This chapter describes the development and implementation of two Decision Support Systems (DSS).
First the input parameters for a DSS of the T.O. assembly line are discussed. The values of these input parameters are based on the case study results in chapter 6. However, as mentioned in the beginning of this report, the goal is not only to develop a DSS for the T.O. assembly line, but also for the Car Wash.
Time limitations restricted this research to one extensive case study. Based on the input parameters that are most appropriate for a DSS for the T.O. assembly line, one case study is also performed for the Car Wash. The results of this simulation are discussed in section 7.1. After the first section discussed all input values of both DSS, section 7.2 provides the development of the DSS and discusses several comments regarding the use of the DSS. Finally, in section 7.3 the implementation process of the DSS is provided.
7.1 Input parameters
In collaboration with C.RO it has been chosen to develop a DSS that uses input parameters of scenario 3. The following list provides an overview of these parameters and a reasoning for their choice:
Forecasts: at the moment of implementation (February 2016), no monthly or yearly forecasts are available for 2016. Forecasts might become available in the weeks after this study. However, the increase in the expected performance is relatively small when yearly or monthly forecasts would have been available. It is therefore chosen that a model that does not need any forecast input is preferred, even if monthly forecasts would be available. This is also due to the fact that this model is the easiest to use for the planners.
Minimum production planning horizon: in section 5.2.4.1 two ‘extreme’ minimum production settings are suggested for the last period in the planning horizon; either the minimum production in period T equals ℎ̃𝑇 ≥ 𝐷̃(𝑇) or ℎ̃𝑇 ≥ 𝐴̃(𝑇). When leaving all other parameters unchanged, all scenarios that included ℎ̃𝑇 ≥ 𝐷̃(𝑇) as minimum production performed better in terms of net savings than scenarios that adopted ℎ̃𝑇 ≥ 𝐴̃(𝑇) as minimum production. However, according to the planners and management at C.RO the increase in throughput time when assuming ℎ̃𝑇 ≥ 𝐷̃(𝑇) instead of ℎ̃𝑇 ≥ 𝐴̃(𝑇) is not in proportion to the increase in expected savings. They therefore preferred ℎ̃𝑇 ≥ 𝐴̃(𝑇) as the minimum production for period t = T.
Length of planning horizon T: when no monthly or yearly forecast is available, the simulations in chapter 6 suggest that a planning horizon of T = 4 is expected to provide the best results. An additional benefit is that a DSS with a planning horizon of T = 4 has a significant shorter computation time than a DSS with a planning horizon of T = 5. This also increases the usability. Consequently the length of the planning horizon will equal 4 periods.
47 7.1.1 Expected savings
The previous list concluded that the input values used in simulation scenario 3 are most appropriate to serve as the basis for a DSS for the workforce planning of the T.O. assembly line. However, as indicated before, a DSS for both the T.O. assembly line and the Car Wash have to be developed. Therefore this section shortly provides the input parameters and results of a case study for the Car Wash. In order to perform a case study for the Car Wash, different assumptions, other contractual agreements and a new piecewise linearization of the production function have been taken into account. Details regarding this are provided in appendix F. As indicated, no more than one case study is performed for the Car Wash.
The results and input parameters are provided in Table 14. These input parameters will also be used as input for the DSS.
Table 14 Summarization of expected savings DSS T.O. assembly line Car Wash
The DSS transforms the mathematical model to a tool that can be used by the planners on a daily basis to optimize the workforce planning. The input values that have been used correspond to what has been suggested in Table 14. Only the forecasts have been transformed from forecasts of 2015 to forecasts of 2016.
Both tools are developed to determine a workforce and production planning for the next 4 days.
However, the forecasts provided in the tools are rarely correct. Consequently, the planning in the DSS is presented as a daily planning and the suggested planning should be revised every day. Additionally, the tools are presented as a decision support system. Indicating that the tools should be used for decision support; the planner is free to adapt the suggestion of the tools, but the planner should be aware that adjusting the output of the models influences the potential cost savings.
The two DSS developed are showed in appendix H. As can be seen in this appendix, the planner first needs to fill in the date for which he would like to create a planning. Next, the planner can indicate
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the amount of cars that have to be finished on the first, second and third day of the planning. Since all demand for the Car Wash and T.O. assembly line has a lead time of 1 – 2 days, it is assumed that no more fields are required to extend the DSS. This would unnecessarily complicate the DSS. After this has been filled in, the planner can press the button ‘provide planning’ and a few seconds later a planning will be suggested in the lower part of the tool.
7.3 Implementation
In the final step of this research, the developed systems have been explained to the planners and management of C.RO Automotive. Periodical meetings and discussions with the planners throughout the project have resulted in cooperation of the planners regarding the implementation. In order to use the DSS, a MS Excel solver add-in has been installed at several computers at C.RO. In addition, the macros have been discussed with the in-house programmers in case C.RO would like to adjust parts of the DSS.
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