PMS-400, the AEGIS Program Office, contracted with DDI to validate WorkFlow’s behavior and to demonstrate model utility. The test utilized data supplied by Bath Iron Works (BIW) for panel production for DDG-51 Class ships. Results validated model operation by reproducing BIW manpower loading and production schedules. Results also demonstrated two unique aspects of model utility. First, WorkFlow simulations identified logical inconsistencies in the data that, when resolved, improved the information used to support production planning. Second, WorkFlow simulations showed how the use of overtime and/or overmanning on selected production tasks could shorten schedules. Simulations also explored how level labor loading could improve productivity on multiple panel lines. Overall, WorkFlow proved itself an extremely valuable tool to assist both the USN and shipyards in the complex process of managing shipbuilding schedules and controlling ship production costs.
 
This report is divided into three sections. The first section recreates the BIW baseline for producing three major panels in the ship assembly process. Simulated schedules match planned schedules and simulated labor (both shipfitters and welders) match planned labor. Anomalies in planned schedule and labor are identified to create a consistent baseline model. The second section builds on the baseline to demonstrate how the introduction of overtime and overmanning can shorten schedules but increase costs. A third section examines level labor loading alternatives to show how WorkFlow could help planners improve productivity by maintaining a more constant work force on each task.
 
Validating a Baseline
 
A Baseline Model was developed to illustrate that WorkFlow can accurately simulate BIW’s panel fabrication process. For this demonstration, three major panel units were modeled. The data used to populate the baseline model included BIW information on labor resources (manning), planned schedule and the basic work breakdown structure (WBS) for the panel fabrication process.
 
The WBS represents the hierarchy of work tasks, from large tasks (assemblies or products) down to smaller tasks that make up the larger project. At the most detailed level in the WBS, tasks were defined according to the type of work being performed, type of labor and amount assigned to the task, and dependent relationships among tasks. This level of detail allowed DDI to validate the Baseline Model by comparing simulation results to BIW’s planned manhour expenditures and schedules dates for individual panels as well as the overall total results.
 
The initial simulation results indicated an inconsistency between BIW’s planned task durations and BIW’s manning profile. The workday schedules (hours per shift) were adjusted in the Baseline Model input data to resolve this anomaly.
 
Figure 1 plots the total manhours expended to complete the three units within the 60-day schedule and the number of personnel assigned. The simulation results corresponded with the BIW’s projected calendar dates, manhour expenditures and manning profile, thereby validating the accuracy of the Baseline Model to reproduce an accurate representation of the panel fabrication process.
 
 
Figure 1: Baseline Total Manhours and Personnel
 
Shortened Schedules
 
To test alternative “what-if?” scenarios, DDI first altered the Baseline Model to plot profiles that more consistently reflect realistic manhour expenditures; the workday schedule was defined as two 8-hour shifts, five days a week. This data modification caused a schedule extension in the Baseline Model from 60 days to 63 days.
 
To analyze the tradeoffs between alternative schedules, work layouts, and resource allocation, an Overtime scenario was created that attempted to shorten the Baseline Model panel fabrication process schedule from 63 days to 51 days. WorkFlow automatically generates the schedule for the Baseline Model by calculating nominal duration required for each task. During simulation, WorkFlow multiplies the amount of desired labor times the number of work hours per day and divides the product into the backlog of labor hours to get the number of days required for each task. The schedule acceleration was achieved by manually entering a completion date for the project that decreased the nominal duration by 12 days.
 
WorkFlow compares the actual start and completion dates for each task against the nominal schedule. If the days required to complete the task exceed the days remaining in the schedule, schedule pressure begins to build. Decreasing the duration in the first two tasks of the Baseline Model triggered a response from WorkFlow that the tasks were behind schedule causing schedule pressure to build. WorkFlow has several management functions that allow the user to specify how to respond to various conditions and situations including schedule pressure.
 
In the Overtime 'what-if?' scenario, two of WorkFlow’s management functions were applied to allow for overtime work to occur and to include a minor influence of fatigue resulting from overtime work. As displayed in Figure 2, the increase in schedule pressure caused overtime work for key labor trades over the course of the project. This allowed the work to be finished 12 days earlier, as expected when the forced deadline was set for 51 days instead of the Baseline Model of 63 days. However, due to fatigue from overtime work, slight productivity losses occurred increasing total manhours in the overtime scenario by 50 hours.
 
 
Figure 2: Schedule Comparison - Overtime
 
When comparing the Baseline Model and Overtime scenario results, one of the factors to consider is the difference between the cost of standard manhours (MHs) and overtime MHs. In Table 1 assume that a standard MH equals $20 and an overtime MH is equivalent to $30.
 

 

Total MHs

Overtime MHs

Overtime Cost

Total MH Cost

Baseline

2,773

                0

                 0

$55,460

Overtime

2,823

             55

       $1,650

$57,110

 
Table 1: Schedule Comparison – Manhour Cost
 
It is obvious from the table that the Overtime scenario has a higher total MH cost. The tradeoff now concerns comparing the savings in scheduled workdays against increased MH costs.
 
A second “what-if?” scenario was simulated to compare the Overtime scenario to an Overmanning scenario. This scenario applies the overmanning management function that increases the level of labor working on a task (with possible productivity losses due to overmanning) in response to the experienced schedule pressure. The plot in Figure 3 indicates the Overmanning scenario also achieved a 12-day acceleration, however, the total number of manhours significantly increased due to much higher productivity losses.
 
 
Figure 3: Schedule Comparison - Overmanning
 
When building the Baseline Model, a minimum, desired and maximum labor level was designated by BIW for the specific labor on each task. If the current level of labor is greater than or less than the desired labor level, the productivity of the labor working on the task may increase or decrease relative to 'normal' productivity depending on the assumptions made in the management functions. In the Overmanning scenario, this function assumed that when the maximum labor level is achieved, there are too many people working on the task to be fully productive.
 
Figure 4 plots the comparison of total personnel assigned (labor) for both the Baseline Model and Overmanning scenario. As Figure 4 depicts, during simulation WorkFlow assigned additional persons to the tasks experiencing schedule pressure in order to accelerate the Overmanning scenario schedule. The maximum number of persons assigned to any task was defined as 12 due to physical limitations.
 
The model behavior in this 'what-if?' comparison reflects what one would expect in a real-world situation. As schedule pressure increases during the simulation, the number of persons assigned increases to accelerate the work accomplished. In the first eight days of the simulation, one to two additional persons are assigned in response to early signs of schedule pressure. As the schedule pressure becomes more significant in days eight through 16, the model responds by doubling the number of additional persons assigned (up to four people).
 
 
Figure 4: Manpower Profile Comparison - Overmanning
 
Again, assuming a standard MH equals $20, a comparison can now be made between the cost of the Baseline, Overtime and Overmanning scenarios. As Table 2 clearly states, overmanning is the most expensive option due to the total number of MHs.
 

 

Total MHs

Cost of MHs

Baseline

2,773

$55,460

Overtime

2,823

$57,110

Overmanning

3,306

$66,120

 
Table 2: Schedule Comparison – Manhour Cost
 
In the Baseline Model it was assumed that productivity losses due to overmanning tasks were more significant than losses incurred from overtime work. When comparing the simulation output results, it is apparent that the Overmanning scenario required additional MHs to complete the same work backlog because of greater productivity losses. WorkFlow helps program managers identify and test sensitive elements in their system. A new scenario that contains a different assumption about productivity losses can be created and simulated in seconds. By offering a control structure to test the consequences of alternative 'what-if?' assumptions and scenarios, WorkFlow can help program managers avoid ineffective actions, reduce program risk, lower support costs, and develop an affordable strategy to meet program requirements.
 
The tradeoff between MH cost and schedule can be clearly seen in Figure 5, which plots a comparison of the Overtime, Overmanning and a third scenario, Overtime and Overmanning. In the Overtime and Overmanning scenario, both overtime work and overmanning were applied to a schedule that was accelerated by 20 days.
 
 
Figure 5: Schedule Comparison – Overmanning and Overtime
 
Again, the tradeoff concerns the additional MH cost associated with shortening the schedule. Depending on the situation a shorter schedule may be worth the additional cost.
 
Level Labor Loading
 
Figure 6 plots a comparison between the Baseline Model and a scenario that more evenly distributes labor usage. The Baseline Model assigns labor according to the type of task and work backlog causing an irregular distribution in the manpower profile with several peaks and dips over the course of the project. The Level Labor scenario assigned the same number of persons to each task. Although there is clearly a significant improvement in the Level Labor scenario manpower distribution, the schedule is extended eight days because less labor is assigned to the tasks with larger backlogs.
 
 
Figure 6: Manpower Distribution Comparison – Level Labor
 
Figures 7 and 8 show variations of other production scenarios involving multiple items produced on two parallel lines. In Figure 7, a second Baseline Model production line was started on project day 16 to use the manpower resources available during the dips in the Baseline Model manpower profile. No additional resources were added to the Baseline Model labor pool of shipfitters and welders. Although the manpower profiles retain the irregular distribution of the Baseline Model, there is enough labor available during the dips in Line One to complete both Line One and Line Two in about 78 days.
 
 
Figure 7: Manpower Distribution Comparison – Multiple Production Lines
 
The ability to quickly incorporate changes in the scenario and compare results is easily demonstrated in the next scenario. Figure 8 plots the results of operating the two production lines using the level labor manpower profile.
 
 
Figure 8: Manpower Distribution Comparison – Multiple Level Labor Production Lines
 
 
Figure 9: Total Manhour Comparison – Multiple Production Lines
 
It is obvious from the output in Figures 8 and 9 that the Baseline Model option finishes earlier than the Level Labor scenario with no significant difference in total manhours. The program manager can now compare the advantages of a level manpower profile against the savings in the project schedule.
 
Conclusion
 
WorkFlow can provide unexpected insights into program management alternatives and support management decision-making. Options become clearer when all assumptions are considered and quantified and when all parties to the decision agree on the same assumptions. When differences arise, alternative values can be tested for their impact on model behavior and outcome. If the results remain unchanged then differences over assumptions become unimportant; if results vary, then the differences can be resolved by further analysis and data. This ability of the model to help focus discussion on key issues while demonstrating the marginality of other issues serves to quickly build consensus for effective decision-making.
 
WorkFlow provides program managers and design engineers with the ability to successfully develop a strategic plan by integrating and managing the multitude of functions that are key to the shipbuilding production process. The results achieved and the output available from simulating with the model include:
 
  • schedules for all tasks and assemblies;
  • overall schedule;
  • labor manning (by shift and by trade);
  • labor hours for all tasks and assemblies; and
  • total labor hours.
 
WorkFlow offers an innovative simulation tool for quantifying manhour cost and schedule tradeoffs, tracking changes in productivity due to internal and external conditions, and tracing the impact of design changes and delays on ship cost and schedule. Program managers and design engineers can use the model to define and test alternative “what-if?” scenarios to search for ways to shorten production times and increase manufacturing productivity.